Acoustic source localization(ASL)and sound event detection(SED)are two widely pursued independent research fields.In recent years,in order to achieve a more complete spatial and temporal representation of sound field,...Acoustic source localization(ASL)and sound event detection(SED)are two widely pursued independent research fields.In recent years,in order to achieve a more complete spatial and temporal representation of sound field,sound event localization and detection(SELD)has become a very active research topic.This paper presents a deep learning-based multioverlapping sound event localization and detection algorithm in three-dimensional space.Log-Mel spectrum and generalized cross-correlation spectrum are joined together in channel dimension as input features.These features are classified and regressed in parallel after training by a neural network to obtain sound recognition and localization results respectively.The channel attention mechanism is also introduced in the network to selectively enhance the features containing essential information and suppress the useless features.Finally,a thourough comparison confirms the efficiency and effectiveness of the proposed SELD algorithm.Field experiments show that the proposed algorithm is robust to reverberation and environment and can achieve higher recognition and localization accuracy compared with the baseline method.展开更多
Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power systems.However,the emergence of cyber-physical a...Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power systems.However,the emergence of cyber-physical attacks,such as dynamic load-altering attacks(DLAAs)has introduced great challenges to the security of smart energy grids.Thus,this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids.The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer.First,a data-driven method was proposed to predict the DLAA sequence in the cyber layer.By designing a double radial basis function network,the influence of disturbances on attack prediction can be eliminated.Based on the prediction results,an unknown input observer-based detection and localization method was further developed for the physical layer.In addition,an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs.Consequently,through the collaborative work of the cyber-physics layer,injected DLAAs were effectively detected and located.Compared with existing methodologies,the simulation results on IEEE 14-bus and 118-bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.展开更多
Ultrasonic guided wave is an attractive monitoring technique for large-scale structures but is vulnerable to changes in environmental and operational conditions(EOC),which are inevitable in the normal inspection of ci...Ultrasonic guided wave is an attractive monitoring technique for large-scale structures but is vulnerable to changes in environmental and operational conditions(EOC),which are inevitable in the normal inspection of civil and mechanical structures.This paper thus presents a robust guided wave-based method for damage detection and localization under complex environmental conditions by singular value decomposition-based feature extraction and one-dimensional convolutional neural network(1D-CNN).After singular value decomposition-based feature extraction processing,a temporal robust damage index(TRDI)is extracted,and the effect of EOCs is well removed.Hence,even for the signals with a very large temperature-varying range and low signal-to-noise ratios(SNRs),the final damage detection and localization accuracy retain perfect 100%.Verifications are conducted on two different experimental datasets.The first dataset consists of guided wave signals collected from a thin aluminum plate with artificial noises,and the second is a publicly available experimental dataset of guided wave signals acquired on a composite plate with a temperature ranging from 20℃to 60℃.It is demonstrated that the proposed method can detect and localize the damage accurately and rapidly,showing great potential for application in complex and unknown EOC.展开更多
Liquid leakage from pipelines is a critical issue in large-scale process plants.Damage in pipelines affects the normal operation of the plant and increases maintenance costs.Furthermore,it causes unsafe and hazardous ...Liquid leakage from pipelines is a critical issue in large-scale process plants.Damage in pipelines affects the normal operation of the plant and increases maintenance costs.Furthermore,it causes unsafe and hazardous situations for operators.Therefore,the detection and localization of leakages is a crucial task for maintenance and condition monitoring.Recently,the use of infrared(IR)cameras was found to be a promising approach for leakage detection in large-scale plants.IR cameras can capture leaking liquid if it has a higher(or lower)temperature than its surroundings.In this paper,a method based on IR video data and machine vision techniques is proposed to detect and localize liquid leakages in a chemical process plant.Since the proposed method is a vision-based method and does not consider the physical properties of the leaking liquid,it is applicable for any type of liquid leakage(i.e.,water,oil,etc.).In this method,subsequent frames are subtracted and divided into blocks.Then,principle component analysis is performed in each block to extract features from the blocks.All subtracted frames within the blocks are individually transferred to feature vectors,which are used as a basis for classifying the blocks.The k-nearest neighbor algorithm is used to classify the blocks as normal(without leakage)or anomalous(with leakage).Finally,the positions of the leakages are determined in each anomalous block.In order to evaluate the approach,two datasets with two different formats,consisting of video footage of a laboratory demonstrator plant captured by an IR camera,are considered.The results show that the proposed method is a promising approach to detect and localize leakages from pipelines using IR videos.The proposed method has high accuracy and a reasonable detection time for leakage detection.The possibility of extending the proposed method to a real industrial plant and the limitations of this method are discussed at the end.展开更多
Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical ...Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical methods for anomalous cell detection cannot adapt to the evolution of networks, and data mining becomes the mainstream. In this paper, we propose a novel kernel density-based local outlier factor(KLOF) to assign a degree of being an outlier to each object. Firstly, the notion of KLOF is introduced, which captures exactly the relative degree of isolation. Then, by analyzing its properties, including the tightness of upper and lower bounds, sensitivity of density perturbation, we find that KLOF is much greater than 1 for outliers. Lastly, KLOFis applied on a real-world dataset to detect anomalous cells with abnormal key performance indicators(KPIs) to verify its reliability. The experiment shows that KLOF can find outliers efficiently. It can be a guideline for the operators to perform faster and more efficient trouble shooting.展开更多
In recent years,local community detection algorithms have developed rapidly because of their nearly linear computing time and the convenience of obtaining the local information of real-world networks.However,there are...In recent years,local community detection algorithms have developed rapidly because of their nearly linear computing time and the convenience of obtaining the local information of real-world networks.However,there are still some issues that need to be further studied.First,there is no local community detection algorithm dedicated to detecting a seed-oriented local community,that is,the local community with the seed as the core.The second and third issues are that the quality of local communities detected by the previous local community detection algorithms are largely dependent on the position of the seed and predefined parameters,respectively.To solve the existing problems,we propose a seed-oriented local community detection algorithm,named SOLCD,that is based on influence spreading.First,we propose a novel measure of node influence named k-core centrality that is based on the k-core value of adjacent nodes.Second,we obtain the seed-oriented local community,which is composed of the may-members and the must-member chain of the seed,by detecting the influence scope of the seed.The may-members and the must-members of the seed are determined by judging the influence relationship between the node and the seed.Five state-of-art algorithms are compared to SOLCD on six real-world networks and three groups of artificial networks.The experimental results show that SOLCD can achieve a high-quality seed-oriented local community for various real-world networks and artificial networks with different parameters.In addition,when taking nodes with different influence as seeds,SOLCD can stably obtain high-quality seed-oriented local communities.展开更多
Fingerprint authentication system is used to verify users' identification according to the characteristics of their fingerprints.However,this system has some security and privacy problems.For example,some artifici...Fingerprint authentication system is used to verify users' identification according to the characteristics of their fingerprints.However,this system has some security and privacy problems.For example,some artificial fingerprints can trick the fingerprint authentication system and access information using real users' identification.Therefore,a fingerprint liveness detection algorithm needs to be designed to prevent illegal users from accessing privacy information.In this paper,a new software-based liveness detection approach using multi-scale local phase quantity(LPQ) and principal component analysis(PCA) is proposed.The feature vectors of a fingerprint are constructed through multi-scale LPQ.PCA technology is also introduced to reduce the dimensionality of the feature vectors and gain more effective features.Finally,a training model is gained using support vector machine classifier,and the liveness of a fingerprint is detected on the basis of the training model.Experimental results demonstrate that our proposed method can detect the liveness of users' fingerprints and achieve high recognition accuracy.This study also confirms that multi-resolution analysis is a useful method for texture feature extraction during fingerprint liveness detection.展开更多
It is known that detecting small moving objects in as- tronomical image sequences is a significant research problem in space surveillance. The new theory, compressive sensing, pro- vides a very easy and computationall...It is known that detecting small moving objects in as- tronomical image sequences is a significant research problem in space surveillance. The new theory, compressive sensing, pro- vides a very easy and computationally cheap coding scheme for onboard astronomical remote sensing. An algorithm for small moving space object detection and localization is proposed. The algorithm determines the measurements of objects by comparing the difference between the measurements of the current image and the measurements of the background scene. In contrast to reconstruct the whole image, only a foreground image is recon- structed, which will lead to an effective computational performance, and a high level of localization accuracy is achieved. Experiments and analysis are provided to show the performance of the pro- posed approach on detection and localization.展开更多
Excess materials are left inside aircraft wings due to manual operation errors,and the removal of excess materials is very crucial.To increase removal efficiency,a continuum robot(CR)with a removal end-effector and a ...Excess materials are left inside aircraft wings due to manual operation errors,and the removal of excess materials is very crucial.To increase removal efficiency,a continuum robot(CR)with a removal end-effector and a stereo camera is used to remove excess objects.The size and weight characteristics of excess materials in aircraft wings are analyzed.A novel negative pressure end-effector and a two-finger gripper are designed based on the CR.The negative pressure end-effector aims to remove nuts,small rivets,and small volumes of aluminum shavings.A two-finger gripper is designed to remove large volumes of aluminum shavings.A stereo camera is used to achieve automatic detection and localization of excess materials.Due to poor lighting conditions in the aircraft wing compartment,supplementary lighting devices are used to improve environmental lighting.Then,You Only Look Once(YOLO)v5 is used to classify and detect excess objects,and two training data sets of excess objects in two wings are constructed.Due to the limited texture features inside the aircraft wings,this paper adopts an image-matching method based on the results of YOLO v5 detection.This matching method avoids the performance instability problem based on Oriented Fast and Rotated BRIEF feature point matching.Experimental verification reveals that the detection accuracy of each type of excess exceeds 90%,and the visual localization error is less than 2 mm for four types of excess objects.Results show the two end-effectors can work well for the task of removing excess material from the aircraft wings using a CR.展开更多
The concept of health monitoring is a key aspect of the field of medicine that has been practiced for a long time. A commonly used diagnostic and health monitoring practice is pulse diagnosis, which can be traced back...The concept of health monitoring is a key aspect of the field of medicine that has been practiced for a long time. A commonly used diagnostic and health monitoring practice is pulse diagnosis, which can be traced back approximately five thousand years in the recorded history of China. With advances in the development of modern technology, the concept of health monitoring of a variety of engineering structures in several applications has begun to attract widespread attention. Of particular interest in this study is the health monitoring of civil structures. It seem natural, and even beneficial, that these two health-monitoring methods, one as applies to the human body and the other to civil structures, should be analyzed and compared. In this paper, the basic concepts and theories of the two monitoring methods are first discussed. Similarities are then summarized and commented upon. It is hoped that this correlation analysis may help provide structural engineers with some insights into the intrinsic concept of using pulse diagnosis in human health monitoring, which may of be some benefit in the development of modern structural health monitoring methods.展开更多
With the advent of Industry 4.0,water treatment systems(WTSs)are recognized as typical industrial cyber-physical systems(iCPSs)that are connected to the open Internet.Advanced information technology(IT)benefits the WT...With the advent of Industry 4.0,water treatment systems(WTSs)are recognized as typical industrial cyber-physical systems(iCPSs)that are connected to the open Internet.Advanced information technology(IT)benefits the WTS in the aspects of reliability,efficiency,and economy.However,the vulnerabilities exposed in the communication and control infrastructure on the cyber side make WTSs prone to cyber attacks.The traditional IT system oriented defense mechanisms cannot be directly applied in safety-critical WTSs because the availability and real-time requirements are of great importance.In this paper,we propose an entropy-based intrusion detection(EBID)method to thwart cyber attacks against widely used controllers(e.g.,programmable logic controllers)in WTSs to address this issue.Because of the varied WTS operating conditions,there is a high false-positive rate with a static threshold for detection.Therefore,we propose a dynamic threshold adjustment mechanism to improve the performance of EBID.To validate the performance of the proposed approaches,we built a high-fidelity WTS testbed with more than 50 measurement points.We conducted experiments under two attack scenarios with a total of 36attacks,showing that the proposed methods achieved a detection rate of 97.22%and a false alarm rate of 1.67%.展开更多
Complex processes often work with multiple operation regions, it is critical to develop effective monitoring approaches to ensure the safety of chemical processes. In this work, a discriminant local consistency Gaussi...Complex processes often work with multiple operation regions, it is critical to develop effective monitoring approaches to ensure the safety of chemical processes. In this work, a discriminant local consistency Gaussian mixture model(DLCGMM) for multimode process monitoring is proposed for multimode process monitoring by integrating LCGMM with modified local Fisher discriminant analysis(MLFDA). Different from Fisher discriminant analysis(FDA) that aims to discover the global optimal discriminant directions, MLFDA is capable of uncovering multimodality and local structure of the data by exploiting the posterior probabilities of observations within clusters calculated from the results of LCGMM. This may enable MLFDA to capture more meaningful discriminant information hidden in the high-dimensional multimode observations comparing to FDA. Contrary to most existing multimode process monitoring approaches, DLCGMM performs LCGMM and MFLDA iteratively, and the optimal subspaces with multi-Gaussianity and the optimal discriminant projection vectors are simultaneously achieved in the framework of supervised and unsupervised learning. Furthermore, monitoring statistics are established on each cluster that represents a specific operation condition and two global Bayesian inference-based fault monitoring indexes are established by combining with all the monitoring results of all clusters. The efficiency and effectiveness of the proposed method are evaluated through UCI datasets, a simulated multimode model and the Tennessee Eastman benchmark process.展开更多
Can WiFi signals be used for sensing purpose? The growing PHY layer capabilities of WiFi has made it possible to reuse WiFi signals for both communication and sensing. Sensing via WiFi would enable remote sensing wit...Can WiFi signals be used for sensing purpose? The growing PHY layer capabilities of WiFi has made it possible to reuse WiFi signals for both communication and sensing. Sensing via WiFi would enable remote sensing without wearable sensors, simultaneous perception and data transmission without extra communication infrastructure, and contactless sensing in privacy-preserving mode. Due to the popularity of WiFi devices and the ubiquitous deployment of WiFi networks, WiFi-based sensing networks, if fully connected, would potentially rank as one of the world's largest wireless sensor networks. Yet the concept of wireless and sensorless sensing is not the simple combination of WiFi and radar. It seeks breakthroughs from dedicated radar systems, and aims to balance between low cost and high accuracy, to meet the rising demand for pervasive environment perception in everyday life. Despite increasing research interest, wireless sensing is still in its infancy. Through introductions on basic principles and working prototypes, we review the feasibilities and limitations of wireless, sensorless, and contactless sensing via WiFi. We envision this article as a brief primer on wireless sensing for interested readers to explore this open and largely unexplored field and create next-generation wireless and mobile computing applications.展开更多
Synthetic aperture radar(SAR)image is severely affected by multiplicative speckle noise,which greatly complicates the edge detection.In this paper,by incorporating the discontinuityadaptive Markov random feld(DAMRF...Synthetic aperture radar(SAR)image is severely affected by multiplicative speckle noise,which greatly complicates the edge detection.In this paper,by incorporating the discontinuityadaptive Markov random feld(DAMRF)and maximum a posteriori(MAP)estimation criterion into edge detection,a Bayesian edge detector for SAR imagery is accordingly developed.In the proposed detector,the DAMRF is used as the a priori distribution of the local mean reflectivity,and a maximum a posteriori estimation of it is thus obtained by maximizing the posteriori energy using gradient-descent method.Four normalized ratios constructed in different directions are computed,based on which two edge strength maps(ESMs)are formed.The fnal edge detection result is achieved by fusing the results of two thresholded ESMs.The experimental results with synthetic and real SAR images show that the proposed detector could effciently detect edges in SAR images,and achieve better performance than two popular detectors in terms of Pratt's fgure of merit and visual evaluation in most cases.展开更多
基金supported by the National Natural Science Foundation of China(61877067)the Foundation of Science and Technology on Near-Surface Detection Laboratory(TCGZ2019A002,TCGZ2021C003,6142414200511)the Natural Science Basic Research Program of Shaanxi(2021JZ-19)。
文摘Acoustic source localization(ASL)and sound event detection(SED)are two widely pursued independent research fields.In recent years,in order to achieve a more complete spatial and temporal representation of sound field,sound event localization and detection(SELD)has become a very active research topic.This paper presents a deep learning-based multioverlapping sound event localization and detection algorithm in three-dimensional space.Log-Mel spectrum and generalized cross-correlation spectrum are joined together in channel dimension as input features.These features are classified and regressed in parallel after training by a neural network to obtain sound recognition and localization results respectively.The channel attention mechanism is also introduced in the network to selectively enhance the features containing essential information and suppress the useless features.Finally,a thourough comparison confirms the efficiency and effectiveness of the proposed SELD algorithm.Field experiments show that the proposed algorithm is robust to reverberation and environment and can achieve higher recognition and localization accuracy compared with the baseline method.
基金supported by the National Nature Science Foundation of China under 62203376the Science and Technology Plan of Hebei Education Department under QN2021139+1 种基金the Nature Science Foundation of Hebei Province under F2021203043the Open Research Fund of Jiangsu Collaborative Innovation Center for Smart Distribution Network,Nanjing Institute of Technology under No.XTCX202203.
文摘Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power systems.However,the emergence of cyber-physical attacks,such as dynamic load-altering attacks(DLAAs)has introduced great challenges to the security of smart energy grids.Thus,this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids.The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer.First,a data-driven method was proposed to predict the DLAA sequence in the cyber layer.By designing a double radial basis function network,the influence of disturbances on attack prediction can be eliminated.Based on the prediction results,an unknown input observer-based detection and localization method was further developed for the physical layer.In addition,an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs.Consequently,through the collaborative work of the cyber-physics layer,injected DLAAs were effectively detected and located.Compared with existing methodologies,the simulation results on IEEE 14-bus and 118-bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.
基金Supported by National Natural Science Foundation of China(Grant Nos.52272433 and 11874110)Jiangsu Provincial Key R&D Program(Grant No.BE2021084)Technical Support Special Project of State Administration for Market Regulation(Grant No.2022YJ11).
文摘Ultrasonic guided wave is an attractive monitoring technique for large-scale structures but is vulnerable to changes in environmental and operational conditions(EOC),which are inevitable in the normal inspection of civil and mechanical structures.This paper thus presents a robust guided wave-based method for damage detection and localization under complex environmental conditions by singular value decomposition-based feature extraction and one-dimensional convolutional neural network(1D-CNN).After singular value decomposition-based feature extraction processing,a temporal robust damage index(TRDI)is extracted,and the effect of EOCs is well removed.Hence,even for the signals with a very large temperature-varying range and low signal-to-noise ratios(SNRs),the final damage detection and localization accuracy retain perfect 100%.Verifications are conducted on two different experimental datasets.The first dataset consists of guided wave signals collected from a thin aluminum plate with artificial noises,and the second is a publicly available experimental dataset of guided wave signals acquired on a composite plate with a temperature ranging from 20℃to 60℃.It is demonstrated that the proposed method can detect and localize the damage accurately and rapidly,showing great potential for application in complex and unknown EOC.
基金funded by the German Federal Ministry for Economic Affairs and Energy(BMWi)(01MD15009F).
文摘Liquid leakage from pipelines is a critical issue in large-scale process plants.Damage in pipelines affects the normal operation of the plant and increases maintenance costs.Furthermore,it causes unsafe and hazardous situations for operators.Therefore,the detection and localization of leakages is a crucial task for maintenance and condition monitoring.Recently,the use of infrared(IR)cameras was found to be a promising approach for leakage detection in large-scale plants.IR cameras can capture leaking liquid if it has a higher(or lower)temperature than its surroundings.In this paper,a method based on IR video data and machine vision techniques is proposed to detect and localize liquid leakages in a chemical process plant.Since the proposed method is a vision-based method and does not consider the physical properties of the leaking liquid,it is applicable for any type of liquid leakage(i.e.,water,oil,etc.).In this method,subsequent frames are subtracted and divided into blocks.Then,principle component analysis is performed in each block to extract features from the blocks.All subtracted frames within the blocks are individually transferred to feature vectors,which are used as a basis for classifying the blocks.The k-nearest neighbor algorithm is used to classify the blocks as normal(without leakage)or anomalous(with leakage).Finally,the positions of the leakages are determined in each anomalous block.In order to evaluate the approach,two datasets with two different formats,consisting of video footage of a laboratory demonstrator plant captured by an IR camera,are considered.The results show that the proposed method is a promising approach to detect and localize leakages from pipelines using IR videos.The proposed method has high accuracy and a reasonable detection time for leakage detection.The possibility of extending the proposed method to a real industrial plant and the limitations of this method are discussed at the end.
基金supported by the National Basic Research Program of China (973 Program: 2013CB329004)
文摘Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical methods for anomalous cell detection cannot adapt to the evolution of networks, and data mining becomes the mainstream. In this paper, we propose a novel kernel density-based local outlier factor(KLOF) to assign a degree of being an outlier to each object. Firstly, the notion of KLOF is introduced, which captures exactly the relative degree of isolation. Then, by analyzing its properties, including the tightness of upper and lower bounds, sensitivity of density perturbation, we find that KLOF is much greater than 1 for outliers. Lastly, KLOFis applied on a real-world dataset to detect anomalous cells with abnormal key performance indicators(KPIs) to verify its reliability. The experiment shows that KLOF can find outliers efficiently. It can be a guideline for the operators to perform faster and more efficient trouble shooting.
基金National Natural Science Foundation of China(Nos.61672179,61370083,61402126)Heilongjiang Province Natural Science Foundation of China(No.F2015030)+1 种基金Science Fund for Youths in Heilongjiang Province(No.QC2016083)Postdoctoral Fellowship in Heilongjiang Province(No.LBH-Z14071).
文摘In recent years,local community detection algorithms have developed rapidly because of their nearly linear computing time and the convenience of obtaining the local information of real-world networks.However,there are still some issues that need to be further studied.First,there is no local community detection algorithm dedicated to detecting a seed-oriented local community,that is,the local community with the seed as the core.The second and third issues are that the quality of local communities detected by the previous local community detection algorithms are largely dependent on the position of the seed and predefined parameters,respectively.To solve the existing problems,we propose a seed-oriented local community detection algorithm,named SOLCD,that is based on influence spreading.First,we propose a novel measure of node influence named k-core centrality that is based on the k-core value of adjacent nodes.Second,we obtain the seed-oriented local community,which is composed of the may-members and the must-member chain of the seed,by detecting the influence scope of the seed.The may-members and the must-members of the seed are determined by judging the influence relationship between the node and the seed.Five state-of-art algorithms are compared to SOLCD on six real-world networks and three groups of artificial networks.The experimental results show that SOLCD can achieve a high-quality seed-oriented local community for various real-world networks and artificial networks with different parameters.In addition,when taking nodes with different influence as seeds,SOLCD can stably obtain high-quality seed-oriented local communities.
基金supported by the NSFC (U1536206,61232016,U1405254,61373133, 61502242)BK20150925the PAPD fund
文摘Fingerprint authentication system is used to verify users' identification according to the characteristics of their fingerprints.However,this system has some security and privacy problems.For example,some artificial fingerprints can trick the fingerprint authentication system and access information using real users' identification.Therefore,a fingerprint liveness detection algorithm needs to be designed to prevent illegal users from accessing privacy information.In this paper,a new software-based liveness detection approach using multi-scale local phase quantity(LPQ) and principal component analysis(PCA) is proposed.The feature vectors of a fingerprint are constructed through multi-scale LPQ.PCA technology is also introduced to reduce the dimensionality of the feature vectors and gain more effective features.Finally,a training model is gained using support vector machine classifier,and the liveness of a fingerprint is detected on the basis of the training model.Experimental results demonstrate that our proposed method can detect the liveness of users' fingerprints and achieve high recognition accuracy.This study also confirms that multi-resolution analysis is a useful method for texture feature extraction during fingerprint liveness detection.
基金supported by the National Natural Science Foundation of China (60903126)the China Postdoctoral Special Science Foundation (201003685)+1 种基金the China Postdoctoral Science Foundation (20090451397)the Northwestern Polytechnical University Foundation for Fundamental Research (JC201120)
文摘It is known that detecting small moving objects in as- tronomical image sequences is a significant research problem in space surveillance. The new theory, compressive sensing, pro- vides a very easy and computationally cheap coding scheme for onboard astronomical remote sensing. An algorithm for small moving space object detection and localization is proposed. The algorithm determines the measurements of objects by comparing the difference between the measurements of the current image and the measurements of the background scene. In contrast to reconstruct the whole image, only a foreground image is recon- structed, which will lead to an effective computational performance, and a high level of localization accuracy is achieved. Experiments and analysis are provided to show the performance of the pro- posed approach on detection and localization.
基金supported in part by the National Natural Science Foundation of China(Grant No.U1813221).
文摘Excess materials are left inside aircraft wings due to manual operation errors,and the removal of excess materials is very crucial.To increase removal efficiency,a continuum robot(CR)with a removal end-effector and a stereo camera is used to remove excess objects.The size and weight characteristics of excess materials in aircraft wings are analyzed.A novel negative pressure end-effector and a two-finger gripper are designed based on the CR.The negative pressure end-effector aims to remove nuts,small rivets,and small volumes of aluminum shavings.A two-finger gripper is designed to remove large volumes of aluminum shavings.A stereo camera is used to achieve automatic detection and localization of excess materials.Due to poor lighting conditions in the aircraft wing compartment,supplementary lighting devices are used to improve environmental lighting.Then,You Only Look Once(YOLO)v5 is used to classify and detect excess objects,and two training data sets of excess objects in two wings are constructed.Due to the limited texture features inside the aircraft wings,this paper adopts an image-matching method based on the results of YOLO v5 detection.This matching method avoids the performance instability problem based on Oriented Fast and Rotated BRIEF feature point matching.Experimental verification reveals that the detection accuracy of each type of excess exceeds 90%,and the visual localization error is less than 2 mm for four types of excess objects.Results show the two end-effectors can work well for the task of removing excess material from the aircraft wings using a CR.
基金the National Science Foundation through the International Collaboration Supplement of Grant No.CMS-0202320the HongKong Research Grants Council via the Competitive Earmarked Research Grant HKUST6220/01E
文摘The concept of health monitoring is a key aspect of the field of medicine that has been practiced for a long time. A commonly used diagnostic and health monitoring practice is pulse diagnosis, which can be traced back approximately five thousand years in the recorded history of China. With advances in the development of modern technology, the concept of health monitoring of a variety of engineering structures in several applications has begun to attract widespread attention. Of particular interest in this study is the health monitoring of civil structures. It seem natural, and even beneficial, that these two health-monitoring methods, one as applies to the human body and the other to civil structures, should be analyzed and compared. In this paper, the basic concepts and theories of the two monitoring methods are first discussed. Similarities are then summarized and commented upon. It is hoped that this correlation analysis may help provide structural engineers with some insights into the intrinsic concept of using pulse diagnosis in human health monitoring, which may of be some benefit in the development of modern structural health monitoring methods.
基金Project supported by the National Natural Science Foundation of China(No.61833015)。
文摘With the advent of Industry 4.0,water treatment systems(WTSs)are recognized as typical industrial cyber-physical systems(iCPSs)that are connected to the open Internet.Advanced information technology(IT)benefits the WTS in the aspects of reliability,efficiency,and economy.However,the vulnerabilities exposed in the communication and control infrastructure on the cyber side make WTSs prone to cyber attacks.The traditional IT system oriented defense mechanisms cannot be directly applied in safety-critical WTSs because the availability and real-time requirements are of great importance.In this paper,we propose an entropy-based intrusion detection(EBID)method to thwart cyber attacks against widely used controllers(e.g.,programmable logic controllers)in WTSs to address this issue.Because of the varied WTS operating conditions,there is a high false-positive rate with a static threshold for detection.Therefore,we propose a dynamic threshold adjustment mechanism to improve the performance of EBID.To validate the performance of the proposed approaches,we built a high-fidelity WTS testbed with more than 50 measurement points.We conducted experiments under two attack scenarios with a total of 36attacks,showing that the proposed methods achieved a detection rate of 97.22%and a false alarm rate of 1.67%.
基金Supported by the National Natural Science Foundation of China(61273167)
文摘Complex processes often work with multiple operation regions, it is critical to develop effective monitoring approaches to ensure the safety of chemical processes. In this work, a discriminant local consistency Gaussian mixture model(DLCGMM) for multimode process monitoring is proposed for multimode process monitoring by integrating LCGMM with modified local Fisher discriminant analysis(MLFDA). Different from Fisher discriminant analysis(FDA) that aims to discover the global optimal discriminant directions, MLFDA is capable of uncovering multimodality and local structure of the data by exploiting the posterior probabilities of observations within clusters calculated from the results of LCGMM. This may enable MLFDA to capture more meaningful discriminant information hidden in the high-dimensional multimode observations comparing to FDA. Contrary to most existing multimode process monitoring approaches, DLCGMM performs LCGMM and MFLDA iteratively, and the optimal subspaces with multi-Gaussianity and the optimal discriminant projection vectors are simultaneously achieved in the framework of supervised and unsupervised learning. Furthermore, monitoring statistics are established on each cluster that represents a specific operation condition and two global Bayesian inference-based fault monitoring indexes are established by combining with all the monitoring results of all clusters. The efficiency and effectiveness of the proposed method are evaluated through UCI datasets, a simulated multimode model and the Tennessee Eastman benchmark process.
文摘Can WiFi signals be used for sensing purpose? The growing PHY layer capabilities of WiFi has made it possible to reuse WiFi signals for both communication and sensing. Sensing via WiFi would enable remote sensing without wearable sensors, simultaneous perception and data transmission without extra communication infrastructure, and contactless sensing in privacy-preserving mode. Due to the popularity of WiFi devices and the ubiquitous deployment of WiFi networks, WiFi-based sensing networks, if fully connected, would potentially rank as one of the world's largest wireless sensor networks. Yet the concept of wireless and sensorless sensing is not the simple combination of WiFi and radar. It seeks breakthroughs from dedicated radar systems, and aims to balance between low cost and high accuracy, to meet the rising demand for pervasive environment perception in everyday life. Despite increasing research interest, wireless sensing is still in its infancy. Through introductions on basic principles and working prototypes, we review the feasibilities and limitations of wireless, sensorless, and contactless sensing via WiFi. We envision this article as a brief primer on wireless sensing for interested readers to explore this open and largely unexplored field and create next-generation wireless and mobile computing applications.
基金supported National Natural Science Foundation of China (No.61102167)
文摘Synthetic aperture radar(SAR)image is severely affected by multiplicative speckle noise,which greatly complicates the edge detection.In this paper,by incorporating the discontinuityadaptive Markov random feld(DAMRF)and maximum a posteriori(MAP)estimation criterion into edge detection,a Bayesian edge detector for SAR imagery is accordingly developed.In the proposed detector,the DAMRF is used as the a priori distribution of the local mean reflectivity,and a maximum a posteriori estimation of it is thus obtained by maximizing the posteriori energy using gradient-descent method.Four normalized ratios constructed in different directions are computed,based on which two edge strength maps(ESMs)are formed.The fnal edge detection result is achieved by fusing the results of two thresholded ESMs.The experimental results with synthetic and real SAR images show that the proposed detector could effciently detect edges in SAR images,and achieve better performance than two popular detectors in terms of Pratt's fgure of merit and visual evaluation in most cases.