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An Enhancement of Object Detection Using YOLO V8 and Mobile Net in Challenging Conditions

SN computer Science 2025年第4期6卷 1-20页

作者： Pasupuleti, Shailaja Ramalakshmi, K. Gunasekaran, Hemalatha Arokiaraj, Rex Macedo Debnath, Saswati Jebaseeli, T. Jemima Department of Computer Science and Engineering Alliance University Karnataka Anekal Bangalore India AU-Centre of Excellence Department of Computer Vision Alliance University Karnataka Anekal Bangalore India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Computer Science and Engineering Alliance University Karnataka Bangalore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

Autonomous drones and deep learning neural networks are becoming popular tools to revolutionize aircraft operations by partially automating visual inspection processes in aircraft maintenance. The research aims to provide a trustworthy method for locating and classifying surface flaws on aircraft, such as dents, fractures, corrosion, and a few other anomalies. To assess the model performance with and without a lightweight network integration, the implementation merges YOLOv8 and Mobile Net architectures, using dropout regularization and data augmentation approaches. To guarantee the model's performance in low-light situations, the dataset has been carefully selected to contain images replicating such settings. Annotation, environment setup, model training, and online dataset scraping are all covered by the thorough technique. A web-scraped dataset is used to train and validate two popular semantic segmentation algorithms, YOLOv8 and Mobile Net, which show enough performance for segmenting and grading aircraft surface problems in practical settings. The results show that precision and recall values are improved using a hybrid technique that ensembles trained Mobile Net and YOLOv8 models. Using COCO metrics, in the proposed approach an assessment of Ensemble WBF, Mobile Net-YOLO, and YOLOv8 outcomes on test and validation sets, guaranteed reliable object recognition and classification abilities. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Aircraft Classification Mobile-Net Object detection Weighted Box Fusion YOLOv8

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Operational Reliability Modeling and Assessment of Battery Energy Storage Based on Lithium-ion Battery Lifetime Degradation

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Journal of Modern Power Systems and Clean Energy 2022年第6期10卷 1738-1749页

作者： Lin Cheng Yuxiang Wan Yanglin Zhou David Wenzhong Gao State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijingChina Department of Electrical and Computer Engineering University of DenverDenverUSA IEEE

Battery energy storage(BES)systems can effectively meet the diversified needs of power system dispatching and assist in renewable energy *** reliability of energy storage is essential to ensure the operational safety of the power ***,BES systems are composed of battery *** suggests that BES performance depends not only on the configuration but also on the operating state over different lifetime *** lack of safety and reliability is the main bottleneck preventing widespread applications of BES ***,a reliability assessment algorithm and a weak-link analytical method for BES systems are proposed while considering battery lifetime ***,a novel lithium-ion battery model is proposed to identify the degradation rate of solid electrolyte interphase film formation and capacity *** impacts of different operating conditions are considered in stress factor ***,a reliability assessment algorithm for a BES system is introduced based on a universal generating *** innovative weak-link analytical method based on the reliability importance index is proposed that combines the evaluation results of state-oriented and state-change-oriented indexes through an entropy weight *** model,algorithm,indexes,and the usefulness are demonstrated in case studies based on aging test data and actual bus operating *** results demonstrate the effects of the battery status and working conditions on BES ***-link analysis is also used to assist BES systems in avoiding short-board batteries to achieve long lifetimes and efficient operation.

关键词： Battery energy storage lifetime degradation operational reliability capacity plummeting weak-link analysis

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In-situ fabrication of vertical heterogeneous nickel diselenide-molybdenum diselenide architectures as bifunctional overall water-splitting electrocatalyst

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Journal of Materials Science & Technology 2024年第10期177卷 256-263页

作者： He Zhou Jingrui Hou Limin Zhang Dezhi Kong Hui Wang Longhui Zeng Zhiwei Ren Tingting Xu Ye Wang Key Laboratory of Material Physics Ministry of EducationSchool of Physics and MicroelectronicsZhengzhou UniversityZhengzhou 450001China Department of Electrical and Computer Engineering University of California at San DiegoSan DiegoCA 92093USA Department of Electronic and Information Engineering The Hong Kong Polytechnic UniversityHung HomKowloon 999077Hong KongChina

Despite the rapid advances in electrocatalysts based on two-dimensional(2D)transition metal dichalco-genides(TMDs)materials,they are subject to serious aggregation,poor conductivity and the presence of inactive basal ***,we have successfully demonstrated the in-situ construction of NiSe_(2)-MoSe_(2) heterostructure arrays on carbon cloth(NiSe_(2)-MoSe_(2)/CC)by a facile two-step hydrothermal *** presence of the synergistic effect in the heterostructures effectively optimizes the poor conductivity and hydrophilicity,and thus enables fast electron transfer,leading to enhanced electrochemical ***-thermore,density functional theory calculations reveal that the electrons redistribution at the heterojunc-tion interface and the reduced Gibbs free energy of hydrogen adsorption for hydrogen evolution reaction(HER)/the Gibbs free energy change value of rate-determining step for oxygen evolution reaction(OER),thus enhancing the HER/OER catalytic ***,the device displays a good performance with a low overpotential of 98 and 310 mV for HER and OER,respectively,and a low cell voltage of 1.59 V for its corresponding electrolyzer(10 mA cm^(-2)).This work presents the high-performance water splitting of bifunctional electrocatalysts based on 2D TMDs materials and offers a novel design concept of interface engineering.

关键词： Bifunctional electrocatalyst Heterostructures Hydrogen/oxygen evolution reaction Water splitting Synergistic effect

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Detecting Pain During Cognitive Tasks via Visibility Graphs of Cortical Signals 22

Detecting Pain During Cognitive Tasks via Visibility Graphs ...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Subramanian, Ashwini Najafizadeh, Laleh Integrated System and NeuroImaging Laboratory Rutgers University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798331520526

Detecting pain is critical for developing adaptive systems in clinical and assistive settings, allowing for timely interventions. This work presents an approach to detect the presence of physical pain during the performance of cognitive tasks from cortical signals. Cortical signals from the motor and prefrontal cortices are acquired using functional near in-frared spectroscopy (fNIRS) during two cognitive tasks, per-formed in the absence and presence of physical pain. Visibil-ity graphs (VGs) of these signals are constructed, and graph metrics of averaged clustering coefficient (C) and edge den-sity (D) are extracted. Statistical analysis reveals differences in these metrics for graphs corresponding to tasks performed under no-pain (NP) versus with-pain (WP) conditions, partic-ularly for signals obtained from the motor cortex. The graph metrics were then used as features to a support vector machine (SVM), achieving an accuracy exceeding 80% in distinguishing between tasks performed with and without pain. Our findings suggest that metrics from VGs of cortical sig-nals may serve as potential biomarkers for pain during cog-nitive tasks. This approach could be especially beneficial for non-verbal patients using assistive brain-computer interfaces. © 2025 IEEE.

关键词： Cognitive tasks fNIRS Pain SVM Visibility graphs

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TopoEEG: A Timesformer-Based Topographic Image Representation Method for Single-Trial Early Detection of P300 22

TopoEEG: A Timesformer-Based Topographic Image Representatio...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Hong, Jiazhen Najafizadeh, Laleh Rutgers University Integrated Systems and NeuroImaging Laboratory Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798331520526

Detecting P300 event-related potentials (ERPs) from electroencephalography (EEG) finds applications in several domains including brain computer interfaces (BCIs). Due to the low signal-to-noise ratio (SNR), however, detecting P300 from single trials has been challenging. As a result, multiple repetitions are typically needed, extending the BCI decision time. To address this problem, in this paper, we present TopoEEG, a novel method that generates a sequence of topography-based images from EEG, to capture both spatial and temporal information. These sequences are subsequently processed using the state-of-the-art video classification technique, TimeSformer. Evaluated using the BCI Competition II (IIb) dataset, results demonstrate that TopoEEG achieves single-trial P300 detection with F1-scores of 0.6830 and 0.7134 within the first 500 ms and 600 ms, respectively. These results highlight TopoEEG's potential to enhance the efficiency and speed of BCIs by reducing the need for multiple repetitions and the time required for decision-making in single-trial P300 detection. © 2025 IEEE.

关键词： Image enhancement

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RAJA Performance Suite: Performance Portability Analysis with Caliper and Thicket

RAJA Performance Suite: Performance Portability Analysis wit...

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Pearce, Olga Burmark, Jason Hornung, Rich Bogale, Befikir Lumsden, Ian Mckinsey, Michael Yokelson, Dewi Boehme, David Brink, Stephanie Taufer, Michela Scogland, Tom Lawrence Livermore National Laboratory LivermoreCA United States University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN United States Texas A&M University Department of Computer Science and Engineering College StationTX United States

ISBN: (纸本)9798350355543

Maintaining performant code in a world of fast-evolving computer architectures and programming models poses a significant challenge to scientists. Typically, benchmark codes are used to model some aspects of a large application code's performance, and are easier to build and run. Such benchmarks can help assess the effects of code or algorithm changes, system updates, and new hardware. However, most performance benchmarks are not written using a wide range of GPU programming models. The RAJA Performance Suite provides a comprehensive set of computational kernels implemented in a variety of programming models. We integrated the performance measurement and analysis tools Caliper and Thicket into the RAJA Performance Suite to facilitate performance comparison across kernel implementations and architectures. This paper describes the RAJA Performance Suite, performance metrics that can be collected, and experimental analysis with case studies. The RAJA Performance Suite and integration of Caliper and Thicket enabled us to effectively measure the variety of implementations on the variety of hardware, and automatically characterize subsets of kernels which exhibit similar bottlenecks - and therefore perform similarly on new architectures which provide a different balance between resources such as FLOPS and memory bandwidth. We definitively demonstrate that the most memory bound kernels show the most performance gains on architectures with high-bandwidth memory (HBM), and that the kernels that have other bottleneck may, to a lesser extent, benefit from the higher-FLOPS GPUs. © 2024 IEEE.

关键词： benchmarking high performance computing performance analysis performance portability

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Age-of-information-aware PI controller for load frequency control

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Protection and Control of Modern Power Systems 2023年第3期8卷 32-46页

作者： Dan Jiao Changzheng Shao Bo Hu Kaigui Xie Chengrong Lin Zhuoxuan Ju State Key Laboratory of Power Transmission Equipment and System Security Chongqing UniversityChongqing 400044China Department of Electrical and Computer Engineering George Washington UniversityDC 20052 WashingtonUSA

Open communication system in modern power systems brings concern about information staleness which may cause power system frequency *** information staleness is often characterized by communication ***,communication delay is a packet-centered metric and cannot reflect the requirement of information freshness for load frequency control(LFC).This paper introduces the age of information(AoI),which is more compre-hensive and informative than the conventional communication delay modeling *** LFC controller and com-munication are integrated into the design for LFC performance *** AoI-aware LFC model is formulated first,and considering each allowable update period of the smart sensor,different AoI-aware PI controllers are then designed according to the exponential decay *** right AoI-aware controller and update period are selected according to the degree of frequency fluctuation of the power *** studies are carried out on one-area and two-area power *** results show the superior performance of the AoI-aware controllers in comparison to the delay-dependent controllers.

关键词： Power system Load frequency control(LFC) Age of information(AoI) PI controller

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Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 66-75页

作者： Lingkang Zeng Wei Yao Ze Hu Hang Shuai Zhouping Li Jinyu Wen Shijie Cheng State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronics EngineeringHuazhong University of Science and TechnologyWuhan 430074China Dispatching and Control Center Central China Branch of State Grid Corporation of ChinaWuhan 430077China. Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA

Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and ***,GTS is composed of offline predetermination and real-time scenario ***,it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power *** improve efficiency of predetermination,this paper proposes a framework of knowledge fusion-based deep reinforcement learning(KF-DRL)for intelligent predetermination of ***,the Markov Decision Process(MDP)for GTS problem is formulated based on transient instability ***,linear action space is developed to reduce dimensionality of action space for multiple controllable ***,KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making *** can enhance the efficiency and learning ***,the graph convolutional network(GCN)is introduced to the policy network for enhanced learning *** simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.

关键词： Deep reinforcement learning generator tripping scheme graph convolutional network invalid action masking knowledgefusion

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Efficient Hardware Design of a Secure Cancellable Biometric Cryptosystem

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Intelligent Automation & Soft Computing 2023年第4期36卷 929-955页

作者： Lamiaa A.Abou Elazm Walid El-Shafai Sameh Ibrahim Mohamed G.Egila H.Shawkey Mohamed K.H.Elsaid Naglaa F.Soliman Hussah Nasser AlEisa Fathi E.Abd El-Samie Department of Microelectronics Electronics Research InstituteAlnozhaEgypt Department of Electronics and Electrical Communications Engineering Ain Shams UniversityCairoEgypt Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh 11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Biometric security is a growing trend,as it supports the authentication of persons using conﬁdential biometric *** of the transmitted data in multi-media systems are susceptible to attacks,which affect the security of these *** systems provide sufﬁcient protection and privacy for *** recently-introduced cancellable biometric recognition systems have not been investigated in the presence of different types of *** addition,they have not been studied on different and large biometric *** point that deserves consideration is the hardware implementation of cancellable biometric recognition *** paper presents a suggested hybrid cancellable biometric recognition system based on a 3D chaotic *** rationale behind the utilization of the 3D chaotic cryptosystem is to guarantee strong encryption of biometric templates,and hence enhance the security and privacy of *** suggested cryptosystem adds signiﬁcant permutation and diffusion to the encrypted biometric *** introduce some sort of attack analysis in this paper to prove the robustness of the proposed cryptosystem against *** addition,a Field Programmable Gate Array(FPGA)implementation of the pro-posed system is *** obtained results with the proposed cryptosystem are compared with those of the traditional encryption schemes,such as Double Random Phase Encoding(DRPE)to reveal superiority,and hence high recogni-tion performance of the proposed cancellable biometric recognition *** obtained results prove that the proposed cryptosystem enhances the security and leads to better efﬁciency of the cancellable biometric recognition system in the presence of different types of attacks.

关键词： Information security cancellable biometric recognition systems cryptanalysis 3D chaotic map encryption FPGA

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Rotor Angle Stability Prediction Using Temporal and Topological Embedding Deep Neural Network Based on Grid-informed Adjacency Matrix

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 695-706页

作者： Peiyuan Sun Long Huo Xin Chen Siyuan Liang the School of Electrical Engineering Xi’an Jiaotong UniversityXi’an 710049China the School of Electrical Engineering and the Center of Nanomaterials for Renewable EnergyState Key Laboratory of Electrical Insulation and Power EquipmentXi’an Jiaotong UniversityXi’an 710049China the Department of Computer Science and Engineering The Chinese University of Hong KongShatin999077Hong KongChina

Rotor angle stability(RAS)prediction is critically essential for maintaining normal operation of the interconnected synchronous machines in power *** wide deployment of phasor measurement units(PMUs)promotes the development of data-driven methods for RAS *** paper proposes a temporal and topological embedding deep neural network(TTEDNN)model to accurately and efficiently predict RAS by extracting the temporal and topological features from the PMU *** grid-informed adjacency matrix incorporates the structural and electrical parameter information of the power *** the small-signal RAS with disturbance under initial operating conditions and the transient RAS with short circuits on transmission lines are *** studies of the IEEE 39-bus and IEEE 300-bus power systems are used to test the performance,scalability,and robustness against measurement uncertainties of the TTEDNN *** show that the TTEDNN model performs best among existing deep learning ***,the superior transfer learning ability from small-signal RAS conditions to transient RAS conditions has been proved.

关键词： Rotor angle stability prediction topological embedding deep learning graph convolution network

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