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  • 1
    Publication Date: 2014-08-07
    Description: Core business is the most important business to the enterprise in diversified business. In this paper, we first introduce the definition and characteristics of the core business and then descript the ant colony clustering algorithm. In order to test the effectiveness of the proposed method, Tianjin Port Logistics Development Co., Ltd. is selected as the research object. Based on the current situation of the development of the company, the core business of the company can be acquired by ant colony clustering algorithm. Thus, the results indicate that the proposed method is an effective way to determine the core business for company.
    Print ISSN: 1024-123X
    Electronic ISSN: 1563-5147
    Topics: Mathematics , Technology
    Published by Hindawi
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  • 2
    Publication Date: 2013-03-20
    Description: The paper deals with nonlinear modeling and identification of an electrohydraulic control system for improving its tracking performance. We build the nonlinear state space model for analyzing the highly nonlinear system and then develop a Hammerstein-Wiener (H-W) model which consists of a static input nonlinear block with two-segment polynomial nonlinearities, a linear time-invariant dynamic block, and a static output nonlinear block with single polynomial nonlinearity to describe it. We simplify the H-W model into a linear-in-parameters structure by using the key term separation principle and then use a modified recursive least square method with iterative estimation of internal variables to identify all the unknown parameters simultaneously. It is found that the proposed H-W model approximates the actual system better than the independent Hammerstein, Wiener, and ARX models. The prediction error of the H-W model is about 13%, 54%, and 58% less than the Hammerstein, Wiener, and ARX models, respectively.
    Print ISSN: 1024-123X
    Electronic ISSN: 1563-5147
    Topics: Mathematics , Technology
    Published by Hindawi
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  • 3
    Publication Date: 2013-11-01
    Description: Accurate forecasting of electrical energy consumption of equipment maintenance plays an important role in maintenance decision making and helps greatly in sustainable energy use. The paper presents an approach for forecasting electrical energy consumption of equipment maintenance based on artificial neural network (ANN) and particle swarm optimization (PSO). A multilayer forward ANN is used for modeling relationships between the input variables and the expected electrical energy consumption, and a new adaptive PSO algorithm is proposed for optimizing the parameters of the ANN. Experimental results demonstrate that our approach provides much better accuracies than some other competitive methods on the test data.
    Print ISSN: 1024-123X
    Electronic ISSN: 1563-5147
    Topics: Mathematics , Technology
    Published by Hindawi
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  • 4
    Publication Date: 2013-01-01
    Description: A novel ultrawideband (UWB) antenna which has a triple-band notch function is presented. The proposed antenna can block interfering signals from C-band satellite communication systems, IEEE802.11a, and HIPERLAN/2 WLAN systems for example. The antenna is excited by using novel common direction rectangular complementary split-ring resonators (CSRR) fabricated on radiating patch of the dielectric substrate with coplanar waveguide (CPW) feed strip line. The voltage standing wave ratio (VSWR) of the proposed antenna is less than 2.0 in the frequency band from 2.8 to 12 GHz, while showing a very sharp band-rejection performance at 3.9 GHz, 5.2 GHz, and 5.9 GHz. The measurement results show that the proposed antenna provides good omnidirectional field pattern over its whole frequency band excluding the rejected band, which is suitable for UWB applications.
    Print ISSN: 1687-5869
    Electronic ISSN: 1687-5877
    Topics: Electrical Engineering, Measurement and Control Technology
    Published by Hindawi
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  • 5
    Publication Date: 2014-01-01
    Description: It is not easy to find marine cracks of structures by directly manual testing. When the cracks of important components are extended under extreme offshore environment, the whole structure would lose efficacy, endanger the staff’s safety, and course a significant economic loss and marine environment pollution. Thus, early discovery of structure cracks is very important. In this paper, a beam structure damage identification model based on intelligent algorithm is firstly proposed to identify partial cracks in supported beams on ocean platform. In order to obtain the replacement mode and strain mode of the beams, the paper takes simple supported beam with single crack and double cracks as an example. The results show that the difference curves of strain mode change drastically only on the injured part and different degrees of injury would result in different mutation degrees of difference curve more or less. While the model based on support vector machine (SVM) and BP neural network can identify cracks of supported beam intelligently, the methods can discern injured degrees of sound condition, single crack, and double cracks. Furthermore, the two methods are compared. The results show that the two methods presented in the paper have a preferable identification precision and adaptation. And damage identification based on support vector machine (SVM) has smaller error results.
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    Electronic ISSN: 1563-5147
    Topics: Mathematics , Technology
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  • 6
    Publication Date: 2014-01-01
    Description: Displacement prediction of tunnel surrounding rock plays an important role in safety monitoring and quality control tunnel construction. In this paper, two methodologies, support vector machines (SVM) and artificial neural network (ANN), are introduced to predict tunnel surrounding rock displacement. Then the two modes are texted with the data ofFangtianchongtunnel, respectively. The comparative results show that solutions gained by SVM seem to be more robust with a smaller standard error compared to ANN. Generally, the comparison between artificial neural network (ANN) and SVM shows that SVM has a higher accuracy prediction than ANN. Results also show that SVM seems to be a powerful tool for tunnel surrounding rock displacement prediction.
    Print ISSN: 1024-123X
    Electronic ISSN: 1563-5147
    Topics: Mathematics , Technology
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  • 7
    Publication Date: 2014-01-01
    Description: An improved SVM model is presented to forecast dry bulk freight index (BDI) in this paper, which is a powerful tool for operators and investors to manage the market trend and avoid price risking shipping industry. The BDI is influenced by many factors, especially the random incidents in dry bulk market, inducing the difficulty in forecasting of BDI. Therefore, to eliminate the impact of random incidents in dry bulk market, wavelet transform is adopted to denoise the BDI data series. Hence, the combined model of wavelet transform and support vector machine is developed to forecast BDI in this paper. Lastly, the BDI data in 2005 to 2012 are presented to test the proposed model. The 84 prior consecutive monthly BDI data are the inputs of the model, and the last 12 monthly BDI data are the outputs of model. The parameters of the model are optimized by genetic algorithm and the final model is conformed through SVM training. This paper compares the forecasting result of proposed method and three other forecasting methods. The result shows that the proposed method has higher accuracy and could be used to forecast the short-term trend of the BDI.
    Print ISSN: 1024-123X
    Electronic ISSN: 1563-5147
    Topics: Mathematics , Technology
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  • 8
    Publication Date: 2014-01-01
    Description: The improvement of social support promotes the mental health and improves the health status. The study aimed to examine the influence of the social support on symptoms of anxiety and depression among patients with silicosis and provide the scientific basis to further alleviate anxiety and depression and to monitor their whole quality of life. We investigated 324 inpatients with silicosis between April 2011 and September 2011. The HADS (the Hospital Anxiety-Depression Scale) was the major methodology used to evaluate anxiety and depression, and the MSPSS (the Multidimensional Scale of Perceived Social Support) to evaluate the social support level. Among patients with silicosis, 99.1% had anxiety symptoms, and 86.1% had depression symptoms. Meanwhile, the social support significantly influenced symptoms of anxiety and depression. The study suggested that patients with silicosis presented more anxiety and depression symptoms, while the social support levels of the patients were relatively low. The influence of social support on symptoms of anxiety and depression among patients with silicosis implied that improving the level of social support and the effective symptomatic treatment might alleviate anxiety and depression symptoms and improve physical and mental status.
    Print ISSN: 2356-6140
    Electronic ISSN: 1537-744X
    Topics: Natural Sciences in General
    Published by Hindawi
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