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A Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller Model Combined with an Improved Particle Swarm Optimization Method for Fall Detection
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作者 Jyun-Guo Wang 《Computer Systems Science & Engineering》 2024年第5期1149-1170,共22页
In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible t... In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous consequences.However,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to acquire.To address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data set.This study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection system.First,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)algorithm.Second,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)information.Third,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search space.The proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data sets.The UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%. 展开更多
关键词 Double interactively recurrent fuzzy cerebellar model articulation controller(D-IRFCMAC) improved particle swarm optimization(IPSO) fall detection
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Driving fatigue fusion detection based on T-S fuzzy neural network evolved by subtractive clustering and particle swarm optimization 被引量:6
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作者 孙伟 张为公 +1 位作者 李旭 陈刚 《Journal of Southeast University(English Edition)》 EI CAS 2009年第3期356-361,共6页
In order to improve the accuracy and reliability of the driving fatigue detection based on a single feature, a new detection algorithm based on multiple features is proposed. Two direct driver's facial features refle... In order to improve the accuracy and reliability of the driving fatigue detection based on a single feature, a new detection algorithm based on multiple features is proposed. Two direct driver's facial features reflecting fatigue and one indirect vehicle behavior feature indicating fatigue are considered. Meanwhile, T-S fuzzy neural network(TSFNN)is adopted to recognize the driving fatigue of drivers. For the structure identification of the TSFNN, subtractive clustering(SC) is used to confirm the fuzzy rules and their correlative parameters. Moreover, the particle swarm optimization (PSO)algorithm is improved to train the TSFNN. Simulation results and experiments on vehicles show that the proposed algorithm can effectively improve the convergence speed and the recognition accuracy of the TSFNN, as well as enhance the correct rate of driving fatigue detection. 展开更多
关键词 driving fatigue fusion detection particle swarm optimization(PSO) subtractive clustering(SC)
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Design and prototyping of the readout electronics for the transition radiation detector in the high energy cosmic radiation detection facility
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作者 Jie-Yu Zhu Yang-Zhou Su +12 位作者 Hai-Bo Yang Fen-Hua Lu Yan Yang Xi-Wen Liu Ping Wei Shu-Cai Wan Hao-Qing Xie Xian-Qin Li Cong Dai Hui-Jun Hu Hong-Bang Liu Shu-Wen Tang Cheng-Xin Zhao 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2024年第4期189-199,共11页
The high energy cosmic-radiation detection(HERD)facility is planned to launch in 2027 and scheduled to be installed on the China Space Station.It serves as a dark matter particle detector,a cosmic ray instrument,and a... The high energy cosmic-radiation detection(HERD)facility is planned to launch in 2027 and scheduled to be installed on the China Space Station.It serves as a dark matter particle detector,a cosmic ray instrument,and an observatory for high-energy gamma rays.A transition radiation detector placed on one of its lateral sides serves dual purpose,(ⅰ)calibrating HERD's electromagnetic calorimeter in the TeV energy range,and(ⅱ)serving as an independent detector for high-energy gamma rays.In this paper,the prototype readout electronics design of the transition radiation detector is demonstrated,which aims to accurately measure the charge of the anodes using the SAMPA application specific integrated circuit chip.The electronic performance of the prototype system is evaluated in terms of noise,linearity,and resolution.Through the presented design,each electronic channel can achieve a dynamic range of 0–100 fC,the RMS noise level not exceeding 0.15 fC,and the integral nonlinearity was<0.2%.To further verify the readout electronic performance,a joint test with the detector was carried out,and the results show that the prototype system can satisfy the requirements of the detector's scientific goals. 展开更多
关键词 HERD Dark matter particle detection TRD Readout electronics SAMPA Data acquisition Performance test
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A Review on Deterministic Lateral Displacement for Particle Separation and Detection 被引量:4
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作者 Thoriq Salafi Yi Zhang Yong Zhang 《Nano-Micro Letters》 SCIE EI CAS CSCD 2019年第4期353-385,共33页
The separation and detection of particles in suspension are essential for a wide spectrum of applications including medical diagnostics.In this field,microfluidic deterministic lateral displacement(DLD)holds a promise... The separation and detection of particles in suspension are essential for a wide spectrum of applications including medical diagnostics.In this field,microfluidic deterministic lateral displacement(DLD)holds a promise due to the ability of continuous separation of particles by size,shape,deformability,and electrical properties with high resolution.DLD is a passive microfluidic separation technique that has been widely implemented for various bioparticle separations from blood cells to exosomes.DLD techniques have been previously reviewed in 2014.Since then,the field has matured as several physics of DLD have been updated,new phenomena have been discovered,and various designs have been presented to achieve a higher separation performance and throughput.Furthermore,some recent progress has shown new clinical applications and ability to use the DLD arrays as a platform for biomolecules detection.This review provides a thorough discussion on the recent progress in DLD with the topics based on the fundamental studies on DLD models and applications for particle separation and detection.Furthermore,current challenges and potential solutions of DLD are also discussed.We believe that a comprehensive understanding on DLD techniques could significantly contribute toward the advancements in the field for various applications.In particular,the rapid,low-cost,and high-throughput particle separation and detection with DLD have a tremendous impact for point-of-care diagnostics. 展开更多
关键词 Microfluidic DETERMINISTIC LATERAL DISPLACEMENT particle SEPARATION particle detection
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Simultaneous Multi-vehicle Detection and Tracking Framework with Pavement Constraints Based on Machine Learning and Particle Filter Algorithm 被引量:3
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作者 WANG Ke HUANG Zhi ZHONG Zhihua 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2014年第6期1169-1177,共9页
Due to the large variations of environment with ever-changing background and vehicles with different shapes, colors and appearances, to implement a real-time on-board vehicle recognition system with high adaptability,... Due to the large variations of environment with ever-changing background and vehicles with different shapes, colors and appearances, to implement a real-time on-board vehicle recognition system with high adaptability, efficiency and robustness in complicated environments, remains challenging. This paper introduces a simultaneous detection and tracking framework for robust on-board vehicle recognition based on monocular vision technology. The framework utilizes a novel layered machine learning and particle filter to build a multi-vehicle detection and tracking system. In the vehicle detection stage, a layered machine learning method is presented, which combines coarse-search and fine-search to obtain the target using the AdaBoost-based training algorithm. The pavement segmentation method based on characteristic similarity is proposed to estimate the most likely pavement area. Efficiency and accuracy are enhanced by restricting vehicle detection within the downsized area of pavement. In vehicle tracking stage, a multi-objective tracking algorithm based on target state management and particle filter is proposed. The proposed system is evaluated by roadway video captured in a variety of traffics, illumination, and weather conditions. The evaluating results show that, under conditions of proper illumination and clear vehicle appearance, the proposed system achieves 91.2% detection rate and 2.6% false detection rate. Experiments compared to typical algorithms show that, the presented algorithm reduces the false detection rate nearly by half at the cost of decreasing 2.7%–8.6% detection rate. This paper proposes a multi-vehicle detection and tracking system, which is promising for implementation in an on-board vehicle recognition system with high precision, strong robustness and low computational cost. 展开更多
关键词 simultaneous detection and tracking pavement segmentation layered machine learning particle filter
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Fault detection and identification for dead reckoning system of mobile robot based on fuzzy logic particle filter 被引量:4
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作者 余伶俐 蔡自兴 +1 位作者 周智 奉振球 《Journal of Central South University》 SCIE EI CAS 2012年第5期1249-1257,共9页
To deal with fault detection and diagnosis with incomplete model for dead reckoning system of mobile robot,an integrative framework of particle filter detection and fuzzy logic diagnosis was devised.Firstly,an adaptiv... To deal with fault detection and diagnosis with incomplete model for dead reckoning system of mobile robot,an integrative framework of particle filter detection and fuzzy logic diagnosis was devised.Firstly,an adaptive fault space is designed for recognizing both known faults and unknown faults,in corresponding modes of modeled and model-free.Secondly,the particle filter is utilized to diagnose the modeled faults and detect model-free fault according to the low particle weight and reliability.Especially,the proposed fuzzy logic diagnosis can further analyze model-free modes and identify some soft faults in unknown fault space.The MORCS-1 experimental results show that the fuzzy diagnosis particle filter(FDPF) combinational framework improves fault detection and identification completeness.Specifically speaking,FDPF is feasible to diagnose the modeled faults in known space.Furthermore,the types of model-free soft faults can also be further identified and diagnosed in unknown fault space. 展开更多
关键词 fault detection and diagnosis particle filter fuzzy logic hard fault soft fault
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Broken Rotor Bar Fault Detection of Induction Motors Using a Joint Algorithm of Trust Region and Modified Bare-bones Particle Swarm Optimization 被引量:1
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作者 Panpan Wang Liping Shi +2 位作者 Yong Zhang Yifan Wang Li Han 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2019年第1期65-78,共14页
A precise detection of the fault feature parameter of motor current is a new research hotspot in the broken rotor bar(BRB) fault diagnosis of induction motors. Discrete Fourier transform(DFT) is the most popular techn... A precise detection of the fault feature parameter of motor current is a new research hotspot in the broken rotor bar(BRB) fault diagnosis of induction motors. Discrete Fourier transform(DFT) is the most popular technique in this field, owing to low computation and easy realization. However, its accuracy is often limited by the data window length, spectral leakage, fence e ect, etc. Therefore, a new detection method based on a global optimization algorithm is proposed. First, a BRB fault current model and a residual error function are designed to transform the fault parameter detection problem into a nonlinear least-square problem. Because this optimization problem has a great number of local optima and needs to be resolved rapidly and accurately, a joint algorithm(called TR-MBPSO) based on a modified bare-bones particle swarm optimization(BPSO) and trust region(TR) is subsequently proposed. In the TR-MBPSO, a reinitialization strategy of inactive particle is introduced to the BPSO to enhance the swarm diversity and global search ability. Meanwhile, the TR is combined with the modified BPSO to improve convergence speed and accuracy. It also includes a global convergence analysis, whose result proves that the TR-MBPSO can converge to the global optimum with the probability of 1. Both simulations and experiments are conducted, and the results indicate that the proposed detection method not only has high accuracy of parameter estimation with short-time data window, e.g., the magnitude and frequency precision of the fault-related components reaches 10^(-4), but also overcomes the impacts of spectral leakage and non-integer-period sampling. The proposed research provides a new BRB detection method, which has enough precision to extract the parameters of the fault feature components. 展开更多
关键词 Fault detection Broken rotor BARS Induction motors Bare-bones particle SWARM optimization Trust region
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Transfer and Detection of barstar Gene to Maize Inbred Line 18-599 (White) by Particle Bombardment 被引量:1
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作者 SUN Qing-quan ZHANG Ying +2 位作者 RONG Ting-zhao DONG Shu-ting ZUO Zhen-peng 《Agricultural Sciences in China》 CAS CSCD 2007年第6期652-656,共5页
In China, the purity of maize hybrid strain is discomforting to the development of seed industrialization. Finding a new method for reproduction of maize hybrid strain is necessary. In this study, using particle bomba... In China, the purity of maize hybrid strain is discomforting to the development of seed industrialization. Finding a new method for reproduction of maize hybrid strain is necessary. In this study, using particle bombardment, barstar gene was transferred into maize inbred line 18-599 (White), which is an antiviral and high quality maize inbred line. By molecular detection of the anther of transgenic maize, two plants transferred with barstar gene were gained in this study, which are two restorer lines. The two plants showed normal male spike, and lively microspores. But the capacity of the two restorer lines should be studied in the future. The aim of this study is to find a new method of reproduction of maize hybrid strain using engineering restorer lines and engineering sterility lines by gene engineering technology. 展开更多
关键词 MAIZE inbred line Barstar gene particle bombardment transgenic plant molecular detection
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Dynamics analysis of vibration process in Particle Impact Noise Detection 被引量:2
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作者 ZHANG Hui ZHOU Chang-lei +1 位作者 WANG Shu-juan ZHAI Guo-fu 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2007年第3期444-448,共5页
Particle Impact Noise Detection (PIND) test is a reliability screening technique for hermetic device that is prescribed by MIL-PRF-39016E. Some test conditions are specified, although MIL-PRF-39016E did not specify ho... Particle Impact Noise Detection (PIND) test is a reliability screening technique for hermetic device that is prescribed by MIL-PRF-39016E. Some test conditions are specified, although MIL-PRF-39016E did not specify how to obtain these condi- tions. This paper establishes the dynamics model of vibration process based on first order mass-spring system. The corresponding Simulink model is also established to simulate vibration process in optional input excitations. The response equations are derived in sinusoidal excitations and the required electromagnetic force waves are computed in order to obtain a given vibration and shock accelerations. Last, some simulation results are given. 展开更多
关键词 particle Impact Noise detection (PIND) VIBRATION Electromagnetic force
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A nano-metallic-particles-based CMOS image sensor for DNA detection 被引量:1
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作者 何进 苏艳梅 +5 位作者 马玉涛 陈沁 王若楠 叶韵 马勇 梁海浪 《Chinese Physics B》 SCIE EI CAS CSCD 2012年第7期416-421,共6页
In this paper we report on a study of the CMOS image sensor detection of DNA based on self-assembled nano- metallic particles, which are selectively deposited on the surface of the passive image sensor. The nano-metal... In this paper we report on a study of the CMOS image sensor detection of DNA based on self-assembled nano- metallic particles, which are selectively deposited on the surface of the passive image sensor. The nano-metallic particles effectively block the optical radiation in the visible spectrum of ordinary light source. When such a technical method is applied to DNA detection, the requirement for a special UV light source in the most popular fluorescence is eliminated. The DNA detection methodology is tested on a CMOS sensor chip fabricated using a standard 0.5 gm CMOS process. It is demonstrated that the approach is highly selective to detecting even a signal-base mismatched DNA target with an extremely-low-concentration DNA sample down to 10 pM under an ordinary light source. 展开更多
关键词 CMOS image sensor nano-metallic particles DNA detection 0.5 gm CMOS process
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Using LBG quantization for particle-based collision detection algorithm 被引量:1
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作者 SAENGHAENGTHAM Nida KANONGCHAIYOS Pizzanu 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2006年第7期1225-1232,共8页
Most collision detection algorithms can be efficiently used only with solid and rigid objects, for instance, Hierarchical methods which must have their bounding representation recalculated every time deformation occur... Most collision detection algorithms can be efficiently used only with solid and rigid objects, for instance, Hierarchical methods which must have their bounding representation recalculated every time deformation occurs. An alternative algorithm using particle-based method is then proposed which can detect the collision among non-rigid deformable polygonal models. However, the original particle-based collision detection algorithm might not be sufficient enough in some situations due to the improper particle dispersion. Therefore, this research presents an improved algorithm which provides a particle to detect in each separated area so that particles always covered all over the object. The surface partitioning can be efficiently performed by using LBG quantization since it can classify object vertices into several groups base on a number of factors as required. A particle is then assigned to move between vertices in a group by the attractive forces received from other particles on neighbouring objects. Collision is detected when the distance between a pair of corresponding particles becomes very small. Lastly, the proposed algo- rithm has been implemented to show that collision detection can be conducted in real-time. 展开更多
关键词 Collision detection Deformable object particle LBG Vector quantization
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A Novel Shilling Attack Detection Model Based on Particle Filter and Gravitation 被引量:1
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作者 Lingtao Qi Haiping Huang +2 位作者 Feng Li Reza Malekian Ruchuan Wang 《China Communications》 SCIE CSCD 2019年第10期112-132,共21页
With the rapid development of e-commerce, the security issues of collaborative filtering recommender systems have been widely investigated. Malicious users can benefit from injecting a great quantities of fake profile... With the rapid development of e-commerce, the security issues of collaborative filtering recommender systems have been widely investigated. Malicious users can benefit from injecting a great quantities of fake profiles into recommender systems to manipulate recommendation results. As one of the most important attack methods in recommender systems, the shilling attack has been paid considerable attention, especially to its model and the way to detect it. Among them, the loose version of Group Shilling Attack Generation Algorithm (GSAGenl) has outstanding performance. It can be immune to some PCC (Pearson Correlation Coefficient)-based detectors due to the nature of anti-Pearson correlation. In order to overcome the vulnerabilities caused by GSAGenl, a gravitation-based detection model (GBDM) is presented, integrated with a sophisticated gravitational detector and a decider. And meanwhile two new basic attributes and a particle filter algorithm are used for tracking prediction. And then, whether an attack occurs can be judged according to the law of universal gravitation in decision-making. The detection performances of GBDM, HHT-SVM, UnRAP, AP-UnRAP Semi-SAD,SVM-TIA and PCA-P are compared and evaluated. And simulation results show the effectiveness and availability of GBDM. 展开更多
关键词 shilling attack detection model collaborative filtering recommender systems gravitation-based detection model particle filter algorithm
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Adaptive stochastic resonance method for weak signal detection based on particle swarm optimization 被引量:6
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作者 XING Hongyan ZHANG Qiang LU Chunxia 《Instrumentation》 2015年第2期3-10,共8页
In order to solve the parameter adjustment problems of adaptive stochastic resonance system in the areas of weak signal detection,this article presents a new method to enhance the detection efficiency and availability... In order to solve the parameter adjustment problems of adaptive stochastic resonance system in the areas of weak signal detection,this article presents a new method to enhance the detection efficiency and availability in the system of two-dimensional Duffing based on particle swarm optimization.First,the influence of different parameters on the detection performance is analyzed respectively.The correlation between parameter adjustment and stochastic resonance effect is also discussed and converted to the problem of multi-parameter optimization.Second,the experiments including typical system and sea clutter data are conducted to verify the effect of the proposed method.Results show that the proposed method is highly effective to detect weak signal from chaotic background,and enhance the output SNR greatly. 展开更多
关键词 Adaptive stochastic resonance two-dimensional Duffing oscillator weak signal detection particle swarm optimization
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Blind Decorrelating Detection Based on Particle Swarm Optimization under Spreading Code Mismatch
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作者 Jhih-Chung Chang Chih-Chang Shen 《Journal of Electronic Science and Technology》 CAS 2014年第3期288-292,共5页
A way of resolving spreading code mismatches in blind multiuser detection with a particle swarm optimization (PSO) approach is proposed. It has been shown that the PSO algorithm incorporating the linear system of th... A way of resolving spreading code mismatches in blind multiuser detection with a particle swarm optimization (PSO) approach is proposed. It has been shown that the PSO algorithm incorporating the linear system of the decorrelating detector, which is termed as decorrelating PSO (DPSO), can significantly improve the bit error rate (BER) and the system capacity. As the code mismatch occurs, the output BER performance is vulnerable to degradation for DPSO. With a blind decorrelating scheme, the proposed blind DPSO (BDPSO) offers more robust capabilities over existing DPSO under code mismatch scenarios. 展开更多
关键词 Code division multiple access code mismatch decorrelating detector multiuser detection particle swarm optimization
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A Particle Filter of Blind Equalization and Multiuser Detection in Asynchronous DS/CDMA Systems
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作者 张静 董建萍 张谦 《Journal of Donghua University(English Edition)》 EI CAS 2008年第3期263-268,共6页
The particle filter (PF) is proposed to be the asynchronous direct-sequence code-division multiple-access (DS/CDMA) multiuser detector without knowing the channel state information. The PF performs symbol detectio... The particle filter (PF) is proposed to be the asynchronous direct-sequence code-division multiple-access (DS/CDMA) multiuser detector without knowing the channel state information. The PF performs symbol detection according to the joint posterior density probability of simulated particles including relative delays, fading gains and symbols via sequential importance sample and resample. A simplified scheme is also proposed by separating the indepent relative delays and fading with symbols. These parameters are modeled as the extended aggressive processes and estimated by the Kalman filter, so as to provide their arbitrary distribution for symbol detection. Simulation results show that the bit error rate of the PF is less than conventional detectors. Moreover, the complexity of PF is moderate comparable to other nonlinear suboptimal approaches. 展开更多
关键词 code-division multiple-access CDMA) spread spectrum multiuser detection interference suppression channel estimation sequential Monte Carlo particle filter
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Image Edge Detection Based on Cellular Neural Network and Particle Swarm Optimization 被引量:1
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作者 Zhengxia Wang Lili Li 《计算机科学与技术汇刊(中英文版)》 2014年第1期1-8,共8页
关键词 粒子群优化算法 图像边缘检测 细胞神经网络 线性矩阵不等式 边缘检测方法 预处理方法 仿真结果 CNN
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An Intrusion Detection Approach Based on Particle Method
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作者 Tao Yu Zhen Liu +1 位作者 Li Fu Yuanning Liu 《通讯和计算机(中英文版)》 2021年第2期6-18,共13页
In recent years,the network continues to enter people’s lives,followed by network security issues that continue to appear,causing substantial economic losses to the world.As an effective method to tackle the network ... In recent years,the network continues to enter people’s lives,followed by network security issues that continue to appear,causing substantial economic losses to the world.As an effective method to tackle the network security issues,intrusion detection system has been widely used and studied.In this paper,the NSL-KDD data set is used to reduce the dimension of data features,remove the features of low correlation and high interference,and improve the computational efficiency.To improve the detection rate and accuracy of intrusion detection,this paper introduces the particle method for the first time that we call it intrusion detection with particle(IDP).To illustrate the effectiveness of this method,experiments are carried out on three kinds of data-before dimension reduction,after dimension reduction and importing particle method based on dimension reduction.By comparing the results of DT,NN,SVM,K-NN,and NB,it is proved that the particle method can effectively improve the intrusion detection rate. 展开更多
关键词 Intrusion detection dimension reduction sort out NSL-KDD particle method
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Research on the Network Intrusion Detection System based on Modified Particle Swarm Optimization Algorithm
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作者 XuesongWang Guangzhan Feng 《International Journal of Technology Management》 2016年第1期56-58,共3页
In this paper, we conduct research on the network intrusion detection system based on the modified particle swarm optimization algorithm. Computer interconnection ability put forward the higher requirements for the sy... In this paper, we conduct research on the network intrusion detection system based on the modified particle swarm optimization algorithm. Computer interconnection ability put forward the higher requirements for the system reliability design, the need to ensure that the system can support various communication protocols to guarantee the reliability and security of the network. At the same time also require network system, the server or products have strong ability of fault tolerance and redundancy, better meet the needs of users, to ensure the safety of the information data and the good operation of the network system. For this target, we propose the novel paradigm for the enhancement of the modern computer network that is innovative. 展开更多
关键词 Intrusion detection NETWORK particle Swarm Optimization MODIFICATION Algorithm.
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Research on the Novel Computer Network Intrusion Detection Model based on Improved Particle Swarm Optimization Algorithm
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作者 Juan Fu Hai Hu Leping Wang 《International Journal of Technology Management》 2016年第8期72-75,共4页
In this paper, we conduct research on the novel computer network intrusion detection model based on improved particle swarmoptimization algorithm. TCP fl ood attack, UDP fl ood attack, ICMP fl ood attack, deformity of... In this paper, we conduct research on the novel computer network intrusion detection model based on improved particle swarmoptimization algorithm. TCP fl ood attack, UDP fl ood attack, ICMP fl ood attack, deformity of message attack, the application layer attack is themost typical DDOS attacks, DDOS attacks are also changing to upgrade at the same time, scholars research on DDOS attack defense measuresbecome more and more has the application value and basic practical signifi cance. Network security protection is a comprehensive project, nomatter what measures to take that safety is always relative, so as the network security administrator, should change with the network securitysituation and the security requirements, moderate to adjust security policies, so as to achieve the target. Under this basis, we propose the newperspective on the IDS system that will then enhance the robustness and safetiness of the overall network system. 展开更多
关键词 particle Swarm Optimization Intrusion detection ALGORITHM Computer Network
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CenterPicker:An Automated Cryo-EM Single-Particle Picking Method Based on Center Point Detection
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作者 Jianquan Ouyang Jinling Wang +1 位作者 Yaowu Wang Tianming Liu 《Journal of Cyber Security》 2022年第2期65-77,共13页
Cryo-electron microscopy(cryo-EM)has become one of the mainstream techniques for determining the structures of proteins andmacromolecular complexes,with prospects for development and significance.Researchers must sele... Cryo-electron microscopy(cryo-EM)has become one of the mainstream techniques for determining the structures of proteins andmacromolecular complexes,with prospects for development and significance.Researchers must select hundreds of thousands of particles from micrographs to acquire the database for single-particle cryo-EM reconstruction.However,existing particle picking methods cannot ensure that the particles are in the center of the bounding box because the signal-to-noise ratio(SNR)of micrographs is extremely low,thereby directly affecting the efficiency and accuracy of 3D reconstruction.We propose an automated particle-picking method(CenterPicker)based on particle center point detection to automatically select a large number of high-quality particles from low signal-to-noise,low-contrast refrigerated microscopy images.The method uses a fully convolutional neural network to generate a keypoint heatmap.The heatmap value represents the probability that a micrograph pixel belongs to a particle center area.CenterPicker can process images of any size and can directly predict the center point and size of the particle.The network implements multiscale feature fusion and introduces an attention mechanism to improve the feature fusion part to obtain more accurate selection results.We have conducted a detailed evaluation of CenterPicker on a range of datasets,and results indicate that it excels in single-particle picking tasks. 展开更多
关键词 Cryo-electron microscope deep learning particle picking object detection
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