The on-board diagnosis network is the nervous system of high-speed Maglev trains, connecting all controller sensors, and corresponding devices to realize the information acquisition and control. In order to study the ...The on-board diagnosis network is the nervous system of high-speed Maglev trains, connecting all controller sensors, and corresponding devices to realize the information acquisition and control. In order to study the on-board diagnosis network＇s security and reliability, a simulation model for the on-board diagnosis network of high-speed Maglev trains with the optimal network engineering tool （OPNET） was built to analyze the network＇s performance, such as response error and bit error rate on the network load, throughput, and node-state response. The simulation model was verified with an actual on-board diagnosis network structure. The results show that the model results obtained are in good agreement with actual system performance and can be used to achieve actual communication network optimization and control algorithms.展开更多
AIM:To study the role of time-intensity curve(TIC) analysis parameters in a complex system of neural networks designed to classify liver tumors.METHODS:We prospectively included 112 patients with hepatocellular carcin...AIM:To study the role of time-intensity curve(TIC) analysis parameters in a complex system of neural networks designed to classify liver tumors.METHODS:We prospectively included 112 patients with hepatocellular carcinoma(HCC)(n = 41),hypervascular(n = 20) and hypovascular(n = 12) liver metastases,hepatic hemangiomas(n = 16) or focal fatty changes(n = 23) who underwent contrast-enhanced ultrasonography in the Research Center of Gastroenterology and Hepatology,Craiova,Romania.We recorded full length movies of all contrast uptake phases and post-processed them offline by selecting two areas of interest(one for the tumor and one for the healthy surrounding parenchyma) and consecutive TIC analysis.The difference in maximum intensities,the time to reaching them and the aspect of the late/portal phase,as quantified by the neural network and a ratio between median intensities of the central and peripheral areas were analyzed by a feed forward back propagation multi-layer neural network which was trained to classify data into five distinct classes,corresponding to each type of liver lesion.RESULTS:The neural network had 94.45% training accuracy(95% CI:89.31%-97.21%) and 87.12% testing accuracy(95% CI:86.83%-93.17%).The automatic classification process registered 93.2% sensitivity,89.7% specificity,94.42% positive predictive value and 87.57% negative predictive value.The artificial neural networks(ANN) incorrectly classified as hemangyomas three HCC cases and two hypervascular metastases,while in turn misclassifying four liver hemangyomas as HCC(one case) and hypervascular metastases(three cases).Comparatively,human interpretation of TICs showed 94.1% sensitivity,90.7% specificity,95.11% positive predictive value and 88.89% negative predictive value.The accuracy and specificity of the ANN diagnosis system was similar to that of human interpretation of the TICs(P = 0.225 and P = 0.451,respectively).Hepatocellular carcinoma cases showed contrast uptake during the arterial phase followed by wash-out in the portal and first seconds of the late phases.For the hypovascular metastases did not show significant contrast uptake during the arterial phase,which resulted in negative differences between the maximum intensities.We registered wash-out in the late phase for most of the hypervascular metastases.Liver hemangiomas had contrast uptake in the arterial phase without agent wash-out in the portallate phases.The focal fatty changes did not show any differences from surrounding liver parenchyma,resulting in similar TIC patterns and extracted parameters.CONCLUSION:Neural network analysis of contrastenhanced ultrasonography-obtained TICs seems a promising field of development for future techniques,providing fast and reliable diagnostic aid for the clinician.展开更多
A sequential diagnosis method is proposed based on a fuzzy neural network realized by ＂the partially-linearized neural network （PNN）＂, by which the fault types of rotating machinery can be precisely and effectivel...A sequential diagnosis method is proposed based on a fuzzy neural network realized by ＂the partially-linearized neural network （PNN）＂, by which the fault types of rotating machinery can be precisely and effectively distinguished at an early stage on the basis of the possibilities of symptom parameters. The non-dimensional symptom parameters in time domain are defined for reflecting the features of time signals measured for the fault diagnosis of rotating machinery. The synthetic detection index is also proposed to evaluate the sensitivity of non-dimensional symptom parameters for detecting faults. The practical example of condition diagnosis for detecting and distinguishing fault states of a centrifugal pump system, such as cavitation, impeller eccentricity which often occur in a centrifugal pump system, are shown to verify the efficiency of the method proposed in this paper.展开更多
According to fault type diversity and fault information uncertainty problem of the hydraulic driven rocket launcher servo system（HDRLSS） , the fault diagnosis method based on the evidence theory and neural network e...According to fault type diversity and fault information uncertainty problem of the hydraulic driven rocket launcher servo system（HDRLSS） , the fault diagnosis method based on the evidence theory and neural network ensemble is proposed. In order to overcome the shortcomings of the single neural network, two improved neural network models are set up at the com-mon nodes to simplify the network structure. The initial fault diagnosis is based on the iron spectrum data and the pressure, flow and temperature（PFT） characteristic parameters as the input vectors of the two improved neural network models, and the diagnosis result is taken as the basic probability distribution of the evidence theory. Then the objectivity of assignment is real-ized. The initial diagnosis results of two improved neural networks are fused by D-S evidence theory. The experimental results show that this method can avoid the misdiagnosis of neural network recognition and improve the accuracy of the fault diagnosis of HDRLSS.展开更多
Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network management.Considering the unique characteristics ...Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network management.Considering the unique characteristics of edge networks,such as limited resources,complex network faults,and the need for high real-time performance,enhancing and optimizing existing network fault diagnosis methods is necessary.Therefore,this paper proposes the lightweight edge-side fault diagnosis approach based on a spiking neural network(LSNN).Firstly,we use the Izhikevich neurons model to replace the Leaky Integrate and Fire(LIF)neurons model in the LSNN model.Izhikevich neurons inherit the simplicity of LIF neurons but also possess richer behavioral characteristics and flexibility to handle diverse data inputs.Inspired by Fast Spiking Interneurons(FSIs)with a high-frequency firing pattern,we use the parameters of FSIs.Secondly,inspired by the connection mode based on spiking dynamics in the basal ganglia(BG)area of the brain,we propose the pruning approach based on the FSIs of the BG in LSNN to improve computational efficiency and reduce the demand for computing resources and energy consumption.Furthermore,we propose a multiple iterative Dynamic Spike Timing Dependent Plasticity(DSTDP)algorithm to enhance the accuracy of the LSNN model.Experiments on two server fault datasets demonstrate significant precision,recall,and F1 improvements across three diagnosis dimensions.Simultaneously,lightweight indicators such as Params and FLOPs significantly reduced,showcasing the LSNN’s advanced performance and model efficiency.To conclude,experiment results on a pair of datasets indicate that the LSNN model surpasses traditional models and achieves cutting-edge outcomes in network fault diagnosis tasks.展开更多
An intermittent connection is one of the major problems that affect the network reliability and communication quality.However,little attention has been paid to the detection,analysis and localization of the intermitte...An intermittent connection is one of the major problems that affect the network reliability and communication quality.However,little attention has been paid to the detection,analysis and localization of the intermittent connections.Partially due to the limitations of the DeviceNet protocol,there is no effective online diagnostic tool available to identify the location of intermittent connection.On the basis of different DeviceNet fault scenarios induced by intermittent connections,a new graph-based diagnostic method is developed to analyze DeviceNet fault patterns,identify the corresponding fault scenarios,and infer the location of the intermittent connection problem by using passively captured network faults.A novel error source analysis tool,which integrates network data-link layer analysis and feature based network physical layer information,is developed to restore the snapshots of the network communication at each intermittent connection induced error.A graph based location identification method is developed to infer the location of the intermittent connections based on the restored error patterns.A 3-node laboratory test-bed,using master-slave polling communication method,is constructed to emulate the intermittent connection induced faults on the network drop cable by using digital switches,whose on/off states are controlled by a computer.During experiments,the network fault diagnosis is conducted by using information collected on trunk cable（backbone）.Experimental study shows that the proposed method is effective to restore the snapshots of the network errors and locate the drop cable that experiences the intermittent connection problem.展开更多
WiFi has become one of the most popular ways to access the Internet.However,in large-scale campus wireless networks,it is challenging for network administrators to provide optimized access quality without knowledge on...WiFi has become one of the most popular ways to access the Internet.However,in large-scale campus wireless networks,it is challenging for network administrators to provide optimized access quality without knowledge on fine-grained traffic characteristics and real network performance.In this paper,we implement PerfMon,a network performance measurement and diagnosis system,which integrates collected multi-source datasets and analysis methods.Based on PerfMon,we first conduct a comprehensive measurement on application-level traffic patterns and behaviors from multiple dimensions in the wireless network of T university(TWLAN),which is one of the largest campus wireless networks.Then we systematically study the application-level network performance.We observe that the application-level traffic behaviors and performance vary greatly across different locations and device types.The performance is far from satisfactory in some cases.To diagnose these problems,we distinguish locations and device types,and further locate the most crucial factors that affect the performance.The results of case studies show that the influential factors can effectively characterize performance changes and explain for performance degradation.展开更多
Diagnosability of a multiprocessor system is one important study topic.Cayley graph network Cay（Tn,Sn） generated by transposition trees Tnis one of the attractive underlying topologies for the multiprocessor system....Diagnosability of a multiprocessor system is one important study topic.Cayley graph network Cay（Tn,Sn） generated by transposition trees Tnis one of the attractive underlying topologies for the multiprocessor system.In this paper,it is proved that diagnosability of Cay（Tn,Sn） is n-1 under the comparison diagnosis model for n ≥ 4.展开更多
Fault detection and diagnosis for pneumatic system of automatic productionline are studied. An expert system using fuzzy-neural network and pneumatic circuit fault diagnosisinstrument are deigned. The mathematical mod...Fault detection and diagnosis for pneumatic system of automatic productionline are studied. An expert system using fuzzy-neural network and pneumatic circuit fault diagnosisinstrument are deigned. The mathematical model of various pneumatic faults and experimental deviceare built. In the end, some experiments are done, which shows that the expert system usingfuzzy-neural network can diagnose fast and truly fault of pneumatic circuit.展开更多
Network fault management is crucial for a wireless sensor network(WSN) to maintain a normal running state because faults(e.g., link failures) often occur. The existing lossy link localization(LLL) approach usually inf...Network fault management is crucial for a wireless sensor network(WSN) to maintain a normal running state because faults(e.g., link failures) often occur. The existing lossy link localization(LLL) approach usually infers the most probable failed link set first, and then gives the fault hypothesis set. However, the inferred failed link set contains many possible failures that do not actually occur. That quantity of redundant information in the inferred set can pose a high computational burden on fault hypothesis inference, and consequently decreases the evaluation accuracy and increases the failure localization time. To address the issue, we propose the conditional information entropy based redundancy elimination(CIERE), a redundant lossy link elimination approach, which can eliminate most redundant information while reserving the important information. Specifically, we develop a probabilistically correlated failure model that can accurately reflect the correlation between link failures and model the nondeterministic fault propagation. Through several rounds of mathematical derivations, the LLL problem is transformed to a set-covering problem. A heuristic algorithm is proposed to deduce the failure hypothesis set. We compare the performance of the proposed approach with those of existing LLL methods in simulation and on a real WSN, and validate the efficiency and effectiveness of the proposed approach.展开更多
基金supported by the National Natural Science Foundation of China (No. 51007074)the Program for New Century Excellent Talents in University(NECT-08-0825)+1 种基金the Research and Development Project of the National Railway Ministry (2011J016-B)The basic research universities special fund operations(SWJTU11CX141)
文摘The on-board diagnosis network is the nervous system of high-speed Maglev trains, connecting all controller sensors, and corresponding devices to realize the information acquisition and control. In order to study the on-board diagnosis network＇s security and reliability, a simulation model for the on-board diagnosis network of high-speed Maglev trains with the optimal network engineering tool （OPNET） was built to analyze the network＇s performance, such as response error and bit error rate on the network load, throughput, and node-state response. The simulation model was verified with an actual on-board diagnosis network structure. The results show that the model results obtained are in good agreement with actual system performance and can be used to achieve actual communication network optimization and control algorithms.
文摘AIM:To study the role of time-intensity curve(TIC) analysis parameters in a complex system of neural networks designed to classify liver tumors.METHODS:We prospectively included 112 patients with hepatocellular carcinoma(HCC)(n = 41),hypervascular(n = 20) and hypovascular(n = 12) liver metastases,hepatic hemangiomas(n = 16) or focal fatty changes(n = 23) who underwent contrast-enhanced ultrasonography in the Research Center of Gastroenterology and Hepatology,Craiova,Romania.We recorded full length movies of all contrast uptake phases and post-processed them offline by selecting two areas of interest(one for the tumor and one for the healthy surrounding parenchyma) and consecutive TIC analysis.The difference in maximum intensities,the time to reaching them and the aspect of the late/portal phase,as quantified by the neural network and a ratio between median intensities of the central and peripheral areas were analyzed by a feed forward back propagation multi-layer neural network which was trained to classify data into five distinct classes,corresponding to each type of liver lesion.RESULTS:The neural network had 94.45% training accuracy(95% CI:89.31%-97.21%) and 87.12% testing accuracy(95% CI:86.83%-93.17%).The automatic classification process registered 93.2% sensitivity,89.7% specificity,94.42% positive predictive value and 87.57% negative predictive value.The artificial neural networks(ANN) incorrectly classified as hemangyomas three HCC cases and two hypervascular metastases,while in turn misclassifying four liver hemangyomas as HCC(one case) and hypervascular metastases(three cases).Comparatively,human interpretation of TICs showed 94.1% sensitivity,90.7% specificity,95.11% positive predictive value and 88.89% negative predictive value.The accuracy and specificity of the ANN diagnosis system was similar to that of human interpretation of the TICs(P = 0.225 and P = 0.451,respectively).Hepatocellular carcinoma cases showed contrast uptake during the arterial phase followed by wash-out in the portal and first seconds of the late phases.For the hypovascular metastases did not show significant contrast uptake during the arterial phase,which resulted in negative differences between the maximum intensities.We registered wash-out in the late phase for most of the hypervascular metastases.Liver hemangiomas had contrast uptake in the arterial phase without agent wash-out in the portallate phases.The focal fatty changes did not show any differences from surrounding liver parenchyma,resulting in similar TIC patterns and extracted parameters.CONCLUSION:Neural network analysis of contrastenhanced ultrasonography-obtained TICs seems a promising field of development for future techniques,providing fast and reliable diagnostic aid for the clinician.
基金Sci-Tech Planning Projects of Chongqing City,China(No.CSTC2007AA7003).
文摘A sequential diagnosis method is proposed based on a fuzzy neural network realized by ＂the partially-linearized neural network （PNN）＂, by which the fault types of rotating machinery can be precisely and effectively distinguished at an early stage on the basis of the possibilities of symptom parameters. The non-dimensional symptom parameters in time domain are defined for reflecting the features of time signals measured for the fault diagnosis of rotating machinery. The synthetic detection index is also proposed to evaluate the sensitivity of non-dimensional symptom parameters for detecting faults. The practical example of condition diagnosis for detecting and distinguishing fault states of a centrifugal pump system, such as cavitation, impeller eccentricity which often occur in a centrifugal pump system, are shown to verify the efficiency of the method proposed in this paper.
文摘According to fault type diversity and fault information uncertainty problem of the hydraulic driven rocket launcher servo system（HDRLSS） , the fault diagnosis method based on the evidence theory and neural network ensemble is proposed. In order to overcome the shortcomings of the single neural network, two improved neural network models are set up at the com-mon nodes to simplify the network structure. The initial fault diagnosis is based on the iron spectrum data and the pressure, flow and temperature（PFT） characteristic parameters as the input vectors of the two improved neural network models, and the diagnosis result is taken as the basic probability distribution of the evidence theory. Then the objectivity of assignment is real-ized. The initial diagnosis results of two improved neural networks are fused by D-S evidence theory. The experimental results show that this method can avoid the misdiagnosis of neural network recognition and improve the accuracy of the fault diagnosis of HDRLSS.
基金supported by National Key R&D Program of China(2019YFB2103202).
文摘Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network management.Considering the unique characteristics of edge networks,such as limited resources,complex network faults,and the need for high real-time performance,enhancing and optimizing existing network fault diagnosis methods is necessary.Therefore,this paper proposes the lightweight edge-side fault diagnosis approach based on a spiking neural network(LSNN).Firstly,we use the Izhikevich neurons model to replace the Leaky Integrate and Fire(LIF)neurons model in the LSNN model.Izhikevich neurons inherit the simplicity of LIF neurons but also possess richer behavioral characteristics and flexibility to handle diverse data inputs.Inspired by Fast Spiking Interneurons(FSIs)with a high-frequency firing pattern,we use the parameters of FSIs.Secondly,inspired by the connection mode based on spiking dynamics in the basal ganglia(BG)area of the brain,we propose the pruning approach based on the FSIs of the BG in LSNN to improve computational efficiency and reduce the demand for computing resources and energy consumption.Furthermore,we propose a multiple iterative Dynamic Spike Timing Dependent Plasticity(DSTDP)algorithm to enhance the accuracy of the LSNN model.Experiments on two server fault datasets demonstrate significant precision,recall,and F1 improvements across three diagnosis dimensions.Simultaneously,lightweight indicators such as Params and FLOPs significantly reduced,showcasing the LSNN’s advanced performance and model efficiency.To conclude,experiment results on a pair of datasets indicate that the LSNN model surpasses traditional models and achieves cutting-edge outcomes in network fault diagnosis tasks.
文摘An intermittent connection is one of the major problems that affect the network reliability and communication quality.However,little attention has been paid to the detection,analysis and localization of the intermittent connections.Partially due to the limitations of the DeviceNet protocol,there is no effective online diagnostic tool available to identify the location of intermittent connection.On the basis of different DeviceNet fault scenarios induced by intermittent connections,a new graph-based diagnostic method is developed to analyze DeviceNet fault patterns,identify the corresponding fault scenarios,and infer the location of the intermittent connection problem by using passively captured network faults.A novel error source analysis tool,which integrates network data-link layer analysis and feature based network physical layer information,is developed to restore the snapshots of the network communication at each intermittent connection induced error.A graph based location identification method is developed to infer the location of the intermittent connections based on the restored error patterns.A 3-node laboratory test-bed,using master-slave polling communication method,is constructed to emulate the intermittent connection induced faults on the network drop cable by using digital switches,whose on/off states are controlled by a computer.During experiments,the network fault diagnosis is conducted by using information collected on trunk cable（backbone）.Experimental study shows that the proposed method is effective to restore the snapshots of the network errors and locate the drop cable that experiences the intermittent connection problem.
基金supported by the National Key Research and Development Program of China(No.2020YFE0200500)。
文摘WiFi has become one of the most popular ways to access the Internet.However,in large-scale campus wireless networks,it is challenging for network administrators to provide optimized access quality without knowledge on fine-grained traffic characteristics and real network performance.In this paper,we implement PerfMon,a network performance measurement and diagnosis system,which integrates collected multi-source datasets and analysis methods.Based on PerfMon,we first conduct a comprehensive measurement on application-level traffic patterns and behaviors from multiple dimensions in the wireless network of T university(TWLAN),which is one of the largest campus wireless networks.Then we systematically study the application-level network performance.We observe that the application-level traffic behaviors and performance vary greatly across different locations and device types.The performance is far from satisfactory in some cases.To diagnose these problems,we distinguish locations and device types,and further locate the most crucial factors that affect the performance.The results of case studies show that the influential factors can effectively characterize performance changes and explain for performance degradation.
基金supported by the National Natural Science Foundation of China(61370001,U1304601)
文摘Diagnosability of a multiprocessor system is one important study topic.Cayley graph network Cay（Tn,Sn） generated by transposition trees Tnis one of the attractive underlying topologies for the multiprocessor system.In this paper,it is proved that diagnosability of Cay（Tn,Sn） is n-1 under the comparison diagnosis model for n ≥ 4.
文摘Fault detection and diagnosis for pneumatic system of automatic productionline are studied. An expert system using fuzzy-neural network and pneumatic circuit fault diagnosisinstrument are deigned. The mathematical model of various pneumatic faults and experimental deviceare built. In the end, some experiments are done, which shows that the expert system usingfuzzy-neural network can diagnose fast and truly fault of pneumatic circuit.
基金Project supported by the National Natural Science Foundation of China(Nos.61401409 and 51577191)
文摘Network fault management is crucial for a wireless sensor network(WSN) to maintain a normal running state because faults(e.g., link failures) often occur. The existing lossy link localization(LLL) approach usually infers the most probable failed link set first, and then gives the fault hypothesis set. However, the inferred failed link set contains many possible failures that do not actually occur. That quantity of redundant information in the inferred set can pose a high computational burden on fault hypothesis inference, and consequently decreases the evaluation accuracy and increases the failure localization time. To address the issue, we propose the conditional information entropy based redundancy elimination(CIERE), a redundant lossy link elimination approach, which can eliminate most redundant information while reserving the important information. Specifically, we develop a probabilistically correlated failure model that can accurately reflect the correlation between link failures and model the nondeterministic fault propagation. Through several rounds of mathematical derivations, the LLL problem is transformed to a set-covering problem. A heuristic algorithm is proposed to deduce the failure hypothesis set. We compare the performance of the proposed approach with those of existing LLL methods in simulation and on a real WSN, and validate the efficiency and effectiveness of the proposed approach.
