• Title/Summary/Keyword: 확률 모델

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An Efficient Location Management Scheme for High-speed Mobile Nodes (고속으로 이동하는 노드들을 위한 효율적인 위치 갱신 기법)

  • 송의성;길준민;황종선
    • Journal of KIISE:Information Networking
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    • v.30 no.5
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    • pp.581-594
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    • 2003
  • Recently, a location management is being more important in mobile communication systems due to an explosive increase of mobile users. Current systems have used a concept of location area. Based on this concept, a mobile user performs a location update whenever it moves into a new location area. However, this scheme can not avoid unnecessary location updates when a mobile user moves around with high movement rate as compared to call arrival rate. That results in tremendous location management cost. To overcome this drawback, our proposal divides service areas into two sets: One is a set of areas that mobile users move with high speed and another is a set of areas that they move with low speed. After establishing these two sets, this paper employs different location tracking schemes for each sets. Generally, most mobile users with high speed have a low CMR and a regular direction until they arrive at their destination. Using such the moving behavior, systems can predict a mobile user's next location area in advance. When the mobile user moves into the predicted location, our proposal does not perform a location update. Thus, it can reduce overall location management cost. The Markov model is used to analyze the performance of our proposal. Using the model, this paper compares our proposal with IS-41 and TLA. The analytic results show that as CMR grows lower, an overall cost of our proposal becomes less, particularly if a mobile user frequently moves into the specific location are predicted by mobile systems. Also, our proposal has a better performance than other two schemes when the communication cost between HLR and VLR is high.

Predicting Cooperative Relationships between Engineering Companies in World Bank's ODA Projects (세계은행 공적개발원조사업의 엔지니어링 기업 간 협력관계 예측모델 개발)

  • Yu, Youngsu;Koo, Bonsang;Lee, Kwanhoon;Han, Seungheon
    • Korean Journal of Construction Engineering and Management
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    • v.20 no.6
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    • pp.107-116
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    • 2019
  • Korean construction engineering firms want to pave the way for expansion of overseas markets through the World Bank's Official Development Assistance (ODA) projects as a way to improve their overseas project performance. However, since the World Bank project competes with global companies for limited projects, building partnerships with suitable business partners is essential to gain an upper hand in bidding competition and meet the institutional conditions of the recipient country. In this regard, many network studies have been conducted in the past through Social Network Analysis (SNA), but few have been analyzed based on the process of changes in the network. So, This study collected winning data from the three Southeast Asian countries that ended after the World Bank's ODA project performed smoothly, and established a learning-based link prediction model that reflected the dynamic nature of the network. As a result, the 11 main variables acting on building a cooperative relationship between winning companies were derived and the effect of each variables on the probability value of cooperation between individual links was identified.

Quantitative Risk Analysis for Railway Tunnels (철도터널 화재에 대한 정량적 위험도 분석)

  • Park, Jung Hyun;Shim, Cha Sang
    • Journal of the Korean Society for Railway
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    • v.20 no.3
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    • pp.400-412
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    • 2017
  • What is particularly noteworthy from Korean and foreign railway tunnel disaster prevention standards is that for the sake of rapid evacuation, more stringent standards for provision of evacuation passages, which require high cost, are being applied. Korean standards stipulate that passage installation should be determined in accordance with the level of risk through a QRA analysis of each tunnel with 1km or longer length. As, however, detailed application criteria as fire occurrence probability, fire occurrence scenario, size of fires and evaluation criteria for level of social risk are not available, additional costs may be incurred due to excessive design. Thus, standards of an appropriate level need to be established. With this backdrop, this study selects detailed application conditions of a reasonable and appropriate level through a study and analysis of relevant documents and analyzes the maximum length of tunnels to which the application of evacuation passages, or the application major evacuation promotion facilities, can be relaxed, together with a QRA analysis of model tunnels (for high speed rail) with different tunnel lengths. In addition, the QRA results on tunnels, including those on the Honam high-speed rail, and analysis results for the model tunnels, are compiled, ; the ultimate results are compared with Korean and other countries' standards related to evacuation promotion facilities, As a result, The appropriateness of application standards are reviewed. These results are expected to be utilized as basic material for establishing a reasonable disaster prevention plan that will consider safety and economies.

Analysis for Applicability of Differential Evolution Algorithm to Geotechnical Engineering Field (지반공학 분야에 대한 차분진화 알고리즘 적용성 분석)

  • An, Joon-Sang;Kang, Kyung-Nam;Kim, San-Ha;Song, Ki-Il
    • Journal of the Korean Geotechnical Society
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    • v.35 no.4
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    • pp.27-35
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    • 2019
  • This study confirmed the applicability to the field of geotechnical engineering for relatively complicated space and many target design variables in back analysis. The Sharan's equation and the Blum's method were used for the tunnel field and the retaining wall as a model for the multi-variate problem of geotechnical engineering. Optimization methods are generally divided into a deterministic method and a stochastic method. In this study, Simulated Annealing Method (SA) was selected as a deterministic method and Differential Evolution Algorithm (DEA) and Particle Swarm Optimization Method (PSO) were selected as stochastic methods. The three selected optimization methods were compared by applying a multi-variate model. The problem of deterministic method has been confirmed in the multi-variate back analysis of geotechnical engineering, and the superiority of DEA can be confirmed. DEA showed an average error rate of 3.12% for Sharan's solution and 2.23% for Blum's problem. The iteration number of DEA was confirmed to be smaller than the other two optimization methods. SA was confirmed to be 117.39~167.13 times higher than DEA and PSO was confirmed to be 2.43~6.91 times higher than DEA. Applying a DEA to the multi-variate back analysis of geotechnical problems can be expected to improve computational speed and accuracy.

Improvement of precipitation forecasting skill of ECMWF data using multi-layer perceptron technique (다층퍼셉트론 기법을 이용한 ECMWF 예측자료의 강수예측 정확도 향상)

  • Lee, Seungsoo;Kim, Gayoung;Yoon, Soonjo;An, Hyunuk
    • Journal of Korea Water Resources Association
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    • v.52 no.7
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    • pp.475-482
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    • 2019
  • Subseasonal-to-Seasonal (S2S) prediction information which have 2 weeks to 2 months lead time are expected to be used through many parts of industry fields, but utilizability is not reached to expectation because of lower predictability than weather forecast and mid- /long-term forecast. In this study, we used multi-layer perceptron (MLP) which is one of machine learning technique that was built for regression training in order to improve predictability of S2S precipitation data at South Korea through post-processing. Hindcast information of ECMWF was used for MLP training and the original data were compared with trained outputs based on dichotomous forecast technique. As a result, Bias score, accuracy, and Critical Success Index (CSI) of trained output were improved on average by 59.7%, 124.3% and 88.5%, respectively. Probability of detection (POD) score was decreased on average by 9.5% and the reason was analyzed that ECMWF's model excessively predicted precipitation days. In this study, we confirmed that predictability of ECMWF's S2S information can be improved by post-processing using MLP even the predictability of original data was low. The results of this study can be used to increase the capability of S2S information in water resource and agricultural fields.

Seismic Reliability Analysis of Offshore Wind Turbine with Twisted Tripod Support using Subset Simulation Method (부분집합 시뮬레이션 방법을 이용한 꼬인 삼각대 지지구조를 갖는 해상풍력발전기의 지진 신뢰성 해석)

  • Park, Kwang-Yeun;Park, Wonsuk
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.32 no.2
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    • pp.125-132
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    • 2019
  • This paper presents a seismic reliability analysis method for an offshore wind turbine with a twisted tripod support structure under earthquake loading. A three dimensional dynamic finite element model is proposed to consider the nonlinearity of the ground-pile interactions and the geometrical characteristics of the twisted tripod support structure where out-of-plane displacement occurs even under in-plane lateral loadings. For the evaluation of seismic reliability, the failure probability was calculated for the maximum horizontal displacement of the pile head, which is calculated from time history analysis using artificial earthquakes for the design return periods. The application of the subset simulation method using the Markov Chain Monte Carlo(MCMC) sampling is proposed for efficient reliability analysis considering the limit state equation evaluation by the nonlinear time history analysis. The proposed method can be applied to the reliability evaluation and design criteria development of the offshore wind turbine with twisted tripod support structure in which two dimensional models and static analysis can not produce accurate results.

Detection of Wildfire Smoke Plumes Using GEMS Images and Machine Learning (GEMS 영상과 기계학습을 이용한 산불 연기 탐지)

  • Jeong, Yemin;Kim, Seoyeon;Kim, Seung-Yeon;Yu, Jeong-Ah;Lee, Dong-Won;Lee, Yangwon
    • Korean Journal of Remote Sensing
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    • v.38 no.5_3
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    • pp.967-977
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    • 2022
  • The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO2), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

Understanding of Generative Artificial Intelligence Based on Textual Data and Discussion for Its Application in Science Education (텍스트 기반 생성형 인공지능의 이해와 과학교육에서의 활용에 대한 논의)

  • Hunkoog Jho
    • Journal of The Korean Association For Science Education
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    • v.43 no.3
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    • pp.307-319
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    • 2023
  • This study aims to explain the key concepts and principles of text-based generative artificial intelligence (AI) that has been receiving increasing interest and utilization, focusing on its application in science education. It also highlights the potential and limitations of utilizing generative AI in science education, providing insights for its implementation and research aspects. Recent advancements in generative AI, predominantly based on transformer models consisting of encoders and decoders, have shown remarkable progress through optimization of reinforcement learning and reward models using human feedback, as well as understanding context. Particularly, it can perform various functions such as writing, summarizing, keyword extraction, evaluation, and feedback based on the ability to understand various user questions and intents. It also offers practical utility in diagnosing learners and structuring educational content based on provided examples by educators. However, it is necessary to examine the concerns regarding the limitations of generative AI, including the potential for conveying inaccurate facts or knowledge, bias resulting from overconfidence, and uncertainties regarding its impact on user attitudes or emotions. Moreover, the responses provided by generative AI are probabilistic based on response data from many individuals, which raises concerns about limiting insightful and innovative thinking that may offer different perspectives or ideas. In light of these considerations, this study provides practical suggestions for the positive utilization of AI in science education.

A Study on Reward-based Home-training App Users Using a Cash-cow User Prediction Model (캐시카우 사용자 예측 모델을 통한 리워드형 홈트레이닝 앱의 운영 및 관리 전략에 관한 연구)

  • Sanghwa Kim;Jinwook Choi;Byungwan Koh
    • Information Systems Review
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    • v.23 no.4
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    • pp.183-198
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    • 2021
  • Due to the Covid-19 pandemic, the home-training app market is growing rapidly and numerous apps are entering the market. It is becoming more difficult for an app to secure the profitability. In this study, by analyzing actual user data of a reward-based home-training app, we propose a model that predicts cash-cow users of the app. Cash-cow users are the users who watch in-stream ads to watch training videos although they cannot earn any rewards by doing so. Thus, these users make profits for the app yet do not incur any costs. The results of this study show that the users who irregularly watch training videos are more likely to be cash-cow users than the users who regularly watch training videos. This result suggests that, paradoxically, for sustainable profitability, home-training apps may need to find a way to retain the users who watch training videos irregularly so that they can be satisfied with the service and continue use the apps.

Habitat Distribution Change Prediction of Asiatic Black Bears (Ursus thibetanus) Using Maxent Modeling Approach (Maxent 모델을 이용한 반달가슴곰의 서식지 분포변화 예측)

  • Kim, Tae-Geun;Yang, DooHa;Cho, YoungHo;Song, Kyo-Hong;Oh, Jang-Geun
    • Korean Journal of Ecology and Environment
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    • v.49 no.3
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    • pp.197-207
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    • 2016
  • This study aims at providing basic data to objectively evaluate the areas suitable for reintroduction of the species of Asiatic black bear (Ursus thibetanus) in order to effectively preserve the Asiatic black bears in the Korean protection areas including national parks, and for the species restoration success. To this end, this study predicted the potential habitats in East Asia, Southeast Asia and India, where there are the records of Asiatic black bears' appearances using the Maxent model and environmental variables related with climate, topography, road and land use. In addition, this study evaluated the effects of the relevant climate and environmental variables. This study also analyzed inhabitation range area suitable for Asiatic black and geographic change according to future climate change. As for the judgment accuracy of the Maxent model widely utilized for habitat distribution research of wildlife for preservation, AUC value was calculated as 0.893 (sd=0.121). This was useful in predicting Asiatic black bears' potential habitat and evaluate the habitat change characteristics according to future climate change. Compare to the distribution map of Asiatic black bears evaluated by IUCN, Habitat suitability by the Maxent model were regionally diverse in extant areas and low in the extinct areas from IUCN map. This can be the result reflecting the regional difference in the environmental conditions where Asiatic black bears inhabit. As for the environment affecting the potential habitat distribution of Asiatic black bears, inhabitation rate was the highest, according to land coverage type, compared to climate, topography and artificial factors like distance from road. Especially, the area of deciduous broadleaf forest was predicted to be preferred, in comparison with other land coverage types. Annual mean precipitation and the precipitation during the driest period were projected to affect more than temperature's annual range, and the inhabitation possibility was higher, as distance was farther from road. The reason is that Asiatic black bears are conjectured to prefer more stable area without human's intervention, as well as prey resource. The inhabitation range was predicted to be expanded gradually to the southern part of India, China's southeast coast and adjacent inland area, and Vietnam, Laos and Malaysia in the eastern coastal areas of Southeast Asia. The following areas are forecast to be the core areas, where Asiatic black bears can inhabit in the Asian region: Jeonnam, Jeonbuk and Gangwon areas in South Korea, Kyushu, Chugoku, Shikoku, Chubu, Kanto and Tohoku's border area in Japan, and Jiangxi, Zhejiang and Fujian border area in China. This study is expected to be used as basic data for the preservation and efficient management of Asiatic black bear's habitat, artificially introduced individual bear's release area selection, and the management of collision zones with humans.