Chun-Hsiung Fang

Bio: Chun-Hsiung Fang is an academic researcher from National Kaohsiung University of Applied Sciences. The author has contributed to research in topics: Robust control & Fuzzy control system. The author has an hindex of 14, co-authored 73 publications receiving 1463 citations. Previous affiliations of Chun-Hsiung Fang include National Sun Yat-sen University.

Predicting survival of individual patients with esophageal cancer by adaptive neuro-fuzzy inference system approach

Abstract: The survival probability comparisons of the three GPS categories between the predicted results from the ANFIS model (dashed lines) and the Kaplan-Meier estimator results of esophageal cancer (solid lines). Based on the adaptive neuro-fuzzy inference system (ANFIS) approach, a hazard modeling and survival prediction system is developed to assist clinicians in prognostic assessment of patients with esophageal cancer and prediction of individual patient survival.The results show that application of ANFIS to prognosis prediction for esophageal cancer is a practical and effective method that can predict the survival of patients more accurately.This may provide valuable prognostic information in addition to AJCC staging and aid the clinicians' decision-making process for risk stratification. Since esophageal cancer has no symptoms in the early stage, it is usually not detected until advanced stages in which treatment is challenging. Integrated treatment provided by a multidisciplinary team is crucial for maximizing the prognosis and survival of patients with esophageal cancer. Currently, clinicians must rely on the cancer staging system for diagnosis and treatment. An accurate and easily applied system for predicting the prognosis of esophageal cancer would be useful for comparing different treatment strategies and for calculating cancer survival probability. This study presents a hazard modeling and survival prediction system based on adaptive neuro-fuzzy inference system (ANFIS) to assist clinicians in prognostic assessment of patients with esophageal cancer and in predicting the survival of individual patients. Expert knowledge was used to construct the fuzzy rule based prognosis inference system for esophageal cancer. Fuzzy logic was used to process the values of input variables rather than categorizing values as normal or abnormal based on cutoffs. After transformation and expansion, censored survival data could be used by the ANFIS for training to establish the risk model for accurately predicting individual survival for different time intervals or for different treatment modalities. Actual values for serum C-reactive protein, albumin, and time intervals were input into the model for use in predicting the survival of individual patients for different time intervals. The curves obtained by the ANFIS approach were fitted to those obtained using the actual values. The comparison results show that the ANFIS is a practical, effective, and accurate method of predicting the survival of esophageal cancer patients.

A simple approach to solving the Diophantine equation

Abstract: Based on the state-space concepts, a simple approach to finding all polynomial matrix solutions of the Diophantine equation is proposed. The procedure presented is very simple in comparison to earlier ones. Unlike earlier ones, it is not necessary to solve any equation. Only two constant matrices which could be selected at random are required. All solutions are expressed in an explicit formula form. >

Doubly coprime matrix-fraction representations using proportional and derivative feedback concepts in generalized state-space systems

Abstract: The concepts of proportional and derivative state feedback are used to derive some explicit formulas for doubly coprime matrix-fraction representations of generalized state-space systems. Specifically, proportional and derivative state feedback is used to determine the doubly coprime matrix-fraction representations and to solve the corresponding generalized Bezout identity in polynomial matrix form. >

Stabilization of Uncertain Linear Systems via Static Output Feedback Control

Abstract: In this paper, an LMI-based approach to designing a static output feedback controller is proposed for the stabilization of uncertain linear systems. The uncertain parameters are assumed to be time-invariant and belong to the convex bounded domains (polytopic type). The results are represented in terms of linear matrix inequalities (LMIs). Based on the feasible solution of derived LMIs, a static output feedback gain can be easily determined for stabiliza-tion of the uncertain systems. Continuous-time cases as well as discrete-time cases are studied simultaneously in this paper. Numerical examples show that the present approach allows larger uncertainties than the existing ones do.

Image Processing

Abstract: MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

Robust stability and stabilization for singular systems with state delay and parameter uncertainty

Abstract: Considers the problems of robust stability and stabilization for uncertain continuous singular systems with state delay. The parametric uncertainty is assumed to be norm bounded. The purpose of the robust stability problem is to give conditions such that the uncertain singular system is regular, impulse free, and stable for all admissible uncertainties, while the purpose of the robust stabilization is to design a state feedback control law such that the resulting closed-loop system is robustly stable. These problems are solved via the notions of generalized quadratic stability and generalized quadratic stabilization, respectively. Necessary and sufficient conditions for generalized quadratic stability and generalized quadratic stabilization are derived. A strict linear matrix inequality (LMI) design approach is developed. An explicit expression for the desired robust state feedback control law is also given. Finally, a numerical example is provided to demonstrate the application of the proposed method.

Stability Analysis of Interval Type-2 Fuzzy-Model-Based Control Systems

Abstract: This paper presents the stability analysis of interval type-2 fuzzy-model-based (FMB) control systems. To investigate the system stability, an interval type-2 Takagi-Sugeno (T-S) fuzzy model, which can be regarded as a collection of a number of type-1 T-S fuzzy models, is proposed to represent the nonlinear plant subject to parameter uncertainties. With the lower and upper membership functions, the parameter uncertainties can be effectively captured. Based on the interval type-2 T-S fuzzy model, an interval type-2 fuzzy controller is proposed to close the feedback loop. To facilitate the stability analysis, the information of the footprint of uncertainty is used to develop some membership function conditions, which allow the introduction of slack matrices to handle the parameter uncertainties in the stability analysis. Stability conditions in terms of linear matrix inequalities are derived using a Lyapunov-based approach. Simulation examples are given to illustrate the effectiveness of the proposed interval type-2 FMB control approach.

Fuzzy-Model-Based Reliable Static Output Feedback $\mathscr{H}_{\infty }$ Control of Nonlinear Hyperbolic PDE Systems

Abstract: This paper investigates the problem of output feedback robust $\mathscr{H}_{\infty }$ control for a class of nonlinear spatially distributed systems described by first-order hyperbolic partial differential equations (PDEs) with Markovian jumping actuator faults. The nonlinear hyperbolic PDE systems are first expressed by Takagi–Sugeno fuzzy models with parameter uncertainties, and then, the objective is to design a reliable distributed fuzzy static output feedback controller guaranteeing the stochastic exponential stability of the resulting closed-loop system with certain $\mathscr{H}_{\infty }$ disturbance attenuation performance. Based on a Markovian Lyapunov functional combined with some matrix inequality convexification techniques, two approaches are developed for reliable fuzzy static output feedback controller design of the underlying fuzzy PDE systems. It is shown that the controller gains can be obtained by solving a set of finite linear matrix inequalities based on the finite-difference method in space. Finally, two examples are presented to demonstrate the effectiveness of the proposed methods.