This paper investigates event-triggered synchronization for complex networks with Markovian jumping parameters.Nonlinear dynamics with Markovian jumping parameters is considered for each node in a complex network. By ...This paper investigates event-triggered synchronization for complex networks with Markovian jumping parameters.Nonlinear dynamics with Markovian jumping parameters is considered for each node in a complex network. By utilizing the proposed event-triggered strategy, and based on the Lyapunov functional method and linear matrix inequality technology,some sufficient conditions for synchronization of complex networks are derived whether the transition rate matrix for the Markov process is completely known or not. Finally, a numerical example is presented to illustrate the effectiveness of the proposed theoretical results.展开更多
In recent years, theoretical and practical research on event-based communication strategies has gained considerable research attention due primarily to their irreplaceable superiority in resource-constrained systems（...In recent years, theoretical and practical research on event-based communication strategies has gained considerable research attention due primarily to their irreplaceable superiority in resource-constrained systems（especially networked systems）. For networked systems, event-based transmission scheme is capable of improving the efficiency in resource utilization and prolonging the lifetime of the network components compared with the widely adopted periodic transmission scheme. As such, it would be interesting to 1） examining how the event-triggering mechanisms affect the control or filtering performance for networked systems, and 2） developing some suitable approaches for the controller and filter design problems. In this paper, a bibliographical review is presented on event-based control and filtering problems for various networked systems. First, the event-driven communication scheme is introduced in detail according to its engineering background, characteristic, and representative research frameworks. Then, different event-based control and filtering（or state estimation） problems are categorized and then discussed. Finally, we conclude the paper by outlining future research challenges for event-based networked systems.展开更多
It has long been a challenging task to detect an anomaly in a crowded scene.In this paper,a selfsupervised framework called the abnormal event detection network(AED-Net),which is composed of a principal component anal...It has long been a challenging task to detect an anomaly in a crowded scene.In this paper,a selfsupervised framework called the abnormal event detection network(AED-Net),which is composed of a principal component analysis network(PCAnet)and kernel principal component analysis(kPCA),is proposed to address this problem.Using surveillance video sequences of different scenes as raw data,the PCAnet is trained to extract high-level semantics of the crowd’s situation.Next,kPCA,a one-class classifier,is trained to identify anomalies within the scene.In contrast to some prevailing deep learning methods,this framework is completely self-supervised because it utilizes only video sequences of a normal situation.Experiments in global and local abnormal event detection are carried out on Monitoring Human Activity dataset from University of Minnesota(UMN dataset)and Anomaly Detection dataset from University of California,San Diego(UCSD dataset),and competitive results that yield a better equal error rate(EER)and area under curve(AUC)than other state-of-the-art methods are observed.Furthermore,by adding a local response normalization(LRN)layer,we propose an improvement to the original AED-Net.The results demonstrate that this proposed version performs better by promoting the framework’s generalization capacity.展开更多
Event correlation is one key technique in network fault management. For the event sample acquisition problem in event correlation, a novel approach is proposed to collect the samples by constructing network simulation...Event correlation is one key technique in network fault management. For the event sample acquisition problem in event correlation, a novel approach is proposed to collect the samples by constructing network simulation platform. The platform designed can set kinds of network faults according to user's demand and generate a lot of network fault events, which will benefit the research on efficient event correlation techniques.展开更多
This paper conducts the analysis on the dissemination mechanism and guiding tactics of public opinion in catastrophic event network. Opinion evolution mechanism can be roughly divided into two classes. One is the beli...This paper conducts the analysis on the dissemination mechanism and guiding tactics of public opinion in catastrophic event network. Opinion evolution mechanism can be roughly divided into two classes. One is the belief of people based on their neighbors, on the basis of the public opinion is in the social network of acquaintances. Such networks are mostly using cellular automata model for data simulation, the results of numerical simulation are speci？ c to stabilize near the critical value show that the system will reach a critical stable state. The network information collection is the source of network public opinion monitoring its breadth and depth determine the monitoring results for the clear theme of public opinion information collection. Under this basis, this paper proposes the novel idea of making the dissemination mechanism easier. The proposed idea is novel and necessary, the effectiveness is proved via the theoretical analysis.展开更多
The Artificial Neural Network (ANN) approach has been successfully used in many hydrological studies especially the rainfall-runoff modeling using continuous data. The present study examines its applicability to model...The Artificial Neural Network (ANN) approach has been successfully used in many hydrological studies especially the rainfall-runoff modeling using continuous data. The present study examines its applicability to model the event-based rainfall-runoff process. A case study has been done for Ajay river basin to develop event-based rainfall-runoff model for the basin to simulate the hourly runoff at Sarath gauging site. The results demonstrate that ANN models are able to provide a good representation of an event-based rainfall-runoff process. The two important parameters, when predicting a flood hydrograph, are the magnitude of the peak discharge and the time to peak discharge. The developed ANN models have been able to predict this information with great accuracy. This shows that ANNs can be very efficient in modeling an event-based rainfall-runoff process for determining the peak discharge and time to the peak discharge very accurately. This is important in water resources design and management applications, where peak discharge and time to peak discharge are important input展开更多
In this paper, we present an approach to improve the accuracy of environmental sound event detection in a wireless acoustic sensor network for home monitoring. Wireless acoustic sensor nodes can capture sounds in the ...In this paper, we present an approach to improve the accuracy of environmental sound event detection in a wireless acoustic sensor network for home monitoring. Wireless acoustic sensor nodes can capture sounds in the home and simultaneously deliver them to a sink node for sound event detection. The proposed approach is mainly composed of three modules, including signal estimation, reliable sensor channel selection, and sound event detection. During signal estimation, lost packets are recovered to improve the signal quality. Next, reliable channels are selected using a multi-channel cross-correlation coefficient to improve the computational efficiency for distant sound event detection without sacrificing performance. Finally, the signals of the selected two channels are used for environmental sound event detection based on bidirectional gated recurrent neural networks using two-channel audio features. Experiments show that the proposed approach achieves superior performances compared to the baseline.展开更多
In hybrid wireless sensor networks, sensor mobility causes the query areas to change dynamically. Aiming at the problem of inefficiency in processing the data aggregation queries in dynamic query areas, this paper pro...In hybrid wireless sensor networks, sensor mobility causes the query areas to change dynamically. Aiming at the problem of inefficiency in processing the data aggregation queries in dynamic query areas, this paper proposes a processing approach for event-based location aware queries （ELAQ）, which includes query dissemination algorithm, maximum distance projection proxy selection algorithm, in-network query propagation, and aggregation algorithm. ELAQs are triggered by the events and the query results are dependent on mobile sensors＇ location, which are the characteristics of ELAQ model. The results show that compared with the TinyDB query processing approach, ELAQ processing approach increases the accuracy of the query result and decreases the query response time.展开更多
The process of transformation of rainfall into runoff over a catchment is very complex and highly nonlinear and exhibits both tempor al and spatial variabilities. In this article, a rainfall-runoff model using th e ar...The process of transformation of rainfall into runoff over a catchment is very complex and highly nonlinear and exhibits both tempor al and spatial variabilities. In this article, a rainfall-runoff model using th e artificial neural networks (ANN) is proposed for simula ting the runoff in storm events. The study uses the data from a coa stal forest catchment located in Seto Inland Sea, Japan. This article studies the accuracy of the short-term rainfall forecast obta ined by ANN time-series analysis techniques and using antecedent rainfa ll depths and stream flow as the input information. The verification results from the proposed model indicate that the approach of ANN rai nfall-runoff model presented in this paper shows a reasonable agreement in rainfall-runoff modeling with high accuracy.展开更多
1 IntroductionNowadays in China, there are more than six hundred million netizens [1]. On April 11, 2015, the nmnbet of simultaneous online users of the Chinese instant message application QQ reached two hundred milli...1 IntroductionNowadays in China, there are more than six hundred million netizens [1]. On April 11, 2015, the nmnbet of simultaneous online users of the Chinese instant message application QQ reached two hundred million [2]. The fast growth ol the lnternet pusnes me rapid development of information technology （IT） and communication technology （CT）. Many traditional IT service and CT equipment providers are facing the fusion of IT and CT in the age of digital transformation, and heading toward ICT enterprises. Large global ICT enterprises, such as Apple, Google, Microsoft, Amazon, Verizon, and AT＆T, have been contributing to the performance improvement of IT service and CT equipment.展开更多
With the highly integration of the Internet world and the real world, Internet information not only provides real-time and effective data for financial investors, but also helps them understand market dynamics, and en...With the highly integration of the Internet world and the real world, Internet information not only provides real-time and effective data for financial investors, but also helps them understand market dynamics, and enables investors to quickly identify relevant financial events that may lead to stock market volatility. However, in the research of event detection in the financial field, many studies are focused on micro-blog, news and other network text information. Few scholars have studied the characteristics of financial time series data. Considering that in the financial field, the occurrence of an event often affects both the online public opinion space and the real transaction space, so this paper proposes a multi-source heterogeneous information detection method based on stock transaction time series data and online public opinion text data to detect hot events in the stock market. This method uses outlier detection algorithm to extract the time of hot events in stock market based on multi-member fusion. And according to the weight calculation formula of the feature item proposed in this paper, this method calculates the keyword weight of network public opinion information to obtain the core content of hot events in the stock market. Finally, accurate detection of stock market hot events is achieved.展开更多
Energy consumption is the core issue in wireless sensor networks (WSN). To generate a node energy model that can accurately reveal the energy consumption of sensor nodes is an extremely important part of protocol deve...Energy consumption is the core issue in wireless sensor networks (WSN). To generate a node energy model that can accurately reveal the energy consumption of sensor nodes is an extremely important part of protocol development, system design and performance evaluation in WSNs. In this paper, by studying component energy consumption in different node states and within state transitions, the authors present the energy models of the node core components, including processors, RF modules and sensors. Furthermore, this paper reveals the energy correlations between node components, and then establishes the node energy model based on the event-trigger mechanism. Finally, the authors simulate the energy models of node components and then evaluate the energy consumption of network protocols based on this node energy model. The proposed model can be used to analyze the WSNs energy consumption, to evaluate communication protocols, to deploy nodes and then to construct WSN applications.展开更多
Safety is the foundation of sustainable development in civil aviation.Although catastrophic accidents are rare,indicators of potential incidents and unsafe events frequently materialize.Therefore,a history of unsafe d...Safety is the foundation of sustainable development in civil aviation.Although catastrophic accidents are rare,indicators of potential incidents and unsafe events frequently materialize.Therefore,a history of unsafe data are considered in predicting safety risks.A deep learning method is adopted for extracting reactions in safety risks.The deep neural network(DNN)model for safety risk prediction is shown to extract complex data characteristics better than a shallow network model.Using extended unsafe data and monthly risk indices,hidden layers and iterations are determined.The effectiveness of DNN is also revealed in comparison with the traditional neural network.Through early risk detection using the method in the paper,airlines and the government can mitigate potential risk and take proactive measures to improve civil aviation safety.展开更多
After the occurrence of unexpected group events of network, the relevant opinion information will spread rapidly through micro-blog, and the negative public opinion information will aggravate the unexpected the group ...After the occurrence of unexpected group events of network, the relevant opinion information will spread rapidly through micro-blog, and the negative public opinion information will aggravate the unexpected the group events to upgrade and expand the scope of harm. It is difficult to deal. So public opinion control is very important. In this paper, we establish an influence model for spreading of public opinion based on SIR model. Through the political analysis, this paper finds that the network group events will subside, but the influence scope, time and ability of event cannot be ignored. As a result of this study, the corresponding strategies are put forward in this paper.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant No.11202084)
文摘This paper investigates event-triggered synchronization for complex networks with Markovian jumping parameters.Nonlinear dynamics with Markovian jumping parameters is considered for each node in a complex network. By utilizing the proposed event-triggered strategy, and based on the Lyapunov functional method and linear matrix inequality technology,some sufficient conditions for synchronization of complex networks are derived whether the transition rate matrix for the Markov process is completely known or not. Finally, a numerical example is presented to illustrate the effectiveness of the proposed theoretical results.
基金supported by National Natural Science Foundation of China(No.61329301)the Royal Society of the UK+2 种基金the Research Fund for the Taishan Scholar Project of Shandong Province of Chinathe China Postdoctoral Science Foundation(No.2016M600547)the Alexander von Humboldt Foundation of Germany
文摘In recent years, theoretical and practical research on event-based communication strategies has gained considerable research attention due primarily to their irreplaceable superiority in resource-constrained systems（especially networked systems）. For networked systems, event-based transmission scheme is capable of improving the efficiency in resource utilization and prolonging the lifetime of the network components compared with the widely adopted periodic transmission scheme. As such, it would be interesting to 1） examining how the event-triggering mechanisms affect the control or filtering performance for networked systems, and 2） developing some suitable approaches for the controller and filter design problems. In this paper, a bibliographical review is presented on event-based control and filtering problems for various networked systems. First, the event-driven communication scheme is introduced in detail according to its engineering background, characteristic, and representative research frameworks. Then, different event-based control and filtering（or state estimation） problems are categorized and then discussed. Finally, we conclude the paper by outlining future research challenges for event-based networked systems.
基金This work is partially supported by the National Key Research and Development Program of China(2016YFE0204200)the National Natural Science Foundation of China(61503017)+3 种基金the Fundamental Research Funds for the Central Universities(YWF-18-BJ-J-221)the Aeronautical Science Foundation of China(2016ZC51022)the Platform CAPSEC(capteurs pour la sécurité)funded by Région Champagne-ArdenneFEDER(fonds européen de développement régional).
文摘It has long been a challenging task to detect an anomaly in a crowded scene.In this paper,a selfsupervised framework called the abnormal event detection network(AED-Net),which is composed of a principal component analysis network(PCAnet)and kernel principal component analysis(kPCA),is proposed to address this problem.Using surveillance video sequences of different scenes as raw data,the PCAnet is trained to extract high-level semantics of the crowd’s situation.Next,kPCA,a one-class classifier,is trained to identify anomalies within the scene.In contrast to some prevailing deep learning methods,this framework is completely self-supervised because it utilizes only video sequences of a normal situation.Experiments in global and local abnormal event detection are carried out on Monitoring Human Activity dataset from University of Minnesota(UMN dataset)and Anomaly Detection dataset from University of California,San Diego(UCSD dataset),and competitive results that yield a better equal error rate(EER)and area under curve(AUC)than other state-of-the-art methods are observed.Furthermore,by adding a local response normalization(LRN)layer,we propose an improvement to the original AED-Net.The results demonstrate that this proposed version performs better by promoting the framework’s generalization capacity.
基金the National Natural Science Foundation of China(69983 0 0 5 )
文摘Event correlation is one key technique in network fault management. For the event sample acquisition problem in event correlation, a novel approach is proposed to collect the samples by constructing network simulation platform. The platform designed can set kinds of network faults according to user's demand and generate a lot of network fault events, which will benefit the research on efficient event correlation techniques.
文摘This paper conducts the analysis on the dissemination mechanism and guiding tactics of public opinion in catastrophic event network. Opinion evolution mechanism can be roughly divided into two classes. One is the belief of people based on their neighbors, on the basis of the public opinion is in the social network of acquaintances. Such networks are mostly using cellular automata model for data simulation, the results of numerical simulation are speci？ c to stabilize near the critical value show that the system will reach a critical stable state. The network information collection is the source of network public opinion monitoring its breadth and depth determine the monitoring results for the clear theme of public opinion information collection. Under this basis, this paper proposes the novel idea of making the dissemination mechanism easier. The proposed idea is novel and necessary, the effectiveness is proved via the theoretical analysis.
文摘The Artificial Neural Network (ANN) approach has been successfully used in many hydrological studies especially the rainfall-runoff modeling using continuous data. The present study examines its applicability to model the event-based rainfall-runoff process. A case study has been done for Ajay river basin to develop event-based rainfall-runoff model for the basin to simulate the hourly runoff at Sarath gauging site. The results demonstrate that ANN models are able to provide a good representation of an event-based rainfall-runoff process. The two important parameters, when predicting a flood hydrograph, are the magnitude of the peak discharge and the time to peak discharge. The developed ANN models have been able to predict this information with great accuracy. This shows that ANNs can be very efficient in modeling an event-based rainfall-runoff process for determining the peak discharge and time to the peak discharge very accurately. This is important in water resources design and management applications, where peak discharge and time to peak discharge are important input
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (NRF2015R1D1A1A01059804)the MSIP (Ministry of Science,ICT and Future Planning),Korea,under the ITRC(Information Technology Research Center) support program (IITP-2016-R2718-16-0011) supervised by the IITP(Institute for Information & communications Technology Promotion)the present Research has been conducted by the Research Grant of Kwangwoon University in 2017
文摘In this paper, we present an approach to improve the accuracy of environmental sound event detection in a wireless acoustic sensor network for home monitoring. Wireless acoustic sensor nodes can capture sounds in the home and simultaneously deliver them to a sink node for sound event detection. The proposed approach is mainly composed of three modules, including signal estimation, reliable sensor channel selection, and sound event detection. During signal estimation, lost packets are recovered to improve the signal quality. Next, reliable channels are selected using a multi-channel cross-correlation coefficient to improve the computational efficiency for distant sound event detection without sacrificing performance. Finally, the signals of the selected two channels are used for environmental sound event detection based on bidirectional gated recurrent neural networks using two-channel audio features. Experiments show that the proposed approach achieves superior performances compared to the baseline.
基金Supported by the National Pre-research Foundation Project of China (513150402)
文摘In hybrid wireless sensor networks, sensor mobility causes the query areas to change dynamically. Aiming at the problem of inefficiency in processing the data aggregation queries in dynamic query areas, this paper proposes a processing approach for event-based location aware queries （ELAQ）, which includes query dissemination algorithm, maximum distance projection proxy selection algorithm, in-network query propagation, and aggregation algorithm. ELAQs are triggered by the events and the query results are dependent on mobile sensors＇ location, which are the characteristics of ELAQ model. The results show that compared with the TinyDB query processing approach, ELAQ processing approach increases the accuracy of the query result and decreases the query response time.
文摘The process of transformation of rainfall into runoff over a catchment is very complex and highly nonlinear and exhibits both tempor al and spatial variabilities. In this article, a rainfall-runoff model using th e artificial neural networks (ANN) is proposed for simula ting the runoff in storm events. The study uses the data from a coa stal forest catchment located in Seto Inland Sea, Japan. This article studies the accuracy of the short-term rainfall forecast obta ined by ANN time-series analysis techniques and using antecedent rainfa ll depths and stream flow as the input information. The verification results from the proposed model indicate that the approach of ANN rai nfall-runoff model presented in this paper shows a reasonable agreement in rainfall-runoff modeling with high accuracy.
基金supported in part by Ministry of Education/China Mobile joint research grant under Project No.5-10Nanjing University of Posts and Telecommunications under Grants No.NY214135 and NY215045
文摘1 IntroductionNowadays in China, there are more than six hundred million netizens [1]. On April 11, 2015, the nmnbet of simultaneous online users of the Chinese instant message application QQ reached two hundred million [2]. The fast growth ol the lnternet pusnes me rapid development of information technology （IT） and communication technology （CT）. Many traditional IT service and CT equipment providers are facing the fusion of IT and CT in the age of digital transformation, and heading toward ICT enterprises. Large global ICT enterprises, such as Apple, Google, Microsoft, Amazon, Verizon, and AT＆T, have been contributing to the performance improvement of IT service and CT equipment.
文摘With the highly integration of the Internet world and the real world, Internet information not only provides real-time and effective data for financial investors, but also helps them understand market dynamics, and enables investors to quickly identify relevant financial events that may lead to stock market volatility. However, in the research of event detection in the financial field, many studies are focused on micro-blog, news and other network text information. Few scholars have studied the characteristics of financial time series data. Considering that in the financial field, the occurrence of an event often affects both the online public opinion space and the real transaction space, so this paper proposes a multi-source heterogeneous information detection method based on stock transaction time series data and online public opinion text data to detect hot events in the stock market. This method uses outlier detection algorithm to extract the time of hot events in stock market based on multi-member fusion. And according to the weight calculation formula of the feature item proposed in this paper, this method calculates the keyword weight of network public opinion information to obtain the core content of hot events in the stock market. Finally, accurate detection of stock market hot events is achieved.
文摘Energy consumption is the core issue in wireless sensor networks (WSN). To generate a node energy model that can accurately reveal the energy consumption of sensor nodes is an extremely important part of protocol development, system design and performance evaluation in WSNs. In this paper, by studying component energy consumption in different node states and within state transitions, the authors present the energy models of the node core components, including processors, RF modules and sensors. Furthermore, this paper reveals the energy correlations between node components, and then establishes the node energy model based on the event-trigger mechanism. Finally, the authors simulate the energy models of node components and then evaluate the energy consumption of network protocols based on this node energy model. The proposed model can be used to analyze the WSNs energy consumption, to evaluate communication protocols, to deploy nodes and then to construct WSN applications.
基金supported by the Joint Funds of the National Natural Science Foundation of China (No. U1833110)
文摘Safety is the foundation of sustainable development in civil aviation.Although catastrophic accidents are rare,indicators of potential incidents and unsafe events frequently materialize.Therefore,a history of unsafe data are considered in predicting safety risks.A deep learning method is adopted for extracting reactions in safety risks.The deep neural network(DNN)model for safety risk prediction is shown to extract complex data characteristics better than a shallow network model.Using extended unsafe data and monthly risk indices,hidden layers and iterations are determined.The effectiveness of DNN is also revealed in comparison with the traditional neural network.Through early risk detection using the method in the paper,airlines and the government can mitigate potential risk and take proactive measures to improve civil aviation safety.
文摘After the occurrence of unexpected group events of network, the relevant opinion information will spread rapidly through micro-blog, and the negative public opinion information will aggravate the unexpected the group events to upgrade and expand the scope of harm. It is difficult to deal. So public opinion control is very important. In this paper, we establish an influence model for spreading of public opinion based on SIR model. Through the political analysis, this paper finds that the network group events will subside, but the influence scope, time and ability of event cannot be ignored. As a result of this study, the corresponding strategies are put forward in this paper.
