• Journal of Information Processing Systems
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• 제11권1호
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• pp.39-56
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• 2015

Continuous multi-interval prediction (CMIP) is used to continuously predict the trend of a data stream based on various intervals simultaneously. The continuous integrated hierarchical temporal memory (CIHTM) network performs well in CMIP. However, it is not suitable for CMIP in real-time mode, especially when the number of prediction intervals is increased. In this paper, we propose a real-time integrated hierarchical temporal memory (RIHTM) network by introducing a new type of node, which is called a Zeta1FirstSpecializedQueueNode (ZFSQNode), for the real-time continuous multi-interval prediction (RCMIP) of data streams. The ZFSQNode is constructed by using a specialized circular queue (sQUEUE) together with the modules of original hierarchical temporal memory (HTM) nodes. By using a simple structure and the easy operation characteristics of the sQUEUE, entire prediction operations are integrated in the ZFSQNode. In particular, we employed only one ZFSQNode in each level of the RIHTM network during the prediction stage to generate different intervals of prediction results. The RIHTM network efficiently reduces the response time. Our performance evaluation showed that the RIHTM was satisfied to continuously predict the trend of data streams with multi-intervals in the real-time mode.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.3913-3934
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• 2021

Real-time prediction of Web service of quality (QoS) provides more convenience for web services in cloud environment, but real-time QoS prediction faces severe challenges, especially under the cold-start situation. Existing literatures of real-time QoS predicting ignore that the QoS of a user/service is related to the QoS of other users/services. For example, users/services belonging to the same group of category will have similar QoS values. All of the methods ignore the group relationship because of the complexity of the model. Based on this, we propose a real-time Matrix Factorization based Clustering model (MFC), which uses category information as a new regularization term of the loss function. Specifically, in order to meet the real-time characteristic of the real-time prediction model, and to minimize the complexity of the model, we first map the QoS values of a large number of users/services to a lower-dimensional space by the PCA method, and then use the K-means algorithm calculates user/service category information, and use the average result to obtain a stable final clustering result. Extensive experiments on real-word datasets demonstrate that MFC outperforms other state-of-the-art prediction algorithms.

• Smart Structures and Systems
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• 제14권6호
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• pp.1269-1289
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• 2014

With the sub-stepping technique, the numerical analysis in real-time dynamic hybrid testing is split into the response analysis and signal generation tasks. Two target computers that operate in real-time may be assigned to implement these two tasks, respectively, for fully extending the simulation scale of the numerical substructure. In this case, the integration time-step of solving the dynamic response of the numerical substructure can be dozens of times bigger than the sampling time-step of the controller. The time delay between the real and desired feedback forces becomes more striking, which challenges the well-developed delay compensation methods in real-time dynamic hybrid testing. This paper focuses on displacement prediction and force correction for delay compensation in the real-time dynamic hybrid testing with a large integration time-step. A new displacement prediction scheme is proposed based on recently-developed explicit integration algorithms and compared with several commonly-used prediction procedures. The evaluation of its prediction accuracy is carried out theoretically, numerically and experimentally. Results indicate that the accuracy and effectiveness of the proposed prediction method are of significance.

• 한국항해항만학회지
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• 제35권9호
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• pp.735-740
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• 2011

It is necessary to develop the one stop system in order to protect our marine environment rapidly from oil spill accident. The purpose of this study is to develop real time database for oil spill prediction modeling and implement real time prediction modelling with ESI and server-client GIS based user interface. The existing oil spill prediction model cannot provide one stop information system for public and government who should protect sea from oil spill accident. The development of multi user based information system permits integrated handling of real time meteorological data from external ftp. A server-client GIS based model is integrated on the basis of real time database and ESI map to provide the result of the oil spill prediction model. End users can access through the client interface and request analysis such as oil spill prediction and GIS functions on the network as their own purpose.

• 전기학회논문지
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• 제65권12호
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• pp.2084-2093
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• 2016

It is crucial to predict water temperature for aquatic ecosystem studies and management. In this paper, we first address challenging issues in predicting water temperature in a real time manner and propose a distributed computing model to address such issues. Then, we present an Artificial Neural Network (ANN)-based water temperature prediction model developed for the Soyang River and a cyberinfrastructure system called WT-Agabus to run such prediction models in an automated and real time manner. The ANN model is designed to use only weather forecast data (air temperature and rainfall) that can be obtained by invoking the weather forecasting system at Korea Meteorological Administration (KMA) and therefore can facilitate the automated and real time water temperature prediction. This paper also demonstrates how easily and efficiently the real time prediction can be implemented with the WT-Agabus prototype system.

• 한국항행학회논문지
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• 제19권3호
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• pp.199-206
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• 2015

IGS에서는 실시간 정밀 측위에 사용할 수 있도록 궤도 예측값인 IGU (IGS Ultra-rapid)와 실시간 궤도 추정값인 RTS (real-time service) 보정정보를 제공한다. IGU는 데이터 지연시간이 없지만, RTS는 5~30 초의 지연시간을 갖기 때문에 실시간으로 사용하기 위해선 지연시간만큼 예측이 필요하다. 본 논문에서는 실시간 사용 측면에서 RTS와 IGU의 성능 분석을 수행하였다. 한반도 내에서 RTS 제공 비율을 파악하기 위하여 한반도에서 관측되는 위성 대비 RTS 제공 비율을 계산하였으며 그 결과 99.3%로 나타났다. RTS의 정확도를 확인하기 위해 보정정보를 방송궤도력에 적용하여 오차를 분석하였으며 이 때 3D 궤도 RMS 오차는 0.043 m으로 나타났다. 실시간 사용 측면에서 RTS와 IGU를 비교하였는데, 실시간으로 가정하였기 때문에 IGU는 예측 정보만 이용하였고, RTS는 데이터 지연시간동안 다항식 모델로 예측을 수행하였다. RTS와 IGU를 1초 간격으로 각각 외삽, 보간을 수행하였고 그 결과 궤도 예측 성능은 비슷하였으며 시계 예측 성능은 RTS가 0.13 m 더 뛰어났다.

• 한국컴퓨터정보학회논문지
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• 제21권5호
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• pp.91-98
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• 2016

We propose a method to train a model that can predict the probability of a crime being committed. CCTV data by matching criminal events are required to train the crime prediction model. However, collecting CCTV data appropriate for training is difficult. Thus, we collected actual criminal records and converted them to an appropriate format using variables by considering a crime prediction environment and the availability of real-time data collection from CCTV. In addition, we identified new specific crime types according to the characteristics of criminal events and trained and tested the prediction model by applying neural network partial least squares for each crime type. Results show a level of predictive accuracy sufficiently significant to demonstrate the applicability of CCTV to real-time crime prediction.

• 정보처리학회논문지A
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• 제11A권4호
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• pp.243-250
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• 2004

실시간 시스템은 시스템이 적시성을 보장하는지 파악하기 위해 실시간 감시기법을 이용한다. 일반적으로 실시간 감시는 실시간 시스템의 현재 동작상태를 파악하는데 중점을 두는 기법이다. 그러나 실시간 시스템의 안정적인 수행을 지원하기 위해서는, 현재 상태를 파악하는 것뿐 아니라, 실시간 시스템 및 시스템상에서 동작하는 실시간 프로세스들의 수행도 예측할 수 있어야 한다. 그러나 기존 예측모델을 실시간 감시기법에 적용하기에는 몇 가지 한계가 있다. 첫째, 예측기능은 실시간 프로세스가 종료한 시점에서 정적인 분석을 통해 수행된다. 둘째, 예측을 위해 사전 기초 통계분석이 필요하다. 셋째, 예측을 위한 이전확률 및 클러스터 정보가 현재 시점을 정확하게 반영하지 못한다. 본 논문에서는 이러한 문제점들을 해결하고 실시간 감시기법에 적용할 수 있는 학습 기반의 수행 예측모델을 제안한다. 이 모델은 학습기법을 통해 불필요한 전처리과정을 없애고, 현시점의 데이터를 이용해, 보다 정확한 실시간 프로세스의 수행 예측이 가능하도록 한다. 또한 이 모델은 실시간 프로세스 수행 시간의 증가율 분석을 통해 다단계 예측을 지원하며, 무엇보다 실시간 프로세스가 실행되는 동안 예측이 가능한 동적 예측을 지원하도록 설계하였다. 실험 결과를 통해 훈련집합의 크기가 10 이상이면 80% 이상의 판단 정확도를 보이며, 다단계 예측의 경우, 훈련집합의 크기 이상의 수행 횟수를 넘으면 다단계 예측의 예측 차는 최소화되는 것으로 나타났다. 본 논문에서 제안한 예측모델은 가장 단순한 학습 알고리즘을 적용했다는 점과, CPU, 메모리, 입출력 데이터를 다루는 다차원 자원공간 모델을 고려하지 못한 한계가 있어 향후에 관련 연구가 요구된다. 본 논문에서 제안하는 학습기반 수행 예측모델은 실시간 감시 및 제어를 필요로 하는 분야 및 응용 분야에 적용할 수 있다.

• 설비공학논문집
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• 제25권6호
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• pp.310-316
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• 2013

This study was designed to investigate a method for short-term, real-time energy demand prediction, to cope with changing loads for the effective operation and management of buildings. Through a case study, a novel methodology for real-time energy demand prediction with the use of weather forecasting data was suggested. To perform the input and output operations of weather data, and to calculate solar radiation and EnergyPlus, the BCVTB (Building Control Virtual Test Bed) was designed. Through the BCVTB, energy demand prediction for the next 24 hours was carried out, based on 4 real-time weather data and 2 solar radiation calculations. The weather parameters used in a model equation to calculate solar radiation were sourced from the weather data of the KMA (Korea Meteorological Administration). Depending on the local weather forecast data, the results showed their corresponding predicted values. Thus, this methodology was successfully applicable to anywhere that local weather forecast data is available.

• Journal of Positioning, Navigation, and Timing
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• 제12권2호
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• pp.167-176
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• 2023

This study presents the performance analysis of real-time orbit determination and prediction for navigation message generation of Regional Navigation Satellite System (RNSS). Since the accuracy of ephemeris and clock correction in navigation message affects the positioning accuracy of the user, it is essential to construct a ground segment that can generate this information precisely when designing a new navigation satellite system. Based on a real-time architecture by an extended Kalman filter, we simulated orbit determination and prediction of RNSS satellites in order to assess the accuracy of orbit and clock prediction and signal-in-space ranging errors (SISRE). As a result of the simulation, the orbit and clock accuracy was at 0.5 m and 2 m levels for 24 hour determination and six hour prediction after the determination, respectively. From the prediction result, we verified that the SISRE of RNSS for six hour prediction was at a 1 m level.