• 제목/요약/키워드: Network operator

검색결과 393건 처리시간 0.024초

Neural Network를 이용한 고무 타이어의 돌출 문자 인식 (Raised characters rocognition of rubber tires using neural network)

  • 김경민;박중조;김민기;박귀태
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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    • pp.864-869
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    • 1993
  • This paper presents the problem of automatically recognizing embossed or molded characters which are raised from the side wall on rubber tire. In the tire image objects have approximately the same gray-value as the background and because of the tire geometry, illumination of the surface is nonhomogenous. Therefore it is difficult to recognize the raised tire character. In this paper, for describing the process of processing three steps have been proposed: 1) MIN & MAX smoothing operation filter, 2) edge detection algorithm using modified laplacian operator, 3) noise rejection. Afterwards, segmentation is done to attain characters from tire image by projection method. The recognition of the characters in the segmented image is carried out by using multilayered neural network, which is insensitive to the noise.

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신경회로망을 이용한 연삭가공의 트러블 인식 (III) -최적 연삭가공 조건의 설정 - (The Recognition of Grinding Troubles Utilizing the Neural Network(III) - Establishment of Optimal Grinding Conditions-)

  • 곽재섭;송지복
    • 한국정밀공학회지
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    • 제15권2호
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    • pp.162-169
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    • 1998
  • Lacking for the skilled grinding operator possessed of the experiential knowledges in machine shop, there is the just requirement which includes the establishment of the optimal grinding conditions. Accordingly, we attemt to develope the selection system of optimal grinding conditions such as workpiece velocity, depth of cut and wheel velocity and to add the trouble shooting system by means of the neural network. Those systems are robust to the each machine error and environmental unstable state. In addition. we produce the loaming process that is progressed with additional data modified by skilled operators, and excluding is advanced to similarity of input data.

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Machine to Machine Commerce(M2M Commerce) in the New Era of Network Convergence

  • ;이병헌
    • 정보와 통신
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    • 제20권11호
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    • pp.1550-1559
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    • 2003
  • The convergence of fixed and wireless networks in data communication is providing the necessary driver for M2M commerce to take-off. The opportunities provided by M2M Commerce areonly limited by imagination. Automotive Fleet and Freight, Tolling, Water and Power Metering, Supply Chain Management including Asset Management, Remote Monitoring and Diagnostics, Energy Management and Access Control and Security are among the many M2M applications that are currently getting rolled out. ARC Group expects the worldwide solutions market to be worth in excess of US$ 100 billion by 2007. In addition, operator revenues worldwide from the transport of Telematics data alone will rise from US$ 3.5 billion in 2002 to US$ 78 billion by 2007. This paper discusses some of the lifestyle and business opportunities provided by M2M Commerce in the new ear of network convergence. It also provides some case studies to demonstrate the benefits of M2M Commerce across the supply chain. The key focus of the paper is on achieving enhanced lifestyle, cost reduction, improved profitability and enhanced customer relationship management through M2M Commerce.

Time-Delay Neural Network를 이용한 증류탑의 on-line 고장 진단 (On-line fault diagnosis of a distillation column using time-delay neural network)

  • 이상규;박선원
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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    • pp.1109-1114
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    • 1992
  • Modern chemical processes are becoming more complicated. The sophisticated chemical processes have needed the fault diagnosis pxpert systems that can detect and diagnose the fault diagnosis expert systems that can detect and diagnose the faults of some processes and give and advice to the operator in the event of process faults. We present the Time-Delay Neural Network(TDNN) approach for on-line fautl diagnosis. The on-line fault diagnosis system finds the exact origin of the fault of which the symptom is propagated continuously with time. The proposed method has been applied to a pilot distillation column to show the merits and applicability of the TDNN.

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인간의 감정 인식을 위한 신경회로망 기반의 휴먼과 컴퓨터 인터페이스 구현 (Implementation of Human and Computer Interface for Detecting Human Emotion Using Neural Network)

  • 조기호;최호진;정슬
    • 제어로봇시스템학회논문지
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    • 제13권9호
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    • pp.825-831
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    • 2007
  • In this paper, an interface between a human and a computer is presented. The human and computer interface(HCI) serves as another area of human and machine interfaces. Methods for the HCI we used are voice recognition and image recognition for detecting human's emotional feelings. The idea is that the computer can recognize the present emotional state of the human operator, and amuses him/her in various ways such as turning on musics, searching webs, and talking. For the image recognition process, the human face is captured, and eye and mouth are selected from the facial image for recognition. To train images of the mouth, we use the Hopfield Net. The results show 88%$\sim$92% recognition of the emotion. For the vocal recognition, neural network shows 80%$\sim$98% recognition of voice.

An accident diagnosis algorithm using long short-term memory

  • Yang, Jaemin;Kim, Jonghyun
    • Nuclear Engineering and Technology
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    • 제50권4호
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    • pp.582-588
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    • 2018
  • Accident diagnosis is one of the complex tasks for nuclear power plant (NPP) operators. In abnormal or emergency situations, the diagnostic activity of the NPP states is burdensome though necessary. Numerous computer-based methods and operator support systems have been suggested to address this problem. Among them, the recurrent neural network (RNN) has performed well at analyzing time series data. This study proposes an algorithm for accident diagnosis using long short-term memory (LSTM), which is a kind of RNN, which improves the limitation for time reflection. The algorithm consists of preprocessing, the LSTM network, and postprocessing. In the LSTM-based algorithm, preprocessed input variables are calculated to output the accident diagnosis results. The outputs are also postprocessed using softmax to determine the ranking of accident diagnosis results with probabilities. This algorithm was trained using a compact nuclear simulator for several accidents: a loss of coolant accident, a steam generator tube rupture, and a main steam line break. The trained algorithm was also tested to demonstrate the feasibility of diagnosing NPP accidents.

선삭에서 신경망 알고리즘에 의한 칩 형태의 인식과 제어 (Control of Identifier of Chip Form by Adjusting Feedrate Used Neural Network Algorithm)

  • 전재억;하만경;구양
    • 동력기계공학회지
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    • 제4권4호
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    • pp.108-115
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    • 2000
  • The continuous chip in turning operation deteriorates the precision of workpiece and can cause a hazardous condition to operator. Thus the chip form control becomes a very important task for reliable turning process. Using the difference of energy radiated from the chip, the chip form is identified using the neural network of supervised data. The feed mechanism is adjusted in order to break continuous chip according to the result of the chip form recognition and shows a good approach for precision turning operation.

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프리미엄 IP 멀티캐스트 기술 (Premium IP Multicast Technology)

  • 오현우;조기성;김상하
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2006년도 하계종합학술대회
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    • pp.117-118
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    • 2006
  • Recently, the fusion of communication and broadcasting surfaces streaming service such as IPTV with killer application of BcN. In this paper, Premium IP multicast is called as transport service technology that transfer streaming service such as IPTV through integrated, controllable, maintainable network in order to guarantee end-to-end QoS to predefined person. It capacitates billing of multicast service instead of network operator guarantees high quality QoS to subscriber. So, network operators are able to create benefits and find benefit models. The other side, subscribers can use various high quality streaming services.

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Hybrid Priority-based Genetic Algorithm for Multi-stage Reverse Logistics Network

  • Lee, Jeong-Eun;Gen, Mitsuo;Rhee, Kyong-Gu
    • Industrial Engineering and Management Systems
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    • 제8권1호
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    • pp.14-21
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    • 2009
  • We formulate a mathematical model of remanufacturing system as multi-stage reverse Logistics Network Problem (mrLNP) with minimizing the total costs for reverse logistics shipping cost and inventory holding cost at disassembly centers and processing centers over finite planning horizons. For solving this problem, in the 1st and the 2nd stages, we propose a Genetic Algorithm (GA) with priority-based encoding method combined with a new crossover operator called as Weight Mapping Crossover (WMX). A heuristic approach is applied in the 3rd stage where parts are transported from some processing centers to one manufacturer. Computer simulations show the effectiveness and efficiency of our approach. In numerical experiments, the results of the proposed method are better than pnGA (Prufer number-based GA).

환경적 배출량을 고려한 경제급전 문제의 신경회로망 응용 (Environmental Constrained Economic Dispatch Using Neural Network)

  • 이상봉;이재규;김규호;유석구
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1998년도 하계학술대회 논문집 C
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    • pp.1100-1102
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    • 1998
  • This paper presents the Two-Phase Neural Network(TPNN) to slove the Optimal Economic Environmental Dispatch problem of thermal generating units in electric power system. The TPNN, Compared with other Neural Networks, is very accurate and it takes smaller computer time for a optimization problem to converge. In this work, in order to provide useful information to the system operator, we are used the total environmental weight and relative weighting of individual insults(e.g., $SO_2$, $NO_X$ and $CO_2$) also, presented the simulation results of the dispatch changes according to the weights. The Two-Phase Neural Network is tested on a 11-unit 3-pollutant system to prove of effectiveness and applicability.

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