Pub Date : 2022-10-19DOI: 10.1109/ICSTCC55426.2022.9931872
Alessio Moreschini, Mattia Mattioni, S. Monaco, D. Normand-Cyrot
We propose an optimization method obtained by the approximation of a novel discretization approach for gradient dynamics recently proposed by the authors. It is shown that the proposed algorithm ensures convergence for all amplitudes of the step size, contrarily to classical implementations.
{"title":"A gradient descent algorithm built on approximate discrete gradients","authors":"Alessio Moreschini, Mattia Mattioni, S. Monaco, D. Normand-Cyrot","doi":"10.1109/ICSTCC55426.2022.9931872","DOIUrl":"https://doi.org/10.1109/ICSTCC55426.2022.9931872","url":null,"abstract":"We propose an optimization method obtained by the approximation of a novel discretization approach for gradient dynamics recently proposed by the authors. It is shown that the proposed algorithm ensures convergence for all amplitudes of the step size, contrarily to classical implementations.","PeriodicalId":220845,"journal":{"name":"2022 26th International Conference on System Theory, Control and Computing (ICSTCC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122608347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-19DOI: 10.1109/ICSTCC55426.2022.9931899
Florin Leon, P. Cașcaval
This paper addresses the issue of optimal design of the decoder in fault-tolerant RAMs with Single Error Correcting and Double Error Detecting facilities (SECDED). If for the encoding logic it is recommended to generate each control bit independently (a classic implementation), for the decoding logic the authors recommend a simpler synthesis, in order to reduce the complexity as much as possible. This is explained by the fact that the decoding logic no longer has any fault tolerance facilities. Since the decoder is implemented as a network of XOR logic gates, the problem we address is to find the simplest structure using 2-input or 3-input XOR gates as base cells. To this end, a search algorithm has been implemented to identify in the parity-check matrix common sets of bits that can be used to generate multiple error control bits. The efficiency of the solution we propose, in terms of complexity, is demonstrated by comparison with the classic one in which the error bits are generated independently.
{"title":"Search Algorithm for Optimal Synthesis of Decoder for RAMs with Error-Correcting Codes","authors":"Florin Leon, P. Cașcaval","doi":"10.1109/ICSTCC55426.2022.9931899","DOIUrl":"https://doi.org/10.1109/ICSTCC55426.2022.9931899","url":null,"abstract":"This paper addresses the issue of optimal design of the decoder in fault-tolerant RAMs with Single Error Correcting and Double Error Detecting facilities (SECDED). If for the encoding logic it is recommended to generate each control bit independently (a classic implementation), for the decoding logic the authors recommend a simpler synthesis, in order to reduce the complexity as much as possible. This is explained by the fact that the decoding logic no longer has any fault tolerance facilities. Since the decoder is implemented as a network of XOR logic gates, the problem we address is to find the simplest structure using 2-input or 3-input XOR gates as base cells. To this end, a search algorithm has been implemented to identify in the parity-check matrix common sets of bits that can be used to generate multiple error control bits. The efficiency of the solution we propose, in terms of complexity, is demonstrated by comparison with the classic one in which the error bits are generated independently.","PeriodicalId":220845,"journal":{"name":"2022 26th International Conference on System Theory, Control and Computing (ICSTCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131150096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-19DOI: 10.1109/ICSTCC55426.2022.9931834
N. Nistor, L. Baicu, B. Dumitrascu
In this paper an original adaptive method for maximizing the energy transferred to the car's battery during regenerative braking is presented. The paper is based on a simulation in Proteus LabCenter, using a battery model, based on functional criteria, with the energy recovered from a reversible motor. The battery management algorithm was implemented on ATMEGA 328 microcontroller, and a MC34063 DC-to-DC converter control circuit. The voltage variations of reversible motor, recovered during braking are simulated with a variable voltage applied on the system input and the results show that the output voltage of the DC-to-DC converter must be continuously adjusted during the braking process. The efficiency lies in the fact that although the braking sequences do not take place for long periods, they are made at currents recovered from magnetic induction of considerable values.
本文提出了一种新颖的自适应方法，使再生制动过程中向蓄电池传递的能量最大化。本文基于Proteus LabCenter的仿真，使用基于功能标准的电池模型，从可逆电机中回收能量。电池管理算法在atmega328单片机和MC34063 dc - dc转换器控制电路上实现。在系统输入端施加可变电压的情况下，对可逆电机在制动过程中恢复的电压变化进行了仿真，结果表明，在制动过程中，dc - dc变换器的输出电压必须连续调节。效率在于，虽然制动顺序不发生长时间，但它们是在从相当大的磁感应恢复的电流下进行的。
{"title":"Automotive algorithm implemented in the microcontroller for adapting regenerative braking","authors":"N. Nistor, L. Baicu, B. Dumitrascu","doi":"10.1109/ICSTCC55426.2022.9931834","DOIUrl":"https://doi.org/10.1109/ICSTCC55426.2022.9931834","url":null,"abstract":"In this paper an original adaptive method for maximizing the energy transferred to the car's battery during regenerative braking is presented. The paper is based on a simulation in Proteus LabCenter, using a battery model, based on functional criteria, with the energy recovered from a reversible motor. The battery management algorithm was implemented on ATMEGA 328 microcontroller, and a MC34063 DC-to-DC converter control circuit. The voltage variations of reversible motor, recovered during braking are simulated with a variable voltage applied on the system input and the results show that the output voltage of the DC-to-DC converter must be continuously adjusted during the braking process. The efficiency lies in the fact that although the braking sequences do not take place for long periods, they are made at currents recovered from magnetic induction of considerable values.","PeriodicalId":220845,"journal":{"name":"2022 26th International Conference on System Theory, Control and Computing (ICSTCC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134162360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-19DOI: 10.1109/ICSTCC55426.2022.9931865
A. Alexandrescu, N. Botezatu, R. Lupu
In relation to an ever changing epidemiological world context, a category of people that is more subject to be impacted consists of the elderly. Certain steps can be taken in order to improve their quality of life especially in case of illness. One way of achieving this is to have a smart assistive living environment, which includes home automation and medical monitoring. The proposed system expands on an IoT solution for assisted living and introduces a highly flexible rules engine for processing physiological and domotics data obtained from the home environment, and for interacting with the system actuators. As proof-of-concept, there are several use-cases that are discussed depending on the type of patient: diabetic, cardiac, hypertensive, obese, COVID or Alzheimer. These scenarios emphasize the efficiency of the proposed solution and offer an insight on the high degree of abstraction and extensibility of the system.
{"title":"Monitoring and processing of physiological and domotics parameters in an Internet of Things (IoT) assistive living environment","authors":"A. Alexandrescu, N. Botezatu, R. Lupu","doi":"10.1109/ICSTCC55426.2022.9931865","DOIUrl":"https://doi.org/10.1109/ICSTCC55426.2022.9931865","url":null,"abstract":"In relation to an ever changing epidemiological world context, a category of people that is more subject to be impacted consists of the elderly. Certain steps can be taken in order to improve their quality of life especially in case of illness. One way of achieving this is to have a smart assistive living environment, which includes home automation and medical monitoring. The proposed system expands on an IoT solution for assisted living and introduces a highly flexible rules engine for processing physiological and domotics data obtained from the home environment, and for interacting with the system actuators. As proof-of-concept, there are several use-cases that are discussed depending on the type of patient: diabetic, cardiac, hypertensive, obese, COVID or Alzheimer. These scenarios emphasize the efficiency of the proposed solution and offer an insight on the high degree of abstraction and extensibility of the system.","PeriodicalId":220845,"journal":{"name":"2022 26th International Conference on System Theory, Control and Computing (ICSTCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125861519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-19DOI: 10.1109/ICSTCC55426.2022.9931785
Daniel Burk, Andreas Völz, K. Graichen
The major part of the execution time of distributed algorithms is required for the communication between agents. This paper approaches a reduction of the communication effort by reducing the number of edges in the considered graph. This is achieved by partitioning the graph and formulating a super graph. At first, the computational and communication effort is evaluated on an abstract level independent of the distributed algorithm, before the Alternating Direction Method of Multipliers (ADMM) is applied to a system of coupled water tanks. This allows to outline the trade-off between computation and communication time and to evaluate an optimal number of partitions that minimizes the execution time. The influence of the partitioning on the convergence behavior of the distributed algorithm is studied and compared with the concept of neighbor approximation.
{"title":"Improving the Performance of Distributed Model Predictive Control by Applying Graph Partitioning Methods","authors":"Daniel Burk, Andreas Völz, K. Graichen","doi":"10.1109/ICSTCC55426.2022.9931785","DOIUrl":"https://doi.org/10.1109/ICSTCC55426.2022.9931785","url":null,"abstract":"The major part of the execution time of distributed algorithms is required for the communication between agents. This paper approaches a reduction of the communication effort by reducing the number of edges in the considered graph. This is achieved by partitioning the graph and formulating a super graph. At first, the computational and communication effort is evaluated on an abstract level independent of the distributed algorithm, before the Alternating Direction Method of Multipliers (ADMM) is applied to a system of coupled water tanks. This allows to outline the trade-off between computation and communication time and to evaluate an optimal number of partitions that minimizes the execution time. The influence of the partitioning on the convergence behavior of the distributed algorithm is studied and compared with the concept of neighbor approximation.","PeriodicalId":220845,"journal":{"name":"2022 26th International Conference on System Theory, Control and Computing (ICSTCC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129976292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-19DOI: 10.1109/ICSTCC55426.2022.9931848
I. Vasiliev, L. Frangu, M. Cristea
This paper deals with the fault detection of centrifugal pumps, based on measured radial vibrations. The detection method compares the vibration signature of the equipment during normal behavior with the current recorded vibration signal. It raises an alarm if a distance function of the resulted residuum exceeds a predefined threshold. The normal signature and the threshold are learned through a machine learning procedure, based on autoencoding neural networks (NN). Two versions of NNs are trained and evaluated. The detection method proved to be reliable in an industrial application, even when using a single low-cost accelerometer for vibration sensing.
{"title":"Pump Fault Detection Using Autoencoding Neural Network","authors":"I. Vasiliev, L. Frangu, M. Cristea","doi":"10.1109/ICSTCC55426.2022.9931848","DOIUrl":"https://doi.org/10.1109/ICSTCC55426.2022.9931848","url":null,"abstract":"This paper deals with the fault detection of centrifugal pumps, based on measured radial vibrations. The detection method compares the vibration signature of the equipment during normal behavior with the current recorded vibration signal. It raises an alarm if a distance function of the resulted residuum exceeds a predefined threshold. The normal signature and the threshold are learned through a machine learning procedure, based on autoencoding neural networks (NN). Two versions of NNs are trained and evaluated. The detection method proved to be reliable in an industrial application, even when using a single low-cost accelerometer for vibration sensing.","PeriodicalId":220845,"journal":{"name":"2022 26th International Conference on System Theory, Control and Computing (ICSTCC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125081444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-19DOI: 10.1109/ICSTCC55426.2022.9931772
M. Miron, S. Moldovanu, Anisia Culea-Florescu
Diagnosis of breast cancer from ultrasound images (USIs) and images processing are two main stages of medical computing field. In this paper, we propose a Multi-Layer Feed Forward Neural Network (MLFNN) for classification of benign and malignant breast tumors by using a Python based implementation. The neural model is trained using the preprocessed regions of interests (ROIs) from USIs that belong to the Breast Ultrasound Dataset (BUSI dataset). The preprocessing procedure includes extracting the ROIs, resizing, normalizing, and flattening. The ROIs are obtained with our own algorithm that overlaps the original image with its corresponding ground truth image. More images and tumor shapes employed in the training stage of the neural network can lead to better prediction performances. In this study, the binary classification of tumors into benignancy or malignancy gives 99% training accuracy, 86% validation accuracy and 71.43% test accuracy.
{"title":"A Multi-Layer Feed Forward Neural Network for Breast Cancer Diagnosis from Ultrasound Images","authors":"M. Miron, S. Moldovanu, Anisia Culea-Florescu","doi":"10.1109/ICSTCC55426.2022.9931772","DOIUrl":"https://doi.org/10.1109/ICSTCC55426.2022.9931772","url":null,"abstract":"Diagnosis of breast cancer from ultrasound images (USIs) and images processing are two main stages of medical computing field. In this paper, we propose a Multi-Layer Feed Forward Neural Network (MLFNN) for classification of benign and malignant breast tumors by using a Python based implementation. The neural model is trained using the preprocessed regions of interests (ROIs) from USIs that belong to the Breast Ultrasound Dataset (BUSI dataset). The preprocessing procedure includes extracting the ROIs, resizing, normalizing, and flattening. The ROIs are obtained with our own algorithm that overlaps the original image with its corresponding ground truth image. More images and tumor shapes employed in the training stage of the neural network can lead to better prediction performances. In this study, the binary classification of tumors into benignancy or malignancy gives 99% training accuracy, 86% validation accuracy and 71.43% test accuracy.","PeriodicalId":220845,"journal":{"name":"2022 26th International Conference on System Theory, Control and Computing (ICSTCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130871490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-19DOI: 10.1109/ICSTCC55426.2022.9931820
Andrei-Daniel Andreiana, C. Bǎdicǎ, Eugenia Ganea, B. Andreiana
Accurate diagnosis using histopathology images re-quires experienced pathologists, a large amount of work and time. Recent studies show that AI could be a solution to help pathologist by offering a fast and reliable help for setting a diagnosis. This paper offers a review of the latest advancements in renal cancer diagnosis using advanced AI methods, especially Convolutional Neural Networks. It includes both Computer Aided Diagnosis solutions and algorithms or frameworks that use histopathology images as input. It provides extensive data about the input databases, preprocessing methods, feature extraction, classifier architectures and results quantification. Further, it elaborates on the type of classification each algorithm offers, ranging from segmentation to benign-malignant classification and up to renal cancer subtypes differentiation or Fuhrman grade determination.
{"title":"A Review of the Impact of Convolutional Neural Networks in the Process of Renal Cancer Diagnosis","authors":"Andrei-Daniel Andreiana, C. Bǎdicǎ, Eugenia Ganea, B. Andreiana","doi":"10.1109/ICSTCC55426.2022.9931820","DOIUrl":"https://doi.org/10.1109/ICSTCC55426.2022.9931820","url":null,"abstract":"Accurate diagnosis using histopathology images re-quires experienced pathologists, a large amount of work and time. Recent studies show that AI could be a solution to help pathologist by offering a fast and reliable help for setting a diagnosis. This paper offers a review of the latest advancements in renal cancer diagnosis using advanced AI methods, especially Convolutional Neural Networks. It includes both Computer Aided Diagnosis solutions and algorithms or frameworks that use histopathology images as input. It provides extensive data about the input databases, preprocessing methods, feature extraction, classifier architectures and results quantification. Further, it elaborates on the type of classification each algorithm offers, ranging from segmentation to benign-malignant classification and up to renal cancer subtypes differentiation or Fuhrman grade determination.","PeriodicalId":220845,"journal":{"name":"2022 26th International Conference on System Theory, Control and Computing (ICSTCC)","volume":"29 3-4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128828816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-19DOI: 10.1109/ICSTCC55426.2022.9931811
Diana-Andreea Arsene, Alexandru Predescu, Maria Stuparu, Ciprian-Octavian Truică, M. Mocanu, Costin-Gabriel Chiru
Monitoring water consumption has multiple benefits nowadays. Big data collected from the sensors provide a consistent basis for the decision-making processes in terms of establishing the indices and criteria needed to optimize the water demand. In this study, the data provided by four distinct water consumption outlets (hot/cold water sink, toilet, and shower) from multiple households were analyzed. A clustering analysis revealed a visual overview of the consumption events from each outlet. Then, classification methods were used to predict the source of water consumption events using four algorithms based on machine learning and deep learning. The proposed methods and results are promising towards the development of a decision support system for streamlining water consumption in urban water distribution systems.
{"title":"Predicting consumption events in a water monitoring system","authors":"Diana-Andreea Arsene, Alexandru Predescu, Maria Stuparu, Ciprian-Octavian Truică, M. Mocanu, Costin-Gabriel Chiru","doi":"10.1109/ICSTCC55426.2022.9931811","DOIUrl":"https://doi.org/10.1109/ICSTCC55426.2022.9931811","url":null,"abstract":"Monitoring water consumption has multiple benefits nowadays. Big data collected from the sensors provide a consistent basis for the decision-making processes in terms of establishing the indices and criteria needed to optimize the water demand. In this study, the data provided by four distinct water consumption outlets (hot/cold water sink, toilet, and shower) from multiple households were analyzed. A clustering analysis revealed a visual overview of the consumption events from each outlet. Then, classification methods were used to predict the source of water consumption events using four algorithms based on machine learning and deep learning. The proposed methods and results are promising towards the development of a decision support system for streamlining water consumption in urban water distribution systems.","PeriodicalId":220845,"journal":{"name":"2022 26th International Conference on System Theory, Control and Computing (ICSTCC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127255163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-19DOI: 10.1109/ICSTCC55426.2022.9931761
Omar Santander, Vidyashankar Kuppuraj, Christopher A. Harrison, M. Baldea
An integrated deep learning - economic model predictive control (EMPC) framework for large scale processes is presented. The framework is successfully implemented to a realistic fluid catalytic cracker (FCC) - fractionator process. Scenarios under the effect of no disturbances (nominal) and with disturbances are simulated demonstrating fast computation (potentially allowing industrial implementation) and improved performance (taking into account process nonlinear behavior, enhancing the process operating profit).
{"title":"Deep learning economic model predictive control for refinery operation: A fluid catalytic cracker - fractionator case study","authors":"Omar Santander, Vidyashankar Kuppuraj, Christopher A. Harrison, M. Baldea","doi":"10.1109/ICSTCC55426.2022.9931761","DOIUrl":"https://doi.org/10.1109/ICSTCC55426.2022.9931761","url":null,"abstract":"An integrated deep learning - economic model predictive control (EMPC) framework for large scale processes is presented. The framework is successfully implemented to a realistic fluid catalytic cracker (FCC) - fractionator process. Scenarios under the effect of no disturbances (nominal) and with disturbances are simulated demonstrating fast computation (potentially allowing industrial implementation) and improved performance (taking into account process nonlinear behavior, enhancing the process operating profit).","PeriodicalId":220845,"journal":{"name":"2022 26th International Conference on System Theory, Control and Computing (ICSTCC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116841139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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