• 한국공간정보시스템학회 논문지
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• 제10권3호
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• pp.31-43
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• 2008

주행 시간 예측은 첨단 여행정보 시스템 (ATIS) 및 교통관리 시스템 (ITS)에서 필수적이다. 이를 위해 본 연구에서는 대용량의 데이터 분류에서 높은 정확도와 빠른 속도를 보장하는 $Na{\ddot{i}}ve$ Bayesian 분류화 기법을 기반으로 한 주행시간 예측 알고리즘을 제안한다. 제안된 알고리즘은 도로 네트워크 상에서 사용자 지정 주행 경로에 대하여 주행시간 예측이 가능하며, 또한 주어진 경로에 대해 시간대 별 평균 구간 속도를 고려하여 보다 정확한 주행 시간 예측을 수행한다. 제안된 알고리즘을 기존의 링크-기반 예측(link-based prediction)알고리즘[1] 및 Micro T* 알고리즘[2]과 성능 비교를 수행하였다. 성능 비교 결과, 제안된 기법이 타 예측기법에 비해 MARE (mean absolute relative error)가 크게 감소하여 성능이 향상되었음을 보였다.

• Journal of Electrical Engineering and Technology
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• 제13권5호
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• pp.1841-1851
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• 2018

For the safe and stable operation of the power system, accurate wind power prediction is of great significance. A wind power prediction method based on empirical mode decomposition and improved extreme learning machine is proposed in this paper. Firstly, wind power time series is decomposed into several components with different frequency by empirical mode decomposition, which can reduce the non-stationary of time series. The components after decomposing remove the long correlation and promote the different local characteristics of original wind power time series. Secondly, an improved extreme learning machine prediction model is introduced to overcome the sample data updating disadvantages of standard extreme learning machine. Different improved extreme learning machine prediction model of each component is established. Finally, the prediction value of each component is superimposed to obtain the final result. Compared with other prediction models, the simulation results demonstrate that the proposed prediction method has better prediction accuracy for wind power.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.122-127
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• 2001

This study investigates the effectiveness of time delay neural networks(TDNN) for the time dependent prediction domain. Although it is well-known fact that the back-propagation neural network(BPN) performs well in pattern recognition tasks, the method has some limitations in that it can only learn an input mapping of static (or spatial) patterns that are independent of time of sequences. The preliminary results show that the accuracy of TDNN is higher than the standard BPN with time lag. Our proposed approaches are demonstrated by the stork market prediction domain.

• 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.

• 한국인터넷방송통신학회논문지
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• 제10권6호
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• pp.137-143
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• 2010

본 연구에서는 시계열 자료처리를 통해 예측정확도를 개선시키는 방안에 대해 연구하였다. 단일 예측 모형의 단점을 개선하기 위해 유사한 시계열 자료를 선정하여 이들로부터 모델을 유도하였다. 이 모델로부터 유효 규칙을 생성해내 향후 자료의 변화를 예측하였다. 실험을 통해 예측정확도에 있어 유의한 수준의 개선효과가 있었음을 확인하였다. 예측모델 구성을 위해 고정구간과 가변구간을 두고 모델링하여 고정구간, 창이동, 누적구간 방식으로 구분하여 예측정확도를 측정하였다. 이중 누적구간 방식이 가장 정확도가 높게 나왔다.

• Journal of Information Processing Systems
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• 제15권6호
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• pp.1335-1349
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• 2019

Crowdsensing technologies can improve the efficiency of smart parking system in comparison with present sensor based smart parking system because of low install price and no restriction caused by sensor installation. A lot of sensing data is necessary to predict parking lot saturation in real-time. However in real world, it is hard to reach the required number of sensing data. In this paper, we model a saturation predication combining a time-based prediction model and a sensing data-based prediction model. The time-based model predicts saturation in aspects of parking lot location and time. The sensing data-based model predicts the degree of saturation of the parking lot with high accuracy based on the degree of saturation predicted from the first model, the saturation information in the sensing data, and the number of parking spaces in the sensing data. We perform prediction model learning with real sensing data gathered from a specific parking lot. We also evaluate the performance of the predictive model and show its efficiency and feasibility.

• 한국컴퓨터정보학회논문지
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• 제28권2호
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• pp.201-207
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• 2023

본 논문에서는 Multi-Step Time Series의 세 가지 전략을 비교 분석하기 위해 LGBM, MLP, LSTM, GRU를 사용하여 농산물 중장기 가격 예측에 대한 최적의 모형을 제안한다. 제안 모형은 다각도로 전략을 선택하여 모델과 전략간 최적의 조합을 찾도록 설계되었다. 기존 농산물 가격 예측 연구에서는 전통 계량경제 모델인 ARIMA를 비롯하여 LSTM 계열 모델이 주로 사용된 반면 Multi-Step Time Series 관련 농산물 가격 예측 연구는 매우 제한적이다. 본 연구에서는 농산물 가격의 변동성 정도에 따라 두 개의 기간으로 나누어 실험을 진행하였으며, Direct, Hybrid, Multiple Outputs 등 세 전략의 중장기 가격 예측 결과 Hybrid 접근법이 상대적으로 우수한 성능을 보였다.본 연구 결과는 중장기 일별 가격 예측을 고도화할 수 있는 효과적인 대안을 제시한다는 측면에서 학술적, 실무적 의의를 갖는다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회A
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• pp.1588-1593
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• 2008

This paper describes a real-time isometric pinch force prediction algorithm from surface electromyogram (sEMG) using multilayer perceptron (MLP) for human robot interactive applications. The activities of seven muscles which are observable from surface electrodes and also related to the movements of the thumb and index finger joints were recorded during pinch force experiments. For the successful implementation of the real-time prediction algorithm, an off-line analysis was performed using the recorded activities. Four muscles were selected for the force prediction by using the Fisher linear discriminant analysis among seven muscles, and the four muscle activities provided effective information for mapping sEMG to the pinch force. The MLP structure was designed to make training efficient and to avoid both under- and over-fitting problems. The pinch force prediction algorithm was tested on five volunteers and the results were evaluated using two criteria: normalized root mean squared error (NRMSE) and correlation (CORR). The training time for the subjects was only 2 min 29 sec, but the prediction results were successful with NRMSE = 0.112 ${\pm}$ 0.082 and CORR = 0.932 ${\pm}$ 0.058. These results imply that the proposed algorithm is useful to measure the produced pinch force without force sensors in real-time. The possible applications include controlling bionic finger robot systems to overcome finger paralysis or amputation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권5호
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• pp.1963-1978
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• 2015

With the advancement of mobile web environments, identification and analysis of the user behavior play a significant role and remains a challenging task to implement with variations observed in the model. This paper presents an efficient method for mining optimized user behavior prediction model using genetic algorithm on mobile web structure. The framework of optimized user behavior prediction model integrates the temporary and permanent register information and is stored immediately in the form of integrated logs which have higher precision and minimize the time for determining user behavior. Then by applying the temporal characteristics, suitable time interval table is obtained by segmenting the logs. The suitable time interval table that split the huge data logs is obtained using genetic algorithm. Existing cluster based temporal mobile sequential arrangement provide efficiency without bringing down the accuracy but compromise precision during the prediction of user behavior. To efficiently discover the mobile users' behavior, prediction model is associated with region and requested services, a method called optimized user behavior Prediction Model using Genetic Algorithm (PM-GA) on mobile web structure is introduced. This paper also provides a technique called MAA during the increase in the number of models related to the region and requested services are observed. Based on our analysis, we content that PM-GA provides improved performance in terms of precision, number of mobile models generated, execution time and increasing the prediction accuracy. Experiments are conducted with different parameter on real dataset in mobile web environment. Analytical and empirical result offers an efficient and effective mining and prediction of user behavior prediction model on mobile web structure.

• 한국해양공학회지
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• 제24권6호
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• pp.97-102
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• 2010

This paper deals with the variability of short term creep rupture time based on previous creep rupture tests and the statistical methodology of the creep life prediction. The results of creep tests performed using constant uniaxial stresses at 600, 650, and $700^{\circ}C$ elevated temperatures were used for a statistical analysis of the inter-specimen variability of the short term creep rupture time. Even under carefully controlled identical testing conditions, the observed short-term creep rupture time showed obvious inter-specimen variability. The statistical aspect of the short term creep rupture time was analyzed using a Weibull statistical analysis. The effect of creep stress on the variability of the creep rupture time was decreased with an increase in the stress level. The effect of the temperature on the variability also decreased with increasing temperature. A long term creep life prediction method that considers this statistical variability is presented. The presented method is in good agreement with the Lason-Miller Parameter (LMP) life prediction method.