Yu-Chen Lin

Induction of VX2 carcinoma in rabbit liver: comparison of two inoculation methods

Direct injection of VX2 cell suspension into the liver is simple and widely used. Implantation of a fragment of VX2 tumour into the liver using a surgical technique has also been developed in the last decade. In this study, we compared these two methods in order to find a better modality for establishing VX2 liver mass. Forty rabbits, each weighing 2.8-3.2 kg, were divided into two groups, 20 rabbits in each. In Group 1, a tumour cell suspension containing 1×106 cells in a volume of 0.1 ml, was injected slowly into the liver parenchyma using a 27-gauge needle during laparotomy. In Group 2, a 1 mm3 fragment of VX2 carcinoma was inoculated into the sub-capsule of the left anterior lobe of the liver. In Group 1, three rabbits showed no tumour growth and 10 rabbits showed evidence of leakage and tumour seeding outside of the liver. In Group 2, all but one rabbit showed tumour growth and none showed evidence of tumour seeding. The leakage rates were 50% and 0% for Group 1 and Group 2, respectively. Overall, the success inoculation rate was 35% for Group 1 and 95% for Group 2. In conclusion, to create the VX2 liver tumour model in rabbits, direct implantation of VX2 tumour fragment into the liver achieved better results than injecting cell suspension of VX2 tumour into the liver.

Robust Active Vibration Control for Rail Vehicle Pantograph

This paper deals with vibration control for a light rail vehicles pantographs. The concepts of force cancellation, skyhook damper, and contact wire-following spring are jointly applied to constitute an effective controller for vibration suppression. Performance of the control system is evaluated on the basis of variations of displacement and acceleration between the pantograph and contact wire. An active control law is developed by means of a linear quadratic regulator design to derive a stabilizing control law for the pantograph system with the time-varying contact force between the pantograph shoe and catenary. A systematic optimization process with Pareto set and variable weights for the design of active suspension parameters of the pantograph using a constrained multiobjective evolutionary algorithm is developed. Extensive simulations are well performed to verify our proposed design.

Adaptive IPM-based lane filtering for night forward vehicle detection

This paper presents a new vision-based vehicle detection method for Forward Collision Warning System (FCWS) at nighttime. Also, lane detection is performed for assistance. To effectively extract the bright objects of interest, an essential image preprocessing including the tone mapping, contrast enhancement and adaptive binaryzation is applied in the nighttime road scenes. The characteristics of taillights in gray-level image are extracted by night vehicle detection method, and the resulted taillight candidates are verified by their corresponding red-component which results from R and B color channels. The taillight candidates to be performed with pairing algorithm are filtered by our proposed adaptive lane boundaries on the basis of Inverse Perspective Mapping (IPM). In addition, we proposed a new detecting scheme which performs the detecting algorithm on two Region of Interest (ROI) defined by different size each time. The computing burden is then reduced because vehicle detection does not have to be performed on the entire image. Finally, relative distance and Time To Collision (TTC) are estimated to warn the inappropriate driving behavior of the driver. The proposed night vehicle detection which integrates lane detection has successfully implemented in ADI-BF561 600MHz dual-core DSP.

A hybrid evolutionary approach for robust active suspension design of light rail vehicles

This paper is concerned with the design of a robust active suspension controller for light rail vehicles aimed at providing superior ride comfort within the suspensions traveling range. A multibody dynamic model of a three-car train is set up and the control parameters are optimized. Force cancellation, skyhook damper, and track-following concepts are used to synthesize the active controller. Selection of the active suspension parameters is aided by an evolutionary computation algorithm to get the best compromise between ride quality, suspension deflections due to irregular gradient tracks, and robust stability of the control system. A mixed gradient and evolutionary multiobjective optimization approach accompanied with the Pareto set and variable weights are developed to deal with the complicated control design task. Extensive simulations and comparisons are performed to verify the proposed design.

A vision-based obstacle detection system for parking assistance

This paper proposes a monocular vision-based obstacle detection algorithm for parking assistance applications of advance safety vehicle by a rear camera. In order to efficiently detect various moving and stationary obstacles behind the vehicle, the feature of corner into the rear obstacles is firstly estimated by the Features from Accelerated Segment Test (FAST) corner detection methods. Then, the inverse perspective mapping (IPM) image can be used to determine whether every detected feature belongs to an obstacle candidate or to the ground. Based on these results, the segmentation and identification strategies are also proposed in order to determine the degree of collision risk and to filter out the non-hazardous candidates. Finally, the correct obstacle regions in IPM transformed image can be easily and quickly extracted. The system can provide a vision-based alert to the driver, helping to avoid collisions with obstacles behind the host vehicle. Through extensive experiments, we have shown that the rear obstacle detection system in typical urban situations can be used to efficiently extract obstacle regions markings. The proposed algorithm achieves high detecting rate and low computing power and is successfully implemented in ADI-BF561 600MHz dual core DSP.

Bankruptcy Prediction Using Cerebellar Model Neural Networks

In Taiwan, more and more enterprises face the problems of financial distress in recent years. Besides, it is also noted that the volume of outstanding debt to corporations increases in Taiwan. An improvement in distress prediction accuracy can lead to save tens of billions of dollars. The firms which face financial distress will reveal many signs on financial data. Therefore, this study hopes to provide to managers and investors as a reference for decisions making through systematic approach as it can look for firms facing financial distress. In this paper, a novel prediction system is proposed which is based on intelligent classification to distinguish bankruptcy prediction. This method is referred to as a cerebellar model neural network (CMNN). A CMNN can be thought of as a learning mechanism imitating the cerebellum of a human being. Through training, this CMNN can be viewed like an expert of financial analyzer and then it can be applied to bankruptcy prediction. This study uses an artificial neural network, a genetic programming, and the proposed CMNN to construct financial distress prediction models and compare the performance of above three models using some Taiwanese company data, and it confirms CMNN is better than the others. By doing this, it can help understand and predict financial condition of firms and prevent the firms from insolvency. The CMNN yields the best prediction through the efficient infer reasoning of CMNN. Thus, the result is feasible to construct the financial distress prediction model.

Indirect Adaptive Fuzzy Supervisory Control with State Observer for Unknown Nonlinear Time Delay System

This paper proposes an indirect adaptive fuzzy neural network (FNN) controller with state observer and supervisory controller for a class of uncertain nonlinear dynamic time delay systems. First, the approximate function of unknown time delay system is inferred by the adaptive time delay FNN system. Next, a state observer is designed to estimate the unknown system states and the indirect adaptive fuzzy controller is constructed. Finally, the closed loop controller is obtained by incorporating the supervisory controller with the indirect adaptive fuzzy controller. Therefore, if the system tends to unstable, i.e., error dynamics is larger than a prescribed constraint which is determined by designer, the supervisory controller will activate to force the state to be stable. The free parameters of the indirect adaptive FNN controller can be tuned online by observer-based output feedback control law and adaptive laws by means of Lyapunov stability criterion. The resulting simulation example shows that the performance of nonlinear time delay chaotic system is fully tracking the reference trajectory. Meanwhile, simulation results show that the adaptive control effort of the proposed control scheme is much less due to the assist of the supervisory controller.

Tracking control of the Z-tilts error compensation stage of the nano-measuring machine using capacitor insertion method

This paper deals with the design of the capacitor insertion method for the three degree-of-freedom (DOF) flexible and deformation mechanisms aimed to eliminate hysteresis effects in piezoelectric actuators. By inserting a capacitor in series with the piezoelectric actuator is applied a 3-DOF nanoprecision platform and laser-measuring systems. The Z-tilts(z, pitch, and roll motion) error compensation stage of the nano-measuring machine is accomplished. In addition, a high resolution laser interferometer is used to measure position accurately. Therefore, above the method is effectively applied to a piezoelectric actuator are presented that compensate substantial improvements in positioning control precision and control performance. With the aid of positioning control, this system provides +/-60nm positioning resolution over the total range of 1000nm and +/-0.1 arcsec angle resolution over the total range of 3 arcsec for the stage along the z-direction.

An evolutionary approach to active suspension design of rail vehicles

Abstract This paper presents an application of a constrained multiobjective evolutionary algorithm for the design of active suspension controllers for light rail vehicles with the aim of providing superior ride comfort within the suspensions stroke limitation. A multibody dynamic model of a three‐car train is derived and the control parameters are optimized. Force cancellation, skyhook damper, and track‐following are used to synthesize the active controller. Selection of the active suspension parameters is aided by an evolutionary computation algorithm to get the best compromise between ride quality and suspension deflections due to irregular gradient tracks. An evolutionary multiobjective optimization approach accompanied with the Pareto set is proposed to deal with the complicated control design problem.

Adaptive tracking control for nonlinear systems by an adaptive model-based FNNs sliding mode control scheme

This paper concerned with the adaptive tracking control problem of an adaptive model-based fuzzy-neural networks (FNNs) sliding mode control (AFSMC) scheme for a class of nonlinear systems, which are represented by Takagi-Sugeno (T-S) fuzzy model to express a nonlinear systems model. Then, an adaptive parameter estimator is proposed to estimate the unknown nonlinear system parameters. Considering the online estimating error from the estimation model and nonlinear system model, a state estimation based feedback controller is derived by the proposed adaptive FNNs sliding mode control scheme and free parameters can be updated online by adaptive laws based on Lyapunov stability theorem. The proposed control scheme can guarantee that the unknown nonlinear system output can track to the states of reference model for any desired input signals when the stability condition is satisfied. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed approaches.