• Journal of Industrial Technology
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• v.20 no.B
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• pp.201-206
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• 2000

A priori information of object is needed to control in some well known control methods. But we can't always know a priori information of object in real world. In this paper, the inverted pendulum is simulated as a control task with the goal of learning to balance the pendulum with no a priori information using neural network controller. In contrast to other applications of neural networks to the inverted pendulum task, the performance feedback is unavailable on each training step, appearing only as a failure signal when the pendulum falls or reaches the bound of track. To solve this task, the delayed performance evaluation and the learning of nonlinear of nonlinear functions must be dealt. Reinforcement learning method is used for those issues.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.63
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• pp.65-77
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• 2001

A more technical perspective is needed in estimating the effect of the Manufacturing Process for improving the Productivity, there are many statistical evaluation methods, convenience sampling, frequencies, histogram, QC seven tools, control chart etc. It is more important for the companies to use six sigma to reduce defective and improve the process control than the technical definition as a disciplined quantitative approach for improvement of process control and a new way of quality innovation. Process network analysis is a technique which has the potentiality for a wide use to improve the manufacturing process which other techniques can't be used to analyze effectively. It has some problems to analyze the process with feedback loops. The branch probabilities during quality inspections depend upon the number of times the product has been rejected. This paper presents how to improve the manufacturing process by statistical process control using branch probabilities, Moment Generating Function(MGF) and Sensitivity Equation.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1983-1989
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• 2005

Industrial robots are powerful, extremely accurate multi-jointed systems, but they are heavy and highly rigid because of their mechanical structure and motorization. Therefore, sharing the robot working space with its environment is problematic. A novel pneumatic artificial muscle actuator (PAM actuator) has been regarded during the recent decades as an interesting alternative to hydraulic and electric actuators. Its main advantages are high strength and high power/weight ratio, low cost, compactness, ease of maintenance, cleanliness, readily available and cheap power source, inherent safety and mobility assistance to humans performing tasks. The PAM is undoubtedly the most promising artificial muscle for the actuation of new types of industrial robots such as Rubber Actuator and PAM manipulators. However, some limitations still exist, such as the air compressibility and the lack of damping ability of the actuator bring the dynamic delay of the pressure response and cause the oscillatory motion. In addition, the nonlinearities in the PAM manipulator still limit the controllability. Therefore, it is not easy to realize motion with high accuracy and high speed and with respect to various external inertia loads in order to realize a human-friendly therapy robot To overcome these problems a novel controller, which harmonizes a phase plane switching control method with conventional PID controller and the adaptabilities of neural network, is newly proposed. In order to realize satisfactory control performance a variable damper - Magneto-Rheological Brake (MRB) is equipped to the joint of the manipulator. Superb mixture of conventional PID controller and a phase plane switching control using neural network brings us a novel controller. This proposed controller is appropriate for a kind of plants with nonlinearity uncertainties and disturbances. The experiments were carried out in practical PAM manipulator and the effectiveness of the proposed control algorithm was demonstrated through experiments, which had proved that the stability of the manipulator can be improved greatly in a high gain control by using MRB with phase plane switching control using neural network and without regard for the changes of external inertia loads.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.12
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• pp.6484-6487
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• 2013

This paper reports the direct controller for nonlinear plants using a neural network. The controller was composed of an approximate controller and a neural network auxiliary controller. The approximate controller provides rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not place too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network was trained and the system showed stable performance for the inputs it has been trained for. The simulation results showed that it was quite effective and could realize satisfactory control of the nonlinear system.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.687-690
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• 2003

This paper presents the development of Programmable Logic Controller (PLC) and network to design the redundant control system in order to control the high priority process. The industrial process that cannot be shutdown or the effect of the shutting down takes abundantly damage. In this article, we say that the high priority process. The redundant systems are designed for controlling the high priority process that the control system must have many controllers to instead the main controller when it has some error. This paper we designed the redundancy control system by the advantage of the high-speed communication on the PLC’s network. The temperature control system and the traffic light control system used as the case study. Each example processes consist of two sets of controller. Our scheme we can increase the reliability prevents process down time and reduces the cost of opportunity to loss also.

• Journal of Korean Society for Quality Management
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• v.29 no.4
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• pp.38-53
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• 2001

In resolving industrial quality control problems, a vector of multiple quality characteristic variables is involved rather than a single variable. However, it is not guaranteed that a multivariate control chart based on statistical methods can monitor abnormal signal in case that small changes of relationship between each variables causes abnormal production process. Hence a quality control system for real-time monitoring of the multi-dimensional quality characteristic vector under a multivariate normal process is needed to enhance tile production system quality performance. A pattern analysis approach based on self-organizing map (SOM), an unsupervised learning technique of neural network, is applied to the design of such a quality control system. In this study we present a new material quality control system based on pattern analysis approach and illustrate the effectiveness of proposed system using actual electronic company material data.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.371-372
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• 2013

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.7
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• pp.4482-4488
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• 2014

This paper proposes the creation scheme of a threat map for robot global path planning. The threat map was generated using neural network theory by analyzing the robot's armament state and the menace information of an enemy or obstacle. In addition, the performance of the suggested method was verified using the compared result of the damage amount and existing robot path data.

• Journal of Electrical Engineering and Technology
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• v.2 no.3
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• pp.379-385
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• 2007

Recently, vital devices of the railway signaling systems have been computerized in order to ensure safe train operation. Due to this computerization, we have gradually come to need networking interfaces between these devices. Thus it is important that there be reliable communication links in the signaling systems. Network technologies are applied in the real-time industrial control system, and there are numerous studies to be carried out on the computer network technology for vital control systems such as railway signaling systems. For deploying the studies, we consider costs, reliability, safety assurance technique, compatibility, and etc. In this paper, we propose the Ethernet for railway signaling systems and also precisely describe the computer network characteristics of vital railway signaling systems. Then we demonstrate the experimental results of the proposed network algorithm, which is based on switched Ethernet technology with redundancy scheme.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.1
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• pp.119-124
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• 2010

The MAC layer in ad-hoc network which makes network of nodes without infrastructure for a time has became an issue to reduce delay, allocate fairly bandwidth, control TX/RX power and improve throughput. Specially, the problem to reduce power consumption in ad-hoc network is very important part as ad-hoc devices use the limited battery. For solution of the problem, many power control algorithms, such as distribute power control, PCM (Power Control MAC) and F-PCF (Fragmentation based PCM), are proposed to limit power consumption until now. Although the algorithms are designed to minimize power consumption, the latency communication zone is generated by power control of RX/TX nodes. However the algorithms don't suitably reuse the space. In this paper proposes the algorithm to improve data throughput through Spatial Reuse based on a power control method.