• Title/Summary/Keyword: Real-Time Prediction

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Performance Prediction for an Adaptive Optics System Using Two Analysis Methods: Statistical Analysis and Computational Simulation (통계분석 및 전산모사 기법을 이용한 적응광학 시스템 성능 예측)

  • Han, Seok Gi;Joo, Ji Yong;Lee, Jun Ho;Park, Sang Yeong;Kim, Young Soo;Jung, Yong Suk;Jung, Do Hwan;Huh, Joon;Lee, Kihun
    • Korean Journal of Optics and Photonics
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    • v.33 no.4
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    • pp.167-176
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    • 2022
  • Adaptive optics (AO) systems compensate for atmospheric disturbance, especially phase distortion, by introducing counter-wavefront deformation calculated from real-time wavefront sensing or prediction. Because AO system implementations are time-consuming and costly, it is highly desirable to estimate the system's performance during the development of the AO system or its parts. Among several techniques, we mostly apply statistical analysis, computational simulation, and optical-bench tests. Statistical analysis estimates performance based on the sum of performance variances due to all design parameters, but ignores any correlation between them. Computational simulation models every part of an adaptive optics system, including atmospheric disturbance and a closed loop between wavefront sensor and deformable mirror, as close as possible to reality, but there are still some differences between simulation models and reality. The optical-bench test implements an almost identical AO system on an optical bench, to confirm the predictions of the previous methods. We are currently developing an AO system for a 1.6-m ground telescope using a deformable mirror that was recently developed in South Korea. This paper reports the results of the statistical analysis and computer simulation for the system's design and confirmation. For the analysis, we apply the Strehl ratio as the performance criterion, and the median seeing conditions at the Bohyun observatory in Korea. The statistical analysis predicts a Strehl ratio of 0.31. The simulation method similarly reports a slightly larger value of 0.32. During the study, the simulation method exhibits run-to-run variation due to the random nature of atmospheric disturbance, which converges when the simulation time is longer than 0.9 seconds, i.e., approximately 240 times the critical time constant of the applied atmospheric disturbance.

Personalized Exhibition Booth Recommendation Methodology Using Sequential Association Rule (순차 연관 규칙을 이용한 개인화된 전시 부스 추천 방법)

  • Moon, Hyun-Sil;Jung, Min-Kyu;Kim, Jae-Kyeong;Kim, Hyea-Kyeong
    • Journal of Intelligence and Information Systems
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    • v.16 no.4
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    • pp.195-211
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    • 2010
  • An exhibition is defined as market events for specific duration to present exhibitors' main product range to either business or private visitors, and it also plays a key role as effective marketing channels. Especially, as the effect of the opinions of the visitors after the exhibition impacts directly on sales or the image of companies, exhibition organizers must consider various needs of visitors. To meet needs of visitors, ubiquitous technologies have been applied in some exhibitions. However, despite of the development of the ubiquitous technologies, their services cannot always reflect visitors' preferences as they only generate information when visitors request. As a result, they have reached their limit to meet needs of visitors, which consequently might lead them to loss of marketing opportunity. Recommendation systems can be the right type to overcome these limitations. They can recommend the booths to coincide with visitors' preferences, so that they help visitors who are in difficulty for choices in exhibition environment. One of the most successful and widely used technologies for building recommender systems is called Collaborative Filtering. Traditional recommender systems, however, only use neighbors' evaluations or behaviors for a personalized prediction. Therefore, they can not reflect visitors' dynamic preference, and also lack of accuracy in exhibition environment. Although there is much useful information to infer visitors' preference in ubiquitous environment (e.g., visitors' current location, booth visit path, and so on), they use only limited information for recommendation. In this study, we propose a booth recommendation methodology using Sequential Association Rule which considers the sequence of visiting. Recent studies of Sequential Association Rule use the constraints to improve the performance. However, since traditional Sequential Association Rule considers the whole rules to recommendation, they have a scalability problem when they are adapted to a large exhibition scale. To solve this problem, our methodology composes the confidence database before recommendation process. To compose the confidence database, we first search preceding rules which have the frequency above threshold. Next, we compute the confidences of each preceding rules to each booth which is not contained in preceding rules. Therefore, the confidence database has two kinds of information which are preceding rules and their confidence to each booth. In recommendation process, we just generate preceding rules of the target visitors based on the records of the visits, and recommend booths according to the confidence database. Throughout these steps, we expect reduction of time spent on recommendation process. To evaluate proposed methodology, we use real booth visit records which are collected by RFID technology in IT exhibition. Booth visit records also contain the visit sequence of each visitor. We compare the performance of proposed methodology with traditional Collaborative Filtering system. As a result, our proposed methodology generally shows higher performance than traditional Collaborative Filtering. We can also see some features of it in experimental results. First, it shows the highest performance at one booth recommendation. It detects preceding rules with some portions of visitors. Therefore, if there is a visitor who moved with very a different pattern compared to the whole visitors, it cannot give a correct recommendation for him/her even though we increase the number of recommendation. Trained by the whole visitors, it cannot correctly give recommendation to visitors who have a unique path. Second, the performance of general recommendation systems increase as time expands. However, our methodology shows higher performance with limited information like one or two time periods. Therefore, not only can it recommend even if there is not much information of the target visitors' booth visit records, but also it uses only small amount of information in recommendation process. We expect that it can give real?time recommendations in exhibition environment. Overall, our methodology shows higher performance ability than traditional Collaborative Filtering systems, we expect it could be applied in booth recommendation system to satisfy visitors in exhibition environment.

An Analysis of IT Trends Using Tweet Data (트윗 데이터를 활용한 IT 트렌드 분석)

  • Yi, Jin Baek;Lee, Choong Kwon;Cha, Kyung Jin
    • Journal of Intelligence and Information Systems
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    • v.21 no.1
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    • pp.143-159
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    • 2015
  • Predicting IT trends has been a long and important subject for information systems research. IT trend prediction makes it possible to acknowledge emerging eras of innovation and allocate budgets to prepare against rapidly changing technological trends. Towards the end of each year, various domestic and global organizations predict and announce IT trends for the following year. For example, Gartner Predicts 10 top IT trend during the next year, and these predictions affect IT and industry leaders and organization's basic assumptions about technology and the future of IT, but the accuracy of these reports are difficult to verify. Social media data can be useful tool to verify the accuracy. As social media services have gained in popularity, it is used in a variety of ways, from posting about personal daily life to keeping up to date with news and trends. In the recent years, rates of social media activity in Korea have reached unprecedented levels. Hundreds of millions of users now participate in online social networks and communicate with colleague and friends their opinions and thoughts. In particular, Twitter is currently the major micro blog service, it has an important function named 'tweets' which is to report their current thoughts and actions, comments on news and engage in discussions. For an analysis on IT trends, we chose Tweet data because not only it produces massive unstructured textual data in real time but also it serves as an influential channel for opinion leading on technology. Previous studies found that the tweet data provides useful information and detects the trend of society effectively, these studies also identifies that Twitter can track the issue faster than the other media, newspapers. Therefore, this study investigates how frequently the predicted IT trends for the following year announced by public organizations are mentioned on social network services like Twitter. IT trend predictions for 2013, announced near the end of 2012 from two domestic organizations, the National IT Industry Promotion Agency (NIPA) and the National Information Society Agency (NIA), were used as a basis for this research. The present study analyzes the Twitter data generated from Seoul (Korea) compared with the predictions of the two organizations to analyze the differences. Thus, Twitter data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. To overcome these challenges, we used SAS IRS (Information Retrieval Studio) developed by SAS to capture the trend in real-time processing big stream datasets of Twitter. The system offers a framework for crawling, normalizing, analyzing, indexing and searching tweet data. As a result, we have crawled the entire Twitter sphere in Seoul area and obtained 21,589 tweets in 2013 to review how frequently the IT trend topics announced by the two organizations were mentioned by the people in Seoul. The results shows that most IT trend predicted by NIPA and NIA were all frequently mentioned in Twitter except some topics such as 'new types of security threat', 'green IT', 'next generation semiconductor' since these topics non generalized compound words so they can be mentioned in Twitter with other words. To answer whether the IT trend tweets from Korea is related to the following year's IT trends in real world, we compared Twitter's trending topics with those in Nara Market, Korea's online e-Procurement system which is a nationwide web-based procurement system, dealing with whole procurement process of all public organizations in Korea. The correlation analysis show that Tweet frequencies on IT trending topics predicted by NIPA and NIA are significantly correlated with frequencies on IT topics mentioned in project announcements by Nara market in 2012 and 2013. The main contribution of our research can be found in the following aspects: i) the IT topic predictions announced by NIPA and NIA can provide an effective guideline to IT professionals and researchers in Korea who are looking for verified IT topic trends in the following topic, ii) researchers can use Twitter to get some useful ideas to detect and predict dynamic trends of technological and social issues.

Dynamic O-D Trip estimation Using Real-time Traffic Data in congestion (혼잡 교통류 특성을 반영한 동적 O-D 통행량 예측 모형 개발)

  • Kim Yong-Hoon;Lee Seung-Jae
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.5 no.1 s.9
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    • pp.1-12
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    • 2006
  • In order to estimate a dynamic origin and destination demand between on and off-ramps in the freeways, a traffic flow theory can be used to calculate a link distribution proportion of traffics moving between them. We have developed a dynamic traffic estimation model based on the three-phase traffic theory (Kerner, 2004), which explains the complexity of traffic phenomena based on phase transitions among free-flow, synchronized flow and moving jam phases, and on their complex nonlinear spatiotemporal features. The developed model explains and estimates traffic congestion in terms of speed breakdown, phase transition and queue propagation. We have estimated the link, on and off-ramp volumes at every time interval by using traffic data collected from vehicle detection systems in Korea freeway sections. The analyzed results show that the developed model describes traffic flows adequately.

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Comparison of Different Multiple Linear Regression Models for Real-time Flood Stage Forecasting (실시간 수위 예측을 위한 다중선형회귀 모형의 비교)

  • Choi, Seung Yong;Han, Kun Yeun;Kim, Byung Hyun
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.32 no.1B
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    • pp.9-20
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    • 2012
  • Recently to overcome limitations of conceptual, hydrological and physics based models for flood stage forecasting, multiple linear regression model as one of data-driven models have been widely adopted for forecasting flood streamflow(stage). The objectives of this study are to compare performance of different multiple linear regression models according to regression coefficient estimation methods and determine most effective multiple linear regression flood stage forecasting models. To do this, the time scale was determined through the autocorrelation analysis of input data and different flood stage forecasting models developed using regression coefficient estimation methods such as LS(least square), WLS(weighted least square), SPW(stepwise) was applied to flood events in Jungrang stream. To evaluate performance of established models, fours statistical indices were used, namely; Root mean square error(RMSE), Nash Sutcliffe efficiency coefficient (NSEC), mean absolute error (MAE), adjusted coefficient of determination($R^{*2}$). The results show that the flood stage forecasting model using SPW(stepwise) parameter estimation can carry out the river flood stage prediction better in comparison with others, and the flood stage forecasting model using LS(least square) parameter estimation is also found to be slightly better than the flood stage forecasting model using WLS(weighted least square) parameter estimation.

Benchmark Test of CFD Software Packages for Sunroof Buffeting in Hyundai Simplified Model (차량 썬루프 버페팅 현상에 대한 전산 해석 소프트웨어의 예측 성능 벤치마크 연구)

  • Cho, Munhwan;Oh, Chisung;Kim, HyoungGun;Ih, Kang-duck
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.24 no.3
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    • pp.171-179
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    • 2014
  • Sunroof buffeting is one of the most critical issues in the vehicle wind noise phenomena. The experimental approach to solve this issue typically requires a lot of time and resources. To reduce time and cost, the numerical approach could be taken, which can also privide more insights into physical phenomena involved in sunroof buffeting, only if the accuracy in its predictions are guranteed. The benchmark test of various numerical solvers is carried out for the buffeting behavior of a simplified vehicle body, the Hyundai simplified model(HSM). The results of each solver are compared to the experimental measurements in a Hyundai aeroacoustic wind tunnel(HAWT) at various wind speeds. In particular, acoustic response tests were performed and the results were provided prior to all simulations in order to consider the real world effects that could introduce discrepancies between the numerical and experimental approaches. Through this study, most solvers can demonstrate an acceptable accuracy level for actual commercial development and high precision experimental data and computational prediction priories can be shared in order to promote the numerical accuracy level of each numerical solver.

Characteristics of Brightness Temperature from MTSAT-1R on Lightning Events and Prediction over South Korea (MTSAT-1R 휘도온도를 이용한 낙뢰발생 특성 분석 및 예측)

  • Eom, Hyo-Sik;Suh, Myoung-Seok;Lee, Yun-Jeong
    • Proceedings of the KSRS Conference
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    • 2009.03a
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    • pp.227-236
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    • 2009
  • This study investigates the characteristics of cloud top brightness temperature (CTBT) of WV and IR1 from MTSAT-1R when lightning strikes in South Korea. For temporal and spatial collocations, lightnings, occurred only within ${\pm}5$ minutes from the six minutes added official satellite observation time (e.g., not 0600 UTC but 0606 UTC, considering the real scan time over South Korea), were selected. And the CTBTs corresponding to lightning spots were determined using the nearest pixel within 5 km. The brightness temperature difference (BTD, defined as WV - IR1) between two channels is negatively large when no lightning occurrs, whereas it increases up to positive values (sometimes, +5 K) and the largest frequency distributes around 225 K and 205 K in lightning cases. The probablistic approach for lightning frequency forecast, presented by Machado et al. (2008) in Southern America, was applied over South Korea and new exponential equations, with high coefficients of determination around 0.95 to 0.99, were developed using two channels' BTDs when lightning strikes. Moreover, a case study on 10th June, 2006, the largest number of lightning occurred between 2002 and 2006, was made. The major finding is that lightning activity is closely related to the dramatic decreases in BT and the increases in BTD (esp., equal to or larger than 0 K). Lightning frequency increases exponentially when BTD increases up to 0 K. Therefore, lightning forecast skill will be improved when the integrated strategy (synoptic background and satellite-based CTBT and BTD) is applied. It is believed that this study contributes to the application of the Korean first geostationary satellite (COMS), scheduled to launch at the end of this year, to severe weather detections.

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Development of Grid Based Distributed Rainfall-Runoff Model with Finite Volume Method (유한체적법을 이용한 격자기반의 분포형 강우-유출 모형 개발)

  • Choi, Yun-Seok;Kim, Kyung-Tak;Lee, Jin-Hee
    • Journal of Korea Water Resources Association
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    • v.41 no.9
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    • pp.895-905
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    • 2008
  • To analyze hydrologic processes in a watershed requires both various geographical data and hydrological time series data. Recently, not only geographical data such as DEM(Digital Elevation Model) and hydrologic thematic map but also hydrological time series from numerical weather prediction and rainfall radar have been provided as grid data, and there are studies on hydrologic analysis using these grid data. In this study, GRM(Grid based Rainfall-runoff Model) which is physically-based distributed rainfall-runoff model has been developed to simulate short term rainfall-runoff process effectively using these grid data. Kinematic wave equation is used to simulate overland flow and channel flow, and Green-Ampt model is used to simulate infiltration process. Governing equation is discretized by finite volume method. TDMA(TriDiagonal Matrix Algorithm) is applied to solve systems of linear equations, and Newton-Raphson iteration method is applied to solve non-linear term. Developed model was applied to simplified hypothetical watersheds to examine model reasonability with the results from $Vflo^{TM}$. It was applied to Wicheon watershed for verification, and the applicability to real site was examined, and simulation results showed good agreement with measured hydrographs.

Impact Analysis of Overestimation Sources on the Accuracy of the Worst Case Timing Analysis for RISC Processors (RISC 프로세서를 대상으로 한 최악 실행시간 분석의 정확도에 대한 과예측 원인별 영향 분석)

  • Kim, Seong-Gwan;Min, Sang-Ryeol;Ha, Ran;Kim, Jong-Sang
    • Journal of KIISE:Computing Practices and Letters
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    • v.5 no.4
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    • pp.467-478
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    • 1999
  • 실시간 태스크의 최악 실행시간을 예측할 때 과예측이 발생하는 원인은, 첫째 프로그램의 동적인 최악 실행 행태를 정적으로 분석하는 것이 근본적으로 어렵기 때문이며, 둘째 최근의 RISC 형태 프로세서에 포함되어 있는 파이프라인 실행 구조와 캐쉬 등이 그러한 정적 분석을 더욱 어렵게 만들기 때문이다. 그런데 기존의 연구에서는 각각의 과예측 원인을 해결하기 위한 방법에 대해서만 언급하고 있을 뿐 분석의 정확도에서 각 원인이 차지하는 비중에 대해서는 언급하고 있지 않다. 이에 본 연구에서는 최악 실행시간 예측시 과예측을 유발하는 원인들, 즉 분석 요소들의 영향을 정량적으로 조사함으로써 기존의 최악 실행시간 분석 기법들이 보완해야 할 방향을 제시하고자 한다. 본 연구에서는 실험이 특정 분석 기법에 의존하지 않도록 하기 위하여 시뮬레이션 방법에 기반한다. 이를 위해 분석 요소별 스위치가 포함된 MIPS R3000 프로세서를 위한 시뮬레이터를 구현하였는데, 각 스위치는 해당 분석 요소에 대한 분석의 정확도 수준을 결정한다. 모든 스위치 조합에 대해서 시뮬레이션을 반복 수행한 다음 분산 분석을 수행하여 어떤 분석 요소가 가장 큰 영향을 끼치는지 고찰한다.Abstract Existing analysis techniques for estimating the worst case execution time (WCET) of real-time tasks still suffer from significant overestimation due to two types of overestimation sources. First, it is unavoidably difficult to predict dynamic behavior of programs statically. Second, pipelined execution and caching found in recent RISC-style processors even more complicate such a prediction. Although these overestimation sources have been attacked in many existing analysis techniques, we cannot find in the literature any description about questions like which one is most important. Thus, in this paper, we quantitatively analyze the impacts of overestimation sources on the accuracy of the worst case timing analysis. Using the results, we can identify dominant overestimation sources that should be analyzed more accurately to get tighter WCET estimations. To make our method independent of any existing analysis techniques, we use simulation based methodology. We have implemented a MIPS R3000 simulator equipped with several switches, each of which determines the accuracy level of the timing analysis for the corresponding overestimation source. After repeating simulation for all of the switch combinations, we perform the variance analysis and study which factor has the largest impact on the accuracy of the predicted WCETs.

Human-Oriented Design of Backrest of Office Chair Using Haptic-aided Design and Lumber Angle Prediction (햅틱보조설계 기법과 요추각도의 예측을 이용한 의자등판의 인간중심적인 설계)

  • Lee, Sang-Duck;Lee, Hae-A;Song, Jae-Bok;Chae, Soo-Won
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.34 no.11
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    • pp.1581-1586
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    • 2010
  • Haptic-aided design (HAD) involves the use of a haptic simulator in place of physical prototypes in the design and development of products with which human beings interact physically. The development time and cost can be significantly reduced by adopting this HAD scheme. Although both physical and emotional factors are equally important, only the emotional factors were taken into consideration in the previous HAD process. Consequently, the design of the products was sometimes unsatisfactory from the viewpoint of ergonomics, even though users were emotionally satisfied with the products. To overcome this problem, in this study, we propose a new human-oriented design methodology that is enhanced by taking the physical factors into consideration. The HAD scheme was verified by using a haptic chair simulator to design a tilt mechanism of an office chair for which the stiffness of the backrest can be adjusted; then, the design was simulated using MADYMO. The results show that the proposed method can reflect both the physical and emotional factors to modify the design in real-time.