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A design method of model error compensator for systems with polytopic-type uncertainty and disturbances

SICE Journal of Control, Measurement, and System Integration 2021年第2期14卷 119-127页

作者： Yoshida, Ryuichiro Tanigawa, Yuki Okajima, Hiroshi Matsunaga, Nobutomo Department of Computer Science and Electrical Engineering Kumamoto University Kumamoto Japan

Control systems achieve the desired performance with the model-based controller if the dynamical model of the actual plant is given with sufficient accuracy. However, if there exists a difference between the actual plant and its model dynamics, the model-based controller does not work well and does not achieve the intended desired performance. A model error compensator (MEC) is proposed for overcoming the model error in our previous study. Attaching the compensator for the model error to the actual plant, the output trajectory of the actual plant is made close to that of its model. Then, from the controller, the apparent difference in the dynamics can be smaller, and performance degradation is drastically reduced. MEC is useful for various control systems such as non-linear systems and the control systems with delay, and so on. In this paper, we propose an original design method of the filter parameters in MEC for systems with polytopic-type uncertainty and disturbances. First, we show an analysis method about the robust performance of MEC for the system with the polytopic type uncertainty based on an linear matrix inequality problem. The gain parameters in MEC is designed using particle swarm optimization and the presented analysis method. The effectiveness of the design method for the system with polytopic-type uncertainty and disturbance is evaluated using numerical examples. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Linear matrix inequalities

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Enhanced U-Net and PSO-Optimized ANFIS for Classifying Fish Diseases in Underwater Images

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International Journal of Fuzzy Systems 2024年第8期26卷 2518-2535页

作者： Khabusi, Simon Peter Huang, Yo-Ping Lee, Mong-Fong Tsai, Meng-Chun Department of Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Electrical Engineering National Penghu University of Science and Technology Penghu88046 Taiwan Department of Computer Science and Information Engineering National Taipei University New Taipei City23741 Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung41349 Taiwan Department of Aquaculture National Penghu University of Science and Technology Penghu88046 Taiwan General Competency Center National Penghu University of Science and Technology Penghu88046 Taiwan

Fish diseases are among the major limiting factors to increase global aquaculture production. They lead to increased fish mortality, low breeding and growth rates, and low meat quality. The success of aquaculture is heavily dependent on the timely identification of disease. Therefore, we propose a fuzzy U-Net model to automatically identify fish disease from underwater images. U-Net is enhanced with multi-head channel and spatial attention and used to segment infected fish regions from fish disease images. Color pixel intensity features are then extracted from the localized regions, a form of guided feature extraction. Fuzzy C-means clustering is then used to find the cluster centroids and data distribution within the clusters, for the design of fuzzy membership functions. Moreover, the number of clusters are determined by silhouette score. Adaptive neuro-fuzzy inference system (ANFIS) is then trained, tested, and cross-validated for fish disease identification. The model parameters are optimized using particle swarm optimization (PSO) algorithm and compared with gradient-based methods. For image segmentation, the enhanced U-Net achieved a mean intersection over union (IoU) of 86.29%, mean pixel accuracy of 90.94%, mean precision of 93.58%, and mean recall value of 89.94% on 42 test images. Subsequently, ANFIS with PSO achieved overall superior performance on fish disease identification over gradient-based methods, with accuracy of 99.31%, precision of 99.00%, recall of 99.00%, and F1-score of 99.00%. The high-performance results of the optimized ANFIS confirm the robustness and efficacy of the proposed method to automatically identify fish diseases in aquaculture. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2024.

关键词： Fuzzy inference

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Peer-to-Peer Learning Dynamics of Wide Neural Networks

Peer-to-Peer Learning Dynamics of Wide Neural Networks

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shreyas Chaudhari Srinivasa Pranav Emile Anand José M. F. Moura Electrical and Computer Engineering Carnegie Mellon University School of Computer Science Georgia Institute of Technology

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Peer-to-peer learning is an increasingly popular framework that enables beyond-5G distributed edge devices to collaboratively train deep neural networks in a privacy-preserving manner without the aid of a central server. Neural network training algorithms for emerging environments, e.g., smart cities, have many design considerations that are difficult to tune in deployment settings – such as neural network architectures and hyperparameters. This presents a critical need for characterizing the training dynamics of distributed optimization algorithms used to train highly nonconvex neural networks in peer-to-peer learning environments. In this work, we provide an explicit characterization of the learning dynamics of wide neural networks trained using popular distributed gradient descent (DGD) algorithms. Our results leverage both recent advancements in neural tangent kernel (NTK) theory and extensive previous work on distributed learning and consensus. We validate our analytical results by accurately predicting the parameter and error dynamics of wide neural networks trained for classification tasks.

关键词： Training computer aided instruction Distance learning Heuristic algorithms Neural networks Signal processing algorithms Prediction algorithms Peer-to-peer computing Classification algorithms Optimization

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Fine-Tuned training method for semantic text similarity measurement using SBERT, Bi-LSTM and Attention Network

Fine-Tuned training method for semantic text similarity meas...

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2023 International Conference on Machine Vision, Image Processing and Imaging Technology, MVIPIT 2023

作者： Ali, Zafar Aziz, Abdul Ali, Asad Ullah, Amin Aurangzeb, Muhammad Nazir, Muhammad Ahsan Southeast University School of Computer Science and Engineering Nanjing China Southeast University School of Electrical Engineering Nanjing China Southeast University School of Information Science and Engineering Nanjing China

ISBN: (纸本)9798350306545

The Semantic Textual Similarity (STS) problem measures the degree to which two text fragments are similar. In QA systems, it is a crucial tool for extracting useful information from documents. In STS challenges, optimizing lexical gaps between phrases has been a crucial issue. Text similarity may be affected by the distance between major parts of the sentence and the absence of context in the text. Numerous studies have developed answers to this issue. In recent years, however, Sentence-BERT has become widespread in semantic textual similarity tasks. Siamese BERT-Networks generate embeddings that are more context-aware and semantically intact. In addition, BiDirectional LSTM provides a deeper grasp of the input and captures long-range dependencies, whereas the Attention mechanism highlights the most important information aspects from the data. Due to the success of SBERT and Bi-LSTM in exploiting lexical gaps and semantics, we introduce SBERT-Bi-LSTM-Attention (SBiLA), a training model that combines SBERT, Bi-LSTM, and a simple attention strategy. The model improves similarity by addressing the lexical gap between short and long texts and reduces training loss to 0.052, MSE to 0.042, and RMSE to 0.206 on the SQuAD 2.0 and WikiHow datasets. © 2023 IEEE.

关键词： Semantics

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Motion Segmentation of Pedestrian Trajectories Using Angular Gaussian Mixture Model 23

Motion Segmentation of Pedestrian Trajectories Using Angular...

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5th World Symposium on Software engineering, WSSE 2023

作者： Al-Dhamari, Ahlam Hafeezallah, Adel Abu-Bakar, Syed Abd Rahman Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor81310 Malaysia Faculty of Computer Science and Engineering Hodeidah University Yemen Department of Electrical Engineering Taibah University Madinah Saudi Arabia

ISBN: (纸本)9798400708053

Motion segmentation and identification of structural segments from an ensemble of human trajectories continue to be a challenge. These processes entail distinguishing and categorizing distinct motion patterns manifested by pedestrians within a group, as well as recognizing conspicuous segments that exhibit their overall motion. Accurate motion segmentation is vital in many areas, particularly computer vision, automation, and human behavior investigation and monitoring. However, owing to the complexity and irregularity of human motion, this task demands sophisticated procedures. Addressing this challenge, the Angular Gaussian Mixture Model (AGMM) is proposed in this study for visual motion segmentation. The angular features of the pedestrian trajectories are incorporated into the GMM, allowing the overall proposed framework to accurately expose the similarity between the trajectories and fulfill motion segmentation. The experimental findings conducted on the CUHK benchmark demonstrate that the proposed framework outperforms various state-of-the-art methods. The findings signify the superior performance achieved by the proposed approach in effectively and accurately segmenting motion trajectories within different crowded scenarios. Statistical evaluation approaches, such as normalized mutual information index (NMI), purity, rand index (RI), F-measure, also known as the F1-score (F1), and accuracy (ACC), were utilized employing an immense number of real-world video clips. This study has effectively laid the foundation for an essential and initial stride towards achieving comprehensive, high-level crowd behavior analysis. © 2023 Copyright held by the owner/author(s).

关键词： Trajectories

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Advancing Underwater Image Enhancement Using Hybrid Deep Learning Models

Advancing Underwater Image Enhancement Using Hybrid Deep Lea...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Jain, Upma Boob, Nandini Shirish Anandhi, R.J. Sehgal, Archana Bhavana, Munugapati Kumar, Yogendra Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India

ISBN: (纸本)9798331512965

When attempting to make underwater images better, there are a lot of obstacles to overcome, including water's inherent color distortion, inadequate visibility, and light absorption and dispersion noise. The difficulties in accurately analyzing images caused by these problems apply to marine research, underwater robots, and environmental monitoring are only a few areas which are impacted. To address the limitations of existing picture enhancement techniques, this paper proposes a new hybrid deep learning CNN + GAN model for enhancing underwater photographs. The hybrid model extracts features with GANs and noise reduces with CNNs to produce better photos with better clarity and color correction. I train and validate the model using an underwater picture dataset and it defeats the state of the art approaches on visibility, color correctness, contrast and contrast. In quantitative and qualitative assessments our hybrid deep learning method yields superior generalization in different undersea environments relative to traditional methods. A suggested approach can potentially improve the underwater photography, marine setting data collecting with more precision, as well as better underwater exploration. This work now provides confidence in research progress into autonomous underwater exploration systems and underwater image processing. © 2025 IEEE.

关键词： Convolutional neural networks

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Remote Control of Residential Meter Involving E-Billing 4

Remote Control of Residential Meter Involving E-Billing

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4th International Conference on Electronic and electrical engineering and Intelligent System, ICE3IS 2024

作者： Khan, Sher Dali Alamsyah, Nurwahyu Mufti, Naveed Ul Haq, Qazi Mazhar Mukhtar, Abbas Haq, Muhamad Amirul Malaysia Universitas Muhammadiyah Yogyakarta Information Technology Department Yogyakarta Indonesia UET Mardan Department of Telecommunication Engineering Pakistan Yuan Ze University Department of Computer Science and Engineering and IBPI Taoyuan Taiwan University of Windsor Department of Electrical and Computing Engineering ON Canada Universitas Muhammadiyah Surabaya Computer Science Department Surabaya Indonesia

ISBN: (纸本)9798350378368

In electrical energy distribution, losses are a challenging problem, among these losses electricity theft plays a prominent role. Not only Pakistan but many other countries throughout the world face this challenge. This paper presents a system based on smart meter to detect the approximate location of electricity theft. Along with electricity theft detection, this paper also proposes a system for the elimination of illegal consumers. The approach used is that the total energy is compared with the legally consumed energy. For communication between the meters wireless communication techniques are used including Global System for Mobile Communications (GSM) and Network Repository Function (NRF). © 2024 IEEE.

关键词： Global system for mobile communications

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Thalassemia Screening by Sentiment Analysis on Social Media Platform Twitter

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computers, Materials & Continua 2023年第7期76卷 665-686页

作者： Wadhah Mohammed M.Aqlan Ghassan Ahmed Ali Khairan Rajab Adel Rajab Asadullah Shaikh Fekry Olayah Shehab Abdulhabib Saeed Alzaeemi Kim Gaik Tay Mohd Adib Omar Ernest Mangantig School of Computer Sciences Universiti Sains MalaysiaUSM11800PenangMalaysia College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia86400JohorMalaysia IPPT Universiti Sains MalaysiaUSM11800PenangMalaysia

Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production,resulting in a drop in the size of red blood *** severe forms,it can lead to *** genetic disorder has posed a major burden on public health wherein patients with severe thalassemia need periodic therapy of iron chelation and blood transfusion for ***,controlling thalassemia is extremely important and is made by promoting screening to the general population,particularly among thalassemia *** Twitter is one of the most influential social media platforms for sharing opinions and discussing different topics like people’s health conditions and major public health *** individuals’sentiments in these tweets helps the research centers to formulate strategies to promote thalassemia screening to the *** effective Lexiconbased approach has been introduced in this study by highlighting a classifier called valence aware dictionary for sentiment reasoning(VADER).In this study applied twitter intelligence tool(TWINT),Natural Language Toolkit(NLTK),and VADER constitute the three main *** represents a gold-standard sentiment lexicon,which is basically tailored to attitudes that are communicated by using social *** contribution of this study is to introduce an effective Lexicon-based approach by highlighting a classifier calledVADERto analyze the sentiment of the general population,particularly among thalassemia carriers on the social media platform *** this study,the results showed that the proposed approach achieved 0.829,0.816,and 0.818 regarding precision,recall,together with F-score,*** tweets were crawled using the search keywords,“thalassemia screening,”thalassemia test,“and thalassemia diagnosis”.Finally,results showed that India and Pakistan ranked the highest in mentions in tweets by the public’s conversations on thalassemia screening with 181 and 164 tweets,respectively.

关键词： Social media platform Twitter screening thalassemia lexicon-based VADER

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A Wireless Sensor Network-Based Monitoring System for Transformer Fault Detection and Diagnosis 1

A Wireless Sensor Network-Based Monitoring System for Transf...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Moshakga, Moemedi Captain Yahya, Abid Samikannu, Ravi Kalpana, C. Sivaramkrishnan, M. Begam, K Maruliya Botswana International University of Science and Technology Department of Electrical Computer and Telecommunications Engineering Palapye Botswana Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Karpagam Institute of Technology Department of Computer Science Engineering Coimbatore India Karpagam College of Engineering Department of Electrical and Electronics Engineering Coimbatore India Periyar Maniammai Institute of Science and Technology Department of Electrical and Electronics Engineering Thanjavur India

ISBN: (纸本)9798350359756

This paper presents a new technique for monitoring and diagnosing faults in power transformers using a Wireless Sensor Network (WSN). The study addresses the necessity for an effective and dependable system to detect and diagnose transformer faults in real time, facilitating proactive maintenance and reducing costly downtime. The proposed methodology involves deploying wireless sensors on the transformer to gather data on temperature, oil level, vibration, and electrical parameters. This data is transmitted wirelessly to a central monitoring unit for analysis, where advanced signal processing techniques and machine learning algorithms are used to detect and diagnose transformer faults based on sensor data. Experimental tests were conducted to validate the effectiveness of the proposed system, demonstrating its ability to successfully detect and diagnose various types of transformer faults with high accuracy and reliability while providing real-time alerts for proactive maintenance actions. The significance of this study lies in its contribution to enhancing power distribution systems' reliability and efficiency by enabling early fault detection and diagnosis in transformers. The proposed WSN-based monitoring system offers a practical solution that can be implemented in existing power grids without significant infrastructure modifications. © 2023 IEEE.

关键词： Fault detection

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Artificial Algae Optimization with Deep Belief Network Enabled Ransomware Detection in IoT Environment

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computer Systems science & engineering 2023年第8期46卷 1293-1310页

作者： Mesfer Al Duhayyim Heba G.Mohamed Fadwa Alrowais Fahd N.Al-Wesabi Anwer Mustafa Hilal Abdelwahed Motwakel Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityRiyadhSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science and Art at MuhaeylKing Khalid UniversityAbhaSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The Internet of Things(IoT)has gained more popularity in research because of its large-scale challenges and *** security was the main concern when witnessing the fast development in its applications and *** was a dreary task to independently set security systems in every IoT gadget and upgrade them according to the newer ***,machine learning(ML)techniques optimally use a colossal volume of data generated by IoT *** Learning(DL)related systems were modelled for attack detection in *** the current security systems address restricted attacks and can be utilized outdated datasets for *** study develops an Artificial Algae Optimization Algorithm with Optimal Deep Belief Network(AAA-ODBN)Enabled Ransomware Detection in an IoT *** presented AAAODBN technique mainly intends to recognize and categorize ransomware in the IoT *** presented AAA-ODBN technique follows a three-stage process:feature selection,classification,and parameter *** the first stage,the AAA-ODBN technique uses AAA based feature selection(AAA-FS)technique to elect feature ***,the AAA-ODBN technique employs the DBN model for ransomware *** last,the dragonfly algorithm(DFA)is utilized for the hyperparameter tuning of the DBN technique.A sequence of simulations is implemented to demonstrate the improved performance of the AAA-ODBN *** experimental values indicate the significant outcome of the AAA-ODBN model over other models.

关键词： Internet of things deep learning cybersecurity ransomware detection feature selection

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