Nanchang, China

Nanchang Institute of Technology is a university in Nanchang, Jiangxi, China.Nanchang Institute of Technology is located in the capital city of Jiangxi province - Nanchang, is approved by the Ministry of Education set up a second level of regular undergraduate institutions, a bachelor's degree-granting units. According to April 2014 the school's official website revealed that the campus covers an area of 757.79 acres. 1.19 million square meters have been built school, library books 2,650,000, a variety of educational assets totaled 1.8 billion rmb, teaching equipment worth 176 million rmb. The school has 16 sub-colleges, 42 undergraduate majors, 62 specialties, Jiangxi university "five" key disciplines 2 , there are students in nearly 30,000 people . Wikipedia.


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Yu Y.,Nanchang Institute of Technology
CICC-ITOE 2010 - 2010 International Conference on Innovative Computing and Communication, 2010 Asia-Pacific Conference on Information Technology and Ocean Engineering | Year: 2010

The purpose of this paper is to develop an analytic hierarchy process (AHP) approach to measure service quality in e-commerce environments. First, an e-commerce service quality (ESQ) index system was constructed. AHP was then carried out to find the relative weights of those assessment criteria and sub-indicators. According to the results, the weights for the three criteria are ordered as website design, responsiveness and information quality. In addition, the most important three sub-indicators are safety in online transactions, easy to use website and delivering services on time. Using an empirical example, the quality scores of the nine sub-factors and the global service were calculated according to the perceptions of customers. The proposed model and method can be tailored and applied by e-commerce enterprises to evaluate their service quality and identify which service dimensions or sub-indicators require most efforts in order to create better service. © 2010 IEEE.


Li L.,Nanchang Institute of Technology
International Journal of Advancements in Computing Technology | Year: 2012

This paper proposes a vector learning particle swarm optimization based on the traditional particle swarm optimization (PSO) algorithm. Unlike the traditional PSO algorithm, the algorithm does not update the entire speed or position vector at the same time, but divides the entire speed or position vector into sub-vectors and cyclically updates each speed or position sub-vector in order. By introducing a new speed update learning strategy in speed sub-vector update, the algorithm makes the particles examine ahead the effects of particle evolution and feed the information back during the update process, to finally determine the next generation value of the particle speed sub-vector, which can effectively avoid the blindness of speed evolution. On the other hand, the speed update learning strategy contains a diversity factor that can effectively expand the diversity of particle evolution. The experimental results show that: This algorithm has strong global search ability for most standard composite test functions and stronger optimization ability than popular PSO based improved algorithms and intelligent single particle optimizer.


Ouyang H.Z.,Nanchang Institute of Technology
Advanced Materials Research | Year: 2014

Under the situation of Global highly advocated green packaging from the point of the principle of green packaging design and based on the technical indicators of cardboard, this article analyzes the requirements for different products and provides the reference for the future design of green cardboard packaging. © (2014) Trans Tech Publications, Switzerland.


Che J.,Nanchang Institute of Technology
Neurocomputing | Year: 2015

It has been widely demonstrated in forecasting that combining forecasts can improve the forecast performance compared to individual forecasts. However, how to select the optimal sub-models from all the available models is a difficult problem in combination forecasting model. Consider that the redundancy information among the selected sub-models will reduce the performance of the combination forecasting model, it is advocated to select a sub-model only if it contributes to the redundancy removing mutual information between the outputs of the selected sub-models and the actual outputs. As linear combination method is promising and popular in the field of combination forecast, a novel Max-Linear-Relevance and Min-Linear-Redundancy based selection algorithm is proposed in this paper. The proposed selection algorithm provides a theoretical approach for the optimal sub-models selection, and tries to compute the redundancy removing linear mutual information between the outputs of the selected sub-models and the actual outputs. Three monthly time series from DataMarket are used as illustrative examples to evaluate the forecasting. As a result of the implementation, it is seen that the proposed combination forecasting model produces better forecasts than those produced by other models. © 2014 Elsevier B.V.


Wang H.,Nanchang Institute of Technology
Mathematical Problems in Engineering | Year: 2012

This paper presents a modified barebones particle swarm optimization (OBPSO) to solve constrained nonlinear optimization problems. The proposed approach OBPSO combines barebones particle swarm optimization (BPSO) and opposition-based learning (OBL) to improve the quality of solutions. A novel boundary search strategy is used to approach the boundary between the feasible and infeasible search region. Moreover, an adaptive penalty method is employed to handle constraints. To verify the performance of OBPSO, a set of well-known constrained benchmark functions is used in the experiments. Simulation results show that our approach achieves a promising performance. © 2012 Hui Wang.


Che J.X.,Nanchang Institute of Technology
Applied Soft Computing Journal | Year: 2013

Support vector regression (SVR) has become very promising and popular in the field of machine learning due to its attractive features and profound empirical performance for small sample, nonlinearity and high dimensional data application. However, most existing support vector regression learning algorithms are limited to the parameters selection and slow learning for large sample. This paper considers an adaptive particle swarm optimization (APSO) algorithm for the parameters selection of support vector regression model. In order to accelerate its training process while keeping high accurate forecasting in each parameters selection step of APSO iteration, an optimal training subset (OTS) method is carried out to choose the representation data points of the full training data set. Furthermore, the optimal parameters setting of SVR and the optimal size of OTS are studied preliminary. Experimental results of an UCI data set and electric load forecasting in New South Wales show that the proposed model is effective and produces better generalization performance. © 2013 Elsevier B.V. All rights reserved.


Wang C.,Nanchang Institute of Technology
Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | Year: 2013

Zero order voltage of the multilevel inverter is a serious threat to the performance and the aging of the motor, so restraining zero order voltage to optimize the motor running state and its maintenance has practical value. In order to restrain zero order voltage, this paper analyzes the distribution rule of zero order voltage for n-cell inverters and proposes a optimal modulation algorithm. The proposed algorithm, without adding any hardware, makes zero order voltage change within the minimum range, while it can ensure the quality of output waveform. Finally, the simulation and experimental results prove that the theoretical analysis of the proposed algorithm on restraining zero order voltage is correct and feasible.


Liu Y.,Nanchang Institute of Technology
Procedia Engineering | Year: 2012

Based on Karhunen-Loève expansion of random field, the paper utilizes Hermite polynomial chaos expansion to build the stochastic response surface function, and the unknown coefficients of the function can be calculated by probabilistic collocation approach. Then, the geometric method can be used to calculate the structural reliability. A comparison with Monte Carlo simulation shows that the proposed method can achieve high accuracy and efficiency while analyzing the property of the second-order and the third-order stochastic response surface. © 2012 Published by Elsevier Ltd.


Liang X.,Nanchang Institute of Technology
Applied Mechanics and Materials | Year: 2014

The 3D flow filed of pump is calculated based on the Navier-Stokes equations and k-ε turbulence model, and the simulation results not only are beneficial to analyze the pressure pulsation around pump impellers, but also provide the boundary conditions for modal analysis of pump rotors. And then a novel fault diagnosis method is provided to simulate the nature frequencies of rotor, and research the relationship between rotor structure and rotor abnormal vibration based on the modal analysis. The research results show that, the frequency of water pressure pulsation mainly concentrated in 24.5Hz and 49.0Hz, especially when the blade angle of pump is greater than -2°; the nature frequencies of first order modal and second order modal are closely to the same 49.0Hz, so it is easy to induce the resonance, and the resonance is an important factor which lead to the severe vibration of pump. © (2014) Trans Tech Publications, Switzerland.


Che J.,Nanchang Institute of Technology
International Journal of Electrical Power and Energy Systems | Year: 2014

Context: Current decision development in electricity market needs a variety of forecasting techniques to analysis the nature of electric load series. And the interpretability and forecasting accuracy of the electric load series are two main objectives when establishing the load forecasting model. Objective: Considering that electric load series exhibit repeating seasonal cycles at different level (daily, weekly and annual seasonality), this paper concerns the interpretability of these seasonal cycles and the forecasting accuracy. Method: For the above proposes, the author firstly introduces a multiple linear regression model that involves treating all the seasonal cycles as the input attributes. The result helps the managers to interpret the series structure with multiple seasonal cycles. To improve the forecasting accuracy, a support vector regression model based on optimal training subset (OTS) and adaptive particle swarm optimization (APSO) algorithm is established to forecast the residual series. Thus, a novel hybrid model combining the proposed linear regression model and support vector regression model is built to achieve the above bi-objective short-term load forecasting. Results: The effectiveness of the hybrid model is evaluated by an electrical load forecasting in California electricity market. The proposed modeling algorithm generates not only the seasonal cycle's decomposition for the time series, but also better accuracy predictions. Conclusion: It is concluded that the hybrid model provides a very powerful tool of easy implementation for bi-objective short-term electric load forecasting. © 2014 Elsevier Ltd. All rights reserved.

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