Jae Pil Hwang

Robust tracking control of an electrically driven robot: adaptive fuzzy logic approach

This paper is concerned with the robust tracking control of an electrically driven robot with the model uncertainties in the robot dynamics and the motor dynamics. The motors driving the joints of the robot are assumed to be equipped with only the joint position and the current measurement devices. Adaptive fuzzy logic and adaptive backstepping method are employed to provide the solution to the control problem. The suggested method does not require the measurement of the velocity nor the acceleration. Simulation results from a two-link electrically driven robot show the satisfactory performance of the proposed control scheme even in the presence of internal model uncertainties in both the robot and motor dynamics and external disturbances

A New Evolutionary Particle Filter for the Prevention of Sample Impoverishment

Particle filters perform the nonlinear estimation and have received much attention from many engineering fields over the past decade. Unfortunately, there are some cases in which most particles are concentrated prematurely at a wrong point, thereby losing diversity and causing the estimation to fail. In this paper, genetic algorithms (GAs) are incorporated into a particle filter to overcome this drawback of the filter. By using genetic operators, the premature convergence of the particles is avoided and the search region of particles enlarged. The GA-inspired proposal distribution is proposed and the corresponding importance weight is derived to approximate the given target distribution. Finally, a computer simulation is performed to show the effectiveness of the proposed method.

A new weighted approach to imbalanced data classification problem via support vector machine with quadratic cost function

In this paper, a new weighted approach on Lagrangian support vector machine for imbalanced data classification problem is proposed. The weight parameters are embedded in the Lagrangian SVM formulation. The training method for weighted Lagrangian SVM is presented and its convergence is proven. The weighted Lagrangian SVM classifier is tested and compared with some other SVMs using synthetic and real data to show its effectiveness and feasibility.

Multi-Classifier Based LIDAR and Camera Fusion

We present a sensor fusion system using lidar and camera. We separate the system into two part which is hypothesis generation part and hypothesis verification part. These parts use different single sensors. Hypothesis generation is done using the lidar and hypothesis verification is done using the camera image. In hypothesis generation, we cluster the lidar data and do a perspective mapping to generate the candidate. In hypothesis verification, we used 5-SVMs classifier. Based on the candidate position, the candidate is putted in different SVM. In the result, we compared the result between 5-SVM hypothesis verification and single SVM hypothesis verification. The result showed 2.2% improvement.

A neural network approach to target classification for active safety system using microwave radar

As a sensor in the active safety system of vehicles, the microwave radar (MWR) would be a good choice for the localization of the nearby targets but could be a bad choice for their classification or identification. In this paper, a target classification system using a 24GHz microwave radar sensor is proposed for the active safety system. The basic idea of this paper is that the pedestrians and the vehicles have different reflection characteristics for a microwave. A multilayer perceptron (MLP) neural network is employed to classify the targets and the probabilistic fusion is conduct over time to improve the classification accuracy. Some experiments are performed to show the validity of the proposed system.

CV-SLAM using ceiling boundary

This paper deals with simultaneous localization and mapping(SLAM) problem for a mobile robot that travels around the indoor environments. A single camera looking up the ceiling is used as the only sensor. Line features are extracted from the boundaries between the ceiling and walls and parameterized for SLAM update. Extended Kalman Filter(EKF) is used for simultaneously estimating the current robot pose and building a map with the line features. When the robot is kidnapped, Monte Carlo Localization(MCL) is used for finding the robot pose. To improve the localization performance, the resampling method is modified. The experiment is practiced in our indoor test bed and the proposed algorithms are proved by the experimental results.

A new state estimation method for chaotic signals: Map-particle filter method

Over the past few decades, research has been conducted regarding the applicability of chaotic behavior in consumer and industrial applications, including secure communication. In this paper, we develop a new particle filter termed as MAP-particle filter and apply it to a secure communication. The proposed particle filter estimates not only the chaotic state but also the secure message, thereby improving the secure communication performance. A computer simulation is conducted to demonstrate the effectiveness of our proposed method.

On-road Vehicle Tracking using Laser Scanner with Multiple Hypothesis Assumption

Active safety vehicle devices are getting more attention recently. To prevent traffic accidents, the environment in front and even around the vehicle must be checked and monitored. In the present applications, mainly camera and radar based systems are used as sensing devices. Laser scanner, one of the sensing devices, has the advantage of obtaining accurate measurement of the distance and the geometric information about the objects in the field of view of the laser scanner. However. there is a problem that detecting object occluded by a foreground one is difficult. In this paper, criterions are proposed to manage this problem. Simulation is conducted by vehicle mounted the laser scanner and multiple-hypothesis algorithm tracks the candidate objects. We compare the running times as multi-hypothesis algorithm parameter varies.

Dual margin approach on a Lagrangian support vector machine

In this paper, we propose a new support vector machine (SVM) called dual margin Lagrangian support vectors machine (DMLSVM). Unlike other SVMs which use only support vectors to determine the separating hyperplanes, DMLSVM utilizes all the available training data for training the classifier, thus producing robust performance. The training data are weighted differently depending on whether they are in a marginal region or surplus region. For fast training, DMLSVM borrows its training algorithm from Lagrangian SVM (LSVM) and tailors the algorithm to its formulation. The convergence of our training method is rigorously proven and its validity is tested on a synthetic test set and UCI dataset. The proposed method can be used in a variety of applications such as a recommender systems for web contents of IPTV services.

Multiclass Lagrangian support vector machine

A support vector machine (SVM) has been developed for two-class problems, although its application to multiclass problems is not straightforward. This paper proposes a new Lagrangian SVM (LSVM) for application to multiclass problems. The multiclass Lagrangian SVM is formulated as a single optimization problem considering all the classes together, and a training method tailored to the multiclass problem is presented. A multiclass output representation matrix is defined to simplify the optimization formulation and associated training method. The proposed method is applied to some benchmark datasets in repository, and its effectiveness is demonstrated via simulation.