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24th International Conference on control Systems and computer Science, CSCS 2023

作者： Oniga, Maria Sultana, Alina-Elena Popescu, Dan Merezeanu, Daniel-Marian Ichim, Loretta University Politehnica of Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania University Politehnica of Bucharest Faculty of Electronics Telecommunications and Information Technology Bucharest Romania 'St. S. Nicolau' Institute of Virology Bucharest Romania

ISBN: (纸本)9798350313390

Skin cancer is one of the most common types of cancer, and it is caused by a variety of dermatological conditions. Identifying abnormalities from skin images is an important pre-diagnostic step to assist physicians in determining the patient's condition. Thus, to aid dermatologists in the diagnosis process, we proposed five CNN-based classification approaches namely ResNet-101, DenseNet-121, GoogLeNet, VGG16, and MobileNetV2 architectures on which the transfer learning process was applied. The HAM10000-N database consisting of 7,120 images, which was obtained from the original HAM10000 dataset through an augmentation process, was used to train the proposed methods. Moreover, the images from the HAM10000-N were pre-processed by removing hair with the DullRazor algorithm. To evaluate and compare the performance of all networks five metrics were calculated: accuracy, precision, recall, and Fl-score. The best results for the seven-class classification of the HAM10000-N dataset were obtained for DenseNet-121 architecture with 87% accuracy, 0.871 precision, 0.87 recall and 0.872 F1-score. © 2023 IEEE.

关键词： Convolutional neural networks

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Modular Model Reduction of Interconnected Systems: A Top-Down Approach

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IFAC-PapersOnLine 2023年第2期56卷 4246-4251页

作者： Lars A.L. Janssen Bart Besselink Rob H.B. Fey Nathan van de Wouw Dynamics and Control group Department of Mechanical Engineering Eindhoven University of Technology Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen

Complex systems often involve interconnected subsystem models developed by multi-disciplinary teams. To simulate and control such systems, a reduced-order model of the interconnected system is required. In the scope of this paper, we pursue this goal by subsystem reduction to warrant modularity of the reduction approach. However, reducing subsystem models affects not only the subsystems accuracy, but also the interconnected model accuracy, making it difficult to predict a priori the accuracy impact of subsystem reduction. To address this challenge, we introduce a top-down approach using mathematical tools from robust performance analysis, enabling the translation of accuracy requirements from the interconnected model to the subsystem level. This enables independent subsystem reduction while ensuring the desired accuracy of the interconnected model. We demonstrate the effectiveness of our approach through a structural dynamics case study.

关键词： Complex systems Model reduction control of interconnected systems Robust performance Top-down approach Error bounds

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Model-based Policy Optimization using Symbolic World Model

arXiv

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arXiv 2024年

作者： Gorodetskiy, Andrey Mironov, Konstantin Panov, Aleksandr Center of Cognitive Modeling Moscow Institute of Physics and Technology Russia Artificial Intelligence Research Institute Russia Federal Research Center Computer Science and Control Russia

The application of learning-based control methods in robotics presents significant challenges. One is that model-free reinforcement learning algorithms use observation data with low sample efficiency. To address this challenge, a prevalent approach is model-based reinforcement learning, which involves employing an environment dynamics model. We suggest approximating transition dynamics with symbolic expressions, which are generated via symbolic regression. Approximation of a mechanical system with a symbolic model has fewer parameters than approximation with neural networks, which can potentially lead to higher accuracy and quality of extrapolation. We use a symbolic dynamics model to generate trajectories in model-based policy optimization to improve the sample efficiency of the learning algorithm. We evaluate our approach across various tasks within simulated environments. Our method demonstrates superior sample efficiency in these tasks compared to model-free and model-based baseline methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Efficiency

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Weighted Null Space Fitting (WNSF): A Link between The Prediction Error Method and Subspace Identification

arXiv

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arXiv 2024年

作者： He, Jiabao Hjalmarsson, Håkan Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

— Subspace identification method (SIM) has been proven to be very useful and numerically robust for estimating state-space models. However, it is in general not believed to be as accurate as prediction error method (PEM). Conversely, PEM, although more accurate, comes with non-convex optimization problems and requires local non-linear optimization algorithms and good initialization points. This contribution proposes a weighted null space fitting (WNSF) method to identify a state-space model, combining some advantages of the two mainstream approaches aforementioned. It starts with the estimate of a nonparametric model using least-squares, and then the reduction to a state-space model in the observer canonical form is a multi-step least-squares procedure where each step consists of the solution of a quadratic optimization problem. Unlike SIM, which focuses on the range space of the extended observability matrix, WNSF estimates its null space, avoiding the need for singular value decomposition. Moreover, the statistically optimal weighting for the null space fitting problem is derived. It is conjectured that WNSF is asymptotically efficient, which is supported by a simulation study. Copyright © 2024, The Authors. All rights reserved.

关键词： State space methods

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Automated Door to Prevent COVID-19 using Fuzzy Logic 4

Automated Door to Prevent COVID-19 using Fuzzy Logic

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4th International Conference on Electrical, Communication and computer Engineering, ICECCE 2023

作者： Khokhar, Salah-Ud-Din Sohaib, Rana Muneeb, Abdul Bibi, Irfana Noor, Muhammad Yasir Imran, Muhammad School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Shandong Jinan China Department of Computer Science G.C. University Lahore Punjab Lahore Pakistan Department of Computer Science Faculty of Computing and Information Technology University of Punjab Punjab Lahore Pakistan Department of Electronics G.C. University Lahore Punjab Lahore Pakistan

ISBN: (纸本)9798350369694

Due to the vagueness and uncertainties due to the coronavirus, it is crucial to implement the standard operating procedures (SOPs) issued by the World Health Organization and the authorities. Therefore an effective control system is required that minimizes the vagueness and uncertainties of people who are on the front line during the period of this pandemic checking the entering persons in an indoor area where it is necessary to maintain a distance of six feet from one another to avoid the spread of coronavirus. In this research work, a robust and efficient system has been proposed that takes protective mask and feverishness values as input and applies them using the fuzzy logic algorithm. If these input values are according to the rules defined in the system, then the door will open as an output. Otherwise, the door will not open. It is, finally, analyzing the output values. This fuzzy model had been implemented using a microcontroller. The genuine difference in the duty cycle of the microcontroller’s output pulse was measured using a 100 MHz digital oscilloscope. Our proposed system was compared with different systems reported in the literature. This work reduces the human effort as well as the negligence of people and builds up the management system without the labor in a mint. © 2023 IEEE.

关键词： Fuzzy logic

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Machine Learning Based Intrusion Detection System in Cloud Environment 8

Machine Learning Based Intrusion Detection System in Cloud E...

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8th International Conference on Information technology and Nanotechnology, ITNT 2022

作者： Saeed, Muhammad Salman Saurabh, Raman Bhasme, Sarang Balasaheb Nazarov, Alexey N. Moscow Institute of Physics and Technology Department of Radio Engineering and Cybernetics Moscow Russia Federal Research Center Computer Science and Control of Russian Academy of Sciences Moscow Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

Attacks and intrusions on computer networks often have different characteristics and behaviors that require professional help. The number of attacks is growing in line with the development of computer networks. In fact, expert knowledge is declining over time and should be reviewed and made available in the system, making it necessary to hire an experienced person. Cloud computing is an advancement in IT technologies that provide users with the most up-To-date, highly sought-After virtual services with high flexibility, low infrastructure costs, and minimal maintenance. Protection against network intruders is one of the most important security challenges for cloud computing, as it affects the privacy, availability, and integrity of cloud services. Because cloud computing is a shared environment, it can be vulnerable to various threats. Because building strong access systems are essential to maintaining cloud security, but it remains an obscure goal and a major challenge due to the growing number of cyber-Attacks. In this study, a machine-learning based approach was used to propose an intrusion detection system (IDS) based on a network based cloud computing model. During the previous processing phase, we used a feature selection algorithm from the CICDDoS2019 database to select features. We have used the Naive Bayes Classifier, Decision Tree Classifier, Supporting Classifier, Logistic Regression, and Random Forest Classifier, all of which are well-known categories. Simulation results when compared to these five different classifiers, random forest algorithm show improvements in overall accuracy, precision, recall and F1 score. We explain how various characteristics of machine learning techniques can be used to build efficient IDS. The goal of this research is to develop a new method based on intrusion detection systems and their varied designs for improving intrusion detection accuracy in cloud computing. © 2022 IEEE.

关键词： Decision trees

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A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

引用

Energy and AI 2023年第4期14卷 221-237页

作者： Zhenglong Sun Ram Machlev Qianchao Wang Juri Belikov Yoash Levron Dmitry Baimel Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)JilinChina The Andrew and Erna Viterbi Faculty of Electrical&Computer Engineering Technion-Israel Institute of TechnologyHaifa 3200003Israel Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing210096China Department of Software Science Tallinn University of TechnologyAkadeemia tee 15a12618 TallinnEstonia Department of Electrical and Electronics Engineering Sami Shamoon College of EngineeringBeer-Sheva84100Israel

In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical ***,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a *** view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault *** data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation *** addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor *** package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification *** database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are *** developed code is available online,and is free to use.

关键词： Motor faults Synchronous motors Public data-set Classification Deep-learning CNN LSTM

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A Novel Framework for Real-Time Water Quality Analysis in Agriculture via ANN and Remote Sensing

A Novel Framework for Real-Time Water Quality Analysis in Ag...

引用

Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Hera Naeem Amir Ali Mokhtarzadeh M. Shahzaib Naeem Kiyindou Michel Precieux Sadaf Shah Department of Computer Science Huaiyin Institute of Technology Huai’an China Department of Intelligent Control Zhejiang University of Science and Tchnology Zhejiang China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Human faces a severe water crisis exacerbated by urbanization, industrialization, and population growth. The country is threatened by depleting water resources, poor storage capacity, poor infrastructure, and climate change impacts on glaciers. Water quality monitoring reveals significant contamination, with a large portion of water sources unsafe for consumption. Improved water management is crucial. Irrigation enhances farmers' overall well-being and provides a reliable income. Increasing agricultural productivity through irrigation is a crucial approach to reducing poverty caused by the poor performance of rain-fed systems. Consequently, the study examines the factors contributing to the underutilization of land available for irrigation. Integration of Remote sensing technique and ANN to evaluate inland water quality by examining its images, collected from USGS and Copernicus data space sites. IBM SPSS Modeler used to train and produce ANNs for predictive analytics. TSS (Total Suspended Solids), algal blooms, and Chlorophyll-a are related but distinct. TSS measures suspended particles in water, algal blooms indicate rapid algae growth due to nutrient excess, and Chlorophyll-a measures algae concentration. High TSS and Chlorophyll-a can result from algal blooms, impacting water quality and clarity.

关键词： Irrigation Water quality Artificial neural networks Particle measurements Water pollution Real-time systems Pollution measurement Water resources Remote sensing Monitoring Climate change Indexes

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Automated Recognition and Analysis of Escape Response in Caenorhabditis Elegans

IEEE Transactions on Computational Biology and Bioinformatic...

引用

IEEE Transactions on Computational Biology and Bioinformatics 2025年第3期22卷 1201-1210页

作者： Wenyue Zhan Weiyang Chen Yi Pan School of Cyber Science and Engineering Qufu Normal University Qufu China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Significant progress in medical research in recent years has led to the successful treatment of numerous illnesses. Unfortunately, effective treatments for several neurological disorders—particularly those affecting motor neurons—remain elusive, not only significantly impairing patients' quality of life but also placing a substantial financial burden on both individuals and society. To address this gap, scientists are focusing on model organisms to unravel pathogenic mechanisms and develop treatment strategies. Among various model organisms, Caenorhabditis elegans ( C. elegans) has shown special relevance in neurological disorder research. Escape responses, including reversals and omega turns, are controlled by motor neurons. Thus, the escape response in C. elegans provides researchers with a reliable observational metric to evaluate the functional state of motor neurons. We developed a head and tail position localization model to determine the positions of the nematode's head and tail, enabling automated counting of escape responses. We further clarified the association between escape response and motor neuron function by comparing the escape response of normal nematodes to those with motor neuron deficiencies. Consequently, the automated counting of C. elegans escape responses is a useful tool for assessing the health of motor neurons. It may offer strong backing for the exploration of neurodegenerative disease therapeutics.

关键词： Videos Motors Tail Neurons Head Gray-scale Trajectory Phenotypes Computational modeling Turning

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A Weighted Least-Squares Method for Non-Asymptotic Identification of Markov Parameters from Multiple Trajectories

arXiv

引用

arXiv 2024年

作者： He, Jiabao Rojas, Cristian R. Hjalmarsson, Håkan Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

Markov parameters play a key role in system identification. There exists many algorithms where these parameters are estimated using least-squares in a first, pre-processing, step, including subspace identification and multi-step least-squares algorithms, such as Weighted Null-Space Fitting. Recently, there has been an increasing interest in non-asymptotic analysis of estimation algorithms. In this contribution we identify the Markov parameters using weighted least-squares and present non-asymptotic analysis for such estimator. To cover both stable and unstable systems, multiple trajectories are collected. We show that with the optimal weighting matrix, weighted least-squares gives a tighter error bound than ordinary least-squares for the case of non-uniformly distributed measurement errors. Moreover, as the optimal weighting matrix depends on the system’s true parameters, we introduce two methods to consistently estimate the optimal weighting matrix, where the convergence rate of these estimates is also provided. Numerical experiments demonstrate improvements of weighted least-squares over ordinary least-squares in finite sample settings. Copyright © 2024, The Authors. All rights reserved.

关键词： Asymptotic analysis

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