• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.315-315
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• 2000

In this paper, we perform the time series prediction using the SVM(Support Vector Machine). We make use of two different loss functions and two different kernel functions; i) Quadratic and $\varepsilon$-insensitive loss function are used; ii) GRBF(Gaussian Radial Basis Function) and ERBF(Exponential Radial Basis Function) are used. Mackey-Glass time series are used for prediction. For both cases, we compare the results by the SVM to those by ANN(Artificial Neural Network) and show the better performance by SVM than that by ANN.

• Journal of the Korean Data and Information Science Society
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• 제11권2호
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• pp.173-179
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• 2000

Interval prediction based on the empirical distribution function for the class of time series with time varying coefficients is discussed. To this end, strong mixing property of the model is shown and results due to Fotopoulos et. al.(1994) are employed. A simulation study is presented to assess the accuracy of the proposed interval predictor.

• 정보과학회 논문지
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• 제44권9호
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• pp.954-965
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• 2017

안정적인 전력 공급은 전력 인프라의 유지 보수 및 작동에 매우 중요하며, 이를 위해 정확한 전력 사용량 예측이 요구된다. 대학 캠퍼스는 전력 사용량이 많은 곳이며, 시간과 환경에 따른 전력 사용량 변화폭이 다양하다. 이러한 이유로, 전력계통의 효율적인 운영을 위해서는 전력 사용량을 정확하게 예측할 수 있는 모델이 요구된다. 기존의 시계열 예측 기법은 학습 시점과 예측 시점 간의 차이가 클수록 예측 구간이 넓어짐으로 예측 성능이 크게 떨어진다는 단점이 있다. 본 논문은 이를 보완하려는 방안으로, 먼저 의사결정나무를 이용해 날짜, 요일, 공휴일 여부, 학기 등을 고려하여 시계열 형태가 유사한 전력 데이터를 분류한다. 다음으로 분류된 데이터 셋에 각각의 자기회귀누적이동평균모형을 구성하여, 예측 시점에서 시계열 교차검증을 적용해 대학 캠퍼스의 일간 전력 사용량 예측 기법을 제안한다. 예측의 정확성을 평가하기 위해, 성능 평가 지표를 이용하여 제안한 기법의 타당성을 검증하였다.

• 한국식품과학회지
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• 제20권6호
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• pp.827-833
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• 1988

본 연구는 식품의 동결시간에 영향을 미치는 요인에 대해 비교 검토하고. 시료 두께에 따른 동결시간 예측 모델을 유도하기 위해 정지공기식 동결법을 이용하여 피조개 등 4종의 시료에 대하여 실험하였다. 그 결과, 시료의 두께가 두꺼울수록, 표면열전달계수 및 초기온도에 의한 영향은 상당히 급증하였으며, 표면열전달계수의 값이 낮을수록 시료 두께에 미치는 영향은 크며. $150W/m^2^{\circ}C$ 이상에서는 동결시간에 미치는 영향이 거의 유사하게 나타났다. 또한 각 시료에 있어 초기 빙결점이 낮을수록 초기 수분함량이 많을수록, 밀도가 높을수록 동결시간이 길게 나타났고. 열적 물성치의 영향은 밀도, 수분함량, 비열 및 열전도도의 순으로 나타났다. 그리고 시료 초기온도, 초기 빙결점, 동결매체 온도에 따른 시료 두께별 동결 시간을 예측할 수 있는 중직선 회귀방정식을 유도한 결과 실험치와 비교하여 ${\pm}5%$의 오차를 보여 주었다.

• 산업기술연구
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• 제30권A호
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• pp.111-117
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• 2010

To improve the performance of the prediction system, the system should reflect well the uncertainty of nonlinear data. Thus, this paper presents multiple prediction systems based on Type-2 fuzzy sets. To construct each prediction system, an Interval Type-2 TSK Fuzzy Logic System and difference data were used, because, in general, it has been known that the Type-2 Fuzzy Logic System can deal with the uncertainty of nonlinear data better than the Type-1 Fuzzy Logic System, and the difference data can provide more steady information than that of original data. Also, to improve each rule base of the fuzzy prediction systems, the HCBKA (Hierarchical Correlation Based K-means clustering Algorithm) was applied because it can consider correlationship and statistical characteristics between data at a time. Subsequently, to alleviate complexity of the proposed prediction system, a system selection method was used. Finally, this paper analyzed and compared the performances between the Type-1 prediction system and the Interval Type-2 prediction system using simulations of three typical time series examples.

• Nuclear Engineering and Technology
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• 제54권8호
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• pp.3027-3033
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• 2022

Background: In this study, various types of deep-learning models for predicting in vitro radiosensitivity from gene-expression profiling were compared. Methods: The clonogenic surviving fractions at 2 Gy from previous publications and microarray gene-expression data from the National Cancer Institute-60 cell lines were used to measure the radiosensitivity. Seven different prediction models including three distinct multi-layered perceptrons (MLP), four different convolutional neural networks (CNN) were compared. Folded cross-validation was applied to train and evaluate model performance. The criteria for correct prediction were absolute error < 0.02 or relative error < 10%. The models were compared in terms of prediction accuracy, training time per epoch, training fluctuations, and required calculation resources. Results: The strength of MLP-based models was their fast initial convergence and short training time per epoch. They represented significantly different prediction accuracy depending on the model configuration. The CNN-based models showed relatively high prediction accuracy, low training fluctuations, and a relatively small increase in the memory requirement as the model deepens. Conclusion: Our findings suggest that a CNN-based model with moderate depth would be appropriate when the prediction accuracy is important, and a shallow MLP-based model can be recommended when either the training resources or time are limited.

• Geomechanics and Engineering
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• 제29권5호
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• pp.523-533
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• 2022

Several prediction model of penetration rate (PR) of tunnel boring machines (TBMs) have been focused on applying to design stage. In construction stage, however, the expected PR and its trends are changed during tunneling owing to TBM excavation skills and the gap between the investigated and actual geological conditions. Monitoring the PR during tunneling is crucial to rescheduling the excavation plan in real-time. This study proposes a sequential prediction method applicable in the construction stage. Geological and TBM operating data are collected from Gunpo cable tunnel in Korea, and preprocessed through normalization and augmentation. The results show that the sequential prediction for 1 ring unit prediction distance (UPD) is R²≥0.79; whereas, a one-step prediction is R²≤0.30. In modeling algorithm, a gradient boosted regression tree (GBRT) outperformed a least square-based linear regression in sequential prediction method. For practical use, a simple equation between the R² and UPD is proposed. When UPD increases R² decreases exponentially; In particular, UPD at R²=0.60 is calculated as 28 rings using the equation. Such a time interval will provide enough time for decision-making. Evidently, the UPD can be adjusted depending on other project and the R² value targeted by an operator. Therefore, a calculation process for the equation between the R² and UPD is addressed.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.631-634
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• 2004

In this paper, the Bayesian recurrent neural network (BRNN) is proposed to predict time series data. Among the various traditional prediction methodologies, a neural network method is considered to be more effective in case of non-linear and non-stationary time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one need to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, we sets the weight vector as a state vector of state space method, and estimates its probability distributions in accordance with the Bayesian inference. This approach makes it possible to obtain more exact estimation of the weights. Moreover, in the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent network with Bayesian inference, what we call BRNN, is expected to show higher performance than the normal neural network. To verify the performance of the proposed method, the time series data are numerically generated and a neural network predictor is applied on it. As a result, BRNN is proved to show better prediction result than common feedforward Bayesian neural network.

• IEIE Transactions on Smart Processing and Computing
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• 제5권6호
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• pp.430-439
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• 2016

Estimation and analysis of traffic jams plays a vital role in an intelligent transportation system and advances safety in the transportation system as well as mobility and optimization of environmental impact. For these reasons, many researchers currently mainly focus on the brilliant machine learning-based prediction approaches for traffic prediction systems. This paper primarily addresses the analysis and comparison of prediction accuracy between two machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). Based on the fact that optimized estimation accuracy of these methods mainly depends on a large amount of recounted data and that they require much time to compute the same function heuristically for each action, we propose an approach that applies multi-threading to these heuristic methods. It is obvious that the greater the amount of historical data, the more processing time is necessary. For a real-time system, operational response time is vital, and the proposed system also focuses on the time complexity cost as well as computational complexity. It is experimentally confirmed that K-NN does much better than Naïve Bayes, not only in prediction accuracy but also in processing time. Multi-threading-based K-NN could compute four times faster than classical K-NN, whereas multi-threading-based Naïve Bayes could process only twice as fast as classical Bayes.

• Structural Engineering and Mechanics
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• 제82권4호
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• pp.445-458
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• 2022

Some of the control systems used in engineering structures that use sensors and decision systems have some time delay reducing efficiency of the control system or even might make it unstable. In this research, in addition to considering the effect of the time delay in vibration control process, predictive control is used to compensate the time delay. A semi-active vibration control approach with the help of magneto-rheological dampers is implemented. In addition to using fuzzy inference system to determine the appropriate control voltage for MR damper, structural behavior prediction system and specifying future responses are also used such that the time delays occurring within control process are overcome. For this purpose, determination of prediction horizon is conducted for one, five, and ten steps ahead for single degree of freedom structures with periods ranging from 0.1 to 4 seconds, subjected to twenty earthquake excitations. The amount of time delay applied to the control system is 0.1 seconds. The obtained results indicate that for 0.1 second time delay, average prediction error values compared to the case without time delay is 3.47 percent. Having 0.1 second time delay in a semi-active control system reduces its efficiency by 11.46 percent; while after providing the control system with structure behavior prediction, the difference in the results for the control system without time delay is just 1.35 percent on average; indicating a 10.11 percent performance improvement for the control system.