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Xi Feng,Mingli Zhang,Jianhua Ye 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.4
Consumer relationship proneness (CRP) reflects a consumer’s relatively stable and conscious tendency to engage in relationships. This study takes CRP as an important consumer individual trait and examines the influence of CRP on customer loyalty in service environment from perspective of relational benefits (i.e. confidence benefits, social benefits, special treatment benefits). Concept model is proposed to explain the influence. Then SEM (structural equation modeling) is used to analyze the data collected from online survey to test the proposed model. Empirical results show that CRP has direct influence on customer loyalty. The results also show that CRP indirectly relates to customer loyalty via its impact on perceived confidence benefits and social benefits. This study deepens the understanding about the importance of CRP and provides insights into development and implement of customer relationship management and relationship marketing for service enterprises.
Effects of Hf Incorporation on Indium Zinc Oxide Thin-Film Transistors using Solution Process
Xifeng Li,Enlong Xin,Jianhua Zhang 대한금속·재료학회 2015 ELECTRONIC MATERIALS LETTERS Vol.11 No.1
Thin-film transistors (TFTs) were fabricated by employing amorphoushafnium indium zinc oxide (HIZO) thin films as the active channellayer by the solution process. Thermogravimetry-differential thermalanalysis, transmittance measurements, atomic force microscopy,scanning electron microscopy, x-ray diffraction, and Fourier transforminfrared analysis were used to study the formation, structure, andoptical properties of the HIZO films. The results showed that theaddition of Hf to the IZO system resulted in suppression of carriergeneration. The HIZO TFTs exhibited lower off-currents and higher onoffcurrent ratios than IZO TFTs without Hf doping. HIZO TFTs with aHf doping content of 5 at. % obtained a threshold voltage of 3.7 V, amobility of 0.27 cm2 V−1 s−1, a subthreshold swing of 1.2 V/dec, and anon-off current ratio of 106.
Recognition Algorithm and Optimization Experiments on Tomato Picking Robots
Xifeng Liang,Zhengshuai Jiang,Binrui Wang 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.9
In order to improve the recognition accuracy of vision system on tomato picking robots, the paper proposed a method of feature extraction and recognition for ripe tomato based on illumination irrelevant images and support vector machine (SVM). In this method, we adopted vector median filter (VMF) to process the tomato images to eliminate noise and make the images more smooth firstly. To avoid the effects of natural environment illumination to the vision system, we processed tomato images and obtained the tomato illumination irrelevant images according to color constancy algorithm of the single pixel. Secondly, we segmented illumination irrelevant images using OSTU method, separated multiple objects by a watershed algorithm based on distance transform and got the target area with mathematical morphology. Also we extracted color, shape and textural features of the ripe tomatoes. Finally, we did experiments on recognizing tomatoes using support vector machine (SVM) with different kernel functions. At the same time, in order to obtain optimal model of SVM, we adopted cross validation and grid search method to optimize the model parameters. The experiment results show that illumination irrelevant processing not only can eliminate the influence of light intensity, but save a gray transferring step for further image segmentation. SVM with radial basis function is better than other kernel functions SVM and the tomato recognition accuracy is 95.7%. Through optimizing the parameter C and r of radial basis function, the tomato recognition accuracy reaches up to 96.9% with the increase of 1.2% when C and r is 4 and 16 respectively. This proves that it's feasible and effective to optimize SVM's parameters by cross validation and grid search method, which provide foundation for further research on vision system of tomato picking robots.