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
The energetic particle detector on China's space station can determine the energy, flux, and direction of medium-and highenergy protons, electrons, heavy ions, and neutrons within the path of the station's orb...The energetic particle detector on China's space station can determine the energy, flux, and direction of medium-and highenergy protons, electrons, heavy ions, and neutrons within the path of the station's orbit. It also assesses the linear energy transfer(LET)spectra and radiation dose rates generated by these particles. Neutron detection is a significant component of this work, utilizing a new type of Cs_(2)LiYCl_(6): Ce scintillator material along with plastic scintillators as sensors. In-orbit testing has demonstrated the efficient identification of space neutrons and gamma rays(n/γ). This data plays a crucial role in supporting manned space engineering, scientific research, and other related fields.展开更多
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
Magnetic polyphosphazene(MPZS) particles coated by Ag nanoparticles(MPZS-Ag) have been developed as surface enhanced Raman spectroscopy(SERS) substrates for sensitive detection of melamine in aqueous solutions and mil...Magnetic polyphosphazene(MPZS) particles coated by Ag nanoparticles(MPZS-Ag) have been developed as surface enhanced Raman spectroscopy(SERS) substrates for sensitive detection of melamine in aqueous solutions and milk samples.5,5’-Dithiobis-(2-nitrobenzoic acid)(DTNB) was used as model analyte to test the SERS activity of the MPZS-Ag particles.The prepared MPZS-Ag particles possess both magnetic responsiveness and excellent SERS properties.SERS detection of different concentrations of melamine aqueous solutions and spiked milk samples were performed by the MPZS-Ag particles.The limit of detection(LOD) of the melamine in aqueous solutions was 10^-7 mol/L(0.0126 mg/L) and 0.6 mg/L in real milk samples using the MPZS-Ag particles as SERS substrates.The LOD of the melamine are much lower than the safety values of Food and Drug Administration and Codex Alimentarius Commission.These results indicate that the MPZS-Ag particles have promising application prospect for SERS analysis in food safety fields.展开更多
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
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
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
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
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
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
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
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
In order to improve the accuracy of free flight conflict detection and reduce the false alarm rate, an improved flight conflict detection algorithm is proposed based on Gauss-Hermite particle filter（GHPF）. The algor...In order to improve the accuracy of free flight conflict detection and reduce the false alarm rate, an improved flight conflict detection algorithm is proposed based on Gauss-Hermite particle filter（GHPF）. The algorithm improves the traditional flight conflict detection method in two aspects：（i） New observation data are integrated into system state transition probability, and Gauss-Hermite Filter（GHF） is used for generating the importance density function.（ii） GHPF is used for flight trajectory prediction and flight conflict probability calculation. The experimental results show that the accuracy of conflict detection and tracing with GHPF is better than that with standard particle filter. The detected conflict probability is more precise with GHPF, and GHPF is suitable for early free flight conflict detection.展开更多
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
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
We developed a single-particle optical particle counter with polarization detection(SOPC)for the real-time measurement of the optical size and depolarization ratio(defined as the ratio of the vertical component to the...We developed a single-particle optical particle counter with polarization detection(SOPC)for the real-time measurement of the optical size and depolarization ratio(defined as the ratio of the vertical component to the parallel component of backward scattering)of atmospheric particles,the polarization ratio(DR)value can reflect the irregularity of the particles.The SOPC can detect aerosol particles with size larger than 500 nm and the maximum particle count rate reaches~1.8×10^(5)particles per liter.The SOPC uses a modulated polarization laser to measure the optical size of particles according to forward scattering signal and the DR value of the particles by backward S and P signal components.The sampling rate of the SOPC was 106#/(sec·channel),and all the raw data were processed online.The calibration curve was obtained by polystyrene latex spheres with sizes of 0.5-10μm,and the average relative deviation of measurement was 3.96% for sub 3μm particles.T-matrix method calculations showed that the DR value of backscatter light at 120°could describe the variations in the aspect ratio of particles in the above size range.We performed insitu observations for the evaluation of the SOPC,the mass concentration constructed by the SOPC showed good agreement with the PM_(2.5)measurements in a nearby state-controlled monitoring site.This instrument could provide useful data for source appointment and regulations against air pollution.展开更多
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
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
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
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
基金The National Key Technologies R & D Program during the 11th Five-Year Plan Period(No.2009BAG13A04)the Ph.D.Programs Foundation of Ministry of Education of China(No.200802861061)the Transportation Science Research Project of Jiangsu Province(No.08X09)
文摘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.
基金This mission was supported by the China Manned Space Office。
文摘The energetic particle detector on China's space station can determine the energy, flux, and direction of medium-and highenergy protons, electrons, heavy ions, and neutrons within the path of the station's orbit. It also assesses the linear energy transfer(LET)spectra and radiation dose rates generated by these particles. Neutron detection is a significant component of this work, utilizing a new type of Cs_(2)LiYCl_(6): Ce scintillator material along with plastic scintillators as sensors. In-orbit testing has demonstrated the efficient identification of space neutrons and gamma rays(n/γ). This data plays a crucial role in supporting manned space engineering, scientific research, and other related fields.
基金the scholarship from NUS Graduate School for integrative science and engineering and funding support from Ministry of Education Academic Research Fund,Singapore(AcRF:R-397-000-270-114,R-397-000-183-112).
文摘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.
基金the financial support of the National Natural Science Foundation of China(Nos.51503040,31771893)the Natural Science Foundation of Fujian Province,China(No. 2018J01766)the Outstanding Youth Research Talent Cultivation Program of Universities in Fujian Province,China (No.601936)
文摘Magnetic polyphosphazene(MPZS) particles coated by Ag nanoparticles(MPZS-Ag) have been developed as surface enhanced Raman spectroscopy(SERS) substrates for sensitive detection of melamine in aqueous solutions and milk samples.5,5’-Dithiobis-(2-nitrobenzoic acid)(DTNB) was used as model analyte to test the SERS activity of the MPZS-Ag particles.The prepared MPZS-Ag particles possess both magnetic responsiveness and excellent SERS properties.SERS detection of different concentrations of melamine aqueous solutions and spiked milk samples were performed by the MPZS-Ag particles.The limit of detection(LOD) of the melamine in aqueous solutions was 10^-7 mol/L(0.0126 mg/L) and 0.6 mg/L in real milk samples using the MPZS-Ag particles as SERS substrates.The LOD of the melamine are much lower than the safety values of Food and Drug Administration and Codex Alimentarius Commission.These results indicate that the MPZS-Ag particles have promising application prospect for SERS analysis in food safety fields.
基金Supported by Open Research Fund of State Key Laboratory of Advanced Technology for Vehicle Body Design & Manufacture of China (Grant No.61075002)Hunan Provincial Natural Science Foundation of China (Grant No.13JJ4033)
文摘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.
基金Project(90820302) supported by the National Natural Science Foundation of ChinaProject(20110491272) supported by China Postdoctoral Science Foundation of China+2 种基金Project(2012QNZT060) supported by the Fundamental Research Fund for the Central Universities of ChinaProject(11B070) supported by the Science Research Foundation of Education Bureau of Hunan Province,ChinaProject(2010-2012) supported by the Postdoctoral Science Foundation of Central South University,China
文摘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.
基金Supported by Fundamental Research Funds for the Central Universities(Grant No.2017XKQY032)
文摘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.
文摘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.
文摘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.
基金Project supported by the Key Program of the National Natural Science Foundation of China (Grant No. 61036004)the Shenzhen Science & Technology Foundation, China (Grant No. CXB201005250031A)+1 种基金the Fundamental Research Project of Shenzhen Science & Technology Foundation, China (Grant No. JC201005280670A)the International Collaboration Project of Shenzhen Science & Technology Foundation, China (Grant No. ZYA2010006030006A)
文摘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.
文摘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.
基金supported by the National Natural Science Foundation of P.R.China(No.61672297)the Key Research and Development Program of Jiangsu Province(Social Development Program,No.BE2017742)+1 种基金The Sixth Talent Peaks Project of Jiangsu Province(No.DZXX-017)Jiangsu Natural Science Foundation for Excellent Young Scholar(No.BK20160089)
文摘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.
基金Supported by the Joint Project of National Natural Science Foundation of ChinaCivil Aviation Administration of China(U1333116)
文摘In order to improve the accuracy of free flight conflict detection and reduce the false alarm rate, an improved flight conflict detection algorithm is proposed based on Gauss-Hermite particle filter（GHPF）. The algorithm improves the traditional flight conflict detection method in two aspects：（i） New observation data are integrated into system state transition probability, and Gauss-Hermite Filter（GHF） is used for generating the importance density function.（ii） GHPF is used for flight trajectory prediction and flight conflict probability calculation. The experimental results show that the accuracy of conflict detection and tracing with GHPF is better than that with standard particle filter. The detected conflict probability is more precise with GHPF, and GHPF is suitable for early free flight conflict detection.
基金supported by the National Natural Science Foundation of China ( Grant No. 61072133)the Production,Learning and Research Joint Innovation Program of Jiangsu Province, China ( Grant Nos. BY2013007-02, SBY201120033)+2 种基金the Major Project Plan for Natural science Research in Colleges and Universities of Jiangsu Province, China( Grant No. 15KJA460008)the Open Topic of Atmospheric Sounding Key Open Laboratory of China Meteorological Administration ( Grant No. KLAS201407)the advantage discipline platform " Information and Communication Engineering" of Jiangsu Province,China
文摘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.
基金supported by the NSC under Grant No.NSC 101-2221-E-275-007
文摘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.
基金supported by the Research and Development of Instruments and Equipments,Chinese Academy of Sciences(No.YJKYYQ20200009)。
文摘We developed a single-particle optical particle counter with polarization detection(SOPC)for the real-time measurement of the optical size and depolarization ratio(defined as the ratio of the vertical component to the parallel component of backward scattering)of atmospheric particles,the polarization ratio(DR)value can reflect the irregularity of the particles.The SOPC can detect aerosol particles with size larger than 500 nm and the maximum particle count rate reaches~1.8×10^(5)particles per liter.The SOPC uses a modulated polarization laser to measure the optical size of particles according to forward scattering signal and the DR value of the particles by backward S and P signal components.The sampling rate of the SOPC was 106#/(sec·channel),and all the raw data were processed online.The calibration curve was obtained by polystyrene latex spheres with sizes of 0.5-10μm,and the average relative deviation of measurement was 3.96% for sub 3μm particles.T-matrix method calculations showed that the DR value of backscatter light at 120°could describe the variations in the aspect ratio of particles in the above size range.We performed insitu observations for the evaluation of the SOPC,the mass concentration constructed by the SOPC showed good agreement with the PM_(2.5)measurements in a nearby state-controlled monitoring site.This instrument could provide useful data for source appointment and regulations against air pollution.
基金Shanghai Municipal Education Commission,China(No.CL200516No.RE559)
文摘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.
文摘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.
文摘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.
基金supported by the National Science and Technology Council under grants NSTC 112-2221-E-320-002the Buddhist Tzu Chi Medical Foundation in Taiwan under Grant TCMMP 112-02-02.
文摘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%.
