Kun Shan University

About: Kun Shan University is a education organization based out in Tainan City, Taiwan. It is known for research contribution in the topics: Heat transfer & Thin film. The organization has 1992 authors who have published 2928 publications receiving 45685 citations. The organization is also known as: Kūnshān Kējì Dàxué.

Isothermal crystallization kinetics and morphology development of isotactic polypropylene blends with atactic polypropylene

Abstract: The crystallization kinetics and morphology development of pure isotactic polypropylene (iPP) homo- polymer and iPP blended with atactic polypropylene (aPP) at different aPP contents and the isothermal crystallization temperatures were studied with differential scanning calo- rimetry, wide-angle X-ray diffraction, and polarized opti- cal microscopy. The spherulitic morphologies of pure iPP and larger amounts of aPP for iPP blends showed the neg- ative spherulite, whereas that of smaller amounts of aPP for the iPP blends showed a combination of positive and negative spherulites. This indicated that the morphology transition of the spherulite may have been due to changes the crystal forms of iPP in the iPP blends during crystalli- zation. Therefore, with smaller amounts of aPP, the spher- ulitic density and overall crystallinity of the iPP blends increased with increasing aPP and presented a lower de- gree of perfection of the g form coexisting with the a form of iPP during crystallization. However, with larger amounts of aPP, the spherulitic density and overall crystallinity of the iPP blends decreased and reduced the g-form crystals with increasing aPP. These results indicate that the aPP molecules hindered the nucleation rate and promoted the molecular motion and growth rate of iPP with smaller amounts of aPP and hindered both the nucleation rate and growth rate of iPP with larger amounts of aPP during isothermal crystallization. 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 1093-1104, 2007

Fault Diagnosis of Steam Turbine-Generator Sets Using an EPSO-Based Support Vector Classifier

Abstract: This paper proposes an enhanced particle swarm optimization (EPSO)-based support vector classifier (SVC) that extracts the support vector from databases, in order to diagnose vibration faults in steam turbine-generator sets (STGS). SVC has been successfully applied to the classification of data with linear or nonlinear features, because it allows generalization. However, the design of the best SVC model for the acquisition of the best hyperplane is often difficult and depends heavily on the operators' experience or on trial-and-error experiments. In this paper, an EPSO algorithm is used to automatically tune the control parameters of an SVC. Since EPSO is an excellent optimization tool, it is easily sufficient for the design of an optimal SVC model. The proposed approach is applied to an STGS, to test its diagnostic accuracy. The test results demonstrate that the proposed EPSO-based SVC method has a higher diagnostic accuracy and a shorter learning time than classical neural network-based methods. This study also provides advice on handling a loss of data features for unknown reasons.

Solar tracking device with springs

Abstract: A solar tracking device is disclosed. In particular, a solar tracking device for overcoming the disadvantages of conventional solar energy system utilizing motors and to lower electricity consumption and decrease cost is provided. The device includes a solar module or solar collector supported by two springs under both ends, and two water tanks on both ends, wherein the solar module or solar collector, similar to a heliostat, is adapted to slowly revolve in response to the imbalanced water tanks filled with different amount of water.