Browsing by Author "Hsieh, SC"

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A modified transfer matrix method for the coupling lateral and torsional vibrations of symmetric rotor-bearing systems
(2006-01-03)
Hsieh, SC
;
Chen, JH
;
Lee, AC
;
機械工程學系
;
Department of Mechanical Engineering
This study develops a modified transfer matrix method for analyzing the coupling lateral and torsional vibrations of the symmetric rotor-bearing system with an external torque. Euler's angles are used to describe the orientations of the shaft element and disk. Additionally, to enhance accuracy, the symmetric rotating shaft is modeled by the Timoshenko beam and considered using a continuous-system concept rather than the conventional "lumped system" concept. Moreover, the harmonic balance method is adopted in this approach to determine the steady-state responses comprising the synchronous and superharmonic whirls. According to our analysis, when the unbalance force and the torque with nx frequency of the rotating speed excite the system simultaneously, the (n + 1) x and (n - 1) x whirls appear along with the synchronous whirl. Finally, several numerical examples are presented to demonstrate the applicability of this approach. (c) 2005 Elsevier Ltd. All rights reserved.
Cascade-CMAC neural network applications on the color scanner to printer calibration
(1997)
Huang, KL
;
Hsieh, SC
;
Fu, HC
;
交大名義發表
;
光電工程學系
This paper presents an application of using a Cascade-CMAC (Cerebellar Model Articulation Controller) neural network to solve some color calibration problems, which include color differences induced from gamuts mis-match and the non-linear transformation characteristics between color scanning input devices and color printing output devices. For this purpose, we proposed a scalable learning architecture ''Cascade-CMAC'' to implement an adaptive color calibration system. By analyzing the preliminary learning situation, the scalable architecture can dynamically create a new learning unit to better represent a finer color resolution, so that the learning capacity as well as the color details of the system can be greatly improved. From the experimental results, the proposed Cascade-CMAC architecture can improve the rate of convergence and also can adjust the learning architecture effectively. The learning speed can be 2 similar to 4 times faster than the conventional CMAC. The effectiveness of this neural network has been tested by observing the differences between the calibrated and the un-calibrated output on a number of known samples. By using Macbeth Color-Checker which contains 24 color ptaches as benchmark, tile average color differences between the original and the calibrated print-out is improved from 15 Delta E-ab to 8 Delta E-ab under the 3 Delta E-ab convergent criterion for training. The calibration performance is somewhat significant.
The failure of threaded fasteners due to vibration
(2005-03-01)
Chen, JH
;
Hsieh, SC
;
Lee, AC
;
機械工程學系
;
Department of Mechanical Engineering
The main objective of this paper is to study the loosening tendency of a threaded fastener. To achieve the objective, the threaded fastener looseness model is constructed through the following procedures. Firstly, static and dynamic threaded fastener models are derived to find the static and dynamic interior forces respectively. Secondly, for investigating the bolt loosening behaviour, the effects of thread lead angle, initial preload, vibration frequency, and the nature of material on the bolt looseness, the threaded fastener looseness model is constructed by combining the above models with a Karnopp frictional model. The proposed approach not only analyses both the static and the dynamic behaviours of the bolt as well as the bolt loosening but also detects whether the bolt is within the elastic range or not. The approach can serve as a guide for designing a thread fastening procedure to avoid the failure of threaded fasteners due to vibration.