Woo-Kyung Choi

Study on method of route choice problem based on user preference

The progress of industrialization and civilization accelerates the complexity of traffic system. To solve the problem of increase of traffic volume and complexity of traffic system, the methods that offer real-time traffic information to drivers like Intelligent Transport System(ITS) are proposed and are researched. Also navigation system that can use in a car is being studied. This paper suggests the selection method of route for the drivers assistant system that can become individual system to driver by addition of drivers tendency. Drivers tendency defines as characteristic of the drivers driving pattern and the selected driving route. This paper infers drivers tendency and characteristics of routes by use of fuzzy logic and simulates the proposed algorithm with Personal Computer(PC) and personal Digital Assistant(PDA).

Multiple sensor fusion and motion control of snake robot based on soft-computing

The recent development in robot filed shows that practical application of robot has transferred from industry to humans daily life. That is, robots which are modeled on human being as well as various animals have shown up. If a robot just moves around certain place as it controls its links, it is not more than a toy for children. A robot has to mount with various sensors to get information from environment, infer environment from sensor information and act properly as human being does with the five senses. In this paper, we made a snake shaped robot mounted with various sensors such as image, gas, temperature and luminosity sensor. The data from sensors is fused by soft-computing method. The snake robot recognizes environment with the fused sensor information and acts according to the result of expert system which is able to infer what proper action is.

Implementation of Intelligent and Human-Friendly Home Service Robot

Robot systems have applied to manufacturing or industrial field for reducing the need for human presence in dangerous and/or repetitive tasks. However, robot applications are transformed from industrial field to human life in recent tendency Nowadays, final goal of robot is to make a intelligent robot that can understand what human say and learn by itself and have internal emotion. For example Home service robots are able to provice functions such as security, housework, entertainment, education and secretary To provide various functions, home robots need to recognize human`s requirement and environment, and it is indispensable to use artificial intelligence technology for implementation of home robots. In this paper, implemented robot system takes data from several sensors and fuses the data to recognize environment information. Also, it can select a proper behavior for environment using soft computing method. Each behavior is composed with intuitive motion and sound in order to let human realize robot behavior well.

Memory Management Model Using Combined ART and Fuzzy Logic

The human being receives a new information from outside and the information shows gradual oblivion with time. But the information remains in memory and isn`t forgotten for a long time if the information is read several times over. For example, we assume that we memorize a telephone number when we listen and never remind we may forget it soon, but we commit to memory long time by repeating. If the human being received new information with strong stimulus, it could remain in memory without recalling repeatedly. The moments of almost losing one`s life in an accident or getting a stroke of luck are rarely forgiven. The human being can keep memory for a long time in spite of the limit of memory for the mechanism mentioned above. In this paper, we propose a model to explain the mechanism mentioned above using a neural network and fuzzy.

Study for intelligent guide system using soft computing

GPS navigation system has been begun to install to the car since the 1990s. The early system was road guide but it is giving much serviceableness to user because various functions are added by the development of various techniques. However the growth of the most important guide thing of navigation system is yet not conspicuous. In this paper, intelligent guide system that infers information of various recommended road and can guide suitable road to personal tendency was proposed. By using fuzzy logic, it updates users driving tendency at regular intervals and infers road state. Also path breakaway inference system that learns users movement path and can secure personal security was proposed by using GPS information.

Learning for Environment and Behavior Pattern Using Recurrent Modular Neural Network Based on Estimated Emotion

Rational sense is affected by emotion. If we add the factor of estimated emotion by environment information into robots, we may get more intelligent and human-friendly robots. However, various sensory information and pattern classification are prescribed for robots to learn emotion so that the networks are suitable for the necessity of robots. Neural network has superior ability to extract character of system but neural network has defect of temporal cross talk and local minimum convergence. To solve the defects, many kinds of modular neural networks have been proposed because they divide a complex problem into simple several subproblems. The modular neural network, introduced by Jacobs and Jordan, shows an excellent ability of recomposition and recombination of complex work. On the other hand, the recurrent network acquires state representations and representations of state make the recurrent neural network suitable for diverse applications such as nonlinear prediction and modeling. In this paper, we applied recurrent network for the expert network in the modular neural network structure to learn data pattern based on emotional assessment. To show the performance of the proposed network, simulation of learning the environment and behavior pattern is proceeded with the real time implementation. The given problem is very complex and has too many cases to learn. The result will show the performance and good ability of the proposed network and will be compared with the result of other method, general modular neural network.

Control of Ubiquitous Environment using Sensors Module

As Ubiquitous era comes, it became necessary to construct environment which can provide more useful information to human in the spaces where people live like homes or offices. On this account, network of the peripheral devices of Ubiquitous should constitute efficiently. For it, this paper researched human pattern by classified motion recognition using sensors module data. (This data processing by Neural network and fuzzy algorithm.) This pattern classification can help control home network system communication. I suggest the system which can control home network system more easily through patterned movement, and control Ubiquitous environment by grasp human`s movement and condition.

A Judgment System for Intelligent Movement Using Soft Computing

This research is to introduce about Judgment System for Intelligent Movement(JSIM) that can perform assistance work of human brain. JSIM can order autonomous command and also it can be directly controlled by user. This research assumes that control object is limited to Mobile Robot(MR) Mobile robot offers image and ultrasonic sensor information to user carrying JSIM and it performs guide to user. JSIM having PDA and Sensor-box controls velocity and direction of the mobile robot by soft-computing method that inputs user`s command and information that is obtained to mobile robot. Also it controls mobile robot to achieve various movement. This paper introduces wearable JSIM that communicates with around devices and that can do intelligent judgment. To verify the possibility of the proposed system, in real environment, the simulation of control and application problem lot mobile robot will be introduced. Intelligent algorithm in the proposed system is generated by mixed hierarchical fuzzy and neural network.

A Study on Human-friendly Path Decision using Fuzzy Logic

Recently many cars are equipping a navigation system. The main purpose of the early system guides a user through the route. A navigation system includes various abilities by development of various technologies and it has given more convenience to user. It can play various records on the tape and announces which are useful information about each road. Also it can use various multi-media contents by DMB device during driving. However, guide function of basic and important road in the navigation system has not grown greatly yet. In this paper, we proposed recommendation method of human-friendly road considering user`s condition through various information of outside environment, user`s velocity intention, a driver`s emotion and a preference of the road. Modules consists of hierarchical structure that can easily correct and add each algorithm and those use fuzzy logic algorithm.

Mobile robot control by MNN using optimal EN

Skills in tracing of the MR divide into following, approaching, avoiding and warning and so on. It is difficult to have all these skills learned as neural network. To make this up for, skills consisted of each module, and Mobile Robot was controlled by the output of module adequate for the situation. A mobile Robot was equipped multi-ultrasonic sensor and a USB Camera, which can be in place of human sense, and the measured environment information data is learned through Modular Neural Network. MNN consisted of optimal combination of activation function in the Expert Network and its structure seemed to improve learning time and errors. The Gating Network(GN) used to control output values of the MNN by switching for angle and speed of the robot. In the paper, EN of Modular Neural network was designed optimal combination. Traveling with a real MR was performed repeatedly to verity the usefulness of the MNN which was proposed in this paper. The robot was properly controlled and driven by the result value and the experimental is rewarded with good fruits.