• Title/Summary/Keyword: prediction system

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A study on the Conceptual Design for the Real-time wind Power Prediction System in Jeju (제주 실시간 풍력발전 출력 예측시스템 개발을 위한 개념설계 연구)

  • Lee, Young-Mi;Yoo, Myoung-Suk;Choi, Hong-Seok;Kim, Yong-Jun;Seo, Young-Jun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.59 no.12
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    • pp.2202-2211
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    • 2010
  • The wind power prediction system is composed of a meteorological forecasting module, calculation module of wind power output and HMI(Human Machine Interface) visualization system. The final information from this system is a short-term (6hr ahead) and mid-term (48hr ahead) wind power prediction value. The meteorological forecasting module for wind speed and direction forecasting is a combination of physical and statistical model. In this system, the WRF(Weather Research and Forecasting) model, which is a three-dimensional numerical weather model, is used as the physical model and the GFS(Global Forecasting System) models is used for initial condition forecasting. The 100m resolution terrain data is used to improve the accuracy of this system. In addition, optimization of the physical model carried out using historic weather data in Jeju. The mid-term prediction value from the physical model is used in the statistical method for a short-term prediction. The final power prediction is calculated using an optimal adjustment between the currently observed data and data predicted from the power curve model. The final wind power prediction value is provided to customs using a HMI visualization system. The aim of this study is to further improve the accuracy of this prediction system and develop a practical system for power system operation and the energy market in the Smart-Grid.

A Study on development of short term electric load prediction system with the genetic algorithm and the fuzzy system (유전자알고리즘과 퍼지시스템을 이용한 단기부하예측 시스템 개발에 관한 연구)

  • Kang, Hwan-Il;Jang, Woo-Seok
    • Journal of the Korean Institute of Intelligent Systems
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    • v.16 no.6
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    • pp.730-735
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    • 2006
  • This paper proposes a time series prediction method for the short term electrical load will) the fuzzy system and the genetic algorithm. At first, we obtain the optimal fuzzy membership function using the genetic algorithm. With the optimal fuzzy rules and its input differences, a better time prediction system may be obtained. We obtain good results for the time prediction of the short term electric load by the proposed algorithm. In addition we implement the graphic user interface for the proposed algorithms. Finally, we implement the regional prediction system for the electric load.

CNN-LSTM based Wind Power Prediction System to Improve Accuracy (정확도 향상을 위한 CNN-LSTM 기반 풍력발전 예측 시스템)

  • Park, Rae-Jin;Kang, Sungwoo;Lee, Jaehyeong;Jung, Seungmin
    • New & Renewable Energy
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    • v.18 no.2
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    • pp.18-25
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    • 2022
  • In this study, we propose a wind power generation prediction system that applies machine learning and data mining to predict wind power generation. This system increases the utilization rate of new and renewable energy sources. For time-series data, the data set was established by measuring wind speed, wind generation, and environmental factors influencing the wind speed. The data set was pre-processed so that it could be applied appropriately to the model. The prediction system applied the CNN (Convolutional Neural Network) to the data mining process and then used the LSTM (Long Short-Term Memory) to learn and make predictions. The preciseness of the proposed system is verified by comparing the prediction data with the actual data, according to the presence or absence of data mining in the model of the prediction system.

Development and Application of Protein-Protein interaction Prediction System, PreDIN (Prediction-oriented Database of Interaction Network)

  • 서정근
    • Proceedings of the Korean Society for Bioinformatics Conference
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    • 2002.06a
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    • pp.5-23
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    • 2002
  • Motivation: Protein-protein interaction plays a critical role in the biological processes. The identification of interacting proteins by bioinformatical methods can provide new lead In the functional studies of uncharacterized proteins without performing extensive experiments. Results: Protein-protein interactions are predicted by a computational algorithm based on the weighted scoring system for domain interactions between interacting protein pairs. Here we propose potential interaction domain (PID) pairs can be extracted from a data set of experimentally identified interacting protein pairs. where one protein contains a domain and its interacting protein contains the other. Every combinations of PID are summarized in a matrix table termed the PID matrix, and this matrix has proposed to be used for prediction of interactions. The database of interacting proteins (DIP) has used as a source of interacting protein pairs and InterPro, an integrated database of protein families, domains and functional sites, has used for defining domains in interacting pairs. A statistical scoring system. named "PID matrix score" has designed and applied as a measure of interaction probability between domains. Cross-validation has been performed with subsets of DIP data to evaluate the prediction accuracy of PID matrix. The prediction system gives about 50% of sensitivity and 98% of specificity, Based on the PID matrix, we develop a system providing several interaction information-finding services in the Internet. The system, named PreDIN (Prediction-oriented Database of Interaction Network) provides interacting domain finding services and interacting protein finding services. It is demonstrated that mapping of the genome-wide interaction network can be achieved by using the PreDIN system. This system can be also used as a new tool for functional prediction of unknown proteins.

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RAM Prediction of Signaling Interlocking System for AREX (공항철도 신호시스템 전자연동장치에 대한 RAM 예측)

  • Song, Mi-Ok;Lim, Sung-Soo;Lee, Chang-Hwan;Kwon, Min-Hyuk
    • Proceedings of the KSR Conference
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    • 2007.05a
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    • pp.255-261
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    • 2007
  • In this paper we introduce the method, procedure and result of RAM prediction for interlocking system which is applied for AREX signaling system. For RAM prediction, we breakdown the interlocking system to the LRU level and select the LRUs of which failure can cause the service delay. The prediction of reliability is based on the Reliability Block Diagram which is the functional diagram composed of selected LRUs and finally, the availability of interlocking system is estimated from the combination of reliability and maintainability.

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Developing an User Location Prediction Model for Ubiquitous Computing based on a Spatial Information Management Technique

  • Choi, Jin-Won;Lee, Yung-Il
    • Architectural research
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    • v.12 no.2
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    • pp.15-22
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    • 2010
  • Our prediction model is based on the development of "Semantic Location Model." It embodies geometrical and topological information which can increase the efficiency in prediction and make it easy to manipulate the prediction model. Data mining is being implemented to extract the inhabitant's location patterns generated day by day. As a result, the self-learning system will be able to semantically predict the inhabitant's location in advance. This context-aware system brings about the key component of the ubiquitous computing environment. First, we explain the semantic location model and data mining methods. Then the location prediction model for the ubiquitous computing system is described in details. Finally, the prototype system is introduced to demonstrate and evaluate our prediction model.

Development of the Drop-outs Prediction Model for Intelligent Drop-outs Prevention System

  • Song, Mi-Young
    • Journal of the Korea Society of Computer and Information
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    • v.22 no.10
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    • pp.9-17
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    • 2017
  • The student dropout prediction is an indispensable for many intelligent systems to measure the educational system and success rate of all university. Therefore, in this paper, we propose an intelligent dropout prediction system that minimizes the situation by adopting the proactive process through an effective model that predicts the students who are at risk of dropout. In this paper, the main data sets for students dropout predictions was used as questionnaires and university information. The questionnaire was constructed based on theoretical and empirical grounds about factor affecting student's performance and causes of dropout. University Information included student grade, interviews, attendance in university life. Through these data sets, the proposed dropout prediction model techniques was classified into the risk group and the normal group using statistical methods and Naive Bays algorithm. And the intelligence dropout prediction system was constructed by applying the proposed dropout prediction model. We expect the proposed study would be used effectively to reduce the students dropout in university.

Marine Disasters Prediction System Model Using Marine Environment Monitoring (해양환경 모니터링을 이용한 해양재해 예측 시스템 모델)

  • Park, Sun;Lee, Seong Ro
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.38C no.3
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    • pp.263-270
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    • 2013
  • Recently, the prediction and analysis technology of marine environment are actively being studied since the ocean resources in the world is taken notice. The prediction of marine disaster by automatic collecting marine environment data and analyzing the collected data can contribute to minimized the damages with respect to marine pollution of oil spill and fisheries damage by red tide blooms and marine environment upsets. However the studies of the marine environment monitoring and analysis system are limited in South Korea. In this paper, we study the marine disasters prediction system model to analyze collection marine information of out sea and near sea. This paper proposes the models for the marine disasters prediction system as communication system model, a marine environment data monitoring system model, prediction and analyzing system model, and situations propagation system model. The red tide prediction model and summarizing and analyzing model is proposed for prediction and analyzing system model.

Development of an AI-based remaining trip time prediction system for nuclear power plants

  • Sang Won Oh;Ji Hun Park;Hye Seon Jo;Man Gyun Na
    • Nuclear Engineering and Technology
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    • v.56 no.8
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    • pp.3167-3179
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    • 2024
  • In abnormal states of nuclear power plants (NPPs), operators undertake mitigation actions to restore a normal state and prevent reactor trips. However, in abnormal states, the NPP condition fluctuates rapidly, which can lead to human error. If human error occurs, the condition of an NPP can deteriorate, leading to reactor trips. Sudden shutdowns, such as reactor trips, can result in the failure of numerous NPP facilities and economic losses. This study develops a remaining trip time (RTT) prediction system as part of an operator support system to reduce possible human errors and improve the safety of NPPs. The RTT prediction system consists of an algorithm that utilizes artificial intelligence (AI) and explainable AI (XAI) methods, such as autoencoders, light gradient-boosting machines, and Shapley additive explanations. AI methods provide diagnostic information about the abnormal states that occur and predict the remaining time until a reactor trip occurs. The XAI method improves the reliability of AI by providing a rationale for RTT prediction results and information on the main variables of the status of NPPs. The RTT prediction system includes an interface that can effectively provide the results of the system.

AP, IP Prediction For Corpus-based Korean Text-To-Speech (코퍼스 방식 음성합성에서의 개선된 운율구 경계 예측)

  • Kwon, O-Hil;Hong, Mun-Ki;Kang, Sun-Mee;Shin, Ji-Young
    • Speech Sciences
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    • v.9 no.3
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    • pp.25-34
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    • 2002
  • One of the most important factor in the performance of Korean text-to-speech system is the prediction of accentual and intonational phrase boundary. The previous method of prediction shows only the 75-85% which is not proper in the practical and commercial system. Therefore, more accurate prediction must be needed in the practical system. In this study, we propose the simple and more accurate method of the prediction of AP, IP.

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