• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2216-2218
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• 2002

A system identification is to measure the output in the presence of a adequate input for the controlled system and to estimate the mathematical model in the basic of input output data. In the system identification, it is possible to estimate the true parameter values by the adjusted least squares method in the input-output case of no observed noise, and it is possible to estimate the true parameter values by the total least squares method in the input-output case with the observed noise. In recent the adjusted least squares method is suggested as a consistent estimation method in the system identification not with the observed noise input but with the observed noise output. In this paper we have developed the adjusted least squares method from the least squares method and have made certain of the efficiency in comparing the estimating results with the generating data by the computer simulations.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.274-284
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• 2019

The control architectures of Chuan Suo (CS) deep submergence rescue vehicle are introduced. The hardware and software architectures are also discussed. The hardware part adopts a distributed control system composed of surface and underwater nodes. A computer is used as a surface control machine. Underwater equipment is based on a multi-board-embedded industrial computer with PC104 BUS, which contains IO, A/D, D/A, eight-channel serial, and power boards. The hardware and software parts complete data transmission through optical fibers. The software part involves an IPC of embedded Vxworks real-time operating system, upon which the operation of I/O, A/D, and D/A boards and serial ports is based on; this setup improves the real-time manipulation. The information flow is controlled by the software part, and the thrust distribution is introduced. A submergence vehicle heeling control method based on ballast water tank regulation is introduced to meet the special heeling requirements of the submergence rescue vehicle during docking. Finally, the feasibility and reliability of the entire system are verified by a pool test.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.23 no.1
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• pp.15-20
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• 2014

The manufacturing equipment on most shop floors consists of numerical control machines, and the condition of each piece of equipment is monitored and controlled by an internal sensor or programmable logic controller (PLC). To control and monitor production lines that consist of an equipment or production module, a separate control and monitoring system such as a manufacturing execution system should be introduced. However, there is no standardized system, and it is costly and difficult to build a system for small or medium-sized plants. In this paper, a PLC-based supervisory system for operation control of a group of production machines is proposed, and the developed PLC-based system is evaluated by applying it to a computer numerical control machine.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.4
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• pp.149-153
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• 1999

This paper proposes a new security system for the next generation mobile telecommunication system The system is based on the network domain security of the 3GPP. The system provides confidentiality and integrity services in the RLC layer, the second layer of 3GPP. Our system has merits in that it can provide security services without any modification to the 3GPP protocol and has low transmission voerhead. Security algorithm and mode are controlled by the RRC layer, the third layer of 3GPP.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.3-8
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• 2002

An intelligent polishing robot automation system is developed. Automatic Tool Change System(A.T.C.), Tool Posture Angle Control, and Robot Program for Polishing Application are developed and integrated into a robotic system that consists of a robot, pneumatic finding tool, and finding abrasives (papers and special films). A.T.C. is specifically designed to exchange whole grinding tool set for complete unmanned operation. Tool Posture Angle Control is developed to give a certain skew angle rather than right angle to tools on the surface for best finishing results. A.T.C. and Tool Posture Angle Control is controlled by a PC and the robot controller. Also, there have been some considerations on enhancing the performance of the system. Some elastic material is inserted between the grinding pad and the holder for better grinding contact. The robot path data is generated automatically from the NC data of previous machining process.

• Journal of Astronomy and Space Sciences
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• v.27 no.2
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• pp.145-152
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• 2010

In this paper, we developed a local oscillator (LO) system of millimeter wave band receiver for radio astronomy observation. We measured the phase and amplitude drift stability of this LO system. The voltage control oscillator (VCO) of this LO system use the 3 mm band Gunn oscillator. We developed the digital phase locked loop (DPLL) module for the LO PLL function that can be computer-controlled. To verify the performance, we measured the output frequency/power and the phase/amplitude drift stability of the developed module and the commercial PLL module, respectively. We show the good performance of the LO system based on the developed PLL module from the measured data analysis. The test results and discussion will be useful tutorial reference to design the LO system for very long baseline interferometry (VLBI) receiver and single dish radio astronomy receiver at the 3 mm frequency band.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.18-21
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• 2001

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network me provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. In the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shoo's that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.3
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• pp.263-272
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• 2000

In this paper we give some geometric condition for a stochastic nonlinear system and we propose a control method for a stochastic nonlinear system using neural networks. Since a competitive learning neural networks has been developed based on the stochastcic approximation method it is regarded as a stochastic recursive filter algorithm. In addition we provide a filtering and control condition for a stochastic nonlinear system called the perfect filtering condition in a viewpoint of stochastic geometry. The stochastic nonlinear system satisfying the perfect filtering condition is decoupled with a deterministic part and purely semi martingale part. Hence the above system can be controlled by conventional control laws and various intelligent control laws. Computer simulation shows that the stochastic nonlinear system satisfying the perfect filtering condition is controllable and the proposed neural controller is more efficient than the conventional LQG controller and the canonical LQ-Neural controller.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.18-18
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• 2000

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network are provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shows that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.8
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• pp.689-695
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• 2004

This paper presents a robot system that combines computer network and an autonomous mobile robot where RTOS is installed. We propose a wireless communication protocol, and also implement it on the RTOS of the robot system. Main controller of the robot processes the control program as a task type in the real-time operating system. Peripheral devices are driven by the device driver functions with the dependency of the hardware. Because the client and server program was implemented to support the multi-platforms by Java SDK and Java JMF, it is easy to analyze programs, maintain system, and correct the errors in the system. End-user can control a robot with a vision showing remote sight over the Internet in real time, and the robot is moved keeping away from the obstacles by itself and command of the server received from end-user at the local client.