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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Rinard, Martin Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA United States

Generative artificial intelligence systems such as large language models (LLMs) exhibit powerful capabilities that many see as the kind of flexible and adaptive intelligence that previously only humans could exhibit. ... 详细信息

ISBN: (纸本)9798400702174

关键词： Software engineering

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Photovoltaics Cell Anomaly Detection Using Deep Learning Techniques 2nd

Photovoltaics Cell Anomaly Detection Using Deep Learning Tec...

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2nd International Conference on Advanced engineering, Technology and Applications, ICAETA 2023

作者： Al-Dulaimi, Abdullah Ahmed Hameed, Alaa Ali Guneser, Muhammet Tahir Jamil, Akhtar Department of Electrical and Electronics Engineering Karabuk University Karabuk Turkey Department of Computer Engineering Istinye University Istanbul Turkey Department of Computer Science National University of Computer and Engineering Sciences Islamabad Pakistan

ISBN: (纸本)9783031509193

Photovoltaic cells play a crucial role in converting sunlight into electrical energy. However, defects can occur during the manufacturing process, negatively impacting these cells’ efficiency and overall performance. Electroluminescence (EL) imaging has emerged as a viable method for defect detection in photovoltaic cells. Developing an accurate and automated detection model capable of identifying and classifying defects in EL images holds significant importance in photovoltaics. This paper introduces a state-of-the-art defect detection model based on the Yolo v.7 architecture designed explicitly for photovoltaic cell electroluminescence images. The model is trained to recognize and categorize five common defect classes, namely black core (Bc), crack (Ck), finger (Fr), star crack (Sc), and thick line (Tl). The proposed model exhibits remarkable performance through experimentation with an average precision of 80%, recall of 87%, and an mAP@.5 score of 86% across all defect classes. Furthermore, a comparative analysis is conducted to evaluate the model’s performance against two recently proposed models. The results affirm the excellent performance of the proposed model, highlighting its superiority in defect detection within the context of photovoltaic cell electroluminescence images. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning Detection Electroluminescence image detection Solar panel

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Fortifying Multi-Agent Architectures in Smart Manufacturing: Leveraging Federated Learning and Blockchain for Security and Resilience 22

Fortifying Multi-Agent Architectures in Smart Manufacturing:...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Tshakwanda, Petro M. Tsegaye, Henok B. Almaayn, Raddad Devetsikiotis, Michael University of New Mexico Department of Electrical and Computer Engineering United States University of Trento Department of Information Engineering and Computer Science Italy

ISBN: (纸本)9798331508050

This work presents a blockchain-based communication architecture for multi-agent smart manufacturing systems, enhanced with federated learning to improve security, data privacy, and scalability. Comparative evaluation with traditional centralized systems across various configurations reveals that our approach achieves up to 12 % higher model accuracy and 15 % greater security, particularly in large-scale deployments, with scalability scores surpassing those of centralized systems by 18 % as agent numbers increase. Although initial latency and energy consumption are higher, these factors improve significantly as the system scales, indicating suitability for complex manufacturing networks. This research underscores the potential of combining federated learning and blockchain to enhance resilience, security, and efficiency in Industry 4.0 smart manufacturing systems. © 2025 IEEE.

关键词： Sensitive data

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A Multi-Modal Smart Switching Based Image Transmission Using Semantic Communication 17

A Multi-Modal Smart Switching Based Image Transmission Using...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Tiwari, Sanjana Tripathi, Sharda Joshi, Sandeep Bhatt, Priyanshu Natarajan, Balasubramaniam Birla Institute of Technology & Science Pilani Department of Electrical & Electronics Engineering 333031 India Kansas State University Department of Electrical & Computer Engineering 66506 United States

ISBN: (纸本)9798331531195

The conventional paradigm of communication primarily concentrates on the transmission of raw data, often disregarding its contextual meaning. However, to tackle the exponential growth in data demands along with the limited availability of transmission bandwidth, there is an increasing need to transition from Shannon's classical information-theoretic communication to a more advanced framework centered on semantics. This work presents a multi-modal semantic-based communication method for the transmission of high-definition images aimed at optimizing the transmitted data volume while maintaining a high throughput and mean intersection over union score. To this end, two architectural models are explored: a denser ResNet-based and a lightweight U-Net-based. Depending on the required QoS and resource availability, the raw image is either semantically segmented to obtain a fine-grained, pixel-level classification of the image or represented as label semantics, which provides only a higher-level, object-based, or region-based classification prior to its transmission. The experimental results show that such an adaptive semantic image processing approach leads to around 63% reduction in the transmitted data volume without compromising on the quality of image reconstruction. © 2025 IEEE.

关键词： Semantic Segmentation

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Hybrid Technique-based Optimal Energy Management in Smart Home Appliances

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IEIE Transactions on Smart Processing and Computing 2024年第5期13卷 425-434页

作者： Shirley, C.P. Depaa, R.A.B. Priya, A. Sarala, R. Solanki, Rajdeep Singh Malini, K.V. Venkatakrishna Reddy, Ch. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Civil Engineering Dr. M.G.R Educational and Research Institute Maduravoyal Chennai95 India School of Architecture Koneru Lakshmaiah Educational Foundation Andhra Pradesh Guntur India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai Rameshwaram High Road Viraganoor Madurai625009 India Department of Computer Science Medi-Caps University Indore India Department of Electrical and Electronics Engineering Sri Sairam College of Engineering Anekal India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad75 India

This manuscript presents a hybrid method for optimal energy management in smart home appliances. The proposed approach combines the Ebola Optimization Search Algorithm (EOSA) with the performance of spiking neural networks (SNN). The key objective of the proposed strategy is cost reduction through day-ahead load scheduling while also considering the best demand response (DR), and self-consumption of photovoltaic (PV). The EOSA method manages air conditioning (AC) and ensures thermal comfort. In addition, the SNN method was used to predict the optimal system control signal. Comparative analysis with existing systems, such as PSO, RFA, and BCO, showed that the proposed strategy resulted in 13% cost savings. The proposed system considered parameters, such as solar radiation, occupant presence, and electricity prices. The proposed technique is implemented in the MATLAB software, and the performance is compared with existing approaches. The efficiency of the proposed approach is higher than the existing techniques. © 2024 The Institute of Electronics and Information Engineers.

关键词： Demand response

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Integrating Advanced Applications in Industrial ADMS for Estimating PV Resources and Topology

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IEEE Transactions on Industry Applications 2022年 1-6页

作者： Bajagain, Surendra Qin, Chuan Pannala, Sanjeev Srivastava, Anurag Dubey, Anamika School of Electrical Engineering and Computer Science Washington State University Pullman WA Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown WV

In contrast to traditional utility monitoring and operational tools, advanced distribution management systems (ADMS) provide the advanced operational features to monitor, secure and operate the distribution system in an integrated man- ner. Large-scale adoption of ADMS at utilities is in the early stage since advanced applications within ADMS environments are still evolving. The topology estimation module is one of the important and challenging ADMS application with enhanced automation. For accurate topology estimation, the topology estimator should capture both uncertainties due to load and PV injection in node measurement data. Load/PV estimation module can provide individual/disaggregated load and PV estimates and supports accurate network estimates. This paper provides a proof-of- the concept for the integration of advanced applications (Load/PV estimation and topology estimation) within an industrial ADMS environment using utility feeder data. IEEE

关键词： Network topology

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Cross-Layer Optimized HARQ Protocol for Underwater Acoustic Wireless Sensor Networks Using Stochastic Network Calculus 2

Cross-Layer Optimized HARQ Protocol for Underwater Acoustic ...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Sivajayaprakash, A. Sukumaran, Rajeev Kumar, C. SRM Institute of Science and Technology Department of Computer Science and Engineering Kattankulathur India Karpagam College of Engineering Department of Electrical and Electronics Engineering Coimbatore641032 India

ISBN: (纸本)9798331534950

UAWSNs face challenges such as long propagation delays, limited bandwidth, and varying channel conditions. To solve these problems, we developed a new protocol called Multi- Hop Cross-Layer Optimized Hybrid Automatic Repeat Request (CLO-HARQ) suitable for UAWSNs. This protocol automatically changes the retransmission strategy and error correction redundancy based on real-time feedback from the physical and network layers. A unique feature of this protocol is the inclusion of environmental factors such as water salinity and temperature in the decision-making process. Also, using Stochastic Network Calculus (SNC), stochastic traffic characteristics predict single-hop and multi-hop communication delay (delay), energy usage (energy usage), and throughput. In the performance evaluation of CLO-HARQ, it has been shown to provide significant improvement in efficiency, throughput, and delay over existing HARQ methods. It is well-suited for long-term underwater monitoring and is a sustainable solution for energy-constrained sensor networks. © 2024 IEEE.

关键词： Network layers

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Accurate State of Health Estimation of Lithium-ion Batteries: An Efficient ResNet-LSTM Approach 4

Accurate State of Health Estimation of Lithium-ion Batteries...

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

作者： Eddin, Ez Eddin Ez Massaoudi, Mohamed Ghrayeb, Ali Qatar University Computer Science and Engineering Qatar Texas A&M University Department of Electrical and Computer Engineering College Station United States Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar

ISBN: (纸本)9798350306262

Accurate estimation of the state of health (SoH) of lithium-ion battery (LIB) application systems is a critical concern in the domain of electric vehicles (EVs). Precise SoH holds significance due to its direct impact on the performance, longevity, and safety of LIBs. This paper proposes an efficient deep learning (DL) method for SoH estimation. The proposed DL model incorporates a residual neural network (ResNet) with long short-term memory (LSTM), extracting spatial-temporal features from LIB data. The ResNet-LSTM approach ensures high accuracy in SoH prediction. Extensive experimental results derived from real-world LIBs are presented, demonstrating the model's superiority and competitiveness in score measures when compared to existing SoH estimation models. The key contributions of this paper include (1) the simulation of a highly effective model explicitly tailored for SoH estimation in LIBs for EVs;(2) empirical verification of the effectiveness and superiority of the proposed model on the widely used NASA battery dataset. Simulation results are carried out to demonstrate the generalization and feasibility of the ResNet-LSTM model in SOH estimation with an overall root-mean-squared error of 0.69%. © 2024 IEEE.

关键词： Long short-term memory

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A Model Driven Framework Enabling the Development of Standardized Text-Free User Interface for Smartphones 21

A Model Driven Framework Enabling the Development of Standar...

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21st International Bhurban Conference on Applied sciences and Technology, IBCAST 2024

作者： Rasheed, Uzair Khalid, Sadia Mehmood, Rabia Ahmad, Muhammad Muneer, Mishal College of Electrical and Mechanical Engineering NUST Department of Computer and Software Engineering Islamabad Pakistan Lahore College for Women University Department of Computer Science Lahore Pakistan International Univesity of Rostock Department of Computer science Germany

ISBN: (纸本)9798331516680

Smartphones offer a wide range of applications globally, and usability is a key factor in their quality. Human-computer Interaction (HCI) continuously evolves to enhance usability by improving user interface (UI) components. However, most UI trends are designed for literate users, neglecting a significant portion of the population who cannot read or write. These users often rely on others for assistance with smartphone applications, and text-based UIs hinder their learning and usage, reducing overall usability. This research addresses the limitations of text-based standards by introducing a standardized framework for developing text-free user interfaces for smartphones. The framework utilizes static and dynamic images instead of text to convey widget or button functions, aiming to enhance usability for illiterate users, including preschoolers. Developed with Obeo Designer and Sirius, the framework's Platform Independent Metamodel (PIM) was validated through case studies representing the target user groups. Future work involves Model to Text (M2T) transformation, and results show the framework's potential for automating applications with text-free UIs. © 2024 IEEE.

关键词： Smartphones

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A Scalable Open-Set ECG Identification System Based on Compressed CNNs

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IEEE Transactions on Neural Networks and Learning Systems 2023年第8期34卷 4966-4980页

作者： Wu, Shun-Chi Wei, Shih-Ying Chang, Chun-Shun Swindlehurst, A. Lee Chiu, Jui-Kun National Tsing Hua University Department of Engineering and System Science Hsinchu30013 Taiwan University of California at Irvine Department of Electrical Engineering and Computer Science IrvineCA92697 United States

Deep learning (DL) is known for its excellence in feature learning and its ability to deliver high-accuracy results. Its application to ECG biometric recognition has received increasing interest but is also accompanied by several deficiencies. In this study, we focus on applying DL, especially convolutional neural networks (CNNs), to ECG biometric identification to address these deficiencies. Using prestored user-specific feature vectors, the proposed scheme can exclude unregistered subjects to realize 'open-set' identification. With the help of its scalable structure and 'transfer learning,' new subjects can be enrolled in an existing system without the need for storing the ECGs of those previously enrolled. Finally, schemes based on the quantum evolutionary algorithm (QEA) are presented to prune unnecessary filters in the proposed CNN model. The performance of the proposed scheme was evaluated using the ECGs of 285 subjects from the PTB dataset. The experimental results demonstrate an identification rate of more than 99% in closed-set identification. Although incorporating the proposed method for unregistered subject exclusion degraded the identification performance slightly, the ability of the approach to resist a dictionary attack was evident. Finally, using the QEA-based filter pruning method and its two-stage extension reduced the number of floating-point operations required to complete one identity recognition to 1.20% and 0.22% of the original value without significantly impacting the identification accuracy. © 2012 IEEE.

关键词： Biometrics

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