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Deep Learning Model for Analyzing EEG Signal Analysis

IEEE Access 2025年 13卷 91034-91045页

作者： Gupta, Varun Kumar, Vivek Prince Singh, Saurabh Lee, Young-Seok Ra, In-Ho Department of Electronics and Communication National Institute of Technology Sikkim India National Institute of Technology Department of Electrical and Electronics Engineering Sikkim India Dongguk University Department of Industrial & Systems Engineering Seoul04620 Korea Republic of Woosong University Department of AI and Bigdata Daejeon34606 Korea Republic of Kunsan National University School of Computer & Software Gunsan Korea Republic of Kunsan National University School of Software Gunsan54150 Korea Republic of

To analyze the physiological information within the acquired EEG signal is very cumbersome due to the possibility of several factors, viz. noise and artifacts, complexity of brain dynamics, and inter-subject variability. To address these issues, this paper compares a U-shaped encoder-decoder network (UNET) and Bat-based UNET signal analysis (BUSA) techniques to classify depression rates in the Electroencephalogram (EEG) datasets. The main objective of including these two techniques is to reveal their effectiveness. It comprises pre-processing, feature extraction, feature selection, and classification stages. The framework excels at noise reduction during pre-processing, enhancing dataset integrity. Feature extraction leverages band power and correlation dimension to extract crucial features. Furthermore, feature selection optimizes classification accuracy by refining the fitness function of bats in the classification layer. The performance of UNET and BUSA are compared based on the following performance evaluating parameters viz. accuracy (Acc), Area Under the Curve (AUC), precision (P), and recall (R) (or sensitivity (Se)). The results indicated that the BUSA technique outperforms the UNET technique. © 2013 IEEE.

关键词： Electroencephalography

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Pattern-based Requirements Elicitation for Sovereign Data Sharing

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Procedia Computer Science 2025年 254卷 147-156页

作者： Julia Pampus Maritta Heisel Fraunhofer Institute for Software and Systems Engineering ISST Speicherstraβe 6 44147 Dortmund Germany University of Duisburg-Essen Bismarckstr 90 47057 Duisburg Germany

The growing data economy increasingly focuses on self-determined and autonomous data sharing, supported by infrastructures that create a trustful and secure environment. A key feature in this context is the offering, negotiation, and enforcement of so-called data usage conditions (DUCs), also known as policies. The design of involved software systems requires a structured and stakeholder-specific elicitation of technical requirements. To address this issue, we define a requirements model, consisting of actor, dataset, and condition entities, for sovereign data sharing and present a method for requirements elicitation in form of a five-step agenda with 13 validation conditions (VCs). The application of this method produces a set of instantiated requirements templates that provide descriptive information about involved actors, identified datasets, and applied DUCs. We demonstrate our method using an established use case from the automotive industry and evaluate it in qualitative expert interviews.

关键词： Data Sharing Sovereignty Data Usage Conditions Data Spaces Patterns Requirements engineering

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Voltage Relaxation Pattern Recognition for Efficient Sorting of Healthy Cells for Second-Life Applications of Retired Electric Vehicle Batteries 25

Voltage Relaxation Pattern Recognition for Efficient Sorting...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Samanta, Akash Karnehm, Dominic Neve, Antje Williamson, Sheldon Ontario Tech University Electrical Computer And Software Engineering Oshawa Canada Institute Of Intelligent Distributed Systems University Of The Bundeswehr Munich Neubiberg Germany

ISBN: (纸本)9798350340266

First-generation electric vehicle (EV) batteries are now retiring from their first life with 70-80%of their initial capacity and are becoming available in the market. To harness the remaining capacity of retired batteries before they reach the end of their life, researchers have proposed various methods, including backup and emergency power supplies for homes, grid-tied stationary energy storage, and storage for renewable energy applications. These applications are collectively termed second-life applications. The first step in utilizing these retired batteries is the selection of healthy cells, as cells in an EV battery pack do not degrade evenly. State-of-the-art cell selection and sorting techniques are either ineffective for industrial-grade applications or highly time-consuming processes. Therefore, this paper proposes a charging voltage relaxation pattern recognition method to efficiently sort healthy cells, powered by data-driven machine learning algorithms. A wide range of battery cycling data collected under different ambient and charging conditions is used for training, testing, and validating the proposed sorting strategy. Furthermore, a comparative analysis is conducted to demonstrate the effectiveness of the proposed strategy compared to state-of-the-art methods. © 2024 IEEE.

关键词： Battery management systems

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Five-layer IoT and Fog Computing Framework Towards Digital Twinning of Battery Management systems for e-Transportation 4

Five-layer IoT and Fog Computing Framework Towards Digital T...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Karnehm, Dominic Samanta, Akash Neve, Antje Williamson, Sheldon University of the Bundeswehr Munich Institute of Intelligent Distributed Systems Neubiberg Germany Ontario Tech University Electrical Computer and Software Engineering Oshawa Canada

ISBN: (纸本)9798350306262

It is needless to mention that the proven capability of cloud computing and digital twin-based monitoring and control systems will play a major role in the implementation of digital twin-based lithium-ion battery management systems (BMS) for e-transportation. In this context, the combination of the Internet of Things (IoT) and fog computing emerges as an enabling technology that facilitates bidirectional data communication and processing. This paper proposes a five-layer IoT framework for the implementation of cloud-based battery monitoring and BMS. Additionally, it critically analyzes the existing IoT frameworks applied to BMS and justifies the suitability of the five-layer architecture. Furthermore, the advantages of fog computing, such as low latency, fault tolerance, limitless systems, multiple applications, and shared responsibility, are discussed. The paper also explores four previously proposed computing-enabled architectures: centralized, hybrid, distributed, and hybrid-distributed computing. Moreover, the potential of utilizing hybrid computing architectures, incorporating fog computing as a supplement to cloud computing, is highlighted to address fault, privacy, or latency issues in cloud-based BMS. © 2024 IEEE.

关键词： Battery management systems

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Realization of software Artifact Knowledge Base with Text Classification

Realization of Software Artifact Knowledge Base with Text Cl...

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2023 International Conference on Frontiers of Robotics and software engineering, FRSE 2023

作者： Liu, Yang Li, Xing Feng, Haojie Kong, Deyan Ji, Zhenyan Beijing Jiaotong University School of Software Engineering Beijing China Systems Engineering Research Institute China State Shipbuilding Corporation Limited Beijing China

ISBN: (纸本)9798350301113

software artifacts are deliverables created by developers throughout the software development lifecycle, including documents, codes, test cases, and other outputs. Effective management of software artifacts is essential for tracking software requirements and enhancing development efficiency. To manage the artifacts effectively and improve software development efficiency, we design and implement a software artifact knowledge base. To further enhance the organization of text software artifacts, we propose a text classification model utilized by software artifact knowledge base to classify software texts artifacts. Experiments are conducted to assess the performance of the text classification model. © 2023 IEEE.

关键词： Efficiency

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MLOps Challenges in Industry 4.0

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SN Computer Science 2023年第6期4卷 1-11页

作者： Faubel, Leonhard Schmid, Klaus Eichelberger, Holger Software Systems Engineering Institute of Computer Science University of Hildesheim Universitätspl. 1 Lower Saxony Hildesheim 31141 Germany

An important part of the Industry 4.0 vision is the use of machine learning (ML) techniques to create novel capabilities and flexibility in industrial production processes. Currently, there is a strong emphasis on MLOps as an enabling collection of practices, techniques, and tools to integrate ML into industrial practice. However, while MLOps is often discussed in the context of pure software systems, Industry 4.0 systems received much less attention. So far, there is only little research focusing on MLOps for Industry 4.0. In this paper, we discuss whether MLOps in Industry 4.0 leads to significantly different challenges compared to typical Internet systems. We provide an initial analysis of MLOps approaches and identify both context-independent MLOps challenges (general challenges) as well as challenges particular to Industry 4.0 (specific challenges) and conclude that MLOps works very similarly in Industry 4.0 systems to pure software systems. This indicates that existing tools and approaches are also mostly suited for the Industry 4.0 context. © 2023, The Author(s).

关键词： Challenges Industry 4.0 Machine learning MLOps

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Audio-Assisted Smart Home Security Monitoring with Few Samples

Audio-Assisted Smart Home Security Monitoring with Few Sampl...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Chi, Haotian Ma, Qi Zhang, Yuxuan Fu, Chenglong Wang, Yuwei Geng, Haijun Du, Xiaojiang Shanxi University School of Automation and Software Engineering Taiyuan China University of North Carolina at Charlotte Department of Software and Information Systems CharlotteNC United States Stevens Institute of Technology Department of Electical and Computer Engineering HobokenNJ United States

ISBN: (纸本)9798350351255

Smart home IoT devices have always been the target of various cyber attacks. By leveraging the smart home monitoring infrastructure, event-based anomaly detection is effective to detect anomalies that cause unfavorable working state of IoT devices. However, IoT events are proven to be vulnerable to event-targeted attacks which could be achieved by exploiting the vulnerabilities embedded in IoT devices, protocols and/or platforms. Thus, existing event-based anomaly detection is not robust in the case of unreliable input. To address this issue, our insight is that the embedded microphone components in many off-the-shelf home devices (e.g., smart doorbells, speakers, cameras, tablets, laptops, etc.) could be utilized to gather acoustic information to help increase the reliability and capability of smart home security monitoring systems. To verify this idea, we propose an audio-assisted framework IoTAudMon for detecting event-targeted attacks. Considering the heterogeneity and sparsity nature of smart homes IoT devices and events, we employ transfer learning to design a practical pipeline for extracting semantic information from audio, eliminating the requirement of human labeling and mitigating the cold start issue in existing solutions. Experiments on public datasets and real devices demonstrate the effectiveness of IoTAudMon. © 2024 IEEE.

关键词： Smart manufacturing

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Application of the ARCADIA Method on a Bulk Carrier Vessel Equipped with a Wind-assistance Device

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INCOSE International Symposium 2024年第1期34卷 24-36页

作者： Dreier, Mathias Bajzek, Matthias Hick, Hannes Michels, Nico Burchardt, Carsten Aoyama, Kazuhiro Institute of Machine Elements and Methods of Development Graz University of Technology Austria Institute Institute of Machine Elements and Methods of Development Graz University of Technology Austria Head of Digital Enterprise Siemens Industry Software GmbH Science Siemens Industry Software GmbH Department of Systems Innovation School of Engineering The University of Tokyo Japan

The maritime industry is undergoing a major transformation to achieve reduction of greenhouse gas emissions. Many new options, such as alternative propulsion systems and fuels, optimized routes, or auxiliary propulsion systems, including wind-assistance devices, need to be integrated and aligned within a wide network of different stakeholders. New ways are necessary to work with and manage the increasing complexity in the maritime industry. Model-based systems engineering approaches are a promising strategy to gain a better understanding of the as-is situation and to develop advanced solutions. This paper shows the application of the ARCADIA method for the maritime industry, with the target of integrating wind-assistance devices to vessels, using the ARCADIA modeling language and the Capella tool. Copyright © 2024 by Dreier et al. Permission granted to INCOSE to publish and use.

关键词： Marine industry

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Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年第12期12卷 19189-19201页

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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UKF‐MOT:An unscented Kalman filter‐based 3D multi‐object tracker

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 1031-1041页

作者： Meng Liu Jianwei Niu Yu Liu Collective Intelligence&Collaboration Laboratory China North Artificial Intelligence and Innovation Research InstituteBeijingChina China North Vehicle Research Institute BeijingChina State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijingChina State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijingChina School of Computer Science and Engineering Beihang UniversityBeijingChina

Multi‐object tracking in autonomous driving is a non‐linear *** better address the tracking problem,this paper leveraged an unscented Kalman filter to predict the object's *** the association stage,the Mahalanobis distance was employed as an affinity metric,and a Non‐minimum Suppression method was designed for *** the detections fed into the tracker and continuous‘predicting‐matching’steps,the states of each object at different time steps were described as their own continuous *** conducted extensive experiments to evaluate tracking accuracy on three challenging datasets(KITTI,nuScenes and Waymo).The experimental results demon-strated that our method effectively achieved multi‐object tracking with satisfactory ac-curacy and real‐time efficiency.

关键词： autonomous vehicle transportation

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