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Chin-Sheng Huang,Su-Wen Kuo,Chia-Cheng Chen 한국증권학회 2010 Asia-Pacific Journal of Financial Studies Vol.39 No.4
The minimum price variation on the Taiwan Stock Exchange reduced for most price categories on March 1, 2005. The present paper simultaneously examines the institutional and endogenous impacts of tick size changes on transaction costs, market liquidity, and trading activity. The empirical evidence suggests that following a reduction in tick size, uniform declines are discernible in transaction costs and market liquidity. In particular, those stocks with a larger relative tick size reduction, higher trading volume, and higher order handling cost components have greater reductions in spread and market depth. Moreover, endogenous tick size reductions have an adverse effect on the trading activity for low-price stocks, due to the relative disadvantage in explicit transaction costs. Finally, the present study observes a general diminution in trade size resulting from a reduction in tick size in the Taiwan Stock Exchange. This study attributes plausible rationales to the fact that after tick size reductions, informed traders employ a smaller trade size to hide private information, or front-runners place a smaller trade size to avoid market turbulence.
Object Recognition Based on Modified Intuitive Corner Detection and Two-stage CornerMatching
Chin Sheng Chen,Ming Fu Tsai,Chun Chan Chiu 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
This paper proposes an object recognition algorithm based on modified intuitive corner detection and two-stage corner matching. The object recognition algorithm consists of two phases: the off-line training phase and the on-line operating phase. The critical purpose is to construct template database in the training phase. Firstly, the corners are extracted from the template image by the modified intuitive corner detection. The multi-resolution patches are then applied to create the full scale corners’ features. Each corner has its own descriptor based on SIFT and PCA. With this information, the algorithm creates the hierarchical structures of multi-resolution patches to improve the speed of corner matching. In the operating phase, the test images are processed in the same manner mentioned above with single resolution patches, and then the corner will be matched with the multi-resolution patches in the training phase’s database. The two-stage corner matching, coarse and fine matching based on hierarchical structures of corner descriptions appears to reduce the range of patch’s candidates, is then adopted toimprove the matching performance. Finally, the Random sample consensus (RANSAC) criterion is applied to reject the remaining outlier. Experimental results show that our proposed object recognition is reliable and real-time.
Optimal design of reinforced concrete plane frames using artificial neural networks
Chin-Sheng Kao,I-Cheng Yeh 사단법인 한국계산역학회 2014 Computers and Concrete, An International Journal Vol.14 No.4
To solve structural optimization problems, it is necessary to integrate a structural analysis package and an optimization package. There have been many packages that can be employed to analyze reinforced concrete plane frames. However, because most structural analysis packages suffer from closeness of systems, it is very difficult to integrate them with optimization packages. To overcome the difficulty, we proposed a possible alternative, DAMDO, which integrates Design, Analysis, Modeling, Definition, and Optimization phases into an integration environment as follows. (1) Design: first generate many possible structural design alternatives. Each design alternative consists of many design variables X. (2) Analysis: employ the structural analysis software to analyze all structural design alternatives to obtain their internal forces and displacements. They are the response variables Y. (3) Modeling: employ artificial neural networks to build the models Y=f(X) to obtain the relationship functions between the design variables X and the response variables Y. (4) Definition: employ the design variables X and the response variables Y to define the objective function and constraint functions. (5) Optimization: employ the optimization software to solve the optimization problem consisting of the objective function and the constraint functions to produce the optimum design variables. The RC frame optimization problem was examined to evaluate the DAMDO approach, and the empirical results showed that it can be solved by the approach.
Optimal design of plane frame structures using artificial neural networks and ratio variables
Chin-Sheng Kao,I-Cheng Yeh 국제구조공학회 2014 Structural Engineering and Mechanics, An Int'l Jou Vol.52 No.4
There have been many packages that can be employed to analyze plane frames. However, because most structural analysis packages suffer from closeness of system, it is very difficult to integrate it with an optimization package. To overcome the difficulty, we proposed a possible alternative, DAMDO, which integrate Design, Analysis, Modeling, Definition, and Optimization phases into an integrative environment. The DAMDO methodology employs neural networks to integrate structural analysis package and optimization package so as not to need directly to integrate these two packages. The key problem of the DAMDO approach is how to generate a set of reasonable random designs in the first phase. According to the characteristics of optimized plane frames, we proposed the ratio variable approach to generate them. The empirical results show that the ratio variable approach can greatly improve the accuracy of the neural networks, and the plane frame optimization problems can be solved by the DAMDO methodology.
Design and simulation of self-tuning PI-type temperature control for an injection molding machine
Chin Sheng Chen,Yen Ting Chen 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
A Proportional-Integral (PI) controller is a typical controller in industry application. For control engineer, it isimportant to find out the optimal PI parameters by observing system response when system model is difficult to identifycorrectly. However, this task is time consuming and boring. This paper proposes a self-tuning methodology with a Gradient Bisection Algorithm (GBA) to find the optimal PI parameters based on the cost function of integral absolute error (IAE) and the design indexes. The control plant for this paper is barrel temperature control of injection moldingmachine with uncertain model. Finally, the simulation results show that our proposed algorithms can dramaticallyreduce the tuning time and control response can achieve the specified performance.
Self-tuning Fuzzy PI Control for Laser Power Stabilization
Chin-Sheng Chen,Bo-Ming Chiu,Chien-Hsu Chen 제어로봇시스템학회 2011 제어로봇시스템학회 국제학술대회 논문집 Vol.2011 No.10
This paper proposes a self-tuning fuzzy PI controller to stabilize the laser power. The laser system is a nonlinear system due to its complexity and easily affected by environment conditions. However, traditional PI controller cannot cope with this nonlinear and time varying system since the parameters of PI controller are fixed. This paper utilizes the fuzzy reasoning engine (FRE) to adjust the parameters of PI controller in order to overcome the above drawbacks. This control algorithm is implemented in Microchip dsPIC33FJ64GS606 microprocessor. The experimental results verify our proposed control algorithm can dramatically stabilize the laser output power and further improve the tracking performance when laser desired power command is changed.
Chin-Sheng Chen,Si-Yu Lin 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
The environmental conditions corresponding to dangerous or collided areas are generally represented by Costmap when the Autonomous Mobile Robot (AMR) is navigated. Here, this paper provides a Costmap 2D layer plugin, Velocity Obstacle layer, it can accurately detect obstacle’s coordination and radius and then estimate the obstacle’s velocity to create Velocity Obstacle which can represent the potential collision vector in the future. In the simulation, we assume the robot’s max velocity is 0.2m/s and an obstacle move forward to the robot with 0.3m/s. The results show the AMR can avoid the obstacle well. In experiment, the AMR also can avoid the people moving toward it in the real world.
Optimal design of plane frame structures using artificial neural networks and ratio variables
Kao, Chin-Sheng,Yeh, I-Cheng Techno-Press 2014 Structural Engineering and Mechanics, An Int'l Jou Vol.52 No.4
There have been many packages that can be employed to analyze plane frames. However, because most structural analysis packages suffer from closeness of system, it is very difficult to integrate it with an optimization package. To overcome the difficulty, we proposed a possible alternative, DAMDO, which integrate Design, Analysis, Modeling, Definition, and Optimization phases into an integrative environment. The DAMDO methodology employs neural networks to integrate structural analysis package and optimization package so as not to need directly to integrate these two packages. The key problem of the DAMDO approach is how to generate a set of reasonable random designs in the first phase. According to the characteristics of optimized plane frames, we proposed the ratio variable approach to generate them. The empirical results show that the ratio variable approach can greatly improve the accuracy of the neural networks, and the plane frame optimization problems can be solved by the DAMDO methodology.
Aina Shasha Hashimi,Riski Titian Ginting,Siew Xian Chin,Kam Sheng Lau,Muhammad Amirul Nazhif Mohd Nohan,Sarani Zakaria,Chi Chin Yap,Chin Hua Chia 한국물리학회 2020 Current Applied Physics Vol.20 No.1
We report the microwave synthesis of copper nanowires (CuNWs) by using alkylamine-mediated approach. The aspect ratio of CuNWs of this study was two–fold compared to the previous microwave-assisted synthesis study. In addition, we showed that microwave synthesis could produce high aspect ratio CuNWs in a much shorter time compared to conventional method. Purification process of CuNWs was done via a simple and fast centrifugation method using water-hydrophobic organic solvent system. We also show the importance of purification process on the performance of the fabricated transparent conductive electrode (TCE) films. Purified CuNWs TCE showed a low sheet resistance of 35 Ω/sq with high transparency of 81% (at λ550 nm). Furthermore, we demonstrated how the retreatment of acetic acid was able to assist CuNWs to regain its high conductivity even after five cycles of repetitive continuous oxidation process.