• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1166-1175
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• 2021

We investigate the current situation and development trend of domestic smart city and emergency vehicle priority signal control system analyzing the existing effectiveness of 1) emergency vehicle priority signal control system and 2) control emergency vehicle priority signal, based on domestic and foreign prior research for signal control system security. The effectiveness of time reduction was analyzed through actual application and test operation to emergency vehicles after establishing the system. In addition, for security management and stable service of real-time signal system control we propose improvement for the technical control items of the ISMS-P certification system to secure golden time to protect citizens' precious lives and property in case of emergency by classifying and mapping the existing ISMS-P certification system and the Korea Internet & Security Agency's cyber security guide according to the items of security threats.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.5
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• pp.116-124
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• 2012

In this paper, we propose an efficient lighting control system design for AVR based LSDM control. This paper, an efficient lighting control system design for LSDM control be design divided as the signal control part for I/O data bus and the timer/counter part for clock signal control according to operating conditions. LSDM control logic be optimization to PORTx and DDRx register by specifying the logical value of each bit for effective control signal processing. And, the LSDM control signal by lighting control program execution of ATmega128 be designed to be LSDM lighting control by control logic operating. In this paper, a proposed lighting control system were measured to power loss rate to proved the power loss reduction about lighting status of LSDM control logic by download the lighting control program to system through serial from host PC. As a measurement result, a proposed lighting control system than the existing lighting control system were proved to be effective to the overall power consumption reduction.

• Journal of Engineering Education Research
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• v.19 no.2
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• pp.3-7
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• 2016

A flow control system was designed and fabricated to control the flow rate of liquid through the pipe. This control system was composed of hardwares and software, hardwares as controller, gate valve, orifice meter and data aquisition board and software as National instruments Labview program. Control of flow rate was executed by adjusting the pneumatic valve located at the center of pipe line based on the control signal generated by LabView PID control algorithm, which converts analog signal measured by pressure difference of orifice to digital signal to adjust pneumatic valve. For the controller setup Ziegler-Nichols tuning technique was applied and control performances were investigated for not only the disturbance but also the set point changes. Developed system showed good control performances in flow control enough to use as teaching tool of feedback control theory and practice in university, and also as industrial application.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.8
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• pp.495-497
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• 2005

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a neural network used for identification of the process dynamics of s signal input and signal output system and it was shown that this method offered superior capability over the conventional back propagation algorithm. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal estimate input signal about controller using neural networks.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.18-20
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• 2005

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a neural network used for identification of the process dynamics of s signal input and signal output system and it was shown that this method offered superior capability over the conventional back propagation algorithm. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal estimate input signal about controller using neural networks.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.3
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• pp.54-62
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• 2002

This paper describes a development of the integrated controller system for car electrical signal and sensor input/output control with CAN communication protocol. In order to improve the system reliability and effectiveness for the conventional controller using the wiring harness, a detailed integrated control system is introduced and discussed. The CAN communication protocol is a robust control method with serial bus system for the control of distributed module in the multiplexed network. Therefore, this has high reliability and flexibility in the overall control system implementation. This paper proposes an integrated system with high reliability and stability for control of various car signal, and evaluates the effectiveness of the system using the actual implementation. For these purposes, after a brief of the main features of the CAN will be addressed, this paper presents the result of development of the integrated hardware system and overall control program.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.2
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• pp.204-210
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• 2018

As the 4th industrial revolution and artificial intelligence technology develop, it is expected that there will be a revolutionary changes in the security robot. However, artificial intelligence system requires enormous hardwares for tremendous computing loads, and there are many challenges that need to be addressed more technologically. This paper introduces precise tracking control technique of autonomous system that need to move repetitive paths for security purpose. The input feedforward signal is generated by using the inverse based iterative learning control theory for the 2 input 2 output nonminimum-phase system which was difficult to overcome by the conventional feedback control system. The simulation results of the input signal generation and precision tracking of given path corresponding to the repetition rate of extreme, such as bandwidth of the system, shows the efficacy of suggested techniques and possibility to be used in military security purposes.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.142-145
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• 2003

Many control applications using Neural Network need a priori information about the objective system. But it is impossible to get exact information about the objective system in real world. To solve this problem, several control methods were proposed. Reinforcement learning control using neural network is one of them. Basically reinforcement learning control doesn't need a priori information of objective system. This method uses reinforcement signal from interaction of objective system and environment and observable states of objective system as input data. But many methods take too much time to apply to real-world. So we focus on faster learning to apply reinforcement learning control to real-world. Two data types are used for reinforcement learning. One is reinforcement signal data. It has only two fixed scalar values that are assigned for each success and fail state. The other is observable state data. There are infinitive states in real-world system. So the number of observable state data is also infinitive. This requires too much learning time for applying to real-world. So we try to reduce the number of observable states by classification of states with Self-Organizing Map. We also use neural dynamic programming for controller design. An inverted pendulum on the cart system is simulated. Failure signal is used for reinforcement signal. The failure signal occurs when the pendulum angle or cart position deviate from the defined control range. The control objective is to maintain the balanced pole and centered cart. And four states that is, position and velocity of cart, angle and angular velocity of pole are used for state signal. Learning controller is composed of serial connection of Self-Organizing Map and two Multi-layer Feed-Forward Neural Networks.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.1
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• pp.42-58
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• 2008

Nowadays the installation of Real-time Traffic Signal Control system is gradually spread, in order to solve the traffic problem which become serious. The most important thing are reliability of data collection and functionality of system in Real-time Traffic Signal Control System. But, the evaluation for those introduction system are defective after system constructing. So, many systems are not working properly to those systems's primarily purpose. This study is executed expansion through field test and analysis which check performance and advise of system operation. It has purpose to establish of the maintenance system of Real-time Traffic Signal Control system. As the result of analysis, we could find the several problems in this study. So, we also could guess that the effective maintenance systems of the Real-time Traffic Signal Control system is necessary within few years.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.4
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• pp.368-374
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• 2011

This paper proposes a robot control human interface using Markov model (HMM) based hand signal recognizer. The command receiving humanoid robot sends webcam images to a client computer. The client computer then extracts the intended commanding hum n's hand motion descriptors. Upon the feature acquisition, the hand signal recognizer carries out the recognition procedure. The recognition result is then sent back to the robot for responsive actions. The system performance is evaluated by measuring the recognition of '48 hand signal set' which is created randomly using fundamental hand motion set. For isolated motion recognition, '48 hand signal set' shows 97.07% recognition rate while the 'baseline hand signal set' shows 92.4%. This result validates the proposed hand signal recognizer is indeed highly discernable. For the '48 hand signal set' connected motions, it shows 97.37% recognition rate. The relevant experiments demonstrate that the proposed system is promising for real world human-robot interface application.