Shaanxi Academy of Governance

Xi’an, China

Shaanxi Academy of Governance

Xi’an, China
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Yang Y.-S.,Xi'an Jiaotong University | Yang Y.-S.,Shaanxi Academy of Governance | Ming A.-B.,HIGH-TECH | Zhang Y.-Y.,Xi'an Jiaotong University | Zhu Y.-S.,Xi'an Jiaotong University
Mechanical Systems and Signal Processing | Year: 2017

Diesel engines, widely used in engineering, are very important for the running of equipments and their fault diagnosis have attracted much attention. In the past several decades, the image based fault diagnosis methods have provided efficient ways for the diesel engine fault diagnosis. By introducing the class information into the traditional non-negative matrix factorization (NMF), an improved NMF algorithm named as discriminative NMF (DNMF) was developed and a novel imaged based fault diagnosis method was proposed by the combination of the DNMF and the KNN classifier. Experiments performed on the fault diagnosis of diesel engine were used to validate the efficacy of the proposed method. It is shown that the fault conditions of diesel engine can be efficiently classified by the proposed method using the coefficient matrix obtained by DNMF. Compared with the original NMF (ONMF) and principle component analysis (PCA), the DNMF can represent the class information more efficiently because the class characters of basis matrices obtained by the DNMF are more visible than those in the basis matrices obtained by the ONMF and PCA. © 2017 Elsevier Ltd


Ju H.,Shaanxi Academy of Governance
Chemical Engineering Transactions | Year: 2017

Hazardous chemical has the characteristics of easy explosion, toxicity, corrosiveness etc. These characteristics make dangerous chemical not only causing harm to the human body, but also polluting the environment. With the development of the global economy, the application of hazardous chemical is more and more widely. Compared with the traditional supply chain, the hazardous chemical supply chain has more potential factors and faces more risks. These potential risks threaten life and property safety. This is also a great challenge to the management of hazardous chemical, especially the management of the hazardous chemical supply chain. In order to assess the risk of hazardous chemical supply chain, we propose an improved AHP algorithm in this paper. This algorithm combines the PSO algorithm with AHP to improve the traditional AHP algorithm. After that, we apply the improved AHP algorithm to assess the risk of the hazardous chemical supply chain and get good results. Copyright © 2017, AIDIC Servizi S.r.l.


Zhang P.,Shaanxi Normal University | Zhang P.,Shaanxi Academy of Governance
Future Information Engineering and Manufacturing Science - Proceedings of the 2014 International Conference on Future Information Engineering and Manufacturing Science, FIEMS 2014 | Year: 2015

The improvement of academic performance depends not only on students’ own efforts, but also on the collaboration of teachers and parents. This paper, using quantitative research, through a questionnaire survey of 98 students from 4 middle schools, measured the perceptions of teachers’ expectation and parents’ expectation and academic performance of junior middle school students and analyzed the relationship between them. The results indicate that: (1) the students’ perception of teachers’ expectation has significant positive effect on their academic performance; (2) the perception of parents’ expectation has significant mediating effect on their perception of the relationship between teachers’ expectation and academic performance. Such research results will have reference significance for improving the academic performance of junior middle school students. © 2015 Taylor & Francis Group, London.


Yang Y.,Xi'an Jiaotong University | Yang Y.,Shaanxi Academy of Governance | Zhang Y.,Xi'an Jiaotong University | Zhu Y.,Xi'an Jiaotong University
Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University | Year: 2015

In the fault diagnosis field of mechanical equipment, the result of analyzing the collected monitoring data from the equipment is often the high dimensionality of images which contain mass data; so the method of extracting sensitive feature from the high-dimensional information or image is a key technology. We present a new method for fault diagnosis of mechanical equipment based on Multi-Kernel Non-negative Matrix Factorization (MKNMF), which overcomes the defect that the traditional fault diagnosis of mechanical equipment requires signal feature extraction this defect causes loss of information; we reduce dimensions for high dimension information through applying Multi-Kernel Non-negative Matrix Factorization method and then distinguish the dimensionality reduction data with Multi-Kernel Support Vector Machine (MKSVM). The experiments and their analysis show preliminarily that this method can reduce the dimensions of the original monitored data and improve the recognition rate of machine fault diagnosis. ©, 2015, Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University. All right reserved.


Liu Y.,Shaanxi Academy of Governance
Advances in Information Sciences and Service Sciences | Year: 2012

The development of modern economy, make enterprises all around the world face the competition of the fiercer and fiercer domestic and international market. In this kind of competition, if an enterprise wants to make the competition advantage, must improve its whole efficiency and performance constantly. The performances of enterprises come from the staff's performance finally, so, the focal point of improving enterprise's performance is in the improvement of staffs performance. And performance appraisal is the important means to investigate staff's performance. it contributes to the realization of enterprise's goal, to the improvement of staffs behavior, to the promotion of ability. In this paper, we investigate the multiple attribute decision making (MADM) problems for evaluating the performance of human resources with uncertain linguistic information. We utilize the uncertain linguistic weighted averaging (ULWA) operator to aggregate the uncertain linguistic information corresponding to each alternative and get the overall value of the alternatives, then rank the alternatives and select the most desirable one(s). Finally, a numerical example for evaluating the performance of human resources with uncertain linguistic information is used to illustrate the proposed model.


Yang Y.,Xi'an Jiaotong University | Yang Y.,Shaanxi Academy of Governance | Zhang Y.,Xi'an Jiaotong University | Zhu Y.,Xi'an Jiaotong University
Advances in Mechanical Engineering | Year: 2015

Although the computation amount involved in the image processing is very large, image information which is very intuitive and easy to be understood has attracted great many attentions in the fault diagnosis of machines. In order to extract useful features from the images accurately and perfectly, a novel mechanical fault diagnosis method was proposed by the combination of the multi-kernel non-negative matrix factorization and multi-kernel support vector machine. The genetic algorithm was used to optimize the parameters of both multi-kernel non-negative matrix factorization and multi-kernel support vector machine. Experiments were used to validate the efficacy of the proposed method. It is shown that the multi-kernel function combined with the polynomial kernel function and radial-based kernel function can describe the fault feature more perfectly in the kernel space than a single kernel function. Sound accuracy can be obtained in the application of the bearing fault diagnosis. Compared with the fault diagnosis method based on the sparse non-negative matrix factorization, the proposed method is more accurate in the condition identification of rotor. © The Author(s) 2015.


Wei Y.,Xi'an Jiaotong University | Wang K.,Xi'an Jiaotong University | Wang W.,Xi'an Jiaotong University | Liu S.,Xi'an Jiaotong University | And 3 more authors.
Applied Energy | Year: 2014

Smoke-free property of dimethyl ether (DME) was utilized to eliminate the influence of fuel on PM formation. The particulate matter (PM) emission characteristics, including mass emission, number concentration distribution, morphology and the percentage composition of soluble organic fraction, were experimentally studied on a compression ignition engine when it ran on diesel and DME, respectively. The emission characteristics were compared and analyzed to better understand the commons and differences of between fuel and lubricant oil originated PM emissions. Experimental results show that, the number concentrations of DME and diesel engine-out PM emissions both decrease with the engine speed, and increase with engine load; the volumetric mass emission of diesel engine-out PM coincides well with its number emission. However, the volumetric mass emission of DME engine-out PM increases with engine speed and performs typical production-consumption effect with the increasing factor of Ttq{dot operator}en. The diesel engine-out PM emission was composed on average of 84% of non-volatile fractions of soot and sulfates and 16% of soluble organic fractions (SOFs) with about 9% of which originated from diesel fuel and 6.8% from lubricant oil. Less lubricant oil originating products such as SOF and soot were emitted on DME engine than on diesel engine. © 2014 Elsevier Ltd.


Yang Y.,Xi'an Jiaotong University | Yang Y.,Shaanxi Academy of Governance | Li G.,Xi'an Technological University | Zhu Y.,Xi'an Jiaotong University | Zhang Y.,Xi'an Jiaotong University
Applied Mechanics and Materials | Year: 2013

To efficiently find hidden clusters in datasets with complex distributed data,inspired by complementary strategies, a hybrid genetic clustering algorithm was developed, which is on the basis of the geodesic distance metric, and combined with the Fuzzy C-Means clustering (FCM) algorithm. First, instead of using Euclidean distance,the new approach employs geodesic distance based dissimilarity metric during all fitness evaluation. And then, with the help of FCM clustering, some sub-clusters with spherical distribution are partitioned effectively. Next, a genetic algorithm based clustering using geodesic distance metric, named GCGD, is adopted to cluster the clustering centers obtained from FCM clustering. Finally, the final results are acquired based on above two clustering results. Experimental results on eight benchmark datasets clustering questions show the effectiveness of the algorithm as a clustering technique. Compared with conventional GCGD, the hybrid clustering can decrease the computational time obviously, while retaining high clustering correct ratio. © (2013) Trans Tech Publications, Switzerland.

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