Research on Feature Extraction and Classification Method of Vibration Signal of Escalator Sprocket Bearing

In order to improve the accuracy of escalator sprocket bearing fault diagnosis,the problem of the feature extraction method of bearing vibration signal is addressed.In this paper,empirical mode is used to decompose the original signal,and the optimal modal component among the multiple modal components is obtained after the optimization decomposition is selected by the envelope spectrum method,and the multi-angle feature measure is introduced to extract the fault characteristic value.According to the vibration characteristics of the bearing vibration signal data,a bearing signal feature group that is more inclined to the fault feature category information is established,which avoids the absolute problem of extracting a single metric feature.The fuzzy C-means clustering algorithm is used to cluster the sample data with similar characteristics into the same cluster area,which effectively solves the problem that a single measurement analysis cannot characterize the complex internal characteristics ofthe bearing vibration signal.

Basic Features of the Crustal Structure in the Lower Yangtze and Its Neighboring Area in the Chinese Mainland： Review of Deep Seismic Sounding Research

The Deep Seismic Sounding( DSS) projects carried out from the 1970 s in the lower Yangtze region and its neighboring area were reviewed in this paper,then the basic wave group features of those wide angle reflection / refraction record sections,and of the crustal structure are summarized. It shows that there were in total five clear wave groups on the record sections,which include the first arrival Pg,the reflection P1 from the bottom interface of the upper crust,the reflection P3 from the bottom interface of the middle crust,the strong reflection Pm from the Moho boundary,and the refraction Pn from uppermost mantle. In general,these phases are easily consistently traced and compared,despite some first arrivals being delayed or arriving earlier than normal due to the shallow sedimentary cover or bedrocks. In particular,in the Dabie Mountain region the seismic events of a few gathered shots always have weak reflection energy,are twisted,or exhibit disorganized waveforms, which could be attributed to the disruption variations of reflection depth,the broken Moho,and the discontinuity of the reflection boundary within crust. The regional crustal structures are composed of the upper,middle and lower crust,of which the middle and lower layers can be divided into two weak reflection ones. The crustal thickness of the North China and Yangtze platform are 30km- 36 km,and the Moho exhibits a flat geometry despite some local uplifts. The average pressure velocity in lower crust beneath this two tectonic area is 6. 7 ± 0. 3km / s. Nevertheless,beneath the Dabieshan area the crustal thickness is 32km- 41 km,the Moho bends down sharply andtakes an abrupt 4km- 7km dislocation in the vertical direction. The average pressure velocity in the lower crust beneath the Dabieshan area is 6. 8 ± 0. 2km / s.

An Adaptive Padding Correlation Filter With Group Feature Fusion for Robust Visual Tracking

In recent visual tracking research,correlation filter(CF)based trackers become popular because of their high speed and considerable accuracy.Previous methods mainly work on the extension of features and the solution of the boundary effect to learn a better correlation filter.However,the related studies are insufficient.By exploring the potential of trackers in these two aspects,a novel adaptive padding correlation filter(APCF)with feature group fusion is proposed for robust visual tracking in this paper based on the popular context-aware tracking framework.In the tracker,three feature groups are fused by use of the weighted sum of the normalized response maps,to alleviate the risk of drift caused by the extreme change of single feature.Moreover,to improve the adaptive ability of padding for the filter training of different object shapes,the best padding is selected from the preset pool according to tracking precision over the whole video,where tracking precision is predicted according to the prediction model trained by use of the sequence features of the first several frames.The sequence features include three traditional features and eight newly constructed features.Extensive experiments demonstrate that the proposed tracker is superior to most state-of-the-art correlation filter based trackers and has a stable improvement compared to the basic trackers.

The Features of Sedimentary Facies and Copper Enrichment Metallogenic Regularities of Kuzigongsu Group in Sareke Glutenite Type Copper Deposits,Wuqia,Xinjiang

1 Introduction Sareke glutenite-type copper deposit is the large size copper deposit discovered in recent years,and it is located Sarekebayi intracontinental pull-apart basin in the western margin of the Tarim basin.Conglomerate of

Study on fishery biology features of spawning group of Pseudopleuronectes yokamae(Gunther) in the Sheath Bay

Pseudopleuronectes yokamae (Günther) is one of the most important economic fish species in the genus living specially in the northwest Pacific, and is distributed in the southern area of the far east sea of Russia, Japan, Korea, the Huanghai Sea, the Bohai Sea and the northern area of the East China Sea. Usually they live in nearshore waters of these areas as geographic subpopulations. P. yokamae in the Sheath Bay belongs to a local group of the Huanghai-Bohai Sea subpopulation and is distributed in the mouth and adjacent waters of the bay all the year round, and the catch of its spawning group has

A MULTIPLE INTELLIGENT AGENT SYSTEM FOR CREDIT RISK PREDICTION VIA AN OPTIMIZATION OF LOCALIZED GENERALIZATION ERROR WITH DIVERSITY

Company bankruptcies cost billions of dollars in losses to banks each year. Thus credit risk prediction is a critical part of a bank＇s loan approval decision process. Traditional financial models for credit risk prediction are no longer adequate for describing today＇s complex relationship between the financial health and potential bankruptcy of a company. In this work, a multiple classifier system （embedded in a multiple intelligent agent system） is proposed to predict the financial health of a company. In our model, each individual agent （classifier） makes a prediction on the likelihood of credit risk based on only partial information of the company. Each of the agents is an expert, but has limited knowledge （represented by features） about the company. The decisions of all agents are combined together to form a final credit risk prediction. Experiments show that our model out-performs other existing methods using the benchmarking Compustat American Corporations dataset.

Alternative Process Planning Modeling Research

The basic theory of the Petri net model is introduced and the characteristics of alternative process planning are analyzed. Then, the modeling of alternative process planning based on Petri nets is discussed in detail. Themodeling process is partitioned into three stages:the modeling of feature operation chains and grouped feature operation chains, the modeling of a feature group's setup determination and the modeling of part setups and related machine function. This paper focuses on the first and the third stages. Furthermore, a case study and implementation of system are described.