Hebei University of Engineering

www.hebeu.edu.cn
Handan, China

Hebei University of Engineering is a provincial university based in Handan, Hebei province, China.It was established in 2003 from the amalgamation of individual colleges; Hebei Institute of Architectural Science and Technology, North China Institute of Water Conservancy and Hydro-electric Power, Handan Medicine College and Handan Agriculture College.In 2006, Ministry of Education of China authorized the university to changed its name to Hebei University of Engineering. The university now specializes in the fields of civil engineering, together with science disciplines, water power, agriculture and medicine. Wikipedia.

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Zhang S.-H.,Hebei University of Engineering
European review for medical and pharmacological sciences | Year: 2017

OBJECTIVE: Cutaneous squamous cell carcinoma is a malignant tumor, which is mostly common in skin epidermis or appendages. microRNA has been proved to regulate growth and survival of cells. Our study was focused on the effect of microRNA15b on cell viability and apoptosis of cutaneous squamous cell carcinoma SCL-1 cell line.MATERIALS AND METHODS: MicroRNA15b and control microRNA were synthesized and transfected into SCL-1 cells, respectively. Effects of transfection on SCL-1 cells were evaluated by MTT assays and flow cytometry. Western Blot was performed to examine the expression of survivin. MicroRNA15b-transfected SCL-1 cells were further intervened by siRNA targeting survivin or surviving-overexpressing plasmid. Their apoptosis were assessed by flow cytometry.RESULTS: Compared with control microRNA transfection, microRNA15b transfection significantly reduced cell viability, enhanced apoptosis and decreased protein expression of survivin. Inhibition of survivin expression enhanced microRNA15b-induced apoptosis of SCL-1 cells, while enhancement of survivin expression attenuated the apoptosis-promoting effect of microRNA15b on SCL-1 cells.CONCLUSIONS: MicroRNA15b reduced the cell viability and promoted the apoptosis of SCL-1 cells via down-regulating the expression of survivin. MicroRNA15b could be a potential therapeutic target for cutaneous squamous cell carcinoma.


Kang L.,Hebei University of Engineering
Chemical Engineering Transactions | Year: 2017

Ni-P-PTFE composite film was prepared on the surface of high-speed steel ring, and the preparation process of Ni-P-PTFE composite film was studied in order to improve the wear resistance of mechanical seal ring. The surface morphology of Ni-P-PTFE composite film was observed by scanning electron microscopy (SEM). The dry friction properties of Ni-P-PTFE coating/graphite were investigated on a pin-disc friction and wear tester. The results show that a large amount of PTFE particles are encapsulated in the skeleton of Ni-P in Ni-P-PTFE composite membrane. Compared with high-speed steel/graphite, the friction coefficient of Ni-P-PTFE coating/graphite the Ni-P-PTFE coating has lower average friction coefficient and stable, which indicates that the coating has good self-lubricating effect. The Ni-P-Ti (CN) composite coating and Ni-P plating are the second. The Ti (CN) particles dispersed in the composite coating have a very important role in reducing the friction coefficient and reducing the wear rate of the coating. Copyright © 2017, AIDIC Servizi S.r.l.


Xing H.-J.,Hebei University | Ha M.-H.,Hebei University of Engineering
Information Sciences | Year: 2014

In cluster analysis, certain features of a given data set may exhibit higher relevance than others. To address this issue, Feature-Weighted Fuzzy C-Means (FWFCM) approaches have emerged in recent years. However, there are certain deficiencies in the existing FWFCMs, e.g., the elements in a feature-weight vector cannot be adaptively adjusted during the training phase, and the update formulas of a feature-weight vector cannot be derived analytically. In this study, an Improved FWFCM (IFWFCM) is proposed to overcome these shortcomings. The IFWFCM-KD based on the kernelized distance is also proposed. Experimental results reported for five numerical data sets and the color images show that IFWFCM is superior to the existing FWFCMs. An interesting conclusion, that IFWFCM-KD might not improve the performance of IFWFCM, is also obtained by applying IFWFCM-KD to tackle the above-mentioned numerical data sets and color images. © 2014 Elsevier Inc. All rights reserved.


Hou J.,Hebei University of Engineering
Journal of Convergence Information Technology | Year: 2010

With respect to multiple attribute decision making problem with triangular fuzzy linguistic information, in which the attribute weights and expert weights take the form of real numbers, and the preference values take the form of triangular fuzzy linguistic variables, a operator for aggregating triangular fuzzy linguistic variables, such as the fuzzy linguistic weighted harmonic mean (FLWHM) operator is introduced. Based on the FLWHM operators, a practical method is developed for group decision making with triangular fuzzy linguistic variables. Finally, an illustrative example about software selection is given to verify the developed approach.


Wei J.,Hebei University of Engineering
Journal of Convergence Information Technology | Year: 2010

With respect to multiple attribute decision making problems with linguistic information of attribute values and weight values, a decision analysis is proposed. Then, a method based on the ET-WA operator for multiple attribute decision making is presented. In the approach, alternative appraisal values are calculated by the aggregation of 2-tuple linguistic information. Thus, the ranking of alternative or selection of the most desirable alternative(s) is obtained by the comparison of 2-tuple linguistic information. Finally, a numerical example with risk evaluation of high-technology is used to illustrate the applicability and effectiveness of the proposed method.


Hou J.,Hebei University of Engineering
Journal of Convergence Information Technology | Year: 2010

The aim of this paper is to investigate the multiple attribute decision making problems with intuitionistic fuzzy information, in which the information about attribute weights is incompletely known, and the attribute values take the form of intuitionistic fuzzy numbers. In order to get the weight vector of the attribute, we establish an optimization model based on the basic ideal of traditional grey relational analysis (GRA) method, by which the attribute weights can be determined. Then, based on the traditional GRA method, calculation steps for solving intuitionistic fuzzy multiple attribute decision-making problems with completely known weight information are given. The degree of grey relation between every alternative and positive ideal solution is calculated. Then, a relative relational degree is defined to determine the ranking order of all alternatives by calculating the degree of grey relation to both the positive-ideal solution (PIS). Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.


Zhao W.,Hebei University of Engineering
Procedia Engineering | Year: 2011

To improve the adaptability and effect of image enhancement, this paper has proposed an image enhancement method based on Gravitational Search Algorithm (GSA), which is used for optimizing the parameters of the normalized incomplete Beta function using the characteristics of the original image, the acquired function is employed to enhance the degraded image. The simulation results show the method can effectively enhance the global contrast of the image and vision. So this method is practical in the field of gray level image adaptive enhancement. © 2011 Published by Elsevier Ltd.


Wei J.,Hebei University of Engineering
Journal of Convergence Information Technology | Year: 2010

The aim of this paper is to investigate the multiple attribute decision making problems with linguistic information, in which the information about attribute weights is incompletely known, and the attribute values take the form of linguistic variables. We develop a new method to solve linguistic MADM with incomplete weight. In order to get the weight vector of the attribute, we establish an optimization model based on the basic ideal of traditional TOPSIS, by which the attribute weights can be determined. Based on this model, we develop a TOPSIS method to rank alternatives and to select the most desirable one(s). Finally, an example is shown to highlight the procedure of the proposed algorithm at the end of this paper.


This paper proposed an approach of mechanical failure information extraction and recognition in the early fault state variables, combined with the principal component analysis algorithm with FCM algorithm. Principal component analysis algorithm to get the characteristic value of data sets carries enough about fault in the time domain. Then FCM algorithm is used to analysis model is established in the classification feature different fault state. The application results of this method to identify variables deviation fault rotor test bed are acceptable. © 2013 ACADEMY PUBLISHER.


Shi H.,Hebei University of Engineering
International Journal of Computer Applications in Technology | Year: 2012

In this paper, we present an application of support vector machines (SVMs) and particle swarm optimisation (PSO) to fault diagnosis. SVMs have been successfully employed to solve regression problem of nonlinearity and small sample. However, the practicability of SVM is affected due to the difficulty of selecting appropriate SVM parameters. PSO is a new optimisation method, which is motivated by social behaviour of organisms such as bird flocking and fish schooling. The method not only has strong global search capability but also is very easy to implement. Thus, the proposed PSO-SVM model is applied to diagnosis operation of rolling bearing failure in this paper, in which PSO is used to determine free parameters of SVM. The experimental results also indicate that the SVM method can achieve greater accuracy than grey model, artificial neural network under the condition of availability of small training data. Copyright © 2012 Inderscience Enterprises Ltd.

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