Who Is at Fault When It Comes to E-commerce Information Leaks?

According to data from the China E-commerce Complaint and Rights Safeguard Public Service Platform,information leaks are no more in the top 10 complaint issues,showing that e-commerce companies have made improvements to the protection of user information.However,as online purchasing further develops,how to prevent user information leaks and stealing of account information is

Information manifold and fault detection of multi-agent systems

With the increase of the number of agents in multi-agent systems and the rapid increase of the complexity of the overall structure of the system,the fault detection and diagnosis work has brought great challenges.Researchers have carried out considerable research work on fault detection and diagnosis of multi-agent systems,but there is no research on fault state estimation and diagnosis based on the information and state of the whole multi-agent system.Based on the global perspective of information geometry theory,this paper presents two new physical quantities of the information manifold of multi-agent systems,as Lagrangian and energy–momentum tensor,to express the state of the overall information of multi-agent systems,and to characterize the energy state and development trend of faults.In this paper,two new physical parameters are introduced into the research of multi-agent fault detection and diagnosis,and the fault state and trend of multi-agent system are evaluated from the global perspective,which provides more comprehensive theoretical support for designing more scientific and reasonable fault diagnosis and fault recovery strategies.Simulation of the application example confirms the competitive performance of the proposed method.

CONDITION MONITOR OF DEEP-HOLE DRILLING BASED ON MULTI-SENSOR INFORMATION FUSION

A condition monitoring method of deep-hole drilling based on multi-sensor information fusion is discussed. The signal of vibration and cutting force are collected when the condition of deep-hole drilling on stainless steel 0Cr17Ni4Cu4Nb is normal or abnormal. Four eigenvectors are extracted on time-domain and frequency-domain analysis of the signals. Then the four eigenvectors are combined and sent to neural networks to dispose. The fusion results indicate that multi-sensor information fusion is superior to single-sensor information, and that cutting force signal can reflect the condition of cutting tool better than vibration signal.

Fault diagnosis method of hydraulic system based on fusion of neural network and D-S evidence theory

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.

Wavelets applied to early fault analysis of hoist gear box

Using wavelets, the vibration signal of a certain mine hoist gear box was analyzed. By multiple comparison analysis, the rational wavelet basis function was determined. Fault characteristic frequencies of hoist gear box were identified. The research indicates that the hoist＇s fault information is non-stationary, and non-stationary signal is clearly extracted by using db20 wavelet as basis function. The db20 wavelet is the proper wavelet base for vibration signal analysis of the hoist gear box.

Improved performance of process monitoring based on selection of key principal components

Conventional principal component analysis(PCA) can obtain low-dimensional representations of original data space, but the selection of principal components(PCs) based on variance is subjective, which may lead to information loss and poor monitoring performance. To address dimension reduction and information preservation simultaneously, this paper proposes a novel PC selection scheme named full variable expression. On the basis of the proposed relevance of variables with each principal component, key principal components can be determined.All the key principal components serve as a low-dimensional representation of the entire original variables, preserving the information of original data space without information loss. A squared Mahalanobis distance, which is introduced as the monitoring statistic, is calculated directly in the key principal component space for fault detection. To test the modeling and monitoring performance of the proposed method, a numerical example and the Tennessee Eastman benchmark are used.