• Title/Summary/Keyword: Prediction System

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Design of HCBKA-Based TSK Fuzzy Prediction System with Error Compensation (HCBKA 기반 오차 보정형 TSK 퍼지 예측시스템 설계)

  • Bang, Young-Keun;Lee, Chul-Heui
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.59 no.6
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    • pp.1159-1166
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    • 2010
  • To improve prediction quality of a nonlinear prediction system, the system's capability for uncertainty of nonlinear data should be satisfactory. This paper presents a TSK fuzzy prediction system that can consider and deal with the uncertainty of nonlinear data sufficiently. In the design procedures of the proposed system, HCBKA(Hierarchical Correlationship-Based K-means clustering Algorithm) was used to generate the accurate fuzzy rule base that can control output according to input efficiently, and the first-order difference method was applied to reflect various characteristics of the nonlinear data. Also, multiple prediction systems were designed to analyze the prediction tendencies of each difference data generated by the difference method. In addition, to enhance the prediction quality of the proposed system, an error compensation method was proposed and it compensated the prediction error of the systems suitably. Finally, the prediction performance of the proposed system was verified by simulating two typical time series examples.

Neuro-Fuzzy Approaches to Ozone Prediction System (뉴로-퍼지 기법에 의한 오존농도 예측모델)

  • 김태헌;김성신;김인택;이종범;김신도;김용국
    • Journal of the Korean Institute of Intelligent Systems
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    • v.10 no.6
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    • pp.616-628
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    • 2000
  • In this paper, we present the modeling of the ozone prediction system using Neuro-Fuzzy approaches. The mechanism of ozone concentration is highly complex, nonlinear, and nonstationary, the modeling of ozone prediction system has many problems and the results of prediction is not a good performance so far. The Dynamic Polynomial Neural Network(DPNN) which employs a typical algorithm of GMDH(Group Method of Data Handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system. The structure of the final model is compact and the computation speed to produce an output is faster than other modeling methods. In addition to DPNN, this paper also includes a Fuzzy Logic Method for modeling of ozone prediction system. The results of each modeling method and the performance of ozone prediction are presented. The proposed method shows that the prediction to the ozone concentration based upon Neuro-Fuzzy approaches gives us a good performance for ozone prediction in high and low ozone concentration with the ability of superior data approximation and self organization.

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Smart Control System Using Fuzzy and Neural Network Prediction System

  • Kim, Tae Yeun;Bae, Sang Hyun
    • Journal of Integrative Natural Science
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    • v.12 no.4
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    • pp.105-115
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    • 2019
  • In this paper, a prediction system is proposed to control the brightness of smart street lamps by predicting the moving path through the reduction of consumption power and information of pedestrian's past moving direction while meeting the function of existing smart street lamps. The brightness of smart street lamps is adjusted by utilizing the walk tracking vector and soft hand-off characteristics obtained through the motion sensing sensor of smart street lamps. In addition, the motion vector is used to analyze and predict the pedestrian path, and the GPU is used for high-speed computation. Pedestrians were detected using adaptive Gaussian mixing, weighted difference imaging, and motion vectors, and motions of pedestrians were analyzed using the extracted motion vectors. The preprocessing process using linear interpolation is performed to improve the performance of the proposed prediction system. Fuzzy prediction system and neural network prediction system are designed in parallel to improve efficiency and rough set is used for error correction.

Prediction System Design based on An Interval Type-2 Fuzzy Logic System using HCBKA (HCBKA를 이용한 Interval Type-2 퍼지 논리시스템 기반 예측 시스템 설계)

  • Bang, Young-Keun;Lee, Chul-Heui
    • Journal of Industrial Technology
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    • v.30 no.A
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    • pp.111-117
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    • 2010
  • To improve the performance of the prediction system, the system should reflect well the uncertainty of nonlinear data. Thus, this paper presents multiple prediction systems based on Type-2 fuzzy sets. To construct each prediction system, an Interval Type-2 TSK Fuzzy Logic System and difference data were used, because, in general, it has been known that the Type-2 Fuzzy Logic System can deal with the uncertainty of nonlinear data better than the Type-1 Fuzzy Logic System, and the difference data can provide more steady information than that of original data. Also, to improve each rule base of the fuzzy prediction systems, the HCBKA (Hierarchical Correlation Based K-means clustering Algorithm) was applied because it can consider correlationship and statistical characteristics between data at a time. Subsequently, to alleviate complexity of the proposed prediction system, a system selection method was used. Finally, this paper analyzed and compared the performances between the Type-1 prediction system and the Interval Type-2 prediction system using simulations of three typical time series examples.

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Development of the Expert Seasonal Prediction System: an Application for the Seasonal Outlook in Korea

  • Kim, WonMoo;Yeo, Sae-Rim;Kim, Yoojin
    • Asia-Pacific Journal of Atmospheric Sciences
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    • v.54 no.4
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    • pp.563-573
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    • 2018
  • An Expert Seasonal Prediction System for operational Seasonal Outlook (ESPreSSO) is developed based on the APEC Climate Center (APCC) Multi-Model Ensemble (MME) dynamical prediction and expert-guided statistical downscaling techniques. Dynamical models have improved to provide meaningful seasonal prediction, and their prediction skills are further improved by various ensemble and downscaling techniques. However, experienced scientists and forecasters make subjective correction for the operational seasonal outlook due to limited prediction skills and biases of dynamical models. Here, a hybrid seasonal prediction system that grafts experts' knowledge and understanding onto dynamical MME prediction is developed to guide operational seasonal outlook in Korea. The basis dynamical prediction is based on the APCC MME, which are statistically mapped onto the station-based observations by experienced experts. Their subjective selection undergoes objective screening and quality control to generate final seasonal outlook products after physical ensemble averaging. The prediction system is constructed based on 23-year training period of 1983-2005, and its performance and stability are assessed for the independent 11-year prediction period of 2006-2016. The results show that the ESPreSSO has reliable and stable prediction skill suitable for operational use.

Development of Comparative Verification System for Reliability Evaluation of Distribution Line Load Prediction Model (배전 선로 부하예측 모델의 신뢰성 평가를 위한 비교 검증 시스템)

  • Lee, Haesung;Lee, Byung-Sung;Moon, Sang-Keun;Kim, Junhyuk;Lee, Hyeseon
    • KEPCO Journal on Electric Power and Energy
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    • v.7 no.1
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    • pp.115-123
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    • 2021
  • Through machine learning-based load prediction, it is possible to prevent excessive power generation or unnecessary economic investment by estimating the appropriate amount of facility investment in consideration of the load that will increase in the future or providing basic data for policy establishment to distribute the maximum load. However, in order to secure the reliability of the developed load prediction model in the field, the performance comparison verification between the distribution line load prediction models must be preceded, but a comparative performance verification system between the distribution line load prediction models has not yet been established. As a result, it is not possible to accurately determine the performance excellence of the load prediction model because it is not possible to easily determine the likelihood between the load prediction models. In this paper, we developed a reliability verification system for load prediction models including a method of comparing and verifying the performance reliability between machine learning-based load prediction models that were not previously considered, verification process, and verification result visualization methods. Through the developed load prediction model reliability verification system, the objectivity of the load prediction model performance verification can be improved, and the field application utilization of an excellent load prediction model can be increased.

Study for Enhanced Train Control System with Intelligent Full Prediction System (지능형열차도착예상정보 시스템을 이용한 열차제어 시스템의 성능향상에 관한 연구)

  • Kim, Yun-Bae;Yoon, Ho-Seok
    • Proceedings of the KSR Conference
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    • 2007.05a
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    • pp.1375-1381
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    • 2007
  • Optimization system for convergence point control is required for train control system, this paper introduces the way of enhanced optimization for convergence point with data of intelligence full prediction system. Also the result of the intelligence full prediction system is useful for train control system at the convergence point and passenger will take more accurate information from the prediction system.

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Design of Hull Residual Life Prediction System Considering Corrosion and Coating (부식과 도장을 고려한 선체잔여수명예측시스템 설계)

  • Park, Seong-Whan;Lee, Han Min
    • Journal of the Society of Naval Architects of Korea
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    • v.50 no.2
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    • pp.104-110
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    • 2013
  • In this paper, the design procedure and results for 'Residual Life Prediction System Considering Corrosion and Coating' are explained, which is one module of 'Life-cycle Management System of Ship and Offshore Plant's' Operation. This 'Residual Life Prediction System' has two main functions; one is residual life prediction function based on probability processing using corrosion measurement data of ship's major structural members, and another is rust rate prediction function based on visual image processing of inspection photos. The analysis of system user requirements and functions are introduced, and the structure and environment of the developed system are explained.

A Movie Rating Prediction System of User Propensity Analysis based on Collaborative Filtering and Fuzzy System (협업적 필터링 및 퍼지시스템 기반 사용자 성향분석에 의한 영화평가 예측 시스템)

  • Lee, Soo-Jin;Jeon, Tae-Ryong;Baek, Gyeong-Dong;Kim, Sung-Shin
    • Journal of the Korean Institute of Intelligent Systems
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    • v.19 no.2
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    • pp.242-247
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    • 2009
  • Recently an intelligent system is developed for the service what users want not a passive system which just answered user's request. This intelligent system is used for personalized recommendation system and representative techniques are content-based and collaborative filtering. In this study, we propose a prediction system which is based on the techniques of recommendation system using a collaborative filtering and a fuzzy system to solve the collaborative filtering problems. In order to verify the prediction system, we used the data that is user's rating about movies. We predicted the user's rating using this data. The accuracy of this prediction system is determined by computing the RMSE(root mean square error) of the system's prediction against the actual rating about the each movie and is compared with the existing system. Thus, this prediction system can be applied to base technology of recommendation system and also recommendation of multimedia such as music and books.

Development of a Multipurpose-Oriented Environmental Prediction Model for Plant Production System - Construction of the Basic System and its Application - (식물생산시스템의 다목적 환경예측 모델의 개발 -기본 시스템 구축 및 응용-)

  • 손정익;이동근;김문기
    • Journal of Bio-Environment Control
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    • v.2 no.2
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    • pp.126-135
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    • 1993
  • Recently, the characteristic of plant production systems in Korea has been changed with the strong trends of integration and large scale, using environmental control techniques. To satisfy this change successfully, first of all, the environmental prediction inside the system must be preceded. While many environmental prediction models for plant production system were developed by many persons, each model cannot be applied to the every situation without the perfect understanding of source codes and the technical modification. The purpose of this study is building the environmental prediction model to predict and evaluate the environment inside the system numerically, and also developing the multipurpose program available for practical design. The model consisted of the basic system model, the cultivation related model and the environmental control related model. The contents of each model are as follows : the basic system model is dealing with thermal and light environments, soil environment and ventilation : the cultivation related model with soil and hydroponic cultures ; and the environmental control related model with thermal curtain and heat exchanging system. The environmental prediction model was developed using a common simulation program, PCSMP, so that it could be easily understood and modified by anyone. Finally, the model was executed and verified through comparison between simulated and measured results for soil culture, and both results showed good agreements.

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