Moon Hwan Kim

New fuzzy skin model for face detection

We discuss the face detection method by using skin information. Skin color has proven to be a useful and robust cue for face detection, localization and tracking. Numerous techniques for skin color modelling and recognition have been proposed during several past years. In this paper we propose a new fuzzy skin model for face detection and its identification method. The fuzzy skin model comprise of the fuzzy rules with color information. The membership function and structure of fuzzy rule are identified by the proposed linear matrix inequality method. Experimental results demonstrate successful face detection.

Identification of t–s fuzzy classifier via linear matrix inequalities

In this paper a new linear matrix inequality (LMI) based design method for T-S fuzzy classifier is proposed. The various design factors including structure of fuzzy rule and various parameters should be considered to design T-S fuzzy classifier. To determine these design factors, we describe a new and efficient two-step approach that leads to good results for classification problem. At first, LMI based fuzzy clustering is applied to obtain compact fuzzy sets in antecedent. Then consequent parameters are optimized by a LMI optimization method.

Hybrid silhouette extraction method for detecting and tracking the human motion

Human motion analysis is an important research subject in human-robot interaction (HRI). However, before analyzing the human motion, silhouette of human body should be extracted from sequential images obtained by CCD camera. The intelligent robot system requires more robust silhouette extraction method because it has internal vibration and low resolution. In this paper, we discuss the hybrid silhouette extraction method for detecting and tracking the human motion. The proposed method is to combine and optimize the temporal and spatial gradient information. Also, we propose some compensation methods so as not to miss silhouette information due to poor images. Finally, we have shown the effectiveness and feasibility of the proposed method through some experiments.

Waypoint Tracking of Large Diameter Unmanned Underwater Vehicles with X-stern Configuration

This paper focuses on a horizontal waypoint tracking and a speed control of large diameter unmanned underwater vehicles (LDUUVs) with X-stern configuration plane. The concerned design problem is converted into an asymptotic stabilization of the error dynamics with respect to the desired yaw angle and surge speed. It is proved that the error dynamics under the proposed control scheme based on the linear control and the feedback linearization can be considered as a cascade system; the cascade system is asymptotically stable if its nominal systems are so. This stability connection enables to separately deal with the waypoint tracking problem and the speed control one. By using the sector nonlinearity, the nominal system with nonlinearities is modeled as a polytopic linear parameter varying (LPV) system with parametric uncertainties. Then, sufficient linear matrix inequality (LMI) conditions for its asymptotic stabilizability are derived in the sense of Lyapunov stability criterion. An example is given to show the validity of the proposed methodology.

Depth and Speed Control of Large Diameter Unmanned Underwater Vehicles

This paper deals with the depth and speed controls of a class of nonlinear large diameter unmanned underwater vehicles (LDUUVs), while maintaining its attitude. The concerned control problem can be viewed as an asymptotic stabilization of the error model in terms of its desired depth, surge speed and attitude. To tackle its nonlinearities, the linear parameter varying (LPV) model is employed. Sufficient linear matrix inequality (LMI) conditions are provided for its asymptotic stabilization. A numerical simulation is provided to demonstrate the effectiveness of the proposed design methodology.

Design of t–s fuzzy classifier via linear matrix inequality approach

A linear matrix inequality approach to designing accurate classifier with a compact T–S(Takagi–Sugeno) fuzzy-rule is proposed, in which all the elements of the T–S fuzzy classifier design problem have been moved in parameters of a LMI optimization problem. Two-step procedure is used to effectively design the T–S fuzzy classifier with many tuning parameters: antecedent part and consequent part design. Then two LMI optimization problems are formulated in both parts and solved efficiently by using interior-point method. Iris data is used to evaluate the performance of the proposed approach. From the simulation results, the proposed approach showed superior performance over other approaches.