• Title, Summary, Keyword: Off-line mapping

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PMCR-A Power Mapping and Calibration Routing for 600 MWe CANDU-PHW Reactors

  • Oh, Se-Ki;G.Kugler
    • Nuclear Engineering and Technology
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    • v.11 no.4
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    • pp.275-286
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    • 1979
  • In 600 MWe CANDU-PHW reactors PMCR will be used on-site for calibration of the regional overpower system. PMCR will be executed off-line in one of the station computers. The program calculates accurate channel power maps by incorporating a fuel turnup dependent flux to power conversion algorithm. Fuel turnup is calculated by PMCR, hence it is independent of other software. Extensive comparisons with the uniform flux/power conversion approximations were made. Significant improvements in power mapping accuracy are achieved with PMCR.

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Kinematic jacobian uncertainty compensation using neural network (신경회로망을 이용한 기구학적 자코비안의 불확실성 보상 알고리즘)

  • Jung, Seul
    • 제어로봇시스템학회:학술대회논문집
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    • pp.1820-1823
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    • 1997
  • For the Cartesian space position controlled robot, it is required to have the accurate mapping from the Cartesian space to the joint space in order to command the desired joint trajectories correctly. since the actual mapping from Cartesian space to joint space is obtained at the joint coordinate not at the actuator coordinate, uncertainty in Jacobian can be present. In this paper, two feasible neural network schemes are proposed to compensate for the kinematic Jacobian uncertainties. Uncertainties in Jacobian can be compensated by identifying either actuator Jacobian off-line or the inverse of that in on-line fashion. the case study of the stenciling robot is examined.

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A Study on Dynamic Security Assessment by using the Data of Line Power Flows (선로조류를 이용한 전력계통 동태 안전성 평가 연구)

  • Lee, Kwang-Ho
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.48 no.2
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    • pp.107-114
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    • 1999
  • This paper presents an application of artificial neural networks(ANN) to assess the dynamic security of power systems. The basic role of ANN is to provide assessment of the system's stability based on training samples from off-line analysi. The critical clearing time(CCT) is an attribute which provides significant information about the quality of the post-fault system behaviour. The function of ANN is a mapping of the pre-fault, fault-on, and post-fault system conditions into the CCT's. In previous work, a feed forward neural network is used to learn this mapping by using the generation outputs during the fault as the input data. However, it takes significant calculation time to make the input data through the network reduction at a fault as the input data. However, it takes significant calculation time to make the input data through the network reduction at a fault considered. In order to enhance the speed of security assessment, the bus data and line powers are used as the input data of the ANN in thil paper. Test results show that the proposed neural networks have the reasonable accuracy and can be used in on-line security assenssment efficiently.

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A Framework for Wide-area Monitoring of Tree-related High Impedance Faults in Medium-voltage Networks

  • Bahador, Nooshin;Matinfar, Hamid Reza;Namdari, Farhad
    • Journal of Electrical Engineering and Technology
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    • v.13 no.1
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    • pp.1-10
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    • 2018
  • Wide-area monitoring of tree-related high impedance fault (THIF) efficiently contributes to increase reliability of large-scaled network, since the failure to early location of them may results in critical lines tripping and consequently large blackouts. In the first place, this wide-area monitoring of THIF requires managing the placement of sensors across large power grid network according to THIF detection objective. For this purpose, current paper presents a framework in which sensors are distributed according to a predetermined risk map. The proposed risk map determines the possibility of THIF occurrence on every branch in a power network, based on electrical conductivity of trees and their positions to power lines which extracted from spectral data. The obtained possibility value can be considered as a weight coefficient assigned to each branch in sensor placement problem. The next step after sensors deployment is to on-line monitor based on moving data window. In this on-line process, the received data window is evaluated for obtaining a correlation between low frequency and high frequency components of signal. If obtained correlation follows a specified pattern, received signal is considered as a THIF. Thereafter, if several faulted section candidates are found by deployed sensors, the most likely location is chosen from the list of candidates based on predetermined THIF risk map.

A Study on Intelligent On-line Tool Conditon Monitoring System for Turning Operations (선삭공작을 위한 지능형 실시간 공구 감시 시스템에 관한 연구)

  • Choe, Gi-Hong;Choe, Gi-Sang
    • Journal of the Korean Society for Precision Engineering
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    • v.9 no.4
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    • pp.22-35
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    • 1992
  • In highly automated machining centers, intelligent sensor fddeback systems are indispensable on order to monitor their operations, to ensure efficient metal removal, and to initate remedial action in the event of accident. In this study, an on-line tool wear detection system for thrning operations is developed, and experimentally evaluated. The system employs multiple sensors and the signals from these sensors are processed using a multichannel autoegressive (AR) series model. The resulting output from the signal processing block is then fed to a previously tranied artificial neural network (multiayered perceptron) to make a final decision on the state of the cutting tool. To learn the necessary input/output mapping for tool wear detection, the weithts and thresholds of the network are adjusted according to the back propagation (BP) method during off-line training. The results of experimental evaluation show that the system works well over a wide range of cutting conditions, and the ability of the system to detect tool wear is improved due to the generalization, fault-tolearant and self-ofganizing properties of the neural network.

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OBSERVATIONAL TEST STUDY OF TRAO OUTER GALAXY SURVEY COMPARING TO FCRAO OUTER GALAXY SURVEY (대덕전파천문대와 FCRAO의 외은하탐사 비교관측연구)

  • Lee, Y.;Jung, J.H.;Kang, H.W.;Lee, C.H.;Kim, H.G.;Kim, I.S;Kim, B.G.
    • Publications of The Korean Astronomical Society
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    • v.25 no.1
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    • pp.23-28
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    • 2010
  • We present results of a test-study of the large-scale survey using the multi-beam receiver system recently installed on the 14 m telescope at Taeduk Radio Astronomy Observatory (TRAO). We have tested several modes of mapping, and found suitable (time-saving) mapping parameters of 'ON-SOURCE' = 8, 'OFF-SOURCE' = 1 when using 'RPT' = 3 as a position-switching mode. We observed 504 spectra towards the NGC 7538, a star forming molecular cloud in the transition of J = 1 - 0 of $^{12}CO$. From the Outer Galaxy Survey database (Heyer et al., 1998) we obtained 504 spectra for the same region. We compared integrated intensities, line profiles of two databases, and found that they are consistent to each other. From the intensity ratio of these two databases we also found that the value of forward spillover scattering of the TRAO telescope system is 0.58.

A New Study on Vibration Data Acquisition and Intelligent Fault Diagnostic System for Aero-engine

  • Ding, Yongshan;Jiang, Dongxiang
    • Proceedings of the Korean Society of Propulsion Engineers Conference
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    • pp.16-21
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    • 2008
  • Aero-engine, as one kind of rotating machinery with complex structure and high rotating speed, has complicated vibration faults. Therefore, condition monitoring and fault diagnosis system is very important for airplane security. In this paper, a vibration data acquisition and intelligent fault diagnosis system is introduced. First, the vibration data acquisition part is described in detail. This part consists of hardware acquisition modules and software analysis modules which can realize real-time data acquisition and analysis, off-line data analysis, trend analysis, fault simulation and graphical result display. The acquisition vibration data are prepared for the following intelligent fault diagnosis. Secondly, two advanced artificial intelligent(AI) methods, mapping-based and rule-based, are discussed. One is artificial neural network(ANN) which is an ideal tool for aero-engine fault diagnosis and has strong ability to learn complex nonlinear functions. The other is data mining, another AI method, has advantages of discovering knowledge from massive data and automatically extracting diagnostic rules. Thirdly, lots of historical data are used for training the ANN and extracting rules by data mining. Then, real-time data are input into the trained ANN for mapping-based fault diagnosis. At the same time, extracted rules are revised by expert experience and used for rule-based fault diagnosis. From the results of the experiments, the conclusion is obvious that both the two AI methods are effective on aero-engine vibration fault diagnosis, while each of them has its individual quality. The whole system can be developed in local vibration monitoring and real-time fault diagnosis for aero-engine.

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Quantitative Proteomics Towards Understanding Life and Environment

  • Choi, Jong-Soon;Chung, Keun-Yook;Woo, Sun-Hee
    • Korean Journal of Environmental Agriculture
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    • v.25 no.4
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    • pp.371-381
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    • 2006
  • New proteomic techniques have been pioneered extensively in recent years, enabling the high-throughput and systematic analyses of cellular proteins in combination with bioinformatic tools. Furthermore, the development of such novel proteomic techniques facilitates the elucidation of the functions of proteins under stress or disease conditions, resulting in the discovery of biomarkers for responses to environmental stimuli. The ultimate objective of proteomics is targeted toward the entire proteome of life, subcellular localization biochemical activities, and the regulation thereof. Comprehensive analysis strategies of proteomics can be classified into three categories: (i) protein separation via 2-dimensional gel electrophoresis (2-DE) or liquid chromatography (LC), (ii) protein identification via either Edman sequencing or mass spectrometry (MS), and (iii) proteome quantitation. Currently, MS-based proteomics techniques have shifted from qualitative proteome analysis via 2-DE or 2D-LC coupled with off-line matrix assisted laser desorption ionization (MALDI) and on-line electrospray ionization (ESI) MS, respectively, toward quantitative proteome analysis. In vitro quantitative proteomic techniques include differential gel electrophoresis with fluorescence dyes. protein-labeling tagging with isotope-coded affinity tags, and peptide-labeling tagging with isobaric tags for relative and absolute quantitation. In addition, stable isotope-labeled amino acids can be in vivo labeled into live culture cells via metabolic incorporation. MS-based proteomics techniques extend to the detection of the phosphopeptide mapping of biologically crucial proteins, which ale associated with post-translational modification. These complementary proteomic techniques contribute to our current understanding of the manner in which life responds to differing environment.

Implementation of a Virtual Training System on Gas Safety

  • Wouseok Jou;Tae-sik Lim;Kyong-sik Kang;Tae-ok Kim
    • Proceedings of the Safety Management and Science Conference
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    • pp.1-5
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    • 2000
  • With the advent of the internet era, web-based virtual training system is gaining its importance in recent years. Because of the fact that the training can take place in any place and at any time, the virtual system is now replacing many of the conventional off-line classes. Hardware environments such as communication bandwidth and computer performance gets fast enough to accommodate the virtual education. Based on the observations on current virtual training system, this paper proposes three critical design rules required when developing a new virtual training system: i) With conceptual mapping, the menu hierarchy can be organized in a clear-cut manner, ii) Extensive use of multimedia tools can help students keep their attention to the lecture materials, and iii) Provision of interaction mechanisms helps students to gain their identity and motivation.

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A Study on the Diagnosis of Cutting Tool States Using Cutting Conditions and Cutting Force Parameters(II) -Decision Making- (절삭조건과 절삭력 파라메타를 이용한 공구상태 진단에 관한 연구(II) -의사결정 -)

  • 정진용;서남섭
    • Journal of the Korean Society for Precision Engineering
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    • v.15 no.4
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    • pp.105-110
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    • 1998
  • In this study, statistical and neural network methods were used to recognize the cutting tool states. This system employed the tool dynamometer and cutting force signals which are processed from the tool dynamometer sensor using linear discriminent function. To learn the necessary input/output mapping for turning operation diagnosis, the weights and thresholds of the neural network were adjusted according to the error back propagation method during off-line training. The cutting conditions, cutting force ratios and statistical values(standard deviation, coefficient of variation) attained from the cutting force signals were used as the inputs to the neural network. Through the suggested neural network a cutting tool states may be successfully diagnosed.

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