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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mazumder, Md. Sakib Ali Shamrat, F M Javed Mehedi Mahmud, Imran Sharmin, Sharmin Hossain, Tanzim Biplab, Ekramul Kabir Daffodil International University Department of Software Engineering Dhaka Bangladesh Universiti Malaya Department of Computer System and Technology Kuala Lumpur Malaysia Institute of Information Technology University of Dhaka Dhaka Bangladesh

ISBN: (纸本)9798350370249

Diabetic Retinopathy (DR) is a prevalent eye complication among individuals with diabetes. Early detection of DR can prevent vision loss and lower associated problems. Manual diagnosis via retinal fundus images requires the expertise of medical professionals to assess several complex characteristics, including visual acuity tests, pupil dilation, and optical consistency tomography. The process is challenging, time-consuming, and prone to errors. This paper proposes a deep learning (DL) technique, DRDnet22, to detect and categorize DR into five distinct stages. The APTOS 2019 dataset is utilized in this study, which contains 3662 images. The study employs dense block and transition layer with global average pooling (GAP) techniques, and the combination of distinct Convolutional Networks (ConvNets) results in an enhanced representation beyond the utilization of features from a single ConvNet alone. To conduct an ablation analysis, the conventional pre-trained model's performance was compared. The performance evaluation points out that the proposed model achieved 81.6% accuracy, which is an advantage over other DCNN models. Despite facing a few inaccuracies, the model's competence in spotting vital features underscores its diagnostic potential. © 2024 IEEE.

关键词： Convolutional neural networks

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Vulnerability of EV Charging Stations to Cyber Attacks Manipulating Prices

Vulnerability of EV Charging Stations to Cyber Attacks Manip...

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2023 International Conference on Future Energy Solutions, FES 2023

作者： Akbarian, Amirhossein Bahrami, Mahdi Vakilian, Mehdi Lehtonen, Matti Sharif University of Technology Center of Excellence in Power System Management and Control Electrical Engineering Department Tehran Iran Aalto University Department of Electrical Engineering and Automation Espoo Finland

ISBN: (纸本)9798350332308

Electric vehicles (EVs) have experienced an unprecedented increase in their penetration;in well developed countries;due to the advancement of battery technology and the need to make transportation more environmentally friendly. As a result of this trend, EVs have become an integral part of those countries power grid ecosystem, where they may be used as both a source, and a consumer of energy. However, cybersecurity risks associated with large fleets of EVs within the power grids can significantly impact the normal operation of distribution systems. This paper considers the cyber-attack consequences, from attackers' perspective. The cyber attackers under study target the charging price (CP) by increasing the CP in off-peak hours and accordingly subsiding it in on-peak hours. A two-stage optimization framework is proposed to identify the most important ultra-fast charging stations (XFCs) under the cyber-attack, meanwhile, the EV users' response to the compromised CP is considered. Then, the proposed model is implemented on a modified IEEE 123-bus distribution test system, and the effectiveness of the model is proved through some case studies. In particular, they demonstrated that the amount of load shedding is increased by 8.198 MW when the EV user response is taken into account. © 2023 IEEE.

关键词： Crime

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Efficient and stable hybrid perovskite-organic light-emitting diodes with external quantum efficiency exceeding 40 per cent

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Light(Science & Applications) 2024年第7期13卷 1364-1371页

作者： Lingmei Kong Yun Luo Qianqian Wu Xiangtian Xiao Yuanzhi Wang Guo Chen Jianhua Zhang Kai Wang Wallace CHChoy Yong-Biao Zhao Hongbo Li Takayuki Chiba Junji Kido Xuyong Yang Key Laboratory of Advanced Display and System Applications of Ministry of Education Shanghai UniversityShanghai 200072China. Institute of Nanoscience and Applications Department of Electrical and Electronic EngineeringSouthern University of Science and TechnologyShenzhen 518055China. Department of Electrical and Electronic Engineering The University of Hong KongHong KongChina. Department of Physics and Lakeside AR/VR Laboratory International Joint Research Center for Optoelectronic and Engineering ResearchYunnan UniversityKunming 650091China. Experimental Center of Advanced Materials School of Materials Science and EngineeringBeijing Institute of TechnologyBeijing 100081China. Graduate School of Organic Materials Science Frontier Center for Organic MaterialsYamagata University4-3-16 JonanYonezawa 992–8510Japan

Light-emitting diodes(LEDs)based on perovskite semiconductor materials with tunable emission wavelength in visible light range as well as narrow linewidth are potential competitors among current light-emitting display technologies,but still suffer from severe instability driven by electric ***,we develop a stable,efficient and highcolor purity hybrid LED with a tandem structure by combining the perovskite LED and the commercial organic LED technologies to accelerate the practical application of *** LED and organic LED with close photoluminescence peak are selected to maximize photon emission without photon reabsorption and to achieve the narrowed emission *** designing an efficient interconnecting layer with p-type interface doping that provides good opto-electric coupling and reduces Joule heating,the resulting green emitting hybrid LED shows a narrow linewidth of around 30 nm,a peak luminance of over 176,000 cd m^(−2),a maximum external quantum efficiency of over 40%,and an operational half-lifetime of over 42,000 h.

关键词： diodes narrow quantum

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Abnormal Data Identification and Reconstruction Based on Wind Speed Characteristics

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CSEE Journal of Power and Energy systems 2025年第2期11卷 612-622页

作者： Mao Yang Tian Peng Wei Zhang Xin Su Chao Han Fulin Fan Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin 132012China Department of Electronic and Electrical Engineering University of StrathclYdeGlasgow G11XWUnited Kingdom

High availability of wind power data is the basis for wind power research, but there are a large number of abnormal data in actual collected data, which seriously affects analysis of wind power law and reduces prediction accuracy. Measured power data of wind farm are analyzed, influence of wind speed fluctuation characteristics on wind power is discussed, and abnormal points are identified for data of different wind types. The Cluster-Based Local Outlier Factor (CLOF) algorithm based on K-means is used to identify outlier abnormal points, and conditional constraints based on physical background are used to identify accumulation abnormal points. Reconstructed data segment is divided according to fluctuation of wind speed. The Bidirectional Gate Recurrent Unit (BiGRU) model with wind speed as input reconstructs fluctuation segment data, and bi-directional weighted random forest model reconstructs stationary segment data. Based on analysis of measured data of a wind farm, results show the method can effectively identify various abnormal data, and complete high-quality reconstruction of data, thereby improving accuracy of wind power prediction.

关键词： Abnormal data bidirectional weighted random forest BiGRU CLOF reconstruction wind power prediction wind speed characteristics

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Point Cloud Segmentation of Breast Ultrasound Regions to be Scanned by Fusing 2D Image Instance Segmentation and Keypoint Detection 8

Point Cloud Segmentation of Breast Ultrasound Regions to be ...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Tan, Jiyong Qin, Hui Chen, Xinxing Li, Jiawang Li, Yuanwei Li, Bing Leng, Yuquan Fu, Chenglong Southern University of Science and Technology Department of Mechanical and Energy Engineering Shenzhen518055 China Guangdong Medical Rehabilitation Intelligent System Engineering Technology Research Center Shenzhen518052 China

ISBN: (纸本)9798350300178

Automatic segmentation of the region of interest to be scanned (ROIS) is one of the key tasks to achieve autonomous scanning of ultrasound robots. By analyzing breast ROIS in an unstructured environment, a point cloud segmentation framework for the breast ultrasound ROIS that incorporates 2D image instance segmentation and keypoint detection is proposed. Firstly, 2D image instance segmentation based on the improved SOLOv2 is performed to extract the human torso point cloud in the unstructured environment. Then, based on YOLO-Pose, the keypoints of human body are automatically detected, and the upper and lower lines of breast ROIS are obtained according to the constraint matching relationship between breast ROIS and keypoints, and finally the point cloud of breast ROIS is obtained effectively and accurately. Experiments show that the proposed framework can achieve automatic segmentation of breast ROIS within 2 s. © 2023 IEEE.

关键词： Ultrasonic applications

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A Surrogate-Assisted Evolutionary Algorithm for Expensive Dynamic Multimodal Optimzation 13

A Surrogate-Assisted Evolutionary Algorithm for Expensive Dy...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wu, Xunfeng Liu, Songbai Ji, Junkai Ma, Lijia Leung, Victor C. M. Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350308365

Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated promising optimization performance in addressing expensive dynamic optimization problems or expensive multimodal optimization problems. However, none of existing SAEAs are designed specifically for tackling expensive dynamic multimodal optimization problems (EDMMOPs). Therefore, in this paper, a first SAEA for tackling EDMMOPs is proposed. First, a nearest density clustering is designed to divide the population into a number of subpopulations, enhancing the diversity of the population. Then, a surrogate-assisted evolutionary optimizer is developed to construct surrogate models for each subpopulation and evolve all solutions in subpopulations by means of the built surrogate models, accelerating the population's converge towards several optimal solutions rapidly. Finally, a transfer learning-based prediction is devised to generate initial samples for next environment by leveraging the stored training samples in the previous environments. To assess the performance of our proposed algorithm, a set of complex benchmark problems is adopted, and the experimental results confirm its superior performance over several competitive algorithms on most test cases. © 2024 IEEE.

关键词： Digital elevation model

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Data driven prediction of fragment velocity distribution under explosive loading conditions

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Defence technology(防务技术) 2025年第1期43卷 109-119页

作者： Donghwan Noh Piemaan Fazily Songwon Seo Jaekun Lee Seungjae Seo Hoon Huh Jeong Whan Yoon Department of Mechanical Engineering Korea Advanced Institute of Science and Technology291 Daehak-roYuseong-guDaejeon 34141Republic of Korea Warhead System Team Precision Firepower Research DivisionPOONGSAN Defense R&D Institute2606-10 Hoguk-roAngang-OupKyung JuKyung-BukRepublic of Korea School of Engineering Deakin UniversityWauran PondsVIC 3216Australia

This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading *** fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation *** paper details the finite element analysis for fragmentation,the characterizations of the dynamic hardening and fracture models,the generation of comprehensive datasets,and the training of the ANN *** results show the influence of casing dimensions on fragment velocity distributions,with the tendencies indicating increased resultant velocity with reduced thickness,increased length and *** model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets,showing its potential for the real-time prediction of fragmentation performance.

关键词： Data driven prediction Dynamic fracture model Dynamic hardening model Fragmentation Fragment velocity distribution High strain rate Machine learning

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Enhanced Load Frequency Control Using PSO-Tuned PID Controller in Two-Area Interconnected Power systems

Enhanced Load Frequency Control Using PSO-Tuned PID Controll...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Ali, Syed Mustafa Khan, Amjad Ullah, Kaleem Hussain, Tanveer Aziz, Muhammad Osama Basit, Abdul University of Alabama at Birmingham Department of Engineering Management AL35294 United States University of Engineering and Technology Department of Electrical Engineering Peshawar Pakistan University of Engineering and Technology Department of Energy System Engineering Peshawar Pakistan Dominion Energy Systems Operations Center Virginia United States University of Engineering and Technology Buildings Energy Research Center Peshawar Pakistan Npcc Ntdc Manager R&d Islamabad Pakistan

ISBN: (纸本)9798331532895

LFC is significant in controlling stability within interconnected power systems, and many techniques are available in the literature for this process. This paper proposes an application of PSO for tuning PID controllers to improve LFC in two areas of thermal power systems. Both areas are interfaced with PSO-tuned PID controllers, and the tuning parameters are optimized using PSO to enhance response characteristics. The newly derived method is compared to the traditional Ziegler-Nichols tuning method for validation. The simulation results present that the proposed method of tuning the PID controllers with the help of the PSO algorithm provides better control with minimized settling time and number of oscillations, thereby validating the method. All the simulations are carried out in MATLAB/SIMULINK and offer a general system to assess the effectiveness of the controller in the multi-area power system. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Computation method and control strategy of rotating separation flows in hydraulic machinery

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Journal of Hydrodynamics 2022年第6期34卷 1006-1020页

作者： Chao-yue Wang Fu-jun Wang Hao Wang Hao-ru Zhao Zhi-feng Yao Ruo-fu Xiao College of Water Resources and Civil Engineering China Agricultural UniversityBeijing100083China Beijing Engineering Research Center of Safety and Energy Saving Technology for Water Supply Network System Beijing100083China Department of Energy and Power Engineering Tsinghua UniversityBeijing100084China

Rotating separation flow(RSF)in hydraulic machinery is characterized by the large flow separations and complex vortical structures induced by the effects of strong rotation,large curvature and multiple wall surfaces,and conducting efficient engineering computation and putting forward effective control strategy for the RSF are important topics in the inner flow *** meet these engineering requirements,the studies on computational method and control strategy of the RSF are conducted in this *** terms of the computational method,the time-scale-driven(TSD)hybrid unsteady Reynolds-averaged Navier-Stokes/large eddy simulation(URANS/LES)modelling strategy is clarified,and an adaptive TSD hybrid model is established based on the RSF characteristics in hydraulic machinery,thereby avoiding the problem of non-monotonic grid convergence and improving the ***,a novel vortex-feature-driven idea suitable for the RSF is further developed inspired by *** terms of the control strategy,the secondary flow generation mechanism in a rotor domain is revealed,and the relationship between natural secondary flows and blade loading distributions is *** the basis of it,an active control strategy with general significance is proposed,and a general alternate loading technique(GALT)is *** aspects can provide generalized paradigms with expandable potential,which are of benefit to the efficient computation and effective control of the RSF in hydraulic machinery.

关键词： Rotating separation flow hydraulic machinery engineering computation turbulence model flow control blade loading

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Comparative Study and Experimental Validation of Phase-Sensitive-Detection Techniques for Sensor Lock-in Amplifiers

Comparative Study and Experimental Validation of Phase-Sensi...

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2023 IEEE Applied Sensing Conference, APSCON 2023

作者： Patil, Rohan Wani, Mohamad Idris Islam, Tarikul Baghini, Maryam Shojaei Somappa, Laxmeesha Malik, Shahid Indian Institute of Technology Centre for Sensors Instrumentation and Cyber Physical System Engineering New Delhi Delhi India Jamia Millia Islamia Department of Electrical Engineering New Delhi India Indian Institute of Technology Department of Electrical Engineering Bombay India

ISBN: (纸本)9781665461634

Often in a sensor system, the signal of interest from the sensor is smaller than the signal due to noise. Lock-in amplifiers have been used to efficiently differentiate the signal of interest from noise and unwanted signals in such scenarios. Phase-sensitive detection (PSD) is an important building block of a lock-in amplifier. Analog switches and multipliers are the two most widely used techniques for the phase-sensitive detection of sensor lock-in amplifiers. Each technique has some distinct advantages and disadvantages as well. In this paper, we have studied and experimentally validated the performance of these two techniques for the PSD in sensor lock-in amplifiers. The techniques are validated with a sine wave, square-wave excitation, and modulation signals. Further, the methods are validated by integrating with a lock-in amplifier. Finally, the performance analysis of these two techniques is tabulated based on the obtained experimental results. The results show that the multiplier-based system shows an error of less than 1%, whereas the square-wave excitation system shows an error as high as 5%. © 2023 IEEE.

关键词： Locks (fasteners)

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