{"title":"A Multi-criteria Weighting Approach with Application to Internet of Things","authors":"C. Rădulescu, Radu Boncea, A. Vevera","doi":"10.24846/v32i4y202301","DOIUrl":"https://doi.org/10.24846/v32i4y202301","url":null,"abstract":"","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":"399 ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139173269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Dučić, S. Dragićević, Pavle Stepanić, Nebojša Stanković, M. Marjanović
{"title":"Development of Hybrid Model based on Artificial Intelligence for Maximizing Solar Energy Yield","authors":"N. Dučić, S. Dragićević, Pavle Stepanić, Nebojša Stanković, M. Marjanović","doi":"10.24846/v32i4y202309","DOIUrl":"https://doi.org/10.24846/v32i4y202309","url":null,"abstract":"","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":"136 4","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138965059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yeabisra Wubishet ENGDA, Gang Gyoo JIN, Yung-Deug SON
: Magnetic levitation systems are highly nonlinear and unstable systems and their efficacy depends on a well-designed controller for stabilizing the system and for tracking the desired reference signal. This work focuses on the design of a backstepping controller for a magnetic levitation (maglev) system under parameter uncertainty which is load variation. The mathematical model is obtained and the stable controller is designed based on this model and Lyapunov’s theorem. Then, the controller parameters are optimally tuned by minimizing the integral of absolute error and control deviation performance criterion using the particle swarm optimization (PSO) algorithm. For comparison purposes, a Proportional-Integral (PI)- type linear quadratic regulator is also designed. A set of simulation works are carried out in order to verify both the tracking performance and the robustness against disturbance for the proposed controller.
{"title":"Backstepping Control of a Magnetic Levitation System Using PSO","authors":"Yeabisra Wubishet ENGDA, Gang Gyoo JIN, Yung-Deug SON","doi":"10.24846/v32i3y202305","DOIUrl":"https://doi.org/10.24846/v32i3y202305","url":null,"abstract":": Magnetic levitation systems are highly nonlinear and unstable systems and their efficacy depends on a well-designed controller for stabilizing the system and for tracking the desired reference signal. This work focuses on the design of a backstepping controller for a magnetic levitation (maglev) system under parameter uncertainty which is load variation. The mathematical model is obtained and the stable controller is designed based on this model and Lyapunov’s theorem. Then, the controller parameters are optimally tuned by minimizing the integral of absolute error and control deviation performance criterion using the particle swarm optimization (PSO) algorithm. For comparison purposes, a Proportional-Integral (PI)- type linear quadratic regulator is also designed. A set of simulation works are carried out in order to verify both the tracking performance and the robustness against disturbance for the proposed controller.","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135243203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bogdan-Ionuț PAHONȚU, Diana-Andreea ARSENE, Alexandru PREDESCU, Mariana MOCANU, Alexandru GHEORGHIȚĂ
{"title":"Blockchain-based Decision Support System for Water Management","authors":"Bogdan-Ionuț PAHONȚU, Diana-Andreea ARSENE, Alexandru PREDESCU, Mariana MOCANU, Alexandru GHEORGHIȚĂ","doi":"10.24846/v32i3y202312","DOIUrl":"https://doi.org/10.24846/v32i3y202312","url":null,"abstract":"","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135243821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: Velocity planning plays an important role in motion planning of automated driving as it must meet safety, comfort, and traffic regulation requirements. Therefore, it is necessary to consider Jerk constraint and dynamic obstacle constraint. However, the introduction of these constraints makes velocity planning a non-convex optimization problem, significantly increasing computational complexity. To address these challenges, this paper investigates an optimization-based time-optimal velocity planning method. The non-convex and non-linear problems caused by Jerk constraint and dynamic obstacle constraint are addressed by realizing constraint linearization through velocity filtering with acceleration as the threshold. The linear programming (LP) method is then used twice to calculate a time-optimal velocity profile that satisfies the given constraints. Furthermore, when hard constraints are unable to satisfy obstacle avoidance planning, a dynamic constraint frame strategy is proposed to relax the hard constraints and fully utilize the dynamic performance of the ego-vehicle to avoid obstacles. Finally, simulations are conducted in various driving scenarios to validate the effectiveness of the proposed approach. The simulation results demonstrate that the approach proposed in this paper can quickly generate velocity profiles that meet safety and comfort constraints, within a short planning period. Additionally, the dynamic constraint frame strategy can improve the dynamic adaptability of the
{"title":"An Optimization-based Time-optimal Velocity Planning for Autonomous Driving","authors":"Hao HU, Weigang PAN, Song GAO, Xiangmeng TANG","doi":"10.24846/v32i3y202304","DOIUrl":"https://doi.org/10.24846/v32i3y202304","url":null,"abstract":": Velocity planning plays an important role in motion planning of automated driving as it must meet safety, comfort, and traffic regulation requirements. Therefore, it is necessary to consider Jerk constraint and dynamic obstacle constraint. However, the introduction of these constraints makes velocity planning a non-convex optimization problem, significantly increasing computational complexity. To address these challenges, this paper investigates an optimization-based time-optimal velocity planning method. The non-convex and non-linear problems caused by Jerk constraint and dynamic obstacle constraint are addressed by realizing constraint linearization through velocity filtering with acceleration as the threshold. The linear programming (LP) method is then used twice to calculate a time-optimal velocity profile that satisfies the given constraints. Furthermore, when hard constraints are unable to satisfy obstacle avoidance planning, a dynamic constraint frame strategy is proposed to relax the hard constraints and fully utilize the dynamic performance of the ego-vehicle to avoid obstacles. Finally, simulations are conducted in various driving scenarios to validate the effectiveness of the proposed approach. The simulation results demonstrate that the approach proposed in this paper can quickly generate velocity profiles that meet safety and comfort constraints, within a short planning period. Additionally, the dynamic constraint frame strategy can improve the dynamic adaptability of the","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135244413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seham MOAWED, Ali ELDOSOUKY, Amany SARHAN, Sally ELGHAMRAWY
{"title":"A Parallel Pairwise-Clustering Matching Algorithm for Large-Scale Metadata Models Using Levenshtein Distance","authors":"Seham MOAWED, Ali ELDOSOUKY, Amany SARHAN, Sally ELGHAMRAWY","doi":"10.24846/v32i3y202302","DOIUrl":"https://doi.org/10.24846/v32i3y202302","url":null,"abstract":"","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135199721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: Usually, data collected through surveys or by means of sensors is prone to errors and inaccuracies, such as missing data and outliers. Such datasets consist of numerical and string variables, with a high variety of values. Emerging issues, for instance, missing or categorical data lead to errors in running most of the machine learning algorithms. Data analysis and pre-processing are usually more substantial and time-consuming than the implementation of the machine algorithms. Nevertheless, the obtained results are significantly influenced by the way missing data or outliers are approached. This paper presents various methods for coping with null and extreme values. Furthermore, it highlights the significance of encoding and scaling the analysed data and their impact on the performance of the machine learning algorithms. Thus, this paper proposes a methodology for a Missing, Outliers, Encoding & Scaling (MOES) horizontal tuning framework using microservices as applications for data processing in order to obtain the best combination of the employed methods. For exemplification purposes, a real data set from the banking sector is used. Furthermore, the proposed methodology was tested using a second real data set from the utilities sector and the results also showed that both the AUC (Area under the Curve) and execution time were better than in the case of employing the PyCaret Python library.
{"title":"A Horizontal Tuning Framework for Machine Learning Algorithms Using a Microservice-based Architecture","authors":"Simona-Vasilica OPREA, Adela BÂRA, Gabriela DOBRIȚA (ENE), Dragoș-Cătălin BARBU","doi":"10.24846/v32i3y202303","DOIUrl":"https://doi.org/10.24846/v32i3y202303","url":null,"abstract":": Usually, data collected through surveys or by means of sensors is prone to errors and inaccuracies, such as missing data and outliers. Such datasets consist of numerical and string variables, with a high variety of values. Emerging issues, for instance, missing or categorical data lead to errors in running most of the machine learning algorithms. Data analysis and pre-processing are usually more substantial and time-consuming than the implementation of the machine algorithms. Nevertheless, the obtained results are significantly influenced by the way missing data or outliers are approached. This paper presents various methods for coping with null and extreme values. Furthermore, it highlights the significance of encoding and scaling the analysed data and their impact on the performance of the machine learning algorithms. Thus, this paper proposes a methodology for a Missing, Outliers, Encoding & Scaling (MOES) horizontal tuning framework using microservices as applications for data processing in order to obtain the best combination of the employed methods. For exemplification purposes, a real data set from the banking sector is used. Furthermore, the proposed methodology was tested using a second real data set from the utilities sector and the results also showed that both the AUC (Area under the Curve) and execution time were better than in the case of employing the PyCaret Python library.","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135243826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: The paper extends the use of a parameter observer (PO) from first-order to second-order observed systems. A procedure for identifying a linking relationship between the coefficients of a second-order linear system is proposed, based on experiment. The link relation uses link parameters provided by POs from processing the free response of the observed system. By combining the connection relations obtained from several experiments carried out on the system, after the controlled modification of its coefficients, it is possible to calculate the coefficients of the second-order system or the primary parameters that appear in their expressions. The application of the method is illustrated for time-invariant systems. The examples include both tutorial examples and mathematical models of some physical systems. In this context, the suitability of the RLC series circuit as an equivalent model of a capacitor is discussed. The limitations due to the sensitivity of the calculation formulas in relation to the estimation errors of the link parameters, respectively the spectrum of the signal at the PO input, e.g., the voltage at the capacitor terminals, are highlighted.
{"title":"Extended Use of the Parameter Observer on a Class of Second-Order Systems","authors":"Toma-Leonida DRAGOMIR, Dadiana-Valeria CĂIMAN, Corneliu BĂRBULESCU, Sorin NANU","doi":"10.24846/v32i3y202306","DOIUrl":"https://doi.org/10.24846/v32i3y202306","url":null,"abstract":": The paper extends the use of a parameter observer (PO) from first-order to second-order observed systems. A procedure for identifying a linking relationship between the coefficients of a second-order linear system is proposed, based on experiment. The link relation uses link parameters provided by POs from processing the free response of the observed system. By combining the connection relations obtained from several experiments carried out on the system, after the controlled modification of its coefficients, it is possible to calculate the coefficients of the second-order system or the primary parameters that appear in their expressions. The application of the method is illustrated for time-invariant systems. The examples include both tutorial examples and mathematical models of some physical systems. In this context, the suitability of the RLC series circuit as an equivalent model of a capacitor is discussed. The limitations due to the sensitivity of the calculation formulas in relation to the estimation errors of the link parameters, respectively the spectrum of the signal at the PO input, e.g., the voltage at the capacitor terminals, are highlighted.","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135199251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: Due to the high-speed displacement of vehicles, various computing resources in the Internet of Vehicles have such characteristics as limited communication bandwidth, unstable network connections, and dynamic changes in network topology. Therefore, establishing a trusted service offloading location and supplying consumers with dependable and low-latency services in a resource-constrained mobile edge computing system is still a significant difficulty. This paper proposes a “device-edge-cloud” collaborative trusted edge computing-aware network model, and presents an intelligent computing-aware routing service offloading method based on multi-objective particle swarm optimization for this system model. First, a trustworthiness model for data transmission across distributed computing resources in the Internet of Vehicles environment is proposed. Then, the literature overview points out that the trustworthiness of computing resources is relative, dynamic, reflexive, symmetrical, and not transitive. Based on the trustworthiness model, a multi-dimensional QoS attribute model for computing resources is established, and the scheduling problem for computing resources is abstracted into a multi-objective optimization problem. Finally, an intelligent computing-aware routing scheduling method based on a multi-objective particle swarm optimization algorithm is proposed for solving the task scheduling problem in the Internet of Vehicles environment. Simulation results show that in comparison with the random scheduling algorithm and the greedy scheduling algorithm, the MOPSO scheduling algorithm is significantly better with regard to the reliability of calculation results and communication cost.
{"title":"A Novel Trusted Intelligent Computing-aware Routing Service Offloading Method Based on MOPSO for Internet of Vehicles","authors":"Huiyong LI, Furong WANG","doi":"10.24846/v32i3y2023010","DOIUrl":"https://doi.org/10.24846/v32i3y2023010","url":null,"abstract":": Due to the high-speed displacement of vehicles, various computing resources in the Internet of Vehicles have such characteristics as limited communication bandwidth, unstable network connections, and dynamic changes in network topology. Therefore, establishing a trusted service offloading location and supplying consumers with dependable and low-latency services in a resource-constrained mobile edge computing system is still a significant difficulty. This paper proposes a “device-edge-cloud” collaborative trusted edge computing-aware network model, and presents an intelligent computing-aware routing service offloading method based on multi-objective particle swarm optimization for this system model. First, a trustworthiness model for data transmission across distributed computing resources in the Internet of Vehicles environment is proposed. Then, the literature overview points out that the trustworthiness of computing resources is relative, dynamic, reflexive, symmetrical, and not transitive. Based on the trustworthiness model, a multi-dimensional QoS attribute model for computing resources is established, and the scheduling problem for computing resources is abstracted into a multi-objective optimization problem. Finally, an intelligent computing-aware routing scheduling method based on a multi-objective particle swarm optimization algorithm is proposed for solving the task scheduling problem in the Internet of Vehicles environment. Simulation results show that in comparison with the random scheduling algorithm and the greedy scheduling algorithm, the MOPSO scheduling algorithm is significantly better with regard to the reliability of calculation results and communication cost.","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135243664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: In the last few decades, the interval type-2 fuzzy controller has gained popularity in comparison with the type-1 fuzzy controller. This is due to the capability of the interval type-2 fuzzy controller to better handle uncertainty and imprecision. However, modeling an intervaltype-2 fuzzy controller brings about several hurdles. One of the challenges of the design of an interval type-2 fuzzy controller is the generation of the primary membership functions with the purpose of formulating the upper and lower membership functions. This paper proposes a technique by which two Gaussian primary membership functions are generated for an interval type-2 fuzzy set. The means for the Gaussian membership functions are generated by employing two clustering techniques, namely the standard fuzzy c-means clustering algorithm and a recently developed supervised clustering algorithm. The standard deviations of the Gaussian membership functions are optimally selected by using the differential evolution algorithm. Besides, the number of rules required for the proposed model is much smaller. The proposed controller is applied to an armature-controlled DC motor and the obtained simulation results are compared with those obtained for the conventional interval type-2 fuzzy controller. The robustness of the proposed controller is also checked by adding noise and an impulse disturbance during the simulation.
{"title":"Design of an Improved Interval Type-2 Controller Using FCM and Supervised Clustering Algorithms","authors":"Anup Kumar MALLICK, Achintya DAS","doi":"10.24846/v32i3y202308","DOIUrl":"https://doi.org/10.24846/v32i3y202308","url":null,"abstract":": In the last few decades, the interval type-2 fuzzy controller has gained popularity in comparison with the type-1 fuzzy controller. This is due to the capability of the interval type-2 fuzzy controller to better handle uncertainty and imprecision. However, modeling an intervaltype-2 fuzzy controller brings about several hurdles. One of the challenges of the design of an interval type-2 fuzzy controller is the generation of the primary membership functions with the purpose of formulating the upper and lower membership functions. This paper proposes a technique by which two Gaussian primary membership functions are generated for an interval type-2 fuzzy set. The means for the Gaussian membership functions are generated by employing two clustering techniques, namely the standard fuzzy c-means clustering algorithm and a recently developed supervised clustering algorithm. The standard deviations of the Gaussian membership functions are optimally selected by using the differential evolution algorithm. Besides, the number of rules required for the proposed model is much smaller. The proposed controller is applied to an armature-controlled DC motor and the obtained simulation results are compared with those obtained for the conventional interval type-2 fuzzy controller. The robustness of the proposed controller is also checked by adding noise and an impulse disturbance during the simulation.","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135244609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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