• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2013년도 춘계학술대회 논문집
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• pp.71-72
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• 2013

• 한국정보시스템학회지:정보시스템연구
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• 제17권1호
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• pp.155-171
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• 2008

This thesis proposed a method that connecting step motor to image send board which can acquire image to move and remote controlling via streaming image board of PDA(personal digital assistant) based on embedded Linux which is using wireless network There are three embedded Linux system to embody movable image send board. First, though the wireless network a signal of PDA is transmitted to the board which has embedded Linux and a system which is controlled by the expansion I/O port of the board. Second, it's a system streaming realtime image at a PDA which has embedded Linux. The last is a system which controls a process of image board using TCP/IP communication and image send board at PC. These are the system which can use industrial settings and homes. It can also make use of an embodiment method about travelling image robot.

• 디지털콘텐츠학회 논문지
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• 제19권6호
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• pp.1185-1195
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• 2018

산업제어시스템(ICS, Industrial Control System)은 산업 분야 제어 공정에 대한 감시와 제어를 수행하는 시스템을 말하며 수도, 전력, 가스 등 기반시설에서 응용되고 있다. 최근 ICS에 대해 Brutal Kangaroo, Emotional Simian, stuxnet 3.0 등 사이버공격이 지속적으로 증가하고 있고 이와 같은 보안위험은 인명피해나 막대한 금전적 손실을 초래한다. ICS에 대한 공격 방법 중 제어계층에 대한 공격은 제어명령을 조작해 현장장치계층의 장치를 오작동하게 하는 것이다. 따라서, 본 논문에서는 이에 대한 대응으로 산업제어시스템에서 제어계층과 현장장치 계층사이에 SDN을 적용해서 제어명령의 정상 여부를 판별하는 메커니즘을 제안하고 가상의 제어시스템을 구성해 시뮬레이션 결과를 소개한다.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 춘계학술대회 논문집
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• pp.253-257
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• 2004

Neural networks are works are used in the framework of sensor based tracking control of robot manipulators. They learn by practice movements the relationship between PSD(an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple back propagation networks one of which is selected according to which division(corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.394-396
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• 1997

This paper describes RBF network controller based on fuzzy control rules for intelligent control of nonlinear systems. The proposed scheme is derived from the functional equivalence between RBF networks and fuzzy inference systems. The design procedure of the proposed scheme is realized by first transforming the fuzzy control rules into the parameters of RBF networks. The optimized RBF network controller is then performed through the gradient descent learning mechanism to an error function. The proposed method is rigorously tested using a nonlinear and unstable nonlinear system. Simulation is performed to demonstrate the feasibility and effectiveness of the proposed scheme.

• 산업공학
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• 제7권3호
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• pp.69-76
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• 1994

In this paper, we develop a real-time manufacturing control and information management(POP) system that can support realization of CIM and information network of manufacturing system. The POP system systematically collect informal data that is originated from working area and make an offer collected information to planning side. Especially, this study designs a modular POP terminal that directly extract and process momentary information from field source and provide it to control system on real-time. In addition, we apply developed POP system with open architecture to flexibly and optimally operate the manufacturing system based on CIM.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1205-1210
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• 2004

The 4-20mA analog signal used in the various industrial fields to interface sensor in distributed process control has been replaced with relatively simple digital networks, called "fieldbus, and recently by Ethernet. Significant advances in Internet and computer technology have made it possible to develop an Internet based control, monitoring, and operation scheduling system for heating, ventilation and air-conditioning (HVAC) systems. The seamless integration of data networks with control networks allows access to any control point from anywhere. Field compatible field devices become so-called "smart" devices, capable of executing simple control, diagnostic and maintenance functions and providing bidirectional serial communication to higher level controller. The most important HVAC of BAS has received nationwide attention because of higher portion of more than 40% in building sector energy use and limited resources. This paper presents the Internet-based monitoring and control architecture and development of LonWorks control modules for AHU (air handling units) of HVAC in viewpoint of configuring BAS network. This article addresses issues in architecture section, electronics, embedded processors and software, and internet technologies.

• 산업경영시스템학회지
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• 제44권2호
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• pp.115-123
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• 2021

A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.

• 한국지능시스템학회논문지
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• 제17권7호
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• pp.887-892
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• 2007

본 논문에서는 지능형 서비스 로봇의 네트워크에서 제어의 실시간이 보장되면서 많은 데이터를 처리할 수 있는 개방형 표준 이더넷 호환성을 확보한 산업용 이더넷 프로토콜인 EtherCAT을 기반으로 하여 네트워크의 물리 계층을 100BaseFx인 광케이블 인터페이스 모듈을 설계하고 구현하여 센서 및 모터제어 시스템에 적용하고, 테스트를 통해 지능형 서비스 로봇 내부 네트워크로서의 적합성을 제시하고자 한다.

• 산업경영시스템학회지
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• 제43권1호
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• pp.16-25
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• 2020

Vegetation segmentation in a field color image is a process of distinguishing vegetation objects of interests like crops and weeds from a background of soil and/or other residues. The performance of the process is crucial in automatic precision agriculture which includes weed control and crop status monitoring. To facilitate the segmentation, color indices have predominantly been used to transform the color image into its gray-scale image. A thresholding technique like the Otsu method is then applied to distinguish vegetation parts from the background. An obvious demerit of the thresholding based segmentation will be that classification of each pixel into vegetation or background is carried out solely by using the color feature of the pixel itself without taking into account color features of its neighboring pixels. This paper presents a new pixel-based segmentation method which employs a multi-layer perceptron neural network to classify the gray-scale image into vegetation and nonvegetation pixels. The input data of the neural network for each pixel are 2-dimensional gray-level values surrounding the pixel. To generate a gray-scale image from a raw RGB color image, a well-known color index called Excess Green minus Excess Red Index was used. Experimental results using 80 field images of 4 vegetation species demonstrate the superiority of the neural network to existing threshold-based segmentation methods in terms of accuracy, precision, recall, and harmonic mean.