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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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Short-Term Wind Energy Forecasting Using Deep Learning-Based Predictive Analytics

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computers, Materials & Continua 2022年第7期72卷 1017-1033页

作者： Noman Shabbir Lauri Kütt Muhammad Jawad Oleksandr Husev Ateeq Ur Rehman Akber Abid Gardezi Muhammad Shafiq Jin-Ghoo Choi Department of Electrical Power Engineering&Mechatronics Tallinn University of TechnologyTallinn19086Estonia Department of Electrical and Computer Engineering COMSATS University Islamabad(Lahore Campus)54000Pakistan Department of Electrical Engineering Government College UniversityLahore54000Pakistan Department of Computer Science COMSATS University IslamabadIslamabad45550Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Wind energy is featured by instability due to a number of factors,such as weather,season,time of the day,climatic area and so ***,instability in the generation of wind energy brings new challenges to electric power grids,such as reliability,flexibility,and power *** transition requires a plethora of advanced techniques for accurate forecasting of wind *** this context,wind energy forecasting is closely tied to machine learning(ML)and deep learning(DL)as emerging technologies to create an intelligent energy management *** article attempts to address the short-term wind energy forecasting problem in Estonia using a historical wind energy generation data ***,we taxonomically delve into the state-of-the-art ML and DL algorithms for wind energy forecasting and implement different trending ML and DL algorithms for the day-ahead *** the selection of model parameters,a detailed exploratory data analysis is *** models are trained on a real-time Estonian wind energy generation dataset for the first time with a frequency of 1 *** main objective of the study is to foster an efficient forecasting technique for *** comparative analysis of the results indicates that Support Vector Machine(SVM),Non-linear Autoregressive Neural Networks(NAR),and Recurrent Neural Network-Long-Term Short-Term Memory(RNNLSTM)are respectively 10%,25%,and 32%more efficient compared to TSO’s forecasting ***,RNN-LSTM is the best-suited and computationally effective DL method for wind energy forecasting in Estonia and will serve as a futuristic solution.

关键词： Wind energy production energy forecast machine learning

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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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Deep Reinforcement Learning-Based Task Offloading and Service Migrating Policies in Service Caching-Assisted Mobile Edge Computing

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China Communications 2024年第4期21卷 88-103页

作者： Ke Hongchang Wang Hui Sun Hongbin Halvin Yang School of Computer Technology and Engineering Changchun Institute of TechnologyChangchun 130012China College of Computer Science and Engineering Changchun University of TechnologyChangchun 130012China Department of Electronic and Electrical Engineering University College LondonLondonUK

Emerging mobile edge computing(MEC)is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment(MWE)with limited computational resources and *** to the homogeneity of request tasks from one MWE during a longterm time period,it is vital to predeploy the particular service cachings required by the request tasks at the MEC *** this paper,we model a service caching-assisted MEC framework that takes into account the constraint on the number of service cachings hosted by each edge server and the migration of request tasks from the current edge server to another edge server with service caching required by ***,we propose a multiagent deep reinforcement learning-based computation offloading and task migrating decision-making scheme(MBOMS)to minimize the long-term average weighted *** proposed MBOMS can learn the near-optimal offloading and migrating decision-making policy by centralized training and decentralized *** and comprehensive simulation results reveal that our proposed MBOMS can converge well after training and outperforms the other five baseline algorithms.

关键词： deep reinforcement learning mobile edge computing service caching service migrating

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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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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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Robust Emotion Recognition in EEG Signals Based on a Combination of Multiple Domain Adaptation Techniques

Robust Emotion Recognition in EEG Signals Based on a Combina...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Mirzaee, Alireza Kordestani, Mojtaba Rueda, Luis Saif, Mehrdad Electrical Engineering Islamic Azad University Dariun Branch Shiraz Iran Electrical Engineering University of Windsor WindsorON Canada School of Computer Science University of Windsor WindsorON Canada

ISBN: (纸本)9798350337020

Conventional classification approaches for EEG- based emotion recognition cannot often adapt to different domains, such as cross-subject or cross-dataset scenarios, leading to poor performance. To handle this challenge, we introduce a novel fusion method using a combination of multiple domain adaptation techniques to improve the emotional states in EEG datasets via classification accuracy. For this aim, Our proposed approach exploits domain adaptation approaches such as Transfer Component Analysis (TCA), Correlation Alignment (CORAL), Transfer Joint Matching (TJM), Geodesic Flow Kernel (GFK), and Joint Distribution Adaptation (JDA), to enhance the overall classification performance. Later, a new fusion approach called Multiple Domain Adaptation based on a Neuro-Fuzzy Inference System (MDA-NF) is applied to combine the classifiers using proper fuzzy membership functions and deliver maximum separation between classes. The main contribution is by applying the fusion approach using MDA- NF technique, adaptability is sufficiently enhanced. Another advantage is to employ multiple adaptation techniques that improve separation between classes. In experimental test results conducted with cross-subject and cross-dataset scenarios, the MDA-NF approach demonstrates superior performance in terms of accuracy for both the valence and arousal aspects, as observed in two public DEAP and DREAMER datasets. © 2023 IEEE.

关键词： Cross-domain adaptation Domain adaptation EEG Emotion recognition fusion approaches

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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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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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