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第41届中国控制会议

作者： Jing Zhang Yi Hua Jiahui Gu Yu Chen Zhong Yin School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The electroencephalogram(EEG) has been demonstrated to be a useful instrument for analyzing human brain activity and assessing a person's cognitive ***,cross-subject cognitive workload assessment remains a challenging task to date because of the wide variation in EEG statistics across *** this paper,a dynamic hierarchical attention based on transformer(DHAT) is proposed to learn the temporal-spatial-spectral features of EEG *** DHAT starts with a hierarchical attention from extracted features in the frequency and time domains,learning inter-channel features and time domain features by muti-head self-attention *** inferred representation of the dimension vector is calculated by automatically assigning a different weight size to the dimension *** the same time,the position encoding,feed-forward neural networks,dropout components are injected to achieve better ***,the gate recurrent unit(GRU) with residual add was used to dynamically fuse the temporal features obtained from the hierarchical *** was performed on 48 subjects from the STEW database,with a mean accuracy of 76.81% for cognitive workload classification across *** DHAT's advanced capability and competitiveness is demonstrated by the comparison with all existing approaches.

关键词： EEG cognitive workload Transformer hierarchical learning deep learning

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Joint Prediction of SOH and RUL for Lithium Batteries Considering Capacity Self Recovery and Model Drift

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IEEE Internet of Things Journal 2025年第12期12卷 22187-22196页

作者： Jia, Zhen Li, Zhifei Wang, Zhengdong Liu, Zhenbao Vong, Chi-Man Xi'an University of Architecture and Technology School of Mechanical and Electrical Engineering Xi'an710055 China Northwestern Polytechnical University School of Civil Aviation Xi'an710072 China University of Macau Department of Computer and Informa tion Science Macau999078 China

Accurate monitoring of the state of health (SOH) and remaining useful life (RUL) of lithium batteries is a key technology to realize their stable and economic operation. The current data-driven methods have problems such as local prediction errors caused by capacity self-recovery effect, model drift phenomenon, and inaccurate trend prediction caused by complex degradation process. In this paper, we propose a joint SOH and RUL forecasting based on the fusion of Kolmogorov-Arnold Network (KAN), Deep Bidirectional Long Short-Term Memory Network (DBLSTM) and adaptive mechanism method (KAN-HDBLSTM-AM). The method innovatively introduces an adaptive mechanism that considers the capacity degradation trend for different prediction steps, suppresses the accumulation of errors in the model prediction process, and further improves the prediction accuracy by combining the nonlinear approximation capability of KAN and the mastery of data temporal characteristics of DBLSTM. Experimental validation shows that the proposed method can effectively overcome the prediction errors caused by capacity self-recovery and model drift, and can retain sufficient accuracy under long prediction steps, with the prediction trend being more in line with the actual change trend and the prediction error being smaller compared with other methods. © 2014 IEEE.

关键词： Prediction models

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Optimizing Diabetes Prediction Accuracy: A Comprehensive Approach with Advanced Preprocessing and Diverse Machine Learning Classifiers 3

Optimizing Diabetes Prediction Accuracy: A Comprehensive App...

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3rd International Conference on Advancement in electrical and Electronic engineering, ICAEEE 2024

作者： Talukder, Md Simul Hasan Nur, Abdullah Hafez Zaman, Snigdha Noor, Moshiur Rahman Khan, Mohammad Aman Ullah Amir, Fatima Bangladesh Atomic Energy Regulatory Authority Nuclear Safety Security and Safeguards Divison Dhaka Bangladesh International Islamic University Chittagong Department of Electronic and Telecommunication Engineering Chittagong Bangladesh International Islamic University Chittagong Department of Computer Science and Engineering Chittagong Bangladesh Eastern University Department of Electrical and Electronic Engineering Dhaka Bangladesh BRAC University Department of Computer Science and Engineering Dhaka Bangladesh Shanto Mariam University of Creative Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350388282

Diabetes is a prevalent chronic health disease affecting millions of people over the world. Early detection and effective management contribute in preventing its complications. This study investigates the use of machine learning algorithms to predict the risk of acquiring diabetes based on multiple clinical parameters (Indian PIMA Dataset). In the study, advanced data preprocessing techniques like missing data handling, outlier rejection, robust scaling and SOMTE oversampling are consolidated with eight traditional machine learning. Effectiveness of those eight models such as Gradient Boosting (GB), XGBoost (EGB), Random Forest (RF), Light GBM (LGBM), Cat Boost (CB), Ada Boost (AB), Decision Tree (DT) and K-Nearest Neighbor (KNN) are analyzed and compared. The prediction outcomes of each model are contrasted using accuracy, roc score and F1 score. After evaluating the model's performances, GB has provided the highest accuracy of 90.25%, ROC score of 96.38% and F1 score of 86.48%. © 2024 IEEE.

关键词： Forecasting

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A Deep Learning Method for Sentence Embeddings Based on Hadamard Matrix Encodings 16

A Deep Learning Method for Sentence Embeddings Based on Hada...

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16th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2022

作者： Trifan, Mircea Ionescu, Bogdan Ionescu, Dan School of Electrical Engineering and Computer Science University of Ottawa OttawaON Canada

ISBN: (数字)9781665481250

ISBN: (纸本)9781665481250

Sentence Embedding is recently getting an accrued attention from the Natural Language Processing (NLP) community. An embedding maps a sentence to a vector of real numbers with applications to similarity and inference tasks. Our method uses: word embeddings, dependency parsing, Hadamard matrix with spread spectrum algorithm and a deep learning neural network trained on the Sentences Involving Compositional Knowledge (SICK) corpus. The dependency parsing labels are associated with rows in a Hadamard matrix. Words embeddings are stored at corresponding rows in another matrix. Using the spread spectrum encoding algorithm the two matrices are combined into a single unidimensional vector. This embedding is then fed to a neural network achieving 80% accuracy while the best score from the SEMEVAL 2014 competition is 84%. The advantages of this method stem from encoding of any sentence size, using only fully connected neural networks, tacking into account the word order and handling long range word dependencies. © 2022 IEEE.

关键词： Embeddings

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A Boosted Communicational Salp Swarm Algorithm: Performance Optimization and Comprehensive Analysis

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Journal of Bionic engineering 2023年第3期20卷 1296-1332页

作者： Chao Lin Pengjun Wang Ali Asghar Heidari Xuehua Zhao Huiling Chen College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035ZhejiangChina College of Electrical and Electronic Engineering Wenzhou UniversityWenzhou325035China School of Digital Media Shenzhen Institute of Information TechnologyShenzhen518172China

The Salp Swarm Algorithm (SSA) is a recently proposed swarm intelligence algorithm inspired by salps, a marine creature similar to jellyfish. Despite its simple structure and solid exploratory ability, SSA suffers from low convergence accuracy and slow convergence speed when dealing with some complex problems. Therefore, this paper proposes an improved algorithm based on SSA and adds three improvements. First, the Real-time Update Mechanism (RUM) underwrites the role of ensuring that excellent individual information will not be lost and information exchange will not lag in the iterative process. Second, the Communication Strategy (CMS), on the other hand, uses the multiplicative relationship of multiple individuals to regulate the exploration and exploitation process dynamically. Third, the Selective Replacement Strategy (SRS) is designed to adaptively adjust the variance ratio of individuals to enhance the accuracy and depth of convergence. The new proposal presented in this study is named RCSSSA. The global optimization capability of the algorithm was tested against various high-performance and novel algorithms at IEEE CEC 2014, and its constrained optimization capability was tested at IEEE CEC 2011. The experimental results demonstrate that the proposed algorithm can converge faster while obtaining better optimization results than traditional swarm intelligence and other improved algorithms. The statistical data in the table support its optimization capabilities, and multiple graphs deepen the understanding and analysis of the proposed algorithm.

关键词： Salp swarm algorithm Swarm intelligence Global optimization Exploration Exploitation

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Utilization of Encoding, Early Stopping, Hyper Parameter Tuning, and Machine Learning Models for Bank Fraud Detection 9

Utilization of Encoding, Early Stopping, Hyper Parameter Tun...

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9th IEEE International Women in engineering (WIE) Conference on electrical and computer engineering, WIECON-ECE 2023

作者： Islam, Md Aminul Nag, Anindya Chowdhury, Saumik Fahim, SM Firoz Ahmed Ghosh, Arjan Mumtaj, Nahi School of Engineering Computing Mathematics Oxford Brookes University Oxford United Kingdom Computer Science & Engineering Discipline Khulna University Khulna9208 Bangladesh Jashore University of Science & Technology Department of Computer Science & Engineering Jashore7408 Bangladesh American International University Bangladesh Department of Computer Science & Engineering Dhaka Bangladesh BSRM School of Engineering Electrical and Electronics BRAC University Dhaka Bangladesh

ISBN: (纸本)9798350319651

An effective fraud detection system must protect millions of clients for a secure banking system, which can be achieved using machine learning and AI. In this article, authors have applied four supervised machine learning models: k-nearest neighbors (KNN), random forest (RF), decision tree, and logistic regression (LR) algorithm to detect bank fraud for a synthetic dataset having 1,00,000 rows and 32 columns. Adequate preprocessing, decoding, rigorous feature engineering, validation, performance evaluation, and explanation have allowed the readers to understand the whole study. The algorithms' accuracy is similar for label encoding, which is not prescribed. Still, a significant AUC of 98% has been achieved in Gradient Boosting Models. Further application of this study can be done in real-life cases of banks, insurance, and finance institutions. © 2023 IEEE.

关键词： Decision trees

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Compact Model for Trap Assisted Tunneling based GIDL

Compact Model for Trap Assisted Tunneling based GIDL

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2022 Device Research Conference, DRC 2022

作者： Dabhi, Chetan Kumar Pahwa, Girish Salahuddin, Sayeef Hu, Chenming University of California Berkeley Electrical Engineering and Computer Science United States

ISBN: (数字)9781665498838

ISBN: (纸本)9781665498838

State-of-the-art FinFETs exhibit the Gate-Induced-Drain-Leakage (GIDL) current, which cannot be attributed entirely to conventional Band-to-Band Tunneling (BTBT) physics for GIDL [1]. For the strained FinFET technology, the Trap-Assisted Tunneling (TAT) is the governing physical mechanism for most GIDL leakage due to a low gate induced vertical field in the gate-drain overlap region. This work presents the TAT-based GIDL compact model, and the developed model is validated with measurement data and TCAD simulations. The model is implemented as part of the industry-standard BSIM-CMG compact model for FinFETs. © 2022 IEEE.

关键词： FinFET

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Binary Growth Optimizer: For Solving Feature Selection Optimization Problems 15th

Binary Growth Optimizer: For Solving Feature Selection Optim...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Chu, Shu-Chuan Dou, Zhi-Chao Pan, Jeng-Shyang Kong, Lingping Pan, Tien-Szu College of Computer Science and Engineering Shandong University of Science and Technology Shandong Qingdao266590 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Ostrava Czech Republic Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung82445 Taiwan

ISBN: (纸本)9789819700677

The present study introduces a novel binary meta-heuristic optimizer, referred to as the Binary Growth Optimizer (BGO), which is specifically developed to address discrete optimization problems. The BGO utilizes transfer functions to convert positions in a continuous space to discrete positions. To assess the performance of the proposed algorithm, we conducted experiments on 14 publicly available datasets from the UCI Machine Learning Repository. We compared the BGO’s performance with seven state-of-the-art meta-heuristics. Based on convergence accuracy statistics, the results indicate that the BGO outperforms the seven compared meta-heuristics, providing the most favorable outcomes. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heuristic algorithms

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Machine Learning Based Analysis of Cyber-Attacks Targeting Smart Grid Infrastructure 2nd

Machine Learning Based Analysis of Cyber-Attacks Targeting S...

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2nd International Conference on Advances in Computing Research, ACR 2024

作者： Nijim, Mais Kanumuri, Viswas Al Aqqad, Waseem Albataineh, Hisham Department of Electrical Engineering and Computer Science Texas A&M University-Kingsville KingsvilleTX United States Department of Electrical and Computer Engineering West Virginia Tech BeckleyWV United States Department of Physics and Geosciences Texas A&M University-Kingsville KingsvilleTX United States

ISBN: (纸本)9783031569494

The transformation of traditional electric power systems into smart grids has allowed for improved monitoring and control capabilities. However, this evolution has also brought about new challenges in the form of malware and vulnerabilities, creating significant security concerns. To address this issue, sensors are being deployed to the grid, leading to a large influx of data that needs to be analyzed for accurate results. Machine learning algorithms are being utilized to process this data and extract important information. This paper explores the application of various machine learning algorithms in smart grids, with a specific focus on their use in cybersecurity. We identify different types of cyber threats affecting smart grids and examine defense strategies to counter these threats. Through this study, we provide insights into the potential of machine learning in addressing the cybersecurity challenges in smart grids. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cybersecurity

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Insole Design and Optimization Processes for Gait Analysis

Insole Design and Optimization Processes for Gait Analysis

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Orozco Villanueva, Kevin Alejandro Richter, Miguel Villa, Carlos Martinez-Villasenor, Lourdes Ponce, Hiram Barrera-Animas, Ari Yair Universidad Panamericana Faculty of Engineering Mexico City Mexico Massachusetts Institute of Technology Electrical Engineering and Computer Science Boston United States

ISBN: (纸本)9781665430654

Gait analysis is becoming increasingly central in various fields such as biomechanics, medicine, and sports. This complex study assesses how individuals walk, weighing in on aspects like bone alignment, joint range of motion, and neuro-muscular activity. Its profound importance emanates from its capability to identify potential walking anomalies at an early stage, which can offset the need for invasive medical interventions and offer valuable insights for clinicians to make well-informed decisions. Inspiration for this in-depth gait analysis research was sourced from previous attempts that employed tools like step mats and cameras to track and analyze movement. Several design imperatives were consolidated within this framework, including the vital need of user safety, assuring comfort throughout usage, preserving system stability, and the undoubtedly significant feature of keeping the insole lightweight. These priorities were not merely for user comfort but were essential for the fidelity of the data being captured. In this paper, it is described the design and optimization processes of an insole for gait analysis following an agile methodology which involves the following stages: requirements, design, implementation and testing. This study delves deeply into the challenges of designing, implementing, and optimizing instrumented insoles for gait analysis. The requirements were successfully achieved by creating a 3mm-thick, flexible insole using TPU 95 material through 3D printing. This insole effectively encapsulates electronic components, ensuring comfort, durability, and safety. © 2023 IEEE.

关键词： Gait analysis insole unobtrusive wearable

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