Generally Applicable Q-Table Compression Method and Its Application for Constrained Stochastic Graph Traversal Optimization Problems

We analyzed a special class of graph traversal problems, where the distances are stochastic, and the agent is restricted to take a limited range in one go. We showed that both constrained shortest Hamiltonian pathfinding problems and disassembly line balancing problems belong to the class of constrained shortest pathfinding problems, which can be represented as mixed-integer optimization problems. Reinforcement learning (RL) methods have proven their efficiency in multiple complex problems. However, researchers concluded that the learning time increases radically by growing the state- and action spaces. In continuous cases, approximation techniques are used, but these methods have several limitations in mixed-integer searching spaces. We present the Q-table compression method as a multistep method with dimension reduction, state fusion, and space compression techniques that project a mixed-integer optimization problem into a discrete one. The RL agent is then trained using an extended Q-value-based method to deliver a human-interpretable model for optimal action selection. Our approach was tested in selected constrained stochastic graph traversal use cases, and comparative results are shown to the simple grid-based discretization method.

Post Date: 01 April 2024

Utility function-based generalization of sum of ranking differences–country-wise analysis of greenhouse gas emissions

The utility function-based sum of ranking differences (uSRD) method is proposed as a utility function-based multi-criteria decision analysis tool. Our idea is that the transformation functions can be represented by a utility function that can be aggregated with multi-attribute utility functions. We present a framework incorporating utility values as the basis for three different but interconnected analyses. The exemplary application focuses on greenhouse gas emissions and economic indicators of 147 countries. First, the uSRD is applied to the utility values to uncover the hidden relationships of the 40 indicators. A ranking of countries is established to see which sample performs the best and the worst in both emissions and economy. Lastly, mitigation actions are delegated to countries through a three-stage assignment that connects emissions to utilities, sectors, and mitigation actions. The results show that the uSRD excels as a support tool for decision-making.

Post date: 28 February 2024

Fault Diagnostics Based on the Analysis of Probability Distributions Estimated Using a Particle Filter 

 This paper proposes a monitoring procedure based on characterizing state probability distributions estimated using particle filters. The work highlights what types of information can be obtained during state estimation and how the revealed information helps to solve fault diagnosis tasks. If a failure is present in the system, the output predicted by the model is inconsistent with the actual output, which affects the operation of the estimator. The heterogeneity of the probability distribution of states increases, and a large proportion of the particles lose their information content. The correlation structure of the posterior probability density can also be altered by failures. The proposed method uses various indicators that characterize the heterogeneity and correlation structure of the state distribution, as well as the consistency between model predictions and observed behavior, to identify the effects of failures.The applicability of the utilized measures is demonstrated through a dynamic vehicle model, where actuator and sensor failure scenarios are investigated.

Post date:  25 January 2024

Objective well-being level (OWL) composite indicator for sustainable and resilient cities

Well-being is a critical element of the 2030 Agenda for Sustainable Development Goals. Given the complexity of the concept of well-being, it follows that its measurement requires complex, multivariate methods that can characterize the physical, economic, social and environmental aspects along with the mental state of a city. Although it is not sufficient to carry out settlement-level analyses to make cities inclusive, safe, resilient and sustainable. It is necessary to understand patterns within settlements. This work aims to present how the urban macrostructure of urban well-being indicators can be estimated based on GIS-based multilayer analysis. Open-source data, e.g. road networks, points of interest, green spaces and vegetation, are used to estimate urban well-being parameters such as noise levels, air quality and health-related impacts supplemented by climate models to assess urban resilience and sustainability. The proposed methodology integrates 24 models into six categories, namely walkability, environment, health, society, climate change and safety, which are weighted based on a multilevel Principal Component Analysis to minimize information loss for aggregated composite indicators. The study revealed two main components of the macrostructure related to well-being in the studied city: one related to the geometrical features and the other can be derived from the structure of the natural environment. In Veszprém a natural restoration of the detached house area, industrial area and downtown is recommended including developments with green and blue infrastructural elements and nature-based solutions.

 Post date: 09 January 2024 

Time-dependent sequential association rule-based survival analysis: A healthcare application

The analysis of event sequences with temporal dependencies holds substantial importance across various domains, including healthcare. This study introduces a novel approach that combines sequential rule mining and survival analysis to uncover significant associations and temporal patterns within event sequences. By integrating these techniques, we address the limitations linked to the loss of temporal information. The methodology extends traditional sequential rule mining by introducing time-dependent confidence functions, providing a comprehensive understanding of relationships between antecedent and consequent events. The incorporation of the Kaplan-Meier estimator of survival analysis enables the calculation of temporal distributions between events, resulting in time-dependent confidence functions. These confidence functions illuminate the probability of specific event occurrences considering temporal contexts. To present the application of the method, we demonstrated the usage within the healthcare domain. Analyzing the ICD-10 codes and the laboratory events, we successfully identified relevant sequential rules and their time-dependent confidence functions. This empirical validation underscores the potential of methodology to uncover clinically significant associations within intricate medical data.

• The study presents a unique methodology that integrates sequential rule mining and survival analysis.

• The methodology extends traditional sequential rule mining by introducing time-dependent confidence functions.

• The application of the method is demonstrated within the healthcare domain.

Post date: 05 January 2024 

Frequent pattern mining-based log file partition for process mining

Process mining is a technique for exploring models based on event sequences, growing in popularity in the process industry. Process mining algorithms assume that the processed log files contain events generated by only one unknown process, which can lead to extremely complex and inaccurate models when this assumption is not met. To address this issue, this article proposes a frequent pattern mining-based method for log file partitioning, allowing for the exploration of parallel processes. The key idea is that frequent pattern mining can identify grouped events and generate sub-logs of overlapping sub-processes. Thanks to the pre-processing of the log files, more compact and interpretable process models can be identified. We developed a set of goal-oriented metrics to evaluate the complexity of process mining problems and the resulting models. The applicability and effectiveness of the method are demonstrated in the analysis of process alarms of an industrial plant. The results confirm that the proposed method enables the discovery of targeted sub-process models by partitioning the log file using frequent pattern mining, and the effectiveness of the method increases with the number of parallel processes stored in the same log file. We recommend applying the method in every case where there is no clear start and end of the logged events so that the log file can describe different processes.

Post date: 03 April 2023 

Goal-Oriented Tuning of Particle Filters for the Fault Diagnostics of Process Systems

This study introduces particle filtering (PF) for the tracking and fault diagnostics of complex process systems. In process systems, model equations are often nonlinear and environmental noise is non-Gaussian. We propose a method for state estimation and fault detection in a wastewater treatment system. The contributions of the paper are the following: (1) A method is suggested for sensor placement based on the state estimation performance; (2) based on the sensitivity analysis of the particle filter parameters, a tuning method is proposed; (3) a case study is presented to compare the performances of the classical PF and intelligent particle filtering (IPF) algorithms; (4) for fault diagnostics purposes, bias and impact sensor faults were examined; moreover, the efficiency of fault detection was evaluated. The results verify that particle filtering is applicable and highly efficient for tracking and fault diagnostics tasks in process systems.

Post date: 09 March 2023

3D Scanner-Based Identification of Welding Defects—Clustering the Results of Point Cloud Alignment

This paper describes a framework for detecting welding errors using 3D scanner data. The proposed approach employs density-based clustering to compare point clouds and identify deviations. The discovered clusters are then classified according to standard welding fault classes. Six welding deviations defined in the ISO 5817:2014 standard were evaluated. All defects were represented through CAD models, and the method was able to detect five of these deviations. The results demonstrate that the errors can be effectively identified and grouped according to the location of the different points in the error clusters. However, the method cannot separate crack-related defects as a distinct cluster. 

Post date: 01 March 2023

Last Chance to Submit Your Paper to CoDIT'23: Extended Deadline Announced 

The CoDIT’23 conference is the ninth (9th) edition in the series of the International Conference on Control, Decision and Information Technologies. 

It will be held 03-06 July, 2023 at Rome, Italy. 

The paper submission deadline has been extended until February 28, 2023.

The conference  purpose is to be a forum for technical exchange amongst scientists having interests in Control, Automation, Robotics, Optimization, Decision, Cybernetics, Computer Science and Information Technologies. This conference will provide a remarkable opportunity for the academic and industrial communities to address new challenges, share solutions and discuss future research directions. The technical program will include plenary lectures, regular technical sessions, and special sessions.

For more information please visit the website.

Post date: 01 February 2023

Demonstration Laboratory of Industry 4.0 Retrofitting and Operator 4.0 Solutions: Education towards Industry 5.0

One of the main challenges of Industry 4.0 is how advanced sensors and sensing technologies can be applied through the Internet of Things layers of existing manufacturing. This is the so-called Brownfield Industry 4.0, where the different types and ages of machines and processes need to be digitalized. Smart retrofitting is the umbrella term for solutions to show how we can digitalize manufacturing machines. This problem is critical in the case of solutions to support human workers. The Operator 4.0 concept shows how we can efficiently support workers on the shop floor. The key indicator is the readiness level of a company, and the main bottleneck is the technical knowledge of the employees. This study proposes an education framework and a related Operator 4.0 laboratory that prepares students for the development and application of Industry 5.0 technologies. The concept of intelligent space is proposed as a basis of the educational framework, which can solve the problem of monitoring the stochastic nature of operators in production processes. The components of the intelligent space are detailed through the layers of the IoT in the form of a case study conducted at the laboratory. The applicability of indoor positioning systems is described with the integration of machine-, operator- and environment-based sensor data to obtain real-time information from the shop floor. The digital twin of the laboratory is developed in a discrete event simulator, which integrates the data from the shop floor and can control the production based on the simulation results. The presented framework can be utilized to design education for the generation of Industry 5.0.

Post date: 03 January 2023

Expert-Based Modular Simulator for Municipal Waste Processing Technology Design

One of the significant problems in our society is the handling and processing of the vast amount of waste produced by households and industrial processes. Nowadays, packaging material regulations are constantly changing, which can significantly impact the quality of municipal waste, requiring the continuous development and redesign of waste processing plants. Since only a few uncertain measurements (composition, mass, etc.) are available for this task, analysing and redesigning waste processing technologies is challenging. This research aims to develop a modelling and simulation concept that can integrate all the available information and can also handle the uncertainty of the measurements. The proposed modular modelling framework can serve as a basis for designing and redesigning the technologies needed to process ever-changing municipal waste. The most important steps of the framework are as follows: identifying the typical equipment, these are the elements; building models of the elements; determining the characteristic parameters of the equipment; exploring the possible relationships between the elements. For example, the information needed to define the model parameters can be gathered from measurements, industrial experience, and expert knowledge. In many cases, the data obtained represent ranges. The stationary model framework applies efficiency factors and divides the solids into substreams based on expert knowledge. Furthermore, a modular simulator framework was developed to simulate the technological schemes with various connections. The specifications for all widely used waste industrial equipment (shredders, air separators, sieves, magnetic-, eddy current-, optical-, and ballistic separators) were used to construct the developed simulator. This simulator can open new opportunities for the design of waste sorting technological networks. The model was calibrated based on expertise gained from operating the studied technology. The changes in the material parameters can be considered, and the modular simulator can lead to flexible waste sorting technologies capable of adapting to governmental and environmental regulations changes. The main result of the work is that a methodology for designing a modular simulator, model development, and a validation method has been proposed, which provides the possibility to deal with uncertainty. All this is successfully presented through the analysis of an operating waste separation system.

Post date: 08 December 2022

Tamás Ruppert has been awarded VEAB Outstanding Young Researchers Award in the „engineering sciences" category

The prize is awarded to young researchers who have made significant achievements in the field of engineering or the living and non-living sciences.

The award ceremony will take place at the VEAB Headquarters of the Hungarian Academy of Sciences in a lecture session on 7 December 2022 at 14:00.

Post date: 25 November 2022 

Researchers of Abonyilab achieved excellent ranking at the Institutional Scientific Student Conference

The University of Pannonia held the Institutional Scientific Student Conference on November 23, 2022. Éva Kenyeres and Ádám Ipkovich were awarded first place, Gergely Lajos Halász took second place. Therefore, they can participate in the National Scientific Student Conference. Mónika Gugolya was awarded third place.

Éva Kenyeres participated under the Faculty  of Engineering- Engineering Sciences section with a paper entitled: Goal-oriented particle filter state estimation algorithm-based fault diagnostics of process systems.

Ádám Ipkovich participated under the Faculty  of Engineering,  Chemical and Chemical Industry - Modeling section with a paper entitled: Iterative Identifiability Analysis of Composite Material Failure Models.

Gergely Lajos Halász participated under the Faculty  of Engineering- Engineering Sciences section with a paper entitled: Estimation of the operator comfort level and the layout information based on sensor fusion techniques.

Mónika Gugolya  participated under the Faculty  of Engineering,  Chemical and Chemical Industry - Modeling section with a paper entitled: Collaborative work scheduling between humans and robots.

Post date: 25 November 2022 

Cooperation patterns in the ERASMUS student exchange network: an empirical study

The ERASMUS program is the most extensive cooperation network of European higher education institutions. The network involves 90% of European universities and hundreds of thousands of students. The allocated money and number of travelers in the program are growing yearly. By considering the interconnection of institutions, the study asks how the program’s budget performs, whether the program can achieve its expected goals, and how the program contributes to the development of a European identity, interactions among young people from different countries and learning among cultures. Our goal was to review and explore the elements of network structures that can be used to understand the complexity of the whole ERASMUS student mobility network at the institutional level. The results suggest some socioeconomic and individual behavioral factors underpinning the emergence of the network. While the nodes are spatially distributed, geographical distance does not play a role in the network’s structure, although parallel travelling strategies exist, i.e., in terms of preference of short- and long-distance. The European regions of home and host countries also affect the network. One of the most considerable driving forces of edge formation between institutions are the subject areas represented by participating institutions. The study finds that faculties of institutions are connected rather than institutions, and multilayer network model suggested to explore the mechanisms of those connections. The results indicate that the information uncovered by the study is helpful to scholars and policymakers.

Post date: 27 October 2022 

Interview with Tamás Ruppert about the relationship between human, robot and intelligent space today and in the future 

The Gyártástrend Hungarian magasine published an interview with Tamas Ruppert about the next steps of the collaboration between the humans and robots.

The detailed interview is available here.

Post date: 13 October 2022 

Goal-oriented possibilistic fuzzy C-Medoid clustering of human mobility patterns—Illustrative application for the Taxicab trips-based enrichment of public transport services

The discovery of human mobility patterns of cities provides invaluable information for decision-makers who are responsible for redesign of community spaces, traffic, and public transportation systems and building more sustainable cities. The present article proposes a possibilistic fuzzy c-medoid clustering algorithm to study human mobility. The proposed medoid-based clustering approach groups the typical mobility patterns within walking distance to the stations of the public transportation system. The departure times of the clustered trips are also taken into account to obtain recommendations for the scheduling of the designed public transportation lines. The effectiveness of the proposed methodology is revealed in an illustrative case study based on the analysis of the GPS data of Taxicabs recorded during nights over a one-year-long period in Budapest.

Post date: 10 October 2022 

Indicators for climate change-driven urban health impact assessment

Climate change can cause multiply potential health issues in urban areas, which is the most susceptible environment in terms of the presently increasing climate volatility. Urban greening strategies make an important part of the adaptation strategies which can ameliorate the negative impacts of climate change. It was aimed to study the potential impacts of different kinds of greenings against the adverse effects of climate change, including waterborne, vector-borne diseases, heat-related mortality, and surface ozone concentration in a medium-sized Hungarian city. As greening strategies, large and pocket parks were considered, based on our novel location identifier algorithm for climate risk minimization.

A method based on publicly available data sources including satellite pictures, climate scenarios and urban macrostructure has been developed to evaluate the health-related indicator patterns in cities. The modelled future- and current patterns of the indicators have been compared. The results can help the understanding of the possible future state of the studied indicators and the development of adequate greening strategies.

Another outcome of the study is that it is not the type of health indicator but its climate sensitivity that determines the extent to which it responds to temperature rises and how effective greening strategies are in addressing the expected problem posed by the factor.

 Post date: 16 September 2022 

Hypergraph-based analysis and design of intelligent collaborative manufacturing space

A method for hypergraph-based analysis and the design of manufacturing systems has been developed. The reason for its development is the need to integrate the human workforce into Industry 4.0 solutions. The proposed intelligent collaborative manufacturing space enhances collaboration between the operators as well as provides them with valuable information about their performance and the state of the production system. The design of these Operator 4.0 solutions requires a problem-specific description of manufacturing systems, the skills, and states of the operators, as well as of the sensors placed in the intelligent space for the simultaneous monitoring of the cooperative work. The design of this intelligent collaborative manufacturing space requires the systematic analysis of the critical sets of interacting elements. The proposal is that hypergraphs can efficiently represent these sets, moreover, studying the centrality and modularity of the resultant hypergraphs can support the formation of collaboration and interaction schemes and the formation of manufacturing cells. A fully reproducible illustrative example presents the applicability of this concept.

Post date: 06 September 2022 

Information sharing in supply chains – Interoperability in an era of circular economy

In order to realize the goals of Industry 5.0 (I5.0), which has data interoperability as one of its core principles, the future research in the Supply Chain (SC) visibility has to be aligned with socially, economically and environmentally sustainable objectives. Within the purview of circular economy, this paper indicates various aspects and implications of data sharing in the SCs in light of the published research. Taking into consideration the heterogeneity of data sources and standards, this article also catalogs all the major data-sharing technologies being employed in sharing data digitally across the SCs.

Drawing on the published research from 2015 to 2021, following the PRISMA framework, this paper presents the state of research in the field of data sharing in SCs in terms of their standardization, optimization, simulation, automation, security and more notably sustainability. Using the co-occurrence metric, bibliometric analysis has been conducted such that the collected research is categorized under various keyword clusters and regional themes. This article brings together two major themes in reviewing the research in the field. Firstly, the bibliometric analysis of the published articles demonstrates the contours of the current state of research and the future possibilities in the field. Secondly, in synthesizing the research on the foundations of sustainability within the CRoss Industry Standard Process for Data Mining (CRISP-DM) framework, this article deals with various aspects and implications of information sharing in the SCs. By bringing these two themes together, this paper affords a prospective researcher with the research vis-à-vis the information sharing in SC, starting from the actual data standards in use to the modality and consequence of their application within the perspective of the circular economy. This article, in essence, indicates how all the aspects of data sharing in SCs may be brought together in service of the paradigm of I5.0.

Post date: 10 August 2022 

Retrofitting-based development of brownfield Industry 4.0 and Industry 5.0 solutions 

The ongoing Industry 4.0 is characterized by the connectivity between components in the manufacturing system. For modern machines, the Internet of Things is a built-in function. In contrast, there are legacy machines in deployment functioning without digital communication. The need to connect them became popular to improve overall production efficiency. As building a new smart factory as a greenfield investment is a capital-intensive choice, retrofitting the existing infrastructure with IoT capability is more reasonable than replacing them. However, this so-called brownfield development, or retrofitting, requires specific prerequisites, e.g., digitization status assessment, technical and connectivity development, management requirement, and operational need, representing a significant disadvantage: lack of scalability. In the meantime, Industry 5.0 is under human-centric priority, which poses new challenges to the retrofitted system. Aware of the challenge, this paper provides a systematic overview of brownfield development regarding technical difficulties, supporting technologies, and possible applications for the legacy system. The research scope focuses on available Industry 4.0 advancements but considers preparing for the forthcoming Industry 5.0. The proposed retrofitting project approach can be a guideline for manufacturers to transform their factories into intelligent spaces with minimal cost and effort but still gain the most applicable solution for management needs. The future direction for other research in brownfield development for Industry 5.0 is also discussed. 

Post date: 22 June 2022

Tamás Ruppert will be a presenter at the interdisciplinary DAAD Alumni & Friends Colloquium

Tamás Ruppert will present at DAAD Alumni & Friends  Colloquium about Industry 5.0 - Human-Factors in semi-automated Manufacturing. The event takes place on June 2  via Webex.

The DAAD Alumni & Friends  Colloquium takes place on June 2. Since 2016 the regional group Ruhr of DAAD Alumni & Friends has co-organized four colloquia per year since 2019, co-hosted by FH Dortmund. They invite DAAD fellows currently pursuing their research in Germany and other young researchers in NRW to present their ongoing projects. 

More detail about the event is available here.

Post date:  22 May 2022

Multi-agent reinforcement learning-based exploration of optimal operation strategies of semi-batch reactors

The operation of semi-batch reactors requires caution because the feeding reagents can accumulate, leading to hazardous situations due to the loss of control ability. This work aims to develop a method that explores the optimal operational strategy of semi-batch reactors. Since reinforcement learning (RL) is an efficient tool to find optimal strategies, we tested the applicability of this concept. We developed a problem-specific RL-based solution for the optimal control of semi-batch reactors in different operation phases. The RL-controller varies the feeding rate in the feeding phase directly, while in the mixing phase, it works as a master in a cascade control structure. The RL-controllers were trained with different neural network architectures to define the most suitable one. The developed RL-based controllers worked very well and were able to keep the temperature at the desired setpoint in the investigated system. The results confirm the benefit of the proposed problem-specific RL-controller. 

Post date:  05 May 2022

A multi-block clustering algorithm for high dimensional binarized sparse data

We introduce a multidimensional multiblock clustering (MDMBC) algorithm in this paper. MDMBC can generate overlapping clusters with similar values along clusters of dimensions. The parsimonious binary vector representation of multidimensional clusters lends itself to the application of efficient meta-heuristic optimization algorithms. In this paper, a hill-climbing (HC) greedy search algorithm has been presented that can be extended by several stochastic and population-based meta-heuristic frameworks. The benefits of the algorithm are demonstrated in a bi-clustering benchmark problem and in the analysis of the Leiden higher education ranking system, which measures the scientific performance of 903 institutions along four dimensions of 20 indicators representing publication output and collaboration in different scientific fields and time periods.

Post date:  01 April 2022

János Abonyi was awarded the Knight's Cross of the Merit of Hungary

The Parliamentary and Strategic State Secretary of the Ministry of Innovation and Technology, Tamás Schanda, presented awards on the March 17 2022. On the occasion of the event, Dr. János Abonyi was awarded the Knight's Cross of the Order of Merit of Hungary, in recognition of his outstanding research and teaching work in the field of complex systems modelling and data mining, and his achievements in the development of the engineering education system of the University of Pannonia.

Post date: 17 March 2022

Factor analysis, sparse PCA, and Sum of Ranking Differences-based improvements of the Promethee-GAIA multicriteria decision support technique

The Promethee-GAIA method is a multicriteria decision support technique that defines the aggregated ranks of multiple criteria and visualizes them based on Principal Component Analysis (PCA). In the case of numerous criteria, the PCA biplot-based visualization do not perceive how a criterion influences the decision problem. The central question is how the Promethee-GAIA-based decision-making process can be improved to gain more interpretable results that reveal more characteristic inner relationships between the criteria. To improve the Promethee-GAIA method, we suggest three techniques that eliminate redundant criteria as well as clearly outline, which criterion belongs to which factor and explore the similarities between criteria. These methods are the following: A) Principal factoring with rotation and communatily analysis (P-PFA), B) the integration of Sparse PCA into the Promethee II methods (P-sPCA), and C) the Sum of Ranking Differences method (P-SRD). The suggested methods are presented through an I4.0+ dataset that measures the Industry 4.0 readiness of NUTS2-classified regions. The proposed methods are useful tools for handling multicriteria ranking problems, if the number of criteria is numerous. 

Post date:  25 February 2022

János Abonyi and Tamás Ruppert are Special Issue Editors of "Industry 5.0 - the Human Factors in Semi-automated Manufacturing"

With the rapid development of innovative technologies, such as artificial intelligence methods, big data and cloud computing, the new concept of Industry 5.0 has been revolutionizing production and logistics systems by introducing collaborative processes and data-based operator support (so-called Operator 4.0). This Special Issue aims to disseminate advanced research in the theory and application of collaboration in the manufacturing industries (also known by some experts as Industry 5.0).

Topics of interest include, but are not limited to:

• Human–machine interface in IIoT for industrial applications;

• Digital Twin, Device Models, Adaptive- and Automation-Models;

• Human–machine interfaces (HMI) and SCADA supervisory systems;

• Human factors, industrial ergonomics, and safety in smart maintenance;

• Industrial applications of the Internet of Things;

• AI- or ML-based maintenance;

• Risk analysis for Industry Production Systems;

• Smart Manufacturing;

• Smart logistics related to industrial applications;

• Cyberphysical systems;

• Industrial sensor networks;

• Combinations of sensors/sensor networks and Augmented Reality in industrial environments;

• Real-time locating in production and logistics;

• Process modelling and simulation.

Link to the Special Issue "Industry 5.0 - the Human Factors in Semi-automated Manufacturing"

Post date:  07 February 2022

We are on the Cover Story of Data journal

The article "Learning Interpretable Mixture of Weibull Distributions - Exploratory Analysis of How Economic Development Influences the Incidence of COVID-19 Deaths" by R. Csalódi, Z. Birkner, and J. Abonyi is on the cover story of Data journal, Volume 6, Issue 12, pp. 125 (2021).

The journal is available here.

The article is available here. 

Post date:  December 2021

Comprehensible Visualization of Multidimensional Data: Ranking Differences-Based Parallel Coordinates

A novel visualization technique is proposed for the sum of ranking differences method (SRD) based on parallel coordinates. An axis is defined for each variable, on which the data are depicted row-wise. By connecting data, the lines may intersect. The fewer intersections between the variables, the more similar they are and the clearer the figure becomes. Therefore, the visualization depends on what techniques are used to order the variables. The key idea is to employ the SRD method to measure the degree of similarity of the variables, establishing a distance-based order. The distances between the axes are not uniformly distributed in the proposed visualization; their closeness reflects similarity, according to their SRD value. The proposed algorithm identifies false similarities through an iterative approach, where the angles between the SRD values determine which side a variable is plotted. Visualization of the algorithm is provided by MATLAB/Octave source codes. The proposed tool is applied to study how the sources of greenhouse gas emissions can be grouped based on the statistical data of the countries. A comparison to multidimensional scaling (MDS)-based ordering is also given. The use case demonstrates the applicability of the method and the synergies of the incorporation of the SRD method into parallel coordinates. 

Post date:  11 December 2021

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

Reinforcement learning (RL) methods can successfully solve complex optimization problems. Our article gives a systematic overview of major types of RL methods, their applications at the field of Industry 4.0 solutions, and it provides methodological guidelines to determine the right approach that can be fitted better to the different problems, and moreover, it can be a point of reference for R&D projects and further researches.

Post date:  30 November 2021

Ontology-Based Analysis of Manufacturing Processes: Lessons Learned from the Case Study of Wire Harness Production

Effective information management is critical for the development of manufacturing processes. This paper aims to provide an overview of ontologies that can be utilized in building Industry 4.0 applications. The main contributions of the work are that it highlights ontologies that are suitable for manufacturing management and recommends the multilayer-network-based interpretation and analysis of ontology-based databases. This article not only serves as a reference for engineers and researchers on ontologies but also presents a reproducible industrial case study that describes the ontology-based model of a wire harness assembly manufacturing process. 

Post date: 19 November 2021

Identification of sampling points for the detection of SARS-CoV-2 in the sewage system

A suitable tool for monitoring the spread of SARS-CoV-2 is to identify potential sampling points in the wastewater collection system that can be used to monitor the distribution of COVID-19 disease affected clusters within a city. The applicability of the developed methodology is presented through the description of the 72,837 population equivalent wastewater collection system of the city of Nagykanizsa, Hungary and the results of the analytical and epidemiological measurements of the wastewater samples. The wastewater sampling was conducted during the 3rd wave of the COVID-19 epidemic. It was found that the overlap between the road system and the wastewater network is high, it is 82 %. It was showed that the proposed methodological approach, using the tools of network science, determines confidently the zones of the wastewater collection system and provides the ideal monitoring points in order to provide the best sampling resolution in urban areas. The strength of the presented approach is that it estimates the network based on publicly available information. It was concluded that the number of zones or sampling points can be chosen based on relevant epidemiological intervention and mitigation strategies. The algorithm allows for continuous effective monitoring of the population infected by SARS-CoV-2 in small-sized cities. 

Post date: 29 October 2021

Janos Abonyi was invited to join as a program committee member at Innovations in Bio-Inspired Computing and Applications (IBICA) 2021 conference

Janos Abonyi serves as a program committee member at the 12th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA).

The aim of IBICA is to provide a platform for world research leaders and practitioners, to discuss the "full spectrum" of current theoretical developments, emerging technologies and innovative applications of Bio-inspired Computing. Bio-insipired Computing is currently one of the most exciting research ares, and it is continuously demonstrating exceptional strenght in solving complex real life problems. 

The conference will be held online on December 16-18, 2021.

Link to conference website

Post date: 07 September 2021

Janos Abonyi was invited to join as a program committee member at Information Assurance and Security (IAS) 2021 conference

Janos Abonyi serves as a program committee member at the 17th International Conference on Information Assurance and Security (IAS). 

The Conference Theme: Innovative Cyber Secutiry: Protecting National Borders'.

The conference aims to bring together researchers, practitioners, developers, and policy makers in multiple disciplines of information security and assurance to exchange ideas and to learn the latest development in this important field.

The conference is organized by Machine Intelligence Research Labs (MIR Labs) and will be held online on December 14-16.

Link to conference website

Post date: 07 September 2021

Janos Abonyi was invited to join as a program committee member at Hybrid Intelligent Systems (HIS) 2021 conference

Janos Abonyi serves as a program committee member at the 21th International Conference on Hybrid Intelligent Systems (HIS).

The objectives of HIS 2021 are: to increase the awareness of the research community of the broad spectrum of hybrid techniques, to bring together AI researchers from around the world to present their cutting-edge results, to discuss the current trends in HIS research, to develop a collective vision of future opportunities, to establish international collaborative opportunities, and as a result to advance the state of the art of the field. 

The conference will be held online on March 14-16, 2021.

Link to conference website

Post date: 07 September 2021

Event-Tree Based Sequence Mining Using LSTM Deep-Learning Model

During the operation of modern technical systems, the use of the LSTM model for the prediction of process variable values and system states is commonly widespread. The goal of this paper is to expand the application of the LSTM-based models upon obtaining information based on prediction. In this method, by predicting transition probabilities, the output layer is interpreted as a probability model by creating a prediction tree of sequences instead of just a single sequence. By further analyzing the prediction tree, we can take risk considerations into account, extract more complex prediction, and analyze what event trees are yielded from different input sequences, that is, with a given state or input sequence, the upcoming events and the probability of their occurrence are considered. In the case of online application, by utilizing a series of input events and the probability trees, it is possible to predetermine subsequent event sequences. The applicability and performance of the approach are demonstrated via a dataset in which the occurrence of events is predetermined, and further datasets are generated with a higher-order decision tree-based model. The case studies simply and effectively validate the performance of the created tool as the structure of the generated tree, and the determined probabilities reflect the original dataset. 

Post date: 16 August 2021

Indoor Positioning Systems Can Revolutionise Digital Lean

The powerful combination of lean principles and digital technologies accelerates wasteidentification and mitigation faster than traditional lean methods. The new digital lean (also referredto as Lean 4.0) solutions incorporate sensors and digital equipment, yielding innovative solutionsthat extend the reach of traditional lean tools. The tracking of flexible and configurable productionsystems is not as straightforward as in a simple conveyor. This paper examines how the informationprovided by indoor positioning systems (IPS) can be utilised in the digital transformation of flexiblemanufacturing. The proposed IPS-based method enriches the information sources of value streammapping and transforms positional data into key-performance indicators used in Lean Manufacturing.The challenges of flexible and reconfigurable manufacturing require a dynamic value stream mapping.To handle this problem, a process mining-based solution has been proposed. A case study isprovided to show how the proposed method can be employed for monitoring and improvingmanufacturing efficiency. 

Post date:  07 June 2021

Test Plan for the Verification of the Robustness of Sensors and Automotive Electronic Products Using Scenario-Based Noise Deployment (SND)

The targeted shortening of sensor development requires short and convincing verification tests. The goal of the development of novel verification methods is to avoid or reduce an excessive amount of testing and identify tests that guarantee that the assumed failure will not happen in practice. In this paper, a method is presented that results in the test loads of such a verification. The method starts with the identification of the requirements for the product related to robustness using the precise descriptions of those use case scenarios in which the product is assumed to be working. Based on the logic of the Quality Function Deployment (QFD) method, a step-by-step procedure has been developed to translate the robustness requirements through the change in design parameters, their causing phenomena, the physical quantities as causes of these phenomena, until the test loads of the verification. The developed method is applied to the test plan of an automotive sensor. The method is general and can be used for any parts of a vehicle, including mechanical, electrical and mechatronical ones, such as sensors and actuators. Nonetheless, the method is applicable in a much broader application area, even outside of the automotive industry.

Post date: 12 May 2021

Data describing the relationship between world news and sustainable development goals

The data article presents a dataset and a tool for news-based monitoring of sustainable development goals defined by the United Nations. The presented dataset was created by struc- tured queries of the GDELT database based on the categories of the World Bank taxonomy matched to sustainable devel- opment goals. The Google BigQuery SQL scripts and the re- sults of the related network analysis are attached to the data to provide a toolset for the strategic management of sustain- ability issues. The article demonstrates the dataset on the 6th sustainable development goal (Clean Water and Sanita- tion). The network formed based on how countries appear in the same news can be used to explore the potential interna- tional cooperation. The network formed based on how topics of World Bank taxonomy appear in the same news can be used to explore how the problems and solutions of sustain- ability issues are interlinked.

Access to data

Post date: 24 March 2021

We are delivering a couse about "Sensing the future of science"

The course offers to gain ability to get ideas for research that has potential impact on social and economic relevances.

The course material covers the concepts of technology scouting and the exploration of information sources to find 'evidence of the future in the present'.

You can download the course material from:  here 

Post date: 17 March 2021

Integrated Survival Analysis and Frequent Pattern Mining for Course Failure-Based Prediction of Student Dropout

A data-driven method to identify frequent sets of course failures that students should avoid in order to minimize the likelihood of their dropping out from their university training is proposed. The overall probability distribution of the dropout is determined by survival analysis. This result can only describe the mean dropout rate of the undergraduates. However, due to the failure of different courses, the chances of dropout can be highly varied, so the traditional survival model should be extended with event analysis. The study paths of students are represented as events in relation to the lack of completing the required subjects for every semester. Frequent patterns of backlogs are discovered by the mining of frequent sets of these events. The prediction of dropout is personalised by classifying the success of the transitions between the semesters. Based on the explored frequent item sets and classifiers, association rules are formed providing the estimates of the success of the continuation of the studies in the form of confidence metrics. The results can be used to identify critical study paths and courses. Furthermore, based on the patterns of individual uncompleted subjects, it is suitable to predict the chance of continuation in every semester. The analysis of the critical study paths can be used to design personalised actions minimizing the risk of dropout, or to redesign the curriculum aiming the reduction in the dropout rate. The applicability of the method is demonstrated based on the analysis of the progress of chemical engineering students at the University of Pannonia in Hungary. The method is suitable for the examination of more general problems assuming the occurrence of a set of events whose combinations may trigger a set of critical events. 

Post date: 24 February 2021

Inustry 4.0-Driven Development of Optimization Algortihms: A Systematic Overview

The Fourth Industrial Revolution means the digital transformation of production systems. Cyber-physical systems allow for the horizontal and vertical integration of these production systems as well as the exploitation of the benefits via optimization tools. This article reviews the impact of Industry 4.0 solutions concerning optimization tasks and optimization algorithms, in addition to the identification of the new R&D directions driven by new application options. The basic organizing principle of this overview of the literature is to explore the requirements of optimization tasks, which are needed to perform horizontal and vertical integration. This systematic review presents content from 900 articles on Industry 4.0 and optimization as well as 388 articles on Industry 4.0 and scheduling. It is our hope that this work can serve as a starting point for researchers and developers in the field. 

Post date: 13 February 2021

A negyedik ipari forradalom hatása a kompetenciacserélődésre

Az élet számos területén folyamatos változás figyelhető meg, különösen így van ez a gyakorlati életben jelenleg is zajló negyedik ipari forradalom kapcsán. Az Ipar 4.0, a technológiai újításai révén, jelentősen megváltoztatja a munkaerőpiacot és a munkahelyeket. Így elkerülhetetlen a jelenleg is zajló és a várható változáshoz való alkalmazkodás, ugyanakkor nehéz megmondani, hogy milyen kompetenciákra lesz szükség ehhez a jövőben. A kutatás célja, az Ipar 4.0 megoldások azonosítása során, a kompetenciaszükséglet változásának meghatározása a vizsgált vállalatokkal készített strukturált interjúk alapján.A kutatás rávilágít arra, hogy a kompetenciacserélődés és azok fejlesztési folyamatai megkezdődtek. Remélhetőleg a feltárt összefüggések további kutatásokat inspirálnak, támpontot szolgáltatnak a munkavállalók fejlesztését szolgáló képzések kidolgozásában, megújításban, valamint a HRM és az Ipar 4.0 területén hasznos információként szolgálnak, segítve a kompetenciafejlesztési és HR-fejlesztési stratégiák kidolgozását és megvalósítását.

Post date: 12 Janura 2021

Estimation of machine setup and changeover times by survival analysis

The losses associated with changeovers are becoming more significant in manufacturing due to the high variance of products and requirements for just-in-time production. The study is based on the single minute exchange of die (SMED) philosophy, which aims to reduce changeover times. We introduced a method for the analysis of these losses based on models that estimate the product- and operator-dependent changeover times using survival analysis. The root causes of the losses are identified by significance tests of the utilized Cox regression models. The resulting models can be used to design a performance management system that considers the stochastic nature of the work of the operators. An anonymized manufacturing example related to the setup of crimping and wire cutting machines demonstrates the applicability of the method. 

Post date: 23 December 2020

Machine Learning Based Analysis of Human Serum N-glycome Alterations to Follow up Lung Tumor Surgery

The human serum N-glycome is a valuable source of biomarkers for malignant diseases, already utilized in multiple studies. In this paper, the N-glycosylation changes in human serumproteins were analyzed after surgical lung tumor resection. Seventeen lung cancer patients were involved in this study and the N-glycosylation pattern of their serum samples was analyzed before andafter the surgery using capillary electrophoresis separation with laser-induced fluorescent detection. The relative peak areas of 21N-glycans were evaluated from the acquired electropherograms using machine learning-based data analysis. Individual glycans as well as their subclasses were taken into account during the course of evaluation. For the data analysis, both discrete (e.g., smoker or not)and continuous (e.g., age of the patient) clinical parameters were compared against the alterations in these 21N-linked carbohydrate structures. The classification tree analysis resulted in a panel of N-glycans, which could be used to follow up on the effects of lung tumor surgical resection. 

Post date: 09 December 2020

Real-Time Locating System in Production Management 

Real-time monitoring and optimization of production and logistics processes significantlyimprove the efficiency of production systems. Advanced production management solutions requirereal-time information about the status of products, production, and resources. As real-time locatingsystems (also referred to as indoor positioning systems) can enrich the available information, thesesystems started to gain attention in industrial environments in recent years. This paper providesa review of the possible technologies and applications related to production control and logistics,quality management, safety, and efficiency monitoring. This work also provides a workflow to clarifythe steps of a typical real-time locating system project, including the cleaning, pre-processing, andanalysis of the data to provide a guideline and reference for research and development of indoorpositioning-based manufacturing solutions. 

Post date: 26 November 2020

 Data describing the regional Industry 4.0 readiness index

The data article presents a dataset suitable to measure regional Industry 4.0 (I4.0+) readiness. The I4.0+ dataset includes 101 indicators with 248 958 observations, aggregated to NUTS 2 statistical level) based on open data in the field of education (ETER, Erasmus), science (USPTO, MA-Graph, GRID), government (Eurostat) and media coverage (GDELT). Indicators consider the I4.0-specific domain of higher education and lifelong learning, innovation, technological investment, labour market and technological readiness as indicators. A composite indicator, the I4.0+ index was constructed by the Promethee method, to identify regional rank regarding their I4.0 performance. The index is validated with economic (GDP) and innovation indexes (Regional Innovation Index). 

Data accessibility

Post date: 27 October 2020

Directions of membrane separator development for microbial fuel cells: A retrospective analysis using frequent itemset mining and descriptive statistical approach

To increase the efficiency of microbial fuel cells (MFCs), the separator (which is mostly a membrane) placed between the electrodes or their compartments is considered of high importance besides several other biotic and abiotic factors (e.g. configuration, mode of operation, types of inoculum and substrate). Nafion-based proton exchange membranes (PEMs) are the most widespread, although these materials are often criticized on various technological and economical grounds. Therefore, to find alternatives of Nafion, the synthesis, development and testing of novel/commercialized membrane separators with enhanced characteristics have been hot topics. In this study, the goals were to assess the membrane-installed MFCs in a retrospective manner and reveal the trends, the applied practices, frequent setups, etc. via Bayesian classification and frequent itemset mining algorithms. Thereafter, a separate discussion was devoted to examine the current standing of research related to major membrane groups used in MFCs and evaluate in accordance with the big picture how the various systems behave in comparison with each other, especially compared to those applying Nafion PEM. It was concluded that some membrane types seem to be competitive to Nafion, however, the standardization of the experiments would drive the more unambiguous comparison of studies. 

Post date: 21 October 2020

Integration of real-time locating systems into digital twins

Cyber-physical model-based solutions should rely on digital twins in which simulations are integrated with real-time sensory and manufacturing data. This paper highlights the benefits of information fusion with real-time locating systems (RTLS) and demonstrates how position and acceleration data can be utilised for the simulation-based analysis of product-specific activity times. The proposed digital twin is continuously capable to predict the production status and provide information for monitoring of production performance thanks to the real time connections of the RTLS and adaptive simulation models. The presented industrial case study demonstrates how the resulted Simulation 4.0 concept supports the analysis of human resource effectiveness (HRE) in an assembly process.

Post date:  06 October 2020

Development of manufacturing execution systems in accordance with Industry 4.0 requirements: A review of standard- and ontology-based methodologies and tools

This work presents how recent trends in Industry 4.0 (I4.0) solutions are influencing the development of manufacturing execution systems (MESs) and analyzes what kinds of trends will determine the development of the next generation of these technologies. This systematic and thematic review provides a detailed analysis of I4.0-related requirements in terms of MES functionalities and an overview of MES development methods and standards because these three aspects are essential in developing MESs. The analysis highlights that MESs should interconnect all components of cyber-physical systems in a seamless, secure, and trustworthy manner to enable high-level automated smart solutions and that semantic metadata can provide contextual information to support interoperability and modular development. The observed trends show that formal models and ontologies will play an even more essential role in I4.0 systems as interoperability becomes more of a focus and that the new generation of linkable data sources should be based on semantically enriched information. The presented overview can serve as a guide for engineers interested in the development of MESs as well as for researchers interested in finding worthwhile areas of research. 

Post date: 01 September 2020

Analytic Hierarchy Process and Multilayer Network-Based Method for Assembly Line Balancing

Assembly line balancing improves the efficiency of production systems by the optimal assignment of tasks to operators. The optimisation of this assignment requires models that provide information about the activity times, constraints and costs of the assignments. A multilayer network-based representation of the assembly line-balancing problem is proposed, in which the layers of the network represent the skills of the operators, the tools required for their activities and the precedence constraints of their activities. The activity–operator network layer is designed by a multi-objective optimisation algorithm in which the training and equipment costs as well as the precedence of the activities are also taken into account. As these costs are difficult to evaluate, the analytic hierarchy process (AHP) technique is used to quantify the importance of the criteria. The optimisation problem is solved by a multi-level simulated annealing algorithm (SA) that efficiently handles the precedence constraints. The efficiency of the method is demonstrated by a case study from wire harness manufacturing. 

Post date: 05 June 2020

Multilayer network based comparative document analysis (MUNCoDA)

The proposed multilayer network-based comparative document analysis (MUNCoDA) method supports the identification of the common points of a set of documents, which deal with the same subject area. As documents are transformed into networks of informative word-pairs, the collection of documents form a multilayer network that allows the comparative evaluation of the texts. The multilayer network can be visualized and analyzed to highlight how the texts are structured. The topics of the documents can be clustered based on the developed similarity measures. By exploring the network centralities, topic importance values can be assigned. The method is fully automated by KNIME preprocessing tools and MATLAB/Octave code.

•Networks can be formed based on informative word pairs of a multiple documents

•The analysis of the proposed multilayer networks provides information for multi-document summarization

•Words and documents can be clustered based on node similarity and edge overlap measures

V. Sebestyén, E. Domokos, J. Abonyi : Multilayer network based comparative document analysis (MUNCoDA), MethodsX, Volume 7, 2020

Post date: 29 April 2020

Prof. Janos Abonyi was invited to join the program committee of 7th edition of the International conference on Time Series and Forecasting (ITISE 2020) 

Post date: Feb22, 2020 09:00:00 AM

The ITISE 2020 (7th International conference on Time Series and Forecasting) seeks to provide a discussion forum for scientists, engineers, educators and students about the latest ideas and realizations in the foundations, theory, models and applications for interdisciplinary and multidisciplinary research encompassing disciplines of computer science, mathematics, statistics, forecaster, econometric, etc, in the field of time series analysis and forecasting.

The aims of ITISE 2020 is to create a a friendly environment that could lead to the establishment or strengthening of scientific collaborations and exchanges among attendees, and therefore, ITISE 2020 solicits high-quality original research papers (including significant work-in-progress) on any aspect time series analysis and forecasting, in order to motivating the generation, and use of knowledge and new computational techniques and methods on forecasting in a wide range of fields.

Link to the conference website

The Assembly magazine reported about our methodology developed for activity time monitoring: 

Post date: Feb13, 2020 6:00:00 PM

Industry 4.0 and the digital manufacturing revolution are all about collecting—and, more importantly, acting on—data gathered from the assembly process in real time. That’s all well and good when data is coming from sensors, vision systems, fastening tools and other electronic devices. But, how can engineers gather real-time data on largely manual assembly processes, such as wire harness assembly?

To solve this problem, we developed a software- and sensor-based system to measure activity times and performance on a wire harness assembly line. To ensure a real-time connection between assembler performance and varying product complexity, our system relies on fixture sensors and an indoor positioning system (IPS). Our goal was to create a system that could continuously estimate the time consumed by the various elementary activities that make up wire harness assembly. Our system creates a model of a task, compares estimated activity times to the actual performance of assemblers, and generates early warnings when their productivity decreases. 

J. Abonyi, T. Ruppert, Monitoring Activity During Wire Harness Assembly, Assembly, 2020

Janos Abonyi invited to serve as a Committee member!

Post date: Feb10, 2020 9:00:00 PM

Dr. Janos Abonyi has been invited to serve as Technical Program Committee member in the  IEEE Wireless Africa 2020 conference.

IEEE Wireless Africa 2020 is sponsored by the IEEE Vehicular Technology Society and will be hosted in South Africa, from 29-30 November 2020.

The conference aims to provide a platform for wireless researchers to share their results, call for comments and collaborations, and exchange innovative ideas on leading edge research in wireless technologies.

Link to the conference website

Link to the precise track

Fuzzy activity time-based model predictive control of open station assembly lines is published!

Post date: Dec 14, 2019 3:00:00 PM

The sequencing and line balancing of manual mixed-model assembly lines are challenging tasks due to the complexity and uncertainty of operator activities. The control of cycle time and the sequencing of production can mitigate the losses due to non-optimal line balancing in the case of open-station production where the operators can work ahead of schedule and try to reduce their backlog. The objective of this paper is to provide a cycle time control algorithm that can improve the e ciency of assembly lines in such situations based on a specially mixed sequencing strategy. To handle the uncertainty of activity times, a fuzzy model-based solution has been developed. As the production process is modular, the fuzzy sets represent the uncertainty of the elementary activity times related to the processing of the modules. The optimistic and pessimistic estimates of the completion of activity times extracted from the fuzzy model are incorporated into a model predictive control algorithm to ensure the constrained optimization of the cycle time. The applicability of the proposed method is demonstrated based on a wire-harness manufacturing process with a paced conveyor, but the proposed algorithm can handle continuous conveyors as well. The results confirm that the application of the proposed algorithm is widely applicable in cases where a production line of a supply chain is not well balanced and the activity times are uncertain. 

J. Abonyi, Tamas Ruppert, Gyula Dorgo, Fuzzy activity time-based model predictive control of open-station assembly lines, Journal of Manufacturing Systems Volume 54, January 2020, Pages 12-23

Constrained Recursive Input Estimation of Blending and Mixing Systems is published!

Post date: Oct 23, 2019 6:00:00 PM

Blending, mixing processes are often supported by advanced process control systems to maximise margins from available component and heat streams. Since these model-based solutions require accurate and reliable data, in weakly instrumented processes, the unknown inlet concentrations and temperatures should be estimated based on the measured outflows. This work presents a method for the reliable estimation of multiple input variables of process units. The key idea is that the input estimation problem is formulated as a constrained recursive estimation task. The applicability of the method is illustrated based on a benchmark model of a blending system. The performance of the method is compared to the moving window and Kalman Filter based solutions. The results show the superior performance of the proposed method and confirm that the apriori knowledge-based constraints improve the robustness of the estimates.

Abonyi J.: „Constrained Recursive Input Estimation of Blending and Mixing Systems”, Chemical Engineering Transactions, 76, 727-732

Data-driven multilayer complex networks of sustainable development goals is published!

Post date: Oct 08, 2019 6:15:00 PM

This data article presents the formulation of multilayer network for modelling the interconnections among the sustainable development goals (SDGs), targets and includes the correlation based linking of the sustainable development indicators with the available long-term datasets of The World Bank, 2018. The spatial distribution of the time series data allows creating country-specific sustainability assessments. In the related research article “Network Model-Based Analysis of the Goals, Targets and Indicators of Sustainable Development for Strategic Environmental Assessment” the similarities of SDGs for ten regions have been modelled in order to improve the quality of strategic environmental assessments. The datasets of the multilayer networks are available on Mendeley .

Sebestyén V., Bulla M., Rédey Á., Abonyi J.: „Data-driven multilayer complex networks of sustainable development goals”, Data in Brief, Volume 25, 2019, 104049

Genetic programming-based development of thermal runaway criteria is published!

Post date: Oct 08, 2019 6:10:00 PM

Common thermal runaway criteria (e.g. divergence criterion and the Maxi criterion) may predict a thermal runaway unreasonably as the Maximum Allowable Temperature (MAT) is not taken into account. This contribution proposes a method for the goal-oriented construction of reactor runaway criteria by Genetic Programming (GP). The runaway prediction problem is formulated as a critical equation-based classification task, and GP is used to identify the optimal structure of the equations that also take into account the MAT. To demonstrate the applicability of the method, tailored criteria were developed for batch and continuous stirred-tank reactors. The resultant critical equations outperform the well-known criteria in terms of the early and accurate indication of thermal runaways.

Kummer A., Varga T., Abonyi J.: „Genetic programming-based development of thermal runaway criteria”, Computers & Chemical Engineering, 2019, 106582

Learning and predicting operation strategies by sequence mining and deep learning (full paper) is published!

Post date: Jun 15, 2019 7:55:00 PM

The operators of chemical technologies are frequently faced with the problem of determining optimal interventions. Our aim is to develop data-driven models by exploring the consequential relationships in the alarm and event-log database of industrial systems. Our motivation is twofold: (1) to facilitate the work of the operators by predicting future events and (2) analyse how consequent the event series is. The core idea is that machine learning algorithms can learn sequences of events by exploring connected events in databases. First, frequent sequence mining applications are utilised to determine how the event sequences evolve during the operation. Second, a sequence-to-sequence deep learning model is proposed for their prediction. The long short-term memory unit-based model (LSTM) is capable of evaluating rare operation situations and their consequential events. The performance of this methodology is presented with regard to the analysis of the alarm and event-log database of an industrial delayed coker unit. 

Dörgő Gy., Abonyi J.: "Learning and predicting operation strategies by sequence mining and deep learning", Computers & Chemical Engineering, Volume 128, 2 September 2019, Pages 174-187

Operating regime model based multi-objective sensor placement for data reconciliation is published!

Post date: Jun 15, 2019 7:40:00 PM

Although the number of sensors in chemical production plants is increasing thanks to the IoT revolution, it is still a crucial problem what to measure and how to place the sensors as such the resulted sensor network be robust and cost-effectively provide the required information. This problem is especially relevant in flexible multi-purpose, multi-product production plants when there are significant differences among the operating regions. The present work aims the development of a sensor placement methodology that utilizes the advantages of local linear models. Realizing the often conflicting nature of the key objectives of sensor placement, the problem is formulated as a multi-objective optimization task taking into consideration the cost, estimation accuracy, observability and fault detection performance of the designed networks and simultaneously seeking for the optimal solutions under multiple operating regimes. The effectiveness of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II)-based solution of the defined problem is demonstrated through benchmark examples.

Dörgő Gy., Haragovics M., Abonyi J.: "Operating regime model based multi-objective sensor placement for data reconciliation", 29th European Symposium on Computer Aided Process Engineering, Netherlands, Eindhoven, 2019 June 16-19.

P-graph-based multi-objective risk analysis and redundancy allocation in safety-critical energy systems is published!

Post date: May 27, 2019 8:10:00 PM

As most of the energy production and transformation processes are safety-critical, it is vital to develop tools that support the analysis and minimisation of their reliability-related risks. The resultant optimisation problem should reflect the structure of the process which requires the utilisation of flexible and problem-relevant models. This paper highlights that P-graphs extended by logical condition units can be transformed into reliability block diagrams, and based on the cut and path sets of the graph a polynomial risk model can be extracted which opens up new opportunities for the definition optimisation problems related to reliability redundancy allocation. A novel multi-objective optimisation based method has been developed to evaluate the criticality of the units and subsystems. The applicability of the proposed method is demonstrated using a real-life case study related to a reforming reaction system. The results highlight that P-graphs can serve as an interface between process flow diagrams and polynomial risk models and the developed tool can improve the reliability of energy systems in retrofitting projects. 

Süle Z., Baumgartner J., Dörgő Gy., Abonyi J.: "P-graph-based multi-objective risk analysis and redundancy allocation in safety-critical energy systems", Energy (2019), vol. 179, 989-1003.

Network Model-Based Analysis of the Goals, Targets and Indicators of Sustainable Development for Strategic Environmental Assessment is published!

Post date: Mar 07, 2019 2:00:00 PM

Strategic environmental assessment is a decision support technique that evaluates policies, plans and programs in addition to identifying the most appropriate interventions in different scenarios. This work develops a network-based model to study interlinked ecological, economic, environmental and social problems to highlight the synergies between policies, plans, and programs in environmental strategic planning. Our primary goal is to propose a methodology for the data-driven verification and extension of expert knowledge concerning the interconnectedness of the sustainable development goals and their related targets. A multilayer network model based on the time-series indicators of the World Bank open data over the last 55 years was assembled. The results illustrate that by providing an objective and data-driven view of the correlated variables of the World Bank, the proposed layered multipartite network model highlights the previously not discussed interconnections, node centrality measures evaluate the importance of the targets, and network community detection algorithms reveal their strongly connected groups. The results confirm that the proposed methodology can serve as a data-driven decision support tool for the preparation and monitoring of long-term environmental policies. The developed new data-driven network model enables multi-level analysis of the sustainability (goals, targets, indicators) and will make it possible to plan long-term environmental strategic planning. Through relationships among indicators, relationships among targets and goals can be modelled. The results show that sustainable development goals are strongly interconnected, while the 5th goal (gender equality) is linked mostly to 17th (partnerships for the goals) goal. The analysis has also highlighted the importance of the 4th (quality education).

Sebestyén V., Bulla M., Rédey Á., Abonyi J.: "Network Model-Based Analysis of the Goals, Targets and Indicators of Sustainable Development for Strategic Environmental Assessment", Journal of Environmental Management, 2019, 238, 126-135

Evaluation of the Complexity, Controllability and Observability of Heat Exchanger Networks Based on Structural Analysis of Network Representations is published!

Post date: Feb 15, 2019 7:45:00 PM

The design and retrofit of Heat Exchanger Networks (HENs) can be based on several objectives and optimisation algorithms. As each method results in an individual network topology that has a significant effect on the operability of the system, control-relevant HEN design and analysis are becoming more and more essential tasks. This work proposes a network science-based analysis tool for the qualification of controllability and observability of HENs. With the proposed methodology, the main characteristics of HEN design methods are determined, the effect of structural properties of HENs on their dynamical behaviour revealed, and the potentials of the network-based HEN representations discussed. Our findings are based on the systematic analysis of almost 50 benchmark problems related to 20 different design methodologies. 

https://www.mdpi.com/1996-1073/12/3/513

Evaluating the Interconnectedness of the Sustainable Development Goals Based on the Causality Analysis of Sustainability Indicators is published!

Post date: Oct 20, 2018 11:40:00 PM

Policymaking requires an in-depth understanding of the cause-and-effect relationships between the sustainable development goals. However, due to the complex nature of socio-economic and environmental systems, this is still a challenging task. In the present article, the interconnectedness of the United Nations (UN) sustainability goals is measured using the Granger causality analysis of their indicators. The applicability of the causality analysis is validated through the predictions of the World3 model. The causal relationships are represented as a network of sustainability indicators providing the opportunity for the application of network analysis techniques. Based on the analysis of 801 UN indicator types in 283 geographical regions, approximately 4000 causal relationships were identified and the most important global connections were represented in a causal loop network. The results highlight the drastic deficiency of the analysed datasets, the strong interconnectedness of the sustainability targets and the applicability of the extracted causal loop network. The analysis of the causal loop networks emphasised the problems of poverty, proper sanitation and economic support in sustainable development.

Dörgő Gy., Sebestyén V., Abonyi J.:"Evaluating the Interconnectedness of the Sustainable Development Goals Based on the Causality Analysis of Sustainability Indicators", Sustainability 2018, 10(10), 3766, doi:10.3390/su10103766

Our review about  operators and cyber-physical systems is published! 

Enabling Technologies for Operator 4.0: A Survey

The fast development of smart sensors and wearable devices has provided the opportunity to develop intelligent operator workspaces. The resultant Human-Cyber-Physical Systems (H-CPS) integrate the operators into flexible and multi-purpose manufacturing processes. The primary enabling factor of the resultant Operator 4.0 paradigm is the integration of advanced sensor and actuator technologies and communications solutions. This work provides an extensive overview of these technologies and highlights that the design of future workplaces should be based on the concept of intelligent space. 

Ruppert T., Jaskó Sz., Holczinger T., Abonyi J.: "Enabling Technologies for Operator 4.0: A Survey", Applied Sciences, Basel, 2018, 8 (9), 1650, 1-19

Soft Sensors Special Issue

Post date: Aug 30, 2018 8:1:00 PM

We are editing a special issue related to software sensors.

Please forward this link to researchers potentially interested insubmitting a paper.

Slide to promote special issue in conference

http://www.mdpi.com/journal/sensors/special_issues/Soft_Sensors

3rd International Conference on Internet of Things, Big Data and Security - Janos Abonyi became the member of the PC

Post date: Jun 8, 2017 5:09:52 AM

The internet of things (IoT) is a platform that allows a network of devices (sensors, smart meters, etc.) to communicate, analyse data and process information collaboratively in the service of individuals or organisations. The IoT network can generate large amounts of data in a variety of formats and using different protocols which can be stored and processed in the cloud. The conference looks to address the issues surrounding IoT devices, their interconnectedness and services they may offer, including efficient, effective and secure analysis of the data IoT produces using machine learning and other advanced techniques, models and tools, and issues of security, privacy and trust that will emerge as IoT technologies mature and become part of our everyday lives.

CONFERENCE AREAS

1 . Big Data Research 

2 . Emerging Services and Analytics 

3 . Internet of Things (IoT) Fundamentals 

4 . Internet of Things (IoT) Applications 

5 . Big Data for Multi-discipline Services 

6 . Security, Privacy and Trust 

7 . IoT Technologies 

UPCOMING DEADLINES

Regular Paper Submission: October 16, 2017 

Regular Paper Authors Notification: December 15, 2017 

Regular Paper Camera Ready and Registration: January 4, 2018 

We are in the news!

Post date: May 29, 2017 4:38:49 AM

The announcement of the new Lendület groups of the Hungarian Academy of Sciences was reported: 

http://inforadio.hu/belfold/2017/05/17/a_program_ami_miatt_hazaternek_a_magyar_kutatok_kulfoldrol/

https://veol.hu/hirek/abonyi-janos-egyetemi-tanar-is-a-lendulet-program-tamogatottja-1837081

http://www.ng.hu/Tudomany/2017/05/17/Lendulet-kutatocsportok-2017

Network science and control theory  - Our paper in Scientific Reports!

Post date: Mar 11, 2017 2:43:46 PM

Network theory based controllability and observability analysis have become widely used techniques. We realized that most applications are not related to dynamical systems, and mainly the physical topologies of the systems are analysed without deeper considerations. Here, we draw attention to the importance of dynamics inside and between state variables by adding functional relationship defined edges to the original topology. The resulting networks differ from physical topologies of the systems and describe more accurately the dynamics of the conservation of mass, momentum and energy. We define the typical connection types and highlight how the reinterpreted topologies change the number of the necessary sensors and actuators in benchmark networks widely studied in the literature. Additionally, we offer a workflow for network science-based dynamical system analysis, and we also introduce a method for generating the minimum number of necessary actuator and sensor points in the system.

http://www.nature.com/articles/s41598-017-00160-5

https://github.com/abonyilab/NOCAD

Media Cloud

Post date: May 20, 2016 9:58:19 PM

Media Cloud is a project of the Harvard Berkman Center for Internet & Society and the MIT Center for Civic Media. It is worth to take a look. 

Early warning - risk management