• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.275-284
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• 2006

This paper deals with problems of safe and efficient navigation algorithm for autonomous mobile robots in unknown environments. Since the obstacle avoidance algorithms are very important in mobile robot navigation, two obstacle avoidance algorithms: VFH(vector field histogram) algorithm and a fuzzy algorithm are combined to have optimal performance in various environments. And a upper-level supervisor is to select the proper one from VFH algorithm and the fuzzy algorithm according to the situations the robot faces. Computer simulation results show the effectiveness of the proposed navigation algorithm for autonomous mobile robots.

• Journal of Mechanical Science and Technology
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• v.15 no.11
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• pp.1472-1481
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• 2001

In this paper, an advanced shift controller that supervises the shift transients with adaptive compensation is presented. Modern shift control systems for vehicle automatic transmission are designe d to provide smooth transients for passengers' comfort and better component durability. In the conventional methods, lots of testing and calibration works have been done to tune gains of the controller, but it does not assure optimum shift quality at all times owing to system variations often caused by uncertainties in shifting hydraulic systems and external disturbances. In the proposed control scheme, an adaptive compensation controller with intelligent supervisor is implemented to achieve improved shift quality over the system variations. The control input pattern which generates clutch pressure commands in hydraulic actuating systems, is updated through a learning process to adjust for each subsequent shift based on continuous monitoring of shifting performance and environmental changes. The proposed algorithm is implemented and evaluated on the experimental test setup. Results from the experimental studies for several operation modes show both improved performance and adaptability of the proposed shift controller to uncertain changes of the shifting environment in vehicle power transmission systems.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.847-852
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• 1993

In this paper, we present a recursive algorithm for the auto-tuning of PID controllers by optimizing a GPC criterion. Also, we develop an intelligent PID controller by combination of a recursive algorithm together with a supervisor, that allows to adjust the main controller parameters (prediction horizon, control weighting, sample time etc.) using some simple rules which is mainly built up through relay tuning experiments. The intelligent PID controller has been implemented successfully on an IBM PC/AT and some simulation results are presented.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.139-144
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• 1995

LIBL(Linguistic Instruction Based Leaning) is an effective learning algorithm for fuzzy controller which interpretes and uses natural language of human The possibiliy of the LIBL algorithm to the fuzzy control of dynamic systems is investigated in this paper. Rise time, percent overshoot, and steady stste are proposed as suitable meaning elements for dynamic systems. A supervisor is able to give "higer-level linguistic instruction" to the learning algorithm through these three meaning elements Simulation results for a DC servo motor show the validity of the proposed algorithm.

• 한국IT서비스학회:학술대회논문집
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• 2008.11a
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• pp.411-414
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• 2008

해상사고로 인한 송전선로의 보호를 위해 Scan Laser Range Finder를 사용하여 해상에서의 일정한 높이에서 수평으로 레이저빔을 조사한 후 높이 기준을 넘는 선박을 검지하여 좌표와 속도를 측정하여 경보방송 및 무선 통신으로 회항을 유도하여 송전선을 감시 및 보호하는 기술개발로 산업 현장에서의 적응력을 높이는데 기여할수 있을것으로 보인다.

• Journal of the Korea Society of Computer and Information
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• v.19 no.6
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• pp.109-117
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• 2014

A cold storage is a warehouse of a insulated building with cooling installations. It has many different types of cold rooms with temperatures below 0 degrees Celsius, and the sequential workflow such as receiving, picking and packing runs in that rooms. Recently, the cold storages have adopted RFID technology, and consequently, warehouse product management in them are becoming intelligent and network. However, information inconsistency in warehouses caused by physical and logical errors reduces reliability in the RFID cold storage management system and worsens their work efficiency. Therefore, it is necessary to develop an early detection system to identify errors. In this paper, we suggest a supervisory system detecting logical errors on business processes of the RFID cold storage. It is composed of a master supervisor and mobile supervisor. In the master supervisor, the manager can set the constraints conditions and get alerts, and in the mobile supervisor, the workers confirm and deal with these faults directly. The supervisory system improve reliability of the RFID cold storage management system by recognizing a failure to identify physically and logically using these constraint conditions. This paper shows that the supervisory system can reduce the average recovery time to improve reliability by decreasing the time for detecting and analyzing errors in the RFID cold storage management system.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.967-982
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• 2011

This paper presents a FTNCS (Fault-Tolerant Networked Control System) that can tolerate control surface failure and packet delay/loss in an UAV (Unmanned Aerial Vehicle). The proposed method utilizes the benefits of self-diagnosis by smart actuators along with the control allocation technique. A smart actuator is an intelligent actuation system combined with microprocessors to perform self-diagnosis and bi-directional communications. In the event of failure, the smart actuator provides the system supervisor with a set of actuator condition data. The system supervisor then compensate for the effect of faulty actuators by re-allocating redundant control surfaces based on the provided actuator condition data. In addition to the compensation of faulty actuators, the proposed FTNCS also includes an efficient algorithm to deal with network induced delay/packet loss. The proposed algorithm is based on a Lagrange polynomial interpolation method without any mathematical model of the system. Computer simulations with an UAV show that the proposed FTNCS can achieve a fast and accurate tracking performance even in the presence of actuator faults and network induced delays.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.2
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• pp.83-93
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• 2000

This paper proposes the use of the analytic hierarchy process(AHP) controller for obtaining optimal cycle time of traffic intersection signal. By means of simulations that have data from expert traffic estimators, the performance of AHP controller is compared with a fixed-time controller and fuzzy controller. A simulation was executed using data obtained from many expert interviewer of Ansan City hall rotary. The principal issue is how AHP controller algorithm can aid traffic signal supervisor to construct an expert system for traffic intersection signal control. This paper has two steps: (I) we shall present a new methodology based on the work of Satty for calculating a scale of importance of each of the objectives, (2) we also consider that the human's decision mechanism is represented by approximating reasoning where the fuzzy integral is used.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.579-585
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• 2008

Recently, many researches on autonomous mobile system have been proposed, which are possible to recognize its surrounding environment and navigate to destination without supervisor's intervention. Various sensors are mounted on the autonomous systems in order for the systems to move to destination safely without any accident. In this paper, we design an ontology-based autonomous system mounted laser distance sensors and cameras, and propose a method to conceptualize sensing information. We show the validity of the proposed method through the experiments of the system's navigation.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.2
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• pp.249-260
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• 1997

The manufacturing environment is rife with nonlinear processes. In this context, an intelligent production controller should be able to predict the dynamic behavior of various subsystems as they react to transient environmental conditions, the varying internal condition of the manufacturing plant, and the changing demands of the production schedule. This level of adaptive capability may be achieved through a coherent methodology for a learning coordinator to predict nonlinear and stochastic processes. The system is to serve as a real time, online supervisor for routine activities as well as exceptional conditions such as damage, failure, or other anomalies. The complexity inherent in a learning coordinator can be managed by a modular architecture incorporating case based reasoning. In the interest of concreteness, the concepts are presented through a case study involving a knowledge based robotic system.