： This paper proposes a new sequential similarity detection algorithm （SSDA）, which can overcome matching error caused by grayscale distortion; meanwhile, time consumption is much less than that of regular algorith...： This paper proposes a new sequential similarity detection algorithm （SSDA）, which can overcome matching error caused by grayscale distortion; meanwhile, time consumption is much less than that of regular algorithms based on image feature. The algorithm adopts Sobel operator to deal with subgraph and template image, and regards the region which has maximum relevance as final result. In order to solve time-consuming problem existing in original algorithm, a coarse-to-fine matching method is put forward. Besides, the location correlation keeps updating and remains the minimum value in the whole scanning process, which can significantly decrease time consumption. Experiments show that the algorithm proposed in this article can not only overcome gray distortion, but also ensure accuracy. Time consumption is at least one time orders of magnitude shorter than that of primal algorithm.展开更多
Visual fire detection technologies can detect fire and alarm warnings earlier than conventional fire detectors. This study proposes an effective visual fire detection method that combines the statistical fire color mo...Visual fire detection technologies can detect fire and alarm warnings earlier than conventional fire detectors. This study proposes an effective visual fire detection method that combines the statistical fire color model and sequential pattern mining technology to detect fire in an image. Furthermore, the proposed method also supports real-time fire detection by integrating adaptive background subtraction technologies. Experimental results show that the proposed method can effectively detect fire in test images and videos. The detection accuracy of the proposed hybrid method is better than that of Celik＇s method.展开更多
Aimed at the problem of the end effect when using empirical mode decomposition(EMD),a method for constraining the end effect of EMD is proposed based on sequential similarity detection and adaptive filter.The method d...Aimed at the problem of the end effect when using empirical mode decomposition(EMD),a method for constraining the end effect of EMD is proposed based on sequential similarity detection and adaptive filter.The method divides the signal into many wavelets,and it changes the initial wavelet length to select the best initial wavelet that has the minimum error and maximum number of matching seed wavelets,and the wavelet slopes are used for pre-matching and secondary matching to speed up the matching speed.Then,folded self-adaptive threshold is used to select multiple seed wavelets,and finally the end waveform is predicted and expanded according to the adaptive filter method.The proposed method is used to analyze the non-stationary nonlinear simulation signal and experimental signal,and it is compared with the mirror extension and RBF extension methods.The orthogonality index and similarity index of the EMD results of the extended signal after the proposed method are better than those of the other methods.The results show that the proposed method can better constrain the end effect,and has certain validity,accuracy and stability in solving the end effect problem.展开更多
A sequence detector based on Hopfield Neural network(HNN) is presented, which is used to estimate the transmitted sequences from the received signals in mobile communications. In order to avoid the convergence of HNN ...A sequence detector based on Hopfield Neural network(HNN) is presented, which is used to estimate the transmitted sequences from the received signals in mobile communications. In order to avoid the convergence of HNN in local minima, a decreasing step algorithm (DSA) is proposed to search the optimum sequence quickly on the basis of the traditional simulated annealing (SA) algorithm. Computer simulation results show that the new HNN detector provides almost the same performance as that of the Viterbi detector while needs less computations and memory capacity, thus it is more feasible in hardware implementation and long constraint convolutional decoding.展开更多
We consider the problem of automated voice activity detection (VAD), in the presence of noise. To attain this objective, we introduce a Sequential Detection of Change Test (SDCT), designed at the independent mixture o...We consider the problem of automated voice activity detection (VAD), in the presence of noise. To attain this objective, we introduce a Sequential Detection of Change Test (SDCT), designed at the independent mixture of Laplacian and Gaussian distributions. We analyse and numerically evaluate the proposed test for various noisy environments. In addition, we address the problem of effectively recognizing the possible presence of cyber exploits in the voice transmission channel. We then introduce another sequential test, designed to detect rapidly and accurately the presence of such exploits, named Cyber Attacks Sequential Detection of Change Test (CA-SDCT). We analyse and numerically evaluate the latter test. Experimental results and comparisons with other proposed methods are also presented.展开更多
The paper analyzed a new watermarking detection paradigm including double detection thresholds based on sequential hypothesis testing. A joint design of watermarking encoding and detection was proposed. The paradigm h...The paper analyzed a new watermarking detection paradigm including double detection thresholds based on sequential hypothesis testing. A joint design of watermarking encoding and detection was proposed. The paradigm had good immunity to noisy signal attacks and high detection probability. Many experiments proved that the above algorithm can detect watermarks about 66% faster than popular detectors, which could have significant impact on many applications such as video watermarking detection and watermark-searching in a large database of digital contents.展开更多
A number of statistical tests are proposed for the purpose of change-point detection in a general nonparametric regression model under mild conditions. New proofs are given to prove the weak convergence of the underly...A number of statistical tests are proposed for the purpose of change-point detection in a general nonparametric regression model under mild conditions. New proofs are given to prove the weak convergence of the underlying processes which assume remove the stringent condition of bounded total variation of the regression function and need only second moments. Since many quantities, such as the regression function, the distribution of the covariates and the distribution of the errors, are unspecified, the results are not distribution-free. A weighted bootstrap approach is proposed to approximate the limiting distributions. Results of a simulation study for this paper show good performance for moderate samples sizes.展开更多
The aircraft braking system is critical to ensure the safe take-off and landing of the aircraft.However,the braking system is often exposed to high temperatures and strong vibration working environments,which makes th...The aircraft braking system is critical to ensure the safe take-off and landing of the aircraft.However,the braking system is often exposed to high temperatures and strong vibration working environments,which makes the sensor prone to failure.Sensor failure has the potential to compromise aircraft safety.In order to improve the safety of the aircraft braking system,a fault detection and fault-tolerant control(FDFTC)strategy for the aircraft brake pressure sensor is designed.Firstly,a model based on a bidirectional long short-term memory(Bi-LSTM)network is constructed to estimate the brake pressure.Then,the residual sequence is obtained by comparing the measured pressure with the estimated pressure.On this basis,the improved sequential probability ratio test(SPRT)method based on mathematical statistics is applied to analyze the residual sequence to detect the fault.Finally,simulation and hardware-in-the-loop(HIL)testing results indicate that the proposed FDFTC strategy can detect sensor faults in time and efficiently complete braking when faults occur.Hence,the proposed FDFTC strategy can effectively deal with the faults of the aircraft brake pressure sensor,which is of great significance to improve the reliability and safety of the aircraft.展开更多
On the basis of Hartmann Shack sensor imaging analysis, a new method is presented with which the wavefront slope can be obtained when the object is incoherent and extended. This method, which is demonstrated by both ...On the basis of Hartmann Shack sensor imaging analysis, a new method is presented with which the wavefront slope can be obtained when the object is incoherent and extended. This method, which is demonstrated by both theoretical interpreting and computer simulation, explains how to measure the wavefront slope difference between two sub apertures through the determination of image displacements on detector plane. It includes a fast and accurate digital algorithm for detecting wavefront disturbance, which is much suitable for realization in such electrical hardwares as digital signal processors.展开更多
Environmental impact of pollutants can be analyzed effectively by acquiring fish behavioral signals in water with biological behavior sensors. However, a variety of factors, such as the complexity of biological organi...Environmental impact of pollutants can be analyzed effectively by acquiring fish behavioral signals in water with biological behavior sensors. However, a variety of factors, such as the complexity of biological organisms themselves, the device error and the environmental noise, may compromise the accuracy and timeliness of model predictions. The current methods lack prior knowledge about the fish behavioral signals corresponding to characteristic pollutants, and in the event of a pollutant invasion, the fish behavioral signals are poorly discriminated. Therefore, we propose a novel method based on Bayesian sequential,which utilizes multi-channel prior knowledge to calculate the outlier sequence based on wavelet feature followed by calculating the anomaly probability of observed values. Furthermore, the relationship between the anomaly probability and toxicity is analyzed in order to achieve forewarning effectively. At last, our algorithm for fish toxicity detection is verified by integrating the data on laboratory acceptance of characteristic pollutants. The results show that only one false positive occurred in the six experiments, the present algorithm is effective in suppressing false positives and negatives, which increases the reliability of toxicity detections, and thereby has certain applicability and universality in engineering applications.展开更多
Aiming at the poor performance of speech signal detection at low signal-to-noise ratio(SNR),a method is proposed to detect active speech frames based on multi-window time-frequency(T-F)diagrams.First,the T-F diagram o...Aiming at the poor performance of speech signal detection at low signal-to-noise ratio(SNR),a method is proposed to detect active speech frames based on multi-window time-frequency(T-F)diagrams.First,the T-F diagram of the signal is calculated based on a multi-window T-F analysis,and a speech test statistic is constructed based on the characteristic difference between the signal and background noise.Second,the dynamic double-threshold processing is used for preliminary detection,and then the global double-threshold value is obtained using K-means clustering.Finally,the detection results are obtained by sequential decision.The experimental results show that the overall performance of the method is better than that of traditional methods under various SNR conditions and background noises.This method also has the advantages of low complexity,strong robustness,and adaptability to multi-national languages.展开更多
A modified non-coherent sequential detection decision logic based on continuous accumulation to achieve fast PN code acquisition is proposed. To simplify the design and analysis, the equivalent relationship between th...A modified non-coherent sequential detection decision logic based on continuous accumulation to achieve fast PN code acquisition is proposed. To simplify the design and analysis, the equivalent relationship between the likelihood ratio of the current sample and that of all the previous samples is deduced. The scheme is proved to be an optimum sequential detection under certain assumptions. Because the average sample number (ASN) can not be calculated through the methods applied to the conventional sequential detection, an algorithm is also provided, which can estimate both the probability density function (pdf) and the upper threshole of ASN. The desired probabilities of false alarm and detection, as well as faster PN code acquisition compared to the conventional sequential detection can be achieved by employing this structure . In addition, Rayeigh-faded reception case is also taken into consideration. Performances of the proposed schemes are obtained, which suggest that the proposed non-coherent sequential detection is more desirable.展开更多
Ratiometric fluorescent detection of iron(Ⅲ)(Fe^(3+))offers inherent self-calibration and contactless analytic capabilities.However,realizing a dual-emission near-infrared(NIR)nanosensor with a low limit of detection...Ratiometric fluorescent detection of iron(Ⅲ)(Fe^(3+))offers inherent self-calibration and contactless analytic capabilities.However,realizing a dual-emission near-infrared(NIR)nanosensor with a low limit of detection(LOD)is rather challenging.In this work,we report the synthesis of water-dispersible erbium-hyperdoped silicon quantum dots(Si QDs:Er),which emit NIR light at the wavelengths of 810 and 1540 nm.A dual-emission NIR nanosensor based on water-dispersible Si QDs:Er enables ratiometric Fe^(3+)detection with a very low LOD(0.06μM).The effects of pH,recyclability,and the interplay between static and dynamic quenching mechanisms for Fe^(3+)detection have been systematically studied.In addition,we demonstrate that the nanosensor may be used to construct a sequential logic circuit with memory functions.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
Support vector machine (SVM) technique has recently become a research focus in intrusion detection field for its better generalization performance when given less priori knowledge than other soft-computing techniques....Support vector machine (SVM) technique has recently become a research focus in intrusion detection field for its better generalization performance when given less priori knowledge than other soft-computing techniques. But the randomicity of parameter selection in its implement often prevents it achieving expected performance. By utilizing genetic algorithm (GA) to optimize the parameters in data preprocessing and the training model of SVM simultaneously, a hybrid optimization algorithm is proposed in the paper to address this problem. The experimental results demonstrate that it’s an effective method and can improve the performance of SVM-based intrusion detection system further.展开更多
Recent advances in deep learning have led to the creation of various methods for change-point detection(CPD).These methods enhance the ability of CPD tech-niques to handle complex,high-dimensional data,making them mor...Recent advances in deep learning have led to the creation of various methods for change-point detection(CPD).These methods enhance the ability of CPD tech-niques to handle complex,high-dimensional data,making them more adaptable and less dependent on strict assump-tions about data distributions.CPD methods have also demonstrated high accuracy and have been applied across various fields,including manufacturing,healthcare,activity monitoring,finance,and environmental monitoring.This review provides an overview of how these methods are applied,the data sets they use,and how their performance is evaluated.It also organizes techniques into supervised and unsupervised categories,citing key studies.Finally,we explore ongoing challenges and suggest directions for future research to improve interpretability,generalizability,and real-world implementation.展开更多
提出了一种基于CAE_ViT网络模型和顺序层状耦合信息框架(sequential hierarchical coupled information framework,SHCIF)的多粒度多缺陷图像分类识别方法,并结合模糊综合评价(FCE)方法,以桥梁设施为例,对其表面缺陷进行细致的分类及安...提出了一种基于CAE_ViT网络模型和顺序层状耦合信息框架(sequential hierarchical coupled information framework,SHCIF)的多粒度多缺陷图像分类识别方法,并结合模糊综合评价(FCE)方法,以桥梁设施为例,对其表面缺陷进行细致的分类及安全评价。首先,研究提出了SHCIF及对应3个层次粒度的识别模型,并构建和增强了对应不同粒度的数据集。SHCIF框架和跨粒度分类决策旨在通过利用桥梁组件和缺陷类型这两个粒度的信息和准确性,提升对缺陷严重程度的识别。其次,使用迁移学习对CAE_ViT预训练模型进行微调,以满足桥梁缺陷检测的具体需求,并通过跨粒度分类决策进一步提升分类的准确性。最后,基于层次分析法与熵权法(AHP⁃EWM)的权重体系,通过模糊综合评价,综合考虑桥梁部位、桥梁组件、缺陷类型及其严重程度,实现了基于表观健康状态对桥梁安全状态等级的定量评价。实验结果显示,在3个层次粒度的识别模型中的宏平均F1⁃Score分数分别达到94.1%、81.6%和75.3%,而跨粒度分类决策的准确率为82%。最终通过一个桥梁的安全评价案例证明了方法的有效性、系统性和可拓展性。展开更多
基金the National Natural Science Foundation of China(No.61165008)
文摘： This paper proposes a new sequential similarity detection algorithm （SSDA）, which can overcome matching error caused by grayscale distortion; meanwhile, time consumption is much less than that of regular algorithms based on image feature. The algorithm adopts Sobel operator to deal with subgraph and template image, and regards the region which has maximum relevance as final result. In order to solve time-consuming problem existing in original algorithm, a coarse-to-fine matching method is put forward. Besides, the location correlation keeps updating and remains the minimum value in the whole scanning process, which can significantly decrease time consumption. Experiments show that the algorithm proposed in this article can not only overcome gray distortion, but also ensure accuracy. Time consumption is at least one time orders of magnitude shorter than that of primal algorithm.
基金supported by National Science Council under Grant No. NSC98-2221-E-218-046
文摘Visual fire detection technologies can detect fire and alarm warnings earlier than conventional fire detectors. This study proposes an effective visual fire detection method that combines the statistical fire color model and sequential pattern mining technology to detect fire in an image. Furthermore, the proposed method also supports real-time fire detection by integrating adaptive background subtraction technologies. Experimental results show that the proposed method can effectively detect fire in test images and videos. The detection accuracy of the proposed hybrid method is better than that of Celik＇s method.
基金The National Natural Science Foundation of China(No.51675100).
文摘Aimed at the problem of the end effect when using empirical mode decomposition(EMD),a method for constraining the end effect of EMD is proposed based on sequential similarity detection and adaptive filter.The method divides the signal into many wavelets,and it changes the initial wavelet length to select the best initial wavelet that has the minimum error and maximum number of matching seed wavelets,and the wavelet slopes are used for pre-matching and secondary matching to speed up the matching speed.Then,folded self-adaptive threshold is used to select multiple seed wavelets,and finally the end waveform is predicted and expanded according to the adaptive filter method.The proposed method is used to analyze the non-stationary nonlinear simulation signal and experimental signal,and it is compared with the mirror extension and RBF extension methods.The orthogonality index and similarity index of the EMD results of the extended signal after the proposed method are better than those of the other methods.The results show that the proposed method can better constrain the end effect,and has certain validity,accuracy and stability in solving the end effect problem.
文摘A sequence detector based on Hopfield Neural network(HNN) is presented, which is used to estimate the transmitted sequences from the received signals in mobile communications. In order to avoid the convergence of HNN in local minima, a decreasing step algorithm (DSA) is proposed to search the optimum sequence quickly on the basis of the traditional simulated annealing (SA) algorithm. Computer simulation results show that the new HNN detector provides almost the same performance as that of the Viterbi detector while needs less computations and memory capacity, thus it is more feasible in hardware implementation and long constraint convolutional decoding.
文摘We consider the problem of automated voice activity detection (VAD), in the presence of noise. To attain this objective, we introduce a Sequential Detection of Change Test (SDCT), designed at the independent mixture of Laplacian and Gaussian distributions. We analyse and numerically evaluate the proposed test for various noisy environments. In addition, we address the problem of effectively recognizing the possible presence of cyber exploits in the voice transmission channel. We then introduce another sequential test, designed to detect rapidly and accurately the presence of such exploits, named Cyber Attacks Sequential Detection of Change Test (CA-SDCT). We analyse and numerically evaluate the latter test. Experimental results and comparisons with other proposed methods are also presented.
基金This is work is supported by Shanghai Municipal Education Commission (NO.04DC33, NO. 2000SG46)
文摘The paper analyzed a new watermarking detection paradigm including double detection thresholds based on sequential hypothesis testing. A joint design of watermarking encoding and detection was proposed. The paradigm had good immunity to noisy signal attacks and high detection probability. Many experiments proved that the above algorithm can detect watermarks about 66% faster than popular detectors, which could have significant impact on many applications such as video watermarking detection and watermark-searching in a large database of digital contents.
文摘A number of statistical tests are proposed for the purpose of change-point detection in a general nonparametric regression model under mild conditions. New proofs are given to prove the weak convergence of the underlying processes which assume remove the stringent condition of bounded total variation of the regression function and need only second moments. Since many quantities, such as the regression function, the distribution of the covariates and the distribution of the errors, are unspecified, the results are not distribution-free. A weighted bootstrap approach is proposed to approximate the limiting distributions. Results of a simulation study for this paper show good performance for moderate samples sizes.
基金Supported by National Natural Science Foundation of China(Grant No.52205045)National Key Research and Development Program of China(Grant No.2021YFB2011300)+2 种基金Aeronautical Science Foundation of China(Grant No.2022Z029051001)Zhejiang Provincial Natural Science Foundation of China(Grant No.LZ24E050006)Research Fund of State Key Laboratory of Mechanics and Control for Aerospace Structures(Nanjing University of Aeronautics and Astronautics)(Grant No.MCAS-E-0224G01).
文摘The aircraft braking system is critical to ensure the safe take-off and landing of the aircraft.However,the braking system is often exposed to high temperatures and strong vibration working environments,which makes the sensor prone to failure.Sensor failure has the potential to compromise aircraft safety.In order to improve the safety of the aircraft braking system,a fault detection and fault-tolerant control(FDFTC)strategy for the aircraft brake pressure sensor is designed.Firstly,a model based on a bidirectional long short-term memory(Bi-LSTM)network is constructed to estimate the brake pressure.Then,the residual sequence is obtained by comparing the measured pressure with the estimated pressure.On this basis,the improved sequential probability ratio test(SPRT)method based on mathematical statistics is applied to analyze the residual sequence to detect the fault.Finally,simulation and hardware-in-the-loop(HIL)testing results indicate that the proposed FDFTC strategy can detect sensor faults in time and efficiently complete braking when faults occur.Hence,the proposed FDFTC strategy can effectively deal with the faults of the aircraft brake pressure sensor,which is of great significance to improve the reliability and safety of the aircraft.
文摘On the basis of Hartmann Shack sensor imaging analysis, a new method is presented with which the wavefront slope can be obtained when the object is incoherent and extended. This method, which is demonstrated by both theoretical interpreting and computer simulation, explains how to measure the wavefront slope difference between two sub apertures through the determination of image displacements on detector plane. It includes a fast and accurate digital algorithm for detecting wavefront disturbance, which is much suitable for realization in such electrical hardwares as digital signal processors.
基金supported by the National Key R & D Program of China (No. 2019YFD0901100)the Frontier Science Key Program of the Chinese Academy of Sciences (No. QYZDY-SSWDQC004)。
文摘Environmental impact of pollutants can be analyzed effectively by acquiring fish behavioral signals in water with biological behavior sensors. However, a variety of factors, such as the complexity of biological organisms themselves, the device error and the environmental noise, may compromise the accuracy and timeliness of model predictions. The current methods lack prior knowledge about the fish behavioral signals corresponding to characteristic pollutants, and in the event of a pollutant invasion, the fish behavioral signals are poorly discriminated. Therefore, we propose a novel method based on Bayesian sequential,which utilizes multi-channel prior knowledge to calculate the outlier sequence based on wavelet feature followed by calculating the anomaly probability of observed values. Furthermore, the relationship between the anomaly probability and toxicity is analyzed in order to achieve forewarning effectively. At last, our algorithm for fish toxicity detection is verified by integrating the data on laboratory acceptance of characteristic pollutants. The results show that only one false positive occurred in the six experiments, the present algorithm is effective in suppressing false positives and negatives, which increases the reliability of toxicity detections, and thereby has certain applicability and universality in engineering applications.
基金The National Natural Science Foundation of China(No.12174053,91938203,11674057,11874109)the Fundamental Research Funds for the Central Universities(No.2242021k30019).
文摘Aiming at the poor performance of speech signal detection at low signal-to-noise ratio(SNR),a method is proposed to detect active speech frames based on multi-window time-frequency(T-F)diagrams.First,the T-F diagram of the signal is calculated based on a multi-window T-F analysis,and a speech test statistic is constructed based on the characteristic difference between the signal and background noise.Second,the dynamic double-threshold processing is used for preliminary detection,and then the global double-threshold value is obtained using K-means clustering.Finally,the detection results are obtained by sequential decision.The experimental results show that the overall performance of the method is better than that of traditional methods under various SNR conditions and background noises.This method also has the advantages of low complexity,strong robustness,and adaptability to multi-national languages.
文摘A modified non-coherent sequential detection decision logic based on continuous accumulation to achieve fast PN code acquisition is proposed. To simplify the design and analysis, the equivalent relationship between the likelihood ratio of the current sample and that of all the previous samples is deduced. The scheme is proved to be an optimum sequential detection under certain assumptions. Because the average sample number (ASN) can not be calculated through the methods applied to the conventional sequential detection, an algorithm is also provided, which can estimate both the probability density function (pdf) and the upper threshole of ASN. The desired probabilities of false alarm and detection, as well as faster PN code acquisition compared to the conventional sequential detection can be achieved by employing this structure . In addition, Rayeigh-faded reception case is also taken into consideration. Performances of the proposed schemes are obtained, which suggest that the proposed non-coherent sequential detection is more desirable.
基金supported by the National Natural Science Foundation of China(U22A2075,U20A20209)the Fundamental Research Funds for the Central Universities(226-2022-00200)the Qianjiang Distinguished Experts program of Hangzhou.
文摘Ratiometric fluorescent detection of iron(Ⅲ)(Fe^(3+))offers inherent self-calibration and contactless analytic capabilities.However,realizing a dual-emission near-infrared(NIR)nanosensor with a low limit of detection(LOD)is rather challenging.In this work,we report the synthesis of water-dispersible erbium-hyperdoped silicon quantum dots(Si QDs:Er),which emit NIR light at the wavelengths of 810 and 1540 nm.A dual-emission NIR nanosensor based on water-dispersible Si QDs:Er enables ratiometric Fe^(3+)detection with a very low LOD(0.06μM).The effects of pH,recyclability,and the interplay between static and dynamic quenching mechanisms for Fe^(3+)detection have been systematically studied.In addition,we demonstrate that the nanosensor may be used to construct a sequential logic circuit with memory functions.
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.
基金This work was supported by the Research Grant of SEC E-Institute :Shanghai High Institution Grid and the Science Foundation ofShanghai Municipal Commission of Science and Technology No.00JC14052
文摘Support vector machine (SVM) technique has recently become a research focus in intrusion detection field for its better generalization performance when given less priori knowledge than other soft-computing techniques. But the randomicity of parameter selection in its implement often prevents it achieving expected performance. By utilizing genetic algorithm (GA) to optimize the parameters in data preprocessing and the training model of SVM simultaneously, a hybrid optimization algorithm is proposed in the paper to address this problem. The experimental results demonstrate that it’s an effective method and can improve the performance of SVM-based intrusion detection system further.
基金National Natural Science Foundation of China(Grant Nos.NSFC-71932006 and NSFC-72171003).
文摘Recent advances in deep learning have led to the creation of various methods for change-point detection(CPD).These methods enhance the ability of CPD tech-niques to handle complex,high-dimensional data,making them more adaptable and less dependent on strict assump-tions about data distributions.CPD methods have also demonstrated high accuracy and have been applied across various fields,including manufacturing,healthcare,activity monitoring,finance,and environmental monitoring.This review provides an overview of how these methods are applied,the data sets they use,and how their performance is evaluated.It also organizes techniques into supervised and unsupervised categories,citing key studies.Finally,we explore ongoing challenges and suggest directions for future research to improve interpretability,generalizability,and real-world implementation.
文摘提出了一种基于CAE_ViT网络模型和顺序层状耦合信息框架(sequential hierarchical coupled information framework,SHCIF)的多粒度多缺陷图像分类识别方法,并结合模糊综合评价(FCE)方法,以桥梁设施为例,对其表面缺陷进行细致的分类及安全评价。首先,研究提出了SHCIF及对应3个层次粒度的识别模型,并构建和增强了对应不同粒度的数据集。SHCIF框架和跨粒度分类决策旨在通过利用桥梁组件和缺陷类型这两个粒度的信息和准确性,提升对缺陷严重程度的识别。其次,使用迁移学习对CAE_ViT预训练模型进行微调,以满足桥梁缺陷检测的具体需求,并通过跨粒度分类决策进一步提升分类的准确性。最后,基于层次分析法与熵权法(AHP⁃EWM)的权重体系,通过模糊综合评价,综合考虑桥梁部位、桥梁组件、缺陷类型及其严重程度,实现了基于表观健康状态对桥梁安全状态等级的定量评价。实验结果显示,在3个层次粒度的识别模型中的宏平均F1⁃Score分数分别达到94.1%、81.6%和75.3%,而跨粒度分类决策的准确率为82%。最终通过一个桥梁的安全评价案例证明了方法的有效性、系统性和可拓展性。
