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IEEE Networking Letters 2025年第1期7卷 6-10页

作者： Raina, Radhika Jeet Singh, Kamal Kumar, Suman Jain, Sarthak Indian Institute of Technology Ropar Department of Electrical Engineering Rupnagar140001 India Guru Gobind Singh Indraprastha University Computer Science and Engineering New Delhi110078 India

This letter focuses on tackling the challenge of accurately determining the timing of buffalo calving while prioritizing power efficiency. To achieve this, a novel, compact, lightweight and power efficient device is designed for buffalo comfort and can be conveniently attached to the tail. The device wirelessly transmits data to a gateway using Bluetooth Low Energy (BLE). This functionality becomes particularly crucial when the tail movement increases in the last 12 hrs before calving and is regarded as a key behavioral indicator for predicting the onset of labor. Moreover, when an accelerometer is tied to the buffalo's tail, the Z axis, which represents the vertical axis perpendicular to ground is anticipated to show the most notable deflections during this period as discussed in the literature. Thus, to conserve power, data is only transmitted when significant tail movement is detected, typically 12 hrs before calving, i.e., when Mean Z >-3 m/s2. This approach reduces the device's power consumption, extending its battery life to more than 6.08 years (approx.) using 620 mAh / 3V battery. © 2019 IEEE.

关键词： Accelerometers

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A Comprehensive Survey on Machine and Deep Learning for Optical Communications

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IEEE Access 2025年 13卷 88794-88846页

作者： Amirabadi, M.A. Nezamalhosseini, S.A. Kahaei, M.H. Chen, Lawrence R. Ferdowsi University of Mashhad Department of Electrical Engineering Mashhad*** Iran Iran University of Science and Technology School of Electrical Engineering Tehran*** Iran McGill University Department of Electrical and Computer Engineering Montreal Canada

The increasing complexity of optical communication systems and networks necessitates advanced methodologies for extracting valuable insights from vast and heterogeneous datasets. Machine learning (ML) and deep learning (DL) have emerged as pivotal tools in this domain, revolutionizing data analysis and enabling automated self-configuration in optical communication systems. Their adoption is fueled by the growing intricacy of systems and links, driven by numerous adjustable and interdependent parameters. This complexity is particularly evident in areas such as coherent transceivers, advanced digital signal processing, optical performance monitoring, cross-layer network optimizations, and nonlinearity compensation. While ML and DL offer immense potential, their application in optical communications is still in its early stages, with significant opportunities remaining unexplored. Many algorithms have yet to be fully deployed in practical settings, underscoring the emerging nature of this research area. This paper presents a comprehensive survey of ML and DL applications across optical fiber communication (OFC), optical wireless communication (OWC), and optical communication networking (OCN), highlighting the challenges, current advancements, and future potential of these approaches. To address the identified gaps, this survey evaluates and compares ML and DL algorithms in terms of their performance, complexity, objectives, input data, metrics, and applications in optical communication. Specific emphasis is placed on identifying how these algorithms enhance system performance and optimization. Furthermore, the advantages and limitations of existing methods are analyzed, offering a clear perspective on the role of ML and DL in this domain. The survey also includes updated visual representations and domain-specific examples to elucidate the practical applications of ML and DL in OFC, OWC, and OCN. It concludes by discussing the open challenges, proposing potential soluti

关键词： Fiber to the x

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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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Modeling and Simulation of Grid-Connected PV System Faults Using MATLAB/Simulink 22

Modeling and Simulation of Grid-Connected PV System Faults U...

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22nd International Learning and technology Conference, L and T 2025

作者： Alkhawaja, Mayar Miguel, Sandy Shabab, Moutaz Abu Eddin Za'ter, Muhy Batarseh, Majd Ghazi Princess Sumaya University for Technology Electrical Engineering Department Amman Jordan King Abdullah University of Science and Technology Now With the Electrical and Computer Engineering Saudi Arabia University of Central Florida Now With the College of Engineering and Computer Science Orlando United States University of Colorado Boulder Electrical and Computer Engineering Boulder United States

ISBN: (纸本)9798331532215

Grid-connected photovoltaic (PV) systems are crucial to modern renewable energy strategies, but various types of faults can significantly impact their performance. Understanding the behavior of these faults is essential for improving system reliability and reducing maintenance costs. This paper investigates the impact of common faults, including open-circuit, short-circuit, partial shading, and grid faults, by simulating them using MATLAB/Simulink. The focus is on modeling faults that lead to power output reduction. The simulation results demonstrate how different fault types affect critical system parameters, such as grid voltage and active power output. The study highlights the value of simulation as a flexible approach for exploring fault scenarios, providing insights into fault detection and mitigation strategies that can help optimize PV system performance and support the development of robust fault data collection, detection, and classification techniques. © 2025 IEEE.

关键词： Power distribution faults

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Vehicle and pedestrian detection method based on improved YOLOv4-tiny

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Optoelectronics Letters 2023年第10期19卷 623-628页

作者： LI Jing XU Zhengjun XU Liang School of Computer Science and Engineering Tianjin University of TechnologyTianjin300384China School of Electrical Engineering and Automation Tianjin University of TechnologyTianjin300384China

Aiming at the problem of low detection accuracy of vehicle and pedestrian detection models,this paper proposes an improved you only look once v4(YOLOv4)-tiny vehicle and pedestrian target detection *** block attention module(CBAM)is introduced into cross stage partial Darknet-53(CSPDarknet53)-tiny module to enhance feature extraction *** addition,the cross stage partial dense block layer(CSP-DBL)module is used to replace the original simple convolutional module superposition,which compensates for the high-resolution characteristic information and further improves the detection accuracy of the ***,the test results on the BDD100K traffic dataset show that the mean average precision(mAP)value of the final network of the proposed method is 88.74%,and the detection speed reaches 63 frames per second(FPS),which improves the detection accuracy of the network and meets the real-time detection speed.

关键词： replace network method

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Decoding Tomorrow's Gold Prices: A Comparative Study of GRU and CNN-LSTM in the Iranian Market 10

Decoding Tomorrow's Gold Prices: A Comparative Study of GRU ...

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10th International Conference on Web Research, ICWR 2024

作者： Baradaran, Amir Hossein Bohlouli, Masoumeh Motlagh, Mohammad Reza Jahed Iran University of Science and Technology School of Electrical Engineering Tehran Iran Alzahra University Department of Computer Engineering Tehran Iran Iran University of Science and Technology School of Computer Engineering Tehran Iran

ISBN: (纸本)9798350394986

With the rising prominence of gold as a lucrative investment avenue in Iran, this research delves into predicting the future price of 18-carat gold. In pursuit of this objective, a comprehensive comparison is conducted between two neural network architectures: the Gated Recurrent Unit (GRU) as a single structure and a hybrid model combining Convolutional Neural Network (CNN) and Long Short-Term Memory Neural Network (LSTM). The evaluation criteria employed focus on error metrics to gauge the accuracy of price predictions. Results reveal that the CNN-LSTM hybrid neural network exhibits superior performance, showcasing lower error values in predicting the price of 18-carat gold in Iran. Consequently, the chosen model, CNN-LSTM, is employed to forecast the following day's gold prices, providing valuable insights for investors navigating the Iranian market. This research contributes to the ongoing discourse on gold investment strategies by highlighting the effectiveness of advanced neural network models in enhancing predictive accuracy. © 2024 IEEE.

关键词： Long short-term memory

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Robust Deep Transfer Learning Based Object Detection and Tracking Approach

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Intelligent Automation & Soft Computing 2023年第3期35卷 3613-3626页

作者： C.Narmadha T.Kavitha R.Poonguzhali V.Hamsadhwani Ranjan walia Monia B.Jegajothi Electronics and Communication Engineering Periyar Maniammai Institute of Science&Technology(PMIST)Thanjavur613403India Department of Computer Science and Engineering Periyar Maniammai Institute of Science&TechnologyThanjavur613403India Department of Electrical and Electronics Engineering Periyar Maniammai Institute of Science and TechnologyThanjavur613403India Electrical Engineering Department Model Institute of Engineering&Technology(Autonomous)Jammu181122India Department of Computer Science and Engineering Shri Mata Vaishno Devi UniversityKatra182320India Center for Energy Research Chennai Institute of TechnologyChennai600069India

At present days,object detection and tracking concepts have gained more importance among researchers and business ***,deep learning(DL)approaches have been used for object tracking as it increases the perfor-mance and speed of the tracking *** paper presents a novel robust DL based object detection and tracking algorithm using Automated Image Anno-tation with ResNet based Faster regional convolutional neural network(R-CNN)named(AIA-FRCNN)*** AIA-RFRCNN method performs image anno-tation using a Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR)called DCF-CSRT *** AIA-RFRCNN model makes use of Faster RCNN as an object detector and tracker,which involves region proposal network(RPN)and Fast *** RPN is a full convolution network that concurrently predicts the bounding box and score of different *** RPN is a trained model used for the generation of the high-quality region proposals,which are utilized by Fast R-CNN for detection ***,Residual Network(ResNet 101)model is used as a shared convolutional neural network(CNN)for the generation of feature *** performance of the ResNet 101 model is further improved by the use of Adam optimizer,which tunes the hyperparameters namely learning rate,batch size,momentum,and weight ***,softmax layer is applied to classify the *** performance of the AIA-RFRCNN method has been assessed using a benchmark dataset and a detailed comparative analysis of the results takes *** outcome of the experiments indicated the superior characteristics of the AIA-RFRCNN model under diverse aspects.

关键词： Object detection tracking deep learning deep transfer learning image annotation

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Generating animatable 3D cartoon faces from single portraits

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虚拟现实与智能硬件（中英文） 2024年第4期6卷 292-307页

作者： Chuanyu PAN Guowei YANG Taijiang MU Yu-Kun LAI Department of Electrical Engineering and Computer Sciences University of California BerkeleyBerkeley CA 94704USA Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China School of Computer Science and Informatics Cardiff UniversityCardiff CF244AGUK

Background With the development of virtual reality(VR)technology,there is a growing need for customized 3D ***,traditional methods for 3D avatar modeling are either time-consuming or fail to retain the similarity to the person being *** study presents a novel framework for generating animatable 3D cartoon faces from a single portrait *** First,we transferred an input real-world portrait to a stylized cartoon image using *** then proposed a two-stage reconstruction method to recover a 3D cartoon face with detailed *** two-stage strategy initially performs coarse estimation based on template models and subsequently refines the model by nonrigid deformation under landmark ***,we proposed a semantic-preserving face-rigging method based on manually created templates and deformation *** Compared with prior arts,the qualitative and quantitative results show that our method achieves better accuracy,aesthetics,and similarity ***,we demonstrated the capability of the proposed 3D model for real-time facial animation.

关键词： 3D reconstruction Cartoon face reconstruction Face rigging Stylized reconstruction Virtual reality

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Dynamic Optimal Power Flow Method Based on Reinforcement Learning for Offshore Wind Farms Considering Multiple Points of Common Coupling

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Journal of Modern Power Systems and Clean Energy 2024年第6期12卷 1749-1759页

作者： Yang Fu Zixu Ren Shurong Wei Lingling Huang Fangxing Li Yang Liu Engineering Research Center of Offshore Wind Technology Ministry of Education(Shanghai University of Electric Power) Shanghai 200090China Department of Electrical Power Engineering Shanghai University of Electric PowerShanghai 200090China Department of Electrical Engineering and Computer Science The University of TennesseeKnoxvilleTN 37996USA

The widespread adoption of renewable energy sources presents significant challenges for power system *** paper proposes a dynamic optimal power flow(DOPF)method based on reinforcement learning(RL)to ad-dress the dispatching *** proposed method consid-ers a scenario where large-scale offshore wind farms are inter-connected and have access to an onshore power grid through multiple points of common coupling(PCCs).First,the opera-tional area model of the offshore power grid at the PCCs is es-tablished by combining the prediction results and the transmis-sion capacity limit of the offshore power *** upon this,a dynamic optimization model of the power system and its RL en-vironment are constructed with the consideration of offshore power dispatching ***,an improved algorithm based on the conditional generative adversarial network(CGAN)and the soft actor-critic(SAC)algorithm is *** analyzing an improved IEEE 118-node system,the proposed method proves to have the advantage of economy over a longer *** resulting strategy satisfies power system opera-tion constraints,effectively addressing the constraint problem of action space of RL,and it has the added benefit of faster so-lution speeds.

关键词： Offshore power grid optimal scheduling dy-namic optimal power flow(DOPF) reinforcement learning(RL) renewable energy

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A Host-based Intrusion Detection: Using Signature-based and AI-driven Anomaly Detection for Enhanced Cybersecurity∗ 4

A Host-based Intrusion Detection: Using Signature-based and ...

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4th International Conference on Digital Futures and Transformative Technologies, ICoDT2 2024

作者： Rehman, Fazalur Mushtaq, Farhan Zaman, Hafsah National University of Sciences and Technology School of Electrical Engineering and Computer Science Islamabad Pakistan Department of Electrical Engineering Taxila Pakistan

ISBN: (纸本)9798350391862

This research introduces a Hybrid Intrusion Detection System (HIDS) that merges signature-based detection, with AI-powered anomaly detection to enhance the accuracy and effectiveness of identifying cyber threats. The proposed HIDS demonstrates an ability to detect uncommon and sophisticated cyber threats with an accuracy rate of 90.37%. By combining Gradient Boosting and K-Nearest Neighbors (KNN) algorithms the system improves detection precision, speeds up response times, and expands coverage across network traffic. This comprehensive approach overcomes the limitations of traditional methods by enabling threat responses while reducing false positive rates. The study highlights the potential of integrating signature-based and AI-driven techniques to strengthen cybersecurity defences which emphasizes the benefits of this approach. When the system detects a potential threat, alerts are sent to the Security Operations Center (SOC) or Network Operations Center (NOC), with details such as nature of the threat, and the affected system. This study establishes a foundation for real-time cyber threat detection and intelligence sharing in cloud environments, with future Hybrid IDS versions operating across multiple hosts and supported by a web service for cross-platform compatibility and centralized alert system. © 2024 IEEE.

关键词： Network intrusion

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