Joon-Hong Seok

Multiobjective Optimization Approach for Sensor Arrangement in A Complex Indoor Environment

Various meta-heuristic search methods have been employed to resolve the sensor arrangement problem, which is a type of NP-hard, combinational problem. However, the difficulty of weight tuning, when formulating a single objective function, is the chief obstacle to the use of the single-objective optimization methods. Although multiobjective optimization methods have been applied recently to avoid the difficulty involved in weight design, the original multiobjective optimization method still requires a greater number of generations for the solutions to converge to the optimal Pareto front. Moreover, unlike in previous works, we deal with four unknowns to define the sensor arrangement problem more practically: 1) The number of sensors is unknown, 2) no candidate is given for installation, 3) the coverage radii of sensors are variable, and 4) sensors cover a wide area in which obstacles exist in complicated arrangements. To improve the search approach for a sensor arrangement with these requirements, we first propose a representation scheme to encode the sensor arrangement problem as a set of chromosomes. Genetic operators and a repair scheme are also properly employed in the proposed encoding method. In addition, two strategies, i.e., the hierarchical fitness assignment strategy and the hybrid optimization strategy, are proposed to improve convergence. We also perform experiments with two commercial sensors to verify the proposed multiobjective optimization approach for sensor arrangement (MOASA). The results show that the proposed MOASA gives better performance than conventional search methods. The effects of the proposed strategies are investigated with additional experiments in terms of the quality of Pareto solutions.

RFID sensor deployment using differential evolution for indoor mobile robot localization

This paper presents the sensor deployment method to design a RFID sensor network for the mobile robot localization using evolutionary approach. For this purpose, we employ the differential evolution (DE), which is well-known for promising performance. We propose two variation methods, the direct optimization strategy for the maximum usage of initial information intuitively and the full coverage optimization strategy for the dense coverage for the surveillance and the security. In that case, the proper tuning of parameters of DE is essential. We experiment sensor deployment in two maps for providing guidance about parameter tuning. The experimental results show better sensor deployment result according to guided parameter setting. The full coverage optimization strategy also shows proper result using guided parameters from the standard DE case.

Mean-shift tracker with face-adjusted model

Mean-shift algorithm shows robust performances in various object-tracking technologies including face tracking. Due to its robustness and accuracy, mean-shift algorithm is regarded as one of the best ways to apply in object-tracking technology in computer vision fields. However, it has a drawback of getting into a bottleneck state when faced with a speedy object moving beyond its window size within one image frame interval time. The time required to calculate mean-shift vector could be much lessened with lesser memory when color model is adjusted to the previously known target information. This paper shows the building process of target-adjusted model with a non-uniform quantization. The target color model dealt in this paper is the one used for deriving mean-shift vector. It is a kernel model containing both the color and distance information. This paper gives scheme to efficiently deal with color information in the model. Through a proper selection of color bins, unimportant color values were reduced to a small amount. As a result, the computing time of the mean-shift vector in face-tracking was shortened while maintaining robustness and accuracy.

Integrated path planning for a partially unknown outdoor environment

In this paper, we propose an integrated path planning algorithm that reasons on integration between a local path planners based on sensory information and global path planners based on global prior information in a partially unknown outdoor environment. A local path planner typically generates collision-free paths using laser range sensors, sonar sensors, etc., nearby robots or unmanned ground vehicles. In contrast, a global path planner generates efficient paths based on given cost function using initially given maps. To generate efficient collision-free paths in a partially unknown outdoor environment, we utilize both information properly in RRT as the local path planner and A* as global path planner and separate a role of each planner not to overlap with each other. It is assume that a partially unknown outdoor environment only provides obstacle information nearby a robot or a vehicle within a range of a sensor and prior approximate information about the entire map. The proposed integrated path planner improves overall computational time and cost to obtain one of the complete path from start to goal.

Evolutionary design of fuzzy classifiers using intersection points

Chromosome representation to search the optimal intersection points between adjacent fuzzy membership functions is originally presented for optimal design of fuzzy classifiers. Since the proposed representation contains the intersection points directly related to the boundary of classification, it is intuitively expected that redundancy of the search space is reduced and the performance is better in comparison with the conventional encoding scheme. Unlike the previous work, the distances between the intersection points are encoded instead of x-coordinates of intersection points in order to reduce the redundancy due to the combinations of disordered intersection points. The experimental results show that the proposed encoding scheme gives superior or competitive performance in two real-world datasets and gives more interpretable fuzzy classifiers. In addition, this proposed approach provides more interpretable classifiers without additional computational cost and also reduces search space while maintaining performance.

New encoding scheme for evolving fuzzy classifiers

We present a noble encoding method for designing an optimal fuzzy classifier with evolutionary optimization. Evolutionary designs of fuzzy classifiers is divided into design of fuzzy rules and design of fuzzy membership functions. Among these design problems, for an evolutionary design of membership functions, the shapes of each membership function are mainly considered in the previous related works. In other words, design of fuzzy membership functions is formulated as a parameter search problem for tuning the shapes of each function (e.g. center(or mean) and width(or variance) in a gaussian function). In this paper, we newly consider the design of fuzzy membership functions as optimization of intersection positions between adjacent membership functions. According to recent insightful researches, classification boundaries are determined by the points of intersection of membership functions. Therefore, the proposed approach differs from conventional approaches in that the proposed method can search and manipulate the border of classification which directly influences the classification performance. In order to verify the proposed encoding method, simulation study is carried out. For this simulation study, we apply the proposed encoding scheme to the basic genetic algorithm (GA), one of the most widely used evolutionary optimization methods in the recent literatures. The performance of the proposed method is investigated with two real world databases, ‘iris’ and ‘glass’ data.

Adaptive robot control based on multiple incremental fuzzy neural networks

An adaptive control for robot manipulators based on multiple incremental fuzzy neural networks (FNNs) is proposed in this paper. The overall controller is comprised of a feedback controller and multiple FNNs which learn inverse dynamics of the robot manipulator for different tasks. The multiple FNNs are switched or blended to improve the transient response when manipulating objects are changed. The structure and parameters of the FNNs are determined dynamically using an incremental learning algorithm which reduces complexity and computation induced by the use of multiple models considerably. The parameters are refined online to compensate for uncertainties. The closed-loop system with a switching or blending law is proven to be stable in Lyapunov sense. The proposed scheme is applied to control a two-link robot manipulator with varying payloads.

Laser micro-patterning software development for manufacturing of pattern roll for printed electronics

In this paper, laser micro-patterning software developed for manufacturing of printing rolls for printed electronics is presented. The software consists of a servo control module, an image processing module, a configuration module, and a laser patterning module. By the software, laser beams position and irradiation are controlled according to drawing patterns while the pattern roll is rotating. In this operation, control performance is important to determine the quality of the pattern roll and manufactured electronics after printing. The software is developed based on the accurate control algorithm and optimization of laser operation over the whole image for computation time and high-speed patterning. Additionally, overlapping image patterning is considered for the uniformity of the engraving depth and patterning quality.

Temporal Waypoint Revision Method to Solve Path Mismatch Problem of Hierarchical Integrated Path Planning for Mobile Vehicle

Hierarchical IPP (Integrated Path Planning) combining the GPP (Global Path Planner) and the LPP (Local Path Planner) is interesting the researches who study about the mobile vehicle in recent years. However, in this study, there is the path mismatch problem caused by the difference in the map information available to both path planners. If ever a part of the path that was found by the GPP is available to mobile vehicle, the part may be unavailable when the mobile vehicle generates the local path with its built-in sensors while the vehicle moves. This paper proposed the TWR (Temporal Waypoint Reviser) to solve the path mismatch problem of the hierarchical IPP. The results of simulation provide the performance of the IPP with the TWR by comparing with other path planners.

A CHROMOSOME REPRESENTATION ENCODING INTERSECTION POINTS FOR EVOLUTIONARY DESIGN OF FUZZY CLASSIFIERS

ABSTRACT—Unlike the conventional chromosome representation to search the shape of fuzzy membership functions, a novel encoding scheme to search the optimal intersection points between adjacent fuzzy membership functions is originally presented for evolutionary design of fuzzy classifiers. Since the proposed representation contains the intersection points directly related to the boundary of classification, it is intuitively expected that redundancy of the search space is reduced and the performance is better in comparison with the conventional encoding scheme. The experimental results show that the proposed encoding scheme gives superior or competitive performance in two real-world datasets and gives more interpretable fuzzy classifiers. This short paper has provided additional explanation to the previous works introduced in the latest conference. Key Words: Fuzzy classifiers, genetic algorithms, encoding scheme, intersection points of membership functions 1. INTRODUCTION In classical data-driven fuzzy modeling, design of optimal fuzzy classifier has been commonly dealt with as a search problem [1][2]. Optimal design of fuzzy classifiers (FCs) is one of the most complex search problems since there are various feasible solutions according to the combination of fuzzy rules and membership functions (MFs). In other words, the optimal design of FCs is described as a non-convex and multimodal search problem. Besides, the high-order and large scale problems caused by the large number of rules, MFs, and input attributes also exist in the optimal design of FCs. In order to overcome these difficulties, many researchers have applied meta-heuristic search methods to optimally design FCs. Among them, evolutionary algorithms (EAs) such as genetic algorithms (GAs) have been widely used in the much of the literature due to their search ability and reliability [1]-[3]. EAs are basically general-purpose population-based stochastic search algorithms which use multiple candidate solutions simultaneously and find the best solution [4]. Irrespective of the given problem, the first step in applying EAs is to encode solutions for the given problem into a set of parameters: a chromosome. Thus, to optimize fuzzy models in a framework of EAs, the first step is to represent a fuzzy model into a chromosome. For evolutionary design of FCs, an encoding scheme relevant to the given problem is also required because an encoding scheme plays an important role to decide the property of search space [1][4]. The conventional encoding methods for evolutionary design of FCs are mainly designed to find the shape of the fuzzy MFs as a center and width of a Gaussian MF and 3 or 4 edge points of a triangular or trapezoidal MF. However, recent related works [5], providing an intuitive insight for properties of fuzzy rules, argue that the boundary of classification is formed in the intersection points between two adjacent MFs. In other Downloaded by [Iowa State University] at 12:35 10 January 2014