• Title/Summary/Keyword: Data Disturbance

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Development of compression method for fault data of digital protection relay using wavelet transforms (웨이블렛 변환을 이용한 디지털 보호계전기용 고장전류 데이터 압축기법 개발)

  • Choi, Ho-Woong;Kim, Yoon-Hoe;Kim, Byung-Jin;Kim, Bo-In;Kim, Jung-Han
    • Proceedings of the KIEE Conference
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    • 2005.07a
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    • pp.283-285
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    • 2005
  • Wavelet transforms provide basis functions for time-frequency analysis and have properties that are particularly useful for the compression of analogue point on wave transient and disturbance power system signals. This paper evaluates the compression properties of the discrete wavelet transform using actual power system data. The results presented in the paper indicate that reduction ratios up to 10:1 with acceptable distortion are achievable. This paper discussed the application of the reduction method for fault analysis and protection assessment.

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Adaptive control to compensate the modeling error of STT missile (STT 미사일의 모델링 오차 보상을 위한 적응 제어)

  • 최진영;좌동경
    • 제어로봇시스템학회:학술대회논문집
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    • 1996.10b
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    • pp.1292-1295
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    • 1996
  • This paper proposes an adaptive control technique for the autopilot design of STT missile. Dynamics of the missile is highly nonlinear and the equilibrium point is vulnerable to change due to fast maneuvering. Therefore nonlinear control techniques are desirable for the autopilot design of the missile. The nonlinear controller requires the exact model to obtain satisfactory performance. Generally a look-up table is used for the dynamic coefficients of a missile, so there must be coefficients error during actual flight, and the performance of the nonlinear controller using these data can be degraded. The proposed adaptive control technique compensates the nonlinear controller with modeling error resulting from the error of aerodynamic data and disturbance. To investigate the usefulness, the proposed method is applied to autopilot design of STT missile through simulations.

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System Identification of Flexible beam Using Eigensystem Realization Algorithm (Eigensystem Realization Algorithm을 이용한 유연한 빔의 운동방정식 규명)

  • Lee, In-Sung;Lee, Jae-Won;Lee, Soo-Cheol
    • Proceedings of the KSME Conference
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    • 2000.04a
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    • pp.566-572
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    • 2000
  • The System identification is the process of developing or improving a mathematical model of a physical system using experimental data of the input, output and noise relationship. The field of system identification has been an important discipline within the automatic control area. The reason is the requirement that mathematical models having a specified accuracy must be used to apply modem control methods. In this paper, it is confirmed that we can obtain transfer function of flexible beam that is expressed in the forms of identified state-space system matrix A, B, C, D and identified observer gain G using Eigensystem Realization Algorithm including singular value decomposition. And these matrices can be applied to the automatic control. In addition to, it is also confirmed that transfer function can express a system using identified observer gain G, in spite of a noisy data or a periodic disturbance.

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A Study on Error Detection and Diagnosis using Fuzzy Algorithm (퍼지 알고리즘을 이용한 오류 검출 및 진단에 관한 연구)

  • Yu, Byung-Sam;Shin, Doo-Jin;Huh, Uk-Youl;Kim, Jin-Hwan
    • Proceedings of the KIEE Conference
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    • 2000.07d
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    • pp.2485-2487
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    • 2000
  • In this paper, we use a fuzzy algorithm to detect and diagnose the error which is caused by time delay of the computer-controlled system. Generally, a computer-controlled system is composed of computer and process. And they communicate the data each other. In data communication, error occurs by some reasons, such as noise, disturbance, hardware defect, etc. Time delay is one of the reasons. And time delay makes it difficult to distinguish whether the system really has a problem or not. Therefore, we need to detect and diagnose the error from time delay. For difficulty of modeling and ambiguity of classification, we use a fuzzy algorithm. To verify the better performance of the proposed algorithm, we exemplified by some simulation results.

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Subjectivity Study on Smoking Cessation of Korean Adult Males: Q-Methodological Approach (성인 남성의 금연에 대한 주관성 연구: Q 방법론적 접근)

  • Park, Youngrye
    • Journal of Korean Biological Nursing Science
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    • v.21 no.2
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    • pp.141-151
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    • 2019
  • Purpose: The purpose of this study was to identify subjectivity on smoking cessation of Korean adult males, and to provide basic data, for non-smoking policies. Methods: Q-methodology, a method of analyzing subjectivity of each item, was used. Thirty-nine adult males, classified 36 selected Q-statements into normal distribution, using a 9-point scale. Collected data were analyzed, using the pc-QUNAL program. Results: Among the Korean adult males, three types of smoking cessation were identified. The name for Type I was 'pursuit of faith', for Type II, 'factor of relationship disturbance' and for Type III 'ambivalence'. Conclusion: Results of this study indicate that different approaches to intervention on smoking cessation are best served for Korean adult males, based on the three types of smoking cessation, and their characteristics.

Power Quality Warning of High-Speed Rail Based on Multi-Features Similarity

  • Bai, Jingjing;Gu, Wei;Yuan, Xiaodong;Li, Qun;Chen, Bing;Wang, Xuchong
    • Journal of Electrical Engineering and Technology
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    • v.10 no.1
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    • pp.92-101
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    • 2015
  • As one type of power quality (PQ) disturbance sources, high-speed rail (HSR) can have major impacts on the power supply grid. Providing timely and accurate warning information for PQ problems of HSR is important for the safe and stable operation of traction power supply systems and the power supply grid. This study proposes a novel warning approach to identify PQ problems and provide warning prompts based on the monitored data of HSR. To embody the displacement and status change of monitored data, multi-features of different sliding windows are computed. To reflect the relative importance degree of these features in the overall evaluation, an analytic hierarchy process (AHP) is used to analyse the weights of multi-features. Finally, a multi-features similarity algorithm is applied to analyse the difference between monitored data and the reference data of HSR, and PQ warning results based on dynamic thresholds can be analysed to quantify its severity. Cases studies demonstrate that the proposed approach is effective and feasible, and it has now been applied to an actual PQ monitoring platform.

Distance Relaying Algorithm Based on An Adaptive Data Window Using Least Square Error Method (최소자승법을 이용한 적응형 데이터 윈도우의 거리계전 알고리즘)

  • Jeong, Ho-Seong;Choe, Sang-Yeol;Sin, Myeong-Cheol
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.51 no.8
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    • pp.371-378
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    • 2002
  • This paper presents the rapid and accurate algorithm for fault detection and location estimation in the transmission line. This algorithm uses wavelet transform for fault detection and harmonics elimination and utilizes least square error method for fault impedance estimation. Wavelet transform decomposes fault signals into high frequence component Dl and low frequence component A3. The former is used for fault phase detection and fault types classification and the latter is used for harmonics elimination. After fault detection, an adaptive data window technique using LSE estimates fault impedance. It can find a optimal data window length and estimate fault impedance rapidly, because it changes the length according to the fault disturbance. To prove the performance of the algorithm, the authors test relaying signals obtained from EMTP simulation. Test results show that the proposed algorithm estimates fault location within a half cycle after fault irrelevant to fault types and various fault conditions.

Detection of Ecosystem Distribution Plants using Drone Hyperspectral Spectrum and Spectral Angle Mapper (드론 초분광 스펙트럼과 분광각매퍼를 적용한 생태계교란식물 탐지)

  • Kim, Yong-Suk
    • Journal of Environmental Science International
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    • v.30 no.2
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    • pp.173-184
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    • 2021
  • Ecological disturbance plants distributed throughout the country are causing a lot of damage to us directly or indirectly in terms of ecology, economy and health. These plants are not easy to manage and remove because they have a strong fertility, and it is very difficult to express them quantitatively. In this study, drone hyperspectral sensor data and Field spectroradiometer were acquired around the experimental area. In order to secure the quality accuracy of the drone hyperspectral image, GPS survey was performed, and a location accuracy of about 17cm was secured. Spectroscopic libraries were constructed for 7 kinds of plants in the experimental area using a Field spectroradiometer, and drone hyperspectral sensors were acquired in August and October, respectively. Spectral data for each plant were calculated from the acquired hyperspectral data, and spectral angles of 0.08 to 0.36 were derived. In most cases, good values of less than 0.5 were obtained, and Ambrosia trifida and Lactuca scariola, which are common in the experimental area, were extracted. As a result, it was found that about 29.6% of Ambrosia trifida and 31.5% of Lactuca scariola spread in October than in August. In the future, it is expected that better results can be obtained for the detection of ecosystem distribution plants if standardized indicators are calculated by constructing a precise spectral angle standard library based on more data.

Problems of Big Data Analysis Education and Their Solutions (빅데이터 분석 교육의 문제점과 개선 방안 -학생 과제 보고서를 중심으로)

  • Choi, Do-Sik
    • Journal of the Korea Convergence Society
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    • v.8 no.12
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    • pp.265-274
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    • 2017
  • This paper examines the problems of big data analysis education and suggests ways to solve them. Big data is a trend that the characteristic of big data is evolving from V3 to V5. For this reason, big data analysis education must take V5 into account. Because increased uncertainty can increase the risk of data analysis, internal and external structured/semi-structured data as well as disturbance factors should be analyzed to improve the reliability of the data. And when using opinion mining, error that is easy to perceive is variability and veracity. The veracity of the data can be increased when data analysis is performed against uncertain situations created by various variables and options. It is the node analysis of the textom(텍스톰) and NodeXL that students and researchers mainly use in the analysis of the association network. Social network analysis should be able to get meaningful results and predict future by analyzing the current situation based on dark data gained.

A study on the improvement of performance of polishing robot attached to machining center (머시닝센터 장착형 연마 로봇의 성능 향상에 관한 연구)

  • 조영길;이민철;전차수
    • 제어로봇시스템학회:학술대회논문집
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    • 1997.10a
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    • pp.1275-1278
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    • 1997
  • Cutting process has been automated by progress of CNC and CAD/CAM, but polishing process has been depended on only experiential knowledge of expert. To automate the polishing pricess polishing robot with 2 degrees of freedom which is attached to a machining center with 3 degrees of freedom has been developed. this automatic polishing robot is able to keep the polishing tool normal on the curved surface of die to improve a performance of polishing. Polishing task for a curved surface die demands repetitive operation and high precision, but conventional control algorithm can not cope with the problem of disturbance such as a change of load. In this research, we develop robust controller using real time sliding mode algorithm. To obtain gain parameters of sliding model control input, the signal compression method is used to identify polishing robot system. To obtain an effect of 5 degrees of freedom motion, 5 axes NC data for polishing are divided into data of two types for 3 axis machining center and 2 axis polishing are divided into data of two types for 3 axis machining center and 2 axis polishing robot. To find an efficient polishing condition to obtain high quality, various experiments are carried out.

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