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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhao, Linqian Li, Yahan Shang, Junliang Ren, Qianqian Zhang, Yuanyuan Liu, Jin-Xing Qufu Normal University School of Computer Science Rizhao276826 China Qingdao University of Technology School of Information and Control Engineering Qingdao266520 China University of Health and Rehabilitation Sciences School of Health and Life Science Qingdao266113 China

ISBN: (纸本)9798350386226

Single nucleotide polymorphism (SNPs) data have become abundant thanks to the quick advancement of high-throughput sequencing technology, which provides convenience for genome-wide association studies. Single SNPs have been proven to be the cause of some diseases, and the emergence of complex diseases is often thought to be the result of the interaction of multiple SNPs. However, the possible interaction of millions of SNPs imposes a heavy computational burden for uncovering complex disease mechanisms. The existing SNP-SNP interaction detection algorithms frequently have flaws including high computation complexity and poor optimization effectiveness. In this study, a particle swarm optimization algorithm based on multi-population mutual learning (PSOMPML) is proposed to detect SNP-SNP interactions. In this algorithm, the mutual learning strategy is introduced to deal with different particles in different sub-populations to facilitate knowledge exchange. In addition, the elite preservation mechanism is incorporated into PSOMPML, to better preserve the good SNPs in the elite particles. The promising region local search strategy searches the optimal solution along the target solution and its near space to increase the convergence speed of the proposed algorithm. Experiments on simulated data sets and real data also demonstrate the effectiveness of the proposed algorithm. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Research on traffic vehicle target detection method based on improved YOLOv7 24

Research on traffic vehicle target detection method based on...

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7th International Conference on Image and Graphics Processing, ICIGP 2024

作者： Zhang, Feng Sun, Quan Yuan, Shuai School of Electrical and Control Engineering Shenyang Jianzhu University China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center China

ISBN: (纸本)9798400716720

With the increase in the number of vehicles in our country, traffic accidents have become frequent. The real-time detection of dense traffic vehicles is particularly important, which can promote the development of autonomous driving and lay a good foundation for intelligent transportation. Aiming at the problems such as high vehicle overlap rate, high small target missing rate and poor real-time detection in traffic vehicle data set, a traffic vehicle target detection method based on improved YOLOv7 was proposed. Firstly, the neck network structure of the original YOLOv7 is lightweight and introduced into the GhostSlimPAN paradigm structure to reduce the number of model parameters and operation cost, and improve the accuracy of detection of small target vehicles. Then, the original loss function CIoU is improved to replusion loss function to reduce the missed and false detection rate caused by mutual occlusion between vehicles. In order to verify the effect of the improved model, the vehicle dataset was selected for testing and verification. The experimental results show that compared with the original YOLOv7 algorithm, the mAP@0.5 of the improved YOLOv7 algorithm on this dataset reaches 88.2%, which is 1.8% higher than that of the benchmark network. The improved algorithm can be applied to vehicle target detection in daily environment, providing key technical support for intelligent transportation and autonomous driving. © 2024 ACM.

关键词： Autonomous vehicles

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Video Polyp Segmentation: A Deep Learning Perspective

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Machine Intelligence Research 2022年第6期19卷 531-549页

作者： Ge-Peng Ji Guobao Xiao Yu-Cheng Chou Deng-Ping Fan Kai Zhao Geng Chen Luc Van Gool Research School of Engineering Australian National UniversityCanberra 2601Australia College of Computer and Control Engineering Minjiang UniversityFuzhou 350108China Department of Computer Science Johns Hopkins UniversityBaltimore 21218USA Computer Vision Laboratory ETH ZürichZürich 8092Switzerland Department of Radiological Sciences University of CaliforniaLos Angeles 90095USA School of Computer Science and Engineering Northwestern Polytechnical UniversityXi′an 710072China

We present the first comprehensive video polyp segmentation(VPS)study in the deep learning *** the years,developments in VPS are not moving forward with ease due to the lack of a large-scale dataset with fine-grained segmentation *** address this issue,we first introduce a high-quality frame-by-frame annotated VPS dataset,named SUN-SEG,which contains 158690colonoscopy video frames from the well-known *** provide additional annotation covering diverse types,i.e.,attribute,object mask,boundary,scribble,and ***,we design a simple but efficient baseline,named PNS+,which consists of a global encoder,a local encoder,and normalized self-attention(NS)*** global and local encoders receive an anchor frame and multiple successive frames to extract long-term and short-term spatial-temporal representations,which are then progressively refined by two NS *** experiments show that PNS+achieves the best performance and real-time inference speed(170 fps),making it a promising solution for the VPS ***,we extensively evaluate 13 representative polyp/object segmentation models on our SUN-SEG dataset and provide attribute-based ***,we discuss several open issues and suggest possible research directions for the VPS *** project and dataset are publicly available at https://***/GewelsJI/VPS.

关键词： Video polyp segmentation(VPS) dataset self-attention colonoscopy abdomen

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A multi-objective genetic algorithm based on neighborhood coevolution for community detection

A multi-objective genetic algorithm based on neighborhood co...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Bi, Mingyuan Shang, Junliang Kong, Xiaotong Li, Feng Zhang, Yuanyuan Liu, Jin-Xing Qufu Normal University School of Computer Science Rizhao276826 China Qingdao University of Technology School of Information and Control Engineering Qingdao266000 China University of Health and Rehabilitation Sciences School of Health and Life Sciences Qingdao266113 China

ISBN: (纸本)9798350386226

Community detection has attracted growing interest, with multi-objective evolutionary algorithms proving to be highly competitive in this area. In this paper, a community detection method based on a multi-objective neighborhood coevolution genetic algorithm, NCMOGA, is proposed. To improve the computational efficiency in large-scale networks, NCMOGA introduces a network processing strategy to simplify the network before and during evolution. A neighborhood coevolution strategy is proposed, in which the corresponding subpopulation is formed according to the neighborhood of each individual. A series of operations such as crossover, mutation and update are performed in the subpopulation, emphasizing the synergy between individuals and their neighbors. Mating selection and crossover operations are performed based on the center selection idea of density peak clustering, and the most important nodes are selected to generate offspring. The effectiveness of NCMOGA is verified on synthetic networks and real-world networks. In addition, the results in guiding the classification of disease and healthy samples demonstrate the high quality of the modules detected by NCMOGA. © 2024 IEEE.

关键词： Genetic algorithms

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Optimization of BLE Broadcast Mode in Offline Finding Network

arXiv

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arXiv 2025年

作者： Zhang, Li Feng, Cheng Xia, Tian School of Computer and Control Engineering Northeast Forestry University China School of Measurement-control Technology and Communications Engineering Harbin University of Science and Technology China

In the Offline Finding Network(OFN), offline Bluetooth tags broadcast to the surrounding area, the finder devices receiving the broadcast signal and upload location information to the IoT(Internet of Things) cloud servers, thereby achieving offline finding of lost items. This process is essentially a Bluetooth low energy (BLE) neighbor discovery process(NDP). In the process, the variety of Bluetooth scan modes caused by the scan interval and scan window settings affects the discovery latency of finder devices finding the tag broadcast packets. To optimize the experience of searching for lost devices, we propose the CPBIS-mechanism, a certain proportion broadcast-intervals screening mechanism that calculates the most suitable two broadcast intervals and their proportion for offline tags. This reduces discovery latency in the BLE NDP, improves the discovery success rate, further enhances the user experience. To our knowledge, we are the first to propose a comprehensive solution for configuring the broadcast interval parameters of advertisers in BLE NDP, particularly for configurations involving two or more broadcast intervals. We evaluated the results obtained by CPBIS on the nRF52832 chip. The data shows that the CPBIS-mechanism achieves relatively low discovery latencies for multiple scan modes. © 2025, CC BY.

关键词： Bluetooth

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Partial Discharge Classification With 1D Convolutional Neural Network 14

Partial Discharge Classification With 1D Convolutional Neura...

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14th International Conference on the Properties and Applications of Dielectric Materials, ICPADM 2024

作者： Cheypoca, Thepjit Promphanich, Wiboon Thway, Aung Ye Hankae, Angelina Phimpissada Jeenmuang, Siwakorn Pattanadech, Norasage School of Engineering King Mongkut's Institute of Technology Ladkrabang Instrumentation and Control Engineering Dept. Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Computer Engineering Dept. Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Electrical Engineering Dept. Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Mechatronics Engineering Dept. Bangkok Thailand

ISBN: (纸本)9798350374605

This paper introduces a novel approach for classifying with the 1D Convolutional Neural Network model for partial discharge patterns, that consists of corona discharge, surface discharge and internal discharge. The PD measuring circuit suggested in IEC 60270:2000 is used to record Partial discharge signals. Independent parameters such as phase and charge of PD patterns were recorded. The Artificial Neural Network for the classification model was constructed. Moreover, 2×1D CNN feature extraction was utilized to reduce the curse of dimensionality in the dense layer of the proposed PD classification model. 80% of the recorded data will be used as a training data and 20% recorded data was used for testing of the classification models. Impacts of neuron numbers and network architecture on the PD classification performance will be observed. © 2024 IEEE.

关键词： Convolutional neural networks

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Vehicle Route Planning Using A∗ Algorithm in Real-world Roads 4

Vehicle Route Planning Using A∗ Algorithm in Real-world Roa...

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2022 4th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2022

作者： Liu, Xingyu School of Control and Computer Engineering North China Electric Power University Beijing100096 China

With the technical progress in the fields of robotics as well as artificial intelligence, many intelligent algorithms which focus on robot path planning problems have been developed so far. A∗ algorithm is an efficient and technically mature path planning algorithm that can efficiently solve robot path planning problems. In this context, this paper proposes an improved A∗ algorithm to plan roads for automobiles in urban areas, introduce time factors into the cost function, select an urban area, use grid map modeling, and simulate in the MATLAB environment. The final result shows that this kind of algorithm can efficiently solve the problem of automobile road planning. © Published under licence by IOP Publishing Ltd.

关键词： MATLAB

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Error-Based Active Disturbance Rejection control for Maximum Power Point Tracking in Photovoltaic System 17th

Error-Based Active Disturbance Rejection Control for Maximum...

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17th Chinese Intelligent Systems Conference, CISC 2021

作者： Ke, Yin Hou, Guolian School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789811663239

With the recent focus marked on conversion efficiency and renewable energy, more research is being devoted to the high-performance maximum power point tracking technology for photovoltaic applications. However, the intermittence and naturalness of solar energy bring a series of severe challenges to photovoltaic system, such as overload and overvoltage. In addition, photovoltaic system is faced with various parameter uncertainties and disturbances from irradiance. Traditional maximum power point tracking methods, such as perturb & observe and incremental conductance, are limited to tracking speed and tracking accuracy. To this end, a novel maximum power point tracking scheme based on the error-based active disturbance rejection control approach and perturb & observe method is proposed to obtain higher efficiency. In the face of various irradiance changes, disturbances can be eliminated rapidly. Finally, the superior performance of proposed scheme is validated by extensive simulation results. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Errors

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Distributed Power Sharing control of AC Microgrid Based on State Observer Approach

Distributed Power Sharing Control of AC Microgrid Based on S...

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International Conference on Guidance, Navigation and control, ICGNC 2020

作者： Zhang, Yanjing Yu, Mei Wei, Bo Xiao, Feng School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789811581540

This paper proposes a consensus-based distributed algorithm to solve both active and reactive sharing problems, which involves alternative current (AC) microgrids and spatially concentrated dispatchable distributed generators (DDGs). The output feedback control method and the leader-follower scheme are designed for each generator to achieve the consistency of the output proportion. Compared with other control strategies, the controller based on state observer only uses local output information and a set of dynamic communicated compensator. In addition, the protocol can solve the proportional consistency with different parameters, which also meets the balance of supply and demand in power system. Numerical examples verify the effectiveness of this method. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Numerical methods

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An improved neural network model for battery smarter state-of-charge estimation of energy-transportation system

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Green Energy and Intelligent Transportation 2023年第2期2卷 56-65页

作者： Bingzhe Fu Wei Wang Yihuan Li Qiao Peng School of Control and Computer Engineering North China Electric Power UniversityBeijing102206China Group of Information Technology Analytics&OperationsQueen's University BelfastBelfastBT95EEUK

The safety and reliability of battery storage systems are critical to the mass roll-out of electrified transportation and new energy *** achieve safe management and optimal control of batteries,the state of charge(SOC)is one of the important *** machine-learning based SOC estimation methods of lithium-ion batteries have attracted substantial interests in recent ***,a common problem with these models is that their estimation performances are not always stable,which makes them difficult to use in practical *** address this problem,an optimized radial basis function neural network(RBF-NN)that combines the concepts of Golden Section Method(GSM)and Sparrow Search Algorithm(SSA)is proposed in this ***,GSM is used to determine the optimum number of neurons in hidden layer of the RBF-NN model,and its parameters such as radial base center,connection weights and so on are optimized by SSA,which greatly improve the performance of RBF-NN in SOC *** the experiments,data collected from different working conditions are used to demonstrate the accuracy and generalization ability of the proposed model,and the results of the experiment indicate that the maximum error of the proposed model is less than 2%.

关键词： Battery management SOC estimation Data science Neural network Golden section method Sparrow search algorithm

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