• Title/Summary/Keyword: sequential prediction

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Analysis of streamflow prediction performance by various deep learning schemes

  • Le, Xuan-Hien;Lee, Giha
    • Proceedings of the Korea Water Resources Association Conference
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    • 2021.06a
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    • pp.131-131
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    • 2021
  • Deep learning models, especially those based on long short-term memory (LSTM), have presented their superiority in addressing time series data issues recently. This study aims to comprehensively evaluate the performance of deep learning models that belong to the supervised learning category in streamflow prediction. Therefore, six deep learning models-standard LSTM, standard gated recurrent unit (GRU), stacked LSTM, bidirectional LSTM (BiLSTM), feed-forward neural network (FFNN), and convolutional neural network (CNN) models-were of interest in this study. The Red River system, one of the largest river basins in Vietnam, was adopted as a case study. In addition, deep learning models were designed to forecast flowrate for one- and two-day ahead at Son Tay hydrological station on the Red River using a series of observed flowrate data at seven hydrological stations on three major river branches of the Red River system-Thao River, Da River, and Lo River-as the input data for training, validation, and testing. The comparison results have indicated that the four LSTM-based models exhibit significantly better performance and maintain stability than the FFNN and CNN models. Moreover, LSTM-based models may reach impressive predictions even in the presence of upstream reservoirs and dams. In the case of the stacked LSTM and BiLSTM models, the complexity of these models is not accompanied by performance improvement because their respective performance is not higher than the two standard models (LSTM and GRU). As a result, we realized that in the context of hydrological forecasting problems, simple architectural models such as LSTM and GRU (with one hidden layer) are sufficient to produce highly reliable forecasts while minimizing computation time because of the sequential data nature.

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A High-Speed LSF Transformation Algorithm for CELP Vocoders

  • Min, So-Yeon;Bae, Myung-Jin
    • The Journal of the Acoustical Society of Korea
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    • v.20 no.1E
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    • pp.20-24
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    • 2001
  • We propose the computation reduction method of real root method that is mainly used in the CELP(Code Excited Linear Prediction) vocoder. The real root method is that if polynomial equations have the real roots, we are able to find those and transform them into LSF[1]. However, this method takes much time to compute, because the root searching is processed sequentially in frequency region. But, the important characteristic of LSF is that most of coefficients are occurred in specific frequency region. So, the searching frequency region is ordered by each coefficient's distribution. And coefficients are searched in ordered frequency region. Transformation time can be reduced by this method than the sequential searching method in frequency region. When we compare this proposed method with the conventional real root method, the experimental result is that the searching time was reduced about 46% in average.

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Evaluation of Optimization Models for a Dimpled Channel to Enhance Heat Transfer (딤플 유로의 열전달 증진을 위한 최적화모델 비교)

  • Shin, Dong-Yoon;Kim, Kwang-Yong;Samad, Abdus
    • Proceedings of the KSME Conference
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    • 2007.05b
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    • pp.2552-2557
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    • 2007
  • Shape optimization of an internal cooling passage with staggered dimples on single surface is performed and performances of surrogates are evaluated in this paper. Optimizations are performed so that turbulent heat transfer can be enhanced compromising with pressure loss due to friction. The three-dimensional governing differential equations have been solved to find the overall Nusselt number and friction factor which are related to the objective functions of this problem. Three design variables were selected among the dimensionless geometric variables. Basic surrogate models such as second order polynomial response surface approximation (RSA), Kriging meta-modeling technique, radial basis neural network (RBNN), and derived press based averaged (PBA) surrogate model are constructed. The optimal points are searched from the above constructed surrogates by sequential quadratic programming (SQP). It is shown that use of multiple surrogates can increase the robustness in prediction of better design with minimum computational cost.

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Development of Analysis System for Sheet Metal Forming (박판금속 성형공정 해석시스템 개발)

  • 정완진;조진우
    • Transactions of Materials Processing
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    • v.8 no.1
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    • pp.29-37
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    • 1999
  • An analysis system for sheet metal forming(SAT_STAMP) has been developed to improve the design and tryout process by predicting the deformation behavior more precisely. This analysis system consists of forming analysis, springback analysis and post processor modules. The more accurate prediction of stress history can be achieved due to the improved contact algorithm. Continuous simulation of sequential processes can be carried out conveniently without interruption by the improved data management of the developed system. The error of data transfer between forming analysis and springback analysis is minimized using the proper shell element. Several benchmark test results and practical results are presented to show the effectiveness and reliability of this program.

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A Predictive Model for Sensory Difference Tests Accounting for Sequence Effects

  • Lee, Hye-Seong
    • Food Science and Biotechnology
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    • v.17 no.5
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    • pp.1052-1059
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    • 2008
  • Sequential Sensitivity Analysis (SSA) and conditional stimulus model have been developed to describe sequence effects in difference tests and proposed to generate prediction of differences in sensitivity between various test protocols and to assist the appropriate selection of difference test. Yet, such models did not furnish a complete explanation of the relative sensitivity in 4 different versions of 3-alternative forced choice (AFC) tests where various interstimulus rinses were introduced. In the present study, the vector of the contrasts between various conditional stimuli were measured using same-different and 2-AFC and a new 16-distribution conditional stimulus model was developed by refining Lee and O'Mahony's contrast model. This new model gave superior predictions than previous models.

Time Integration in Reduced System Constructed by Two-level Condensation Scheme (임의의 하중 하에서 축소시스템 구성을 통한 구조물의 동적 거동 연구)

  • 김현기;조맹효
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2004.10a
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    • pp.19-26
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    • 2004
  • This study constructs the reduced system by two-level condensation scheme. This scheme consists of two steps. First step selects the candidate area for the primary degrees of freedom by energy estimation in element level. In the second step, the primary degrees of freedom are selected by the sequential elimination scheme. The efficiency and reliability of this scheme is shown through the prediction of eigenvalues of a few numerical examples. Time integration in the reduced system can save the computing time effectively. The well-constructed reduced system can present the accurate behavior of the structure under arbitrary dynamic loads so much as the global system. Through the numerical example, the efficiency and reliability of the proposed scheme will be demonstrated.

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Distortion Correction Modeling Method for Zoom Lens Cameras with Bundle Adjustment

  • Fang, Wei;Zheng, Lianyu
    • Journal of the Optical Society of Korea
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    • v.20 no.1
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    • pp.140-149
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    • 2016
  • For visual measurement under dynamic scenarios, a zoom lens camera is more flexible than a fixed one. However, the challenges of distortion prediction within the whole focal range limit the widespread application of zoom lens cameras greatly. Thus, a novel sequential distortion correction method for a zoom lens camera is proposed in this study. In this paper, a distortion assessment method without coupling effect is depicted by an elaborated chessboard pattern. Then, the appropriate distortion correction model for a zoom lens camera is derived from the comparisons of some existing models and methods. To gain a rectified image at any zoom settings, a global distortion correction modeling method is developed with bundle adjustment. Based on some selected zoom settings, the optimized quadratic functions of distortion parameters are obtained from the global perspective. Using the proposed method, we can rectify all images from the calibrated zoom lens camera. Experimental results of different zoom lens cameras validate the feasibility and effectiveness of the proposed method.

Selection of Optimal Sensor Locations for Thermal Error Model of Machine tools (공작기계 열오차 모델의 최적 센서위치 선정)

  • 안중용
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 1999.10a
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    • pp.345-350
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    • 1999
  • The effectiveness of software error compensation for thermally induced machine tool errors relies on the prediction accuracy of the pre-established thermal error models. The selection of optimal sensor locations is the most important in establishing these empirical models. In this paper, a methodology for the selection of optimal sensor locations is proposed to establish a robust linear model which is not subjected to collinearity. Correlation coefficient and time delay are used as thermal parameters for optimal sensor location. Firstly, thermal deformation and temperatures are measured with machine tools being excited by sinusoidal heat input. And then, after correlation coefficient and time delays are calculated from the measured data, the optimal sensor location is selected through hard c-means clustering and sequential selection method. The validity of the proposed methodology is verified through the estimation of thermal expansion along Z-axis by spindle rotation.

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Prediction of Void Crushing Behavior in Upset & Bloom Forging of Large Ingot (대형인곳의 업셋-블룸단조에서의 기공 압착 거동 예측)

  • Kwon I.K.;Kim K.H.;Youn Y.C.;Song M.C.
    • Proceedings of the Korean Society for Technology of Plasticity Conference
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    • 2004.10a
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    • pp.325-328
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    • 2004
  • This paper deals with void crushing behavior by ingot forging process which consists of sequential operations of upset forging and bloom forging. The predicted results of void crushing behavior by the simplified global-local method using F.E. analysis showed that the inherent void at the top region of the ingots remains incompletely crushed even after several forging operations. From the results of the hot upset forging test using the billets with drilled voids, it was found that the bonding efficiency of the void after forging process increases with an increase in deformation, and a decrease of initial diameter of voids.

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Selection of Important Variables in the Classification Model for Successful Flight Training (조종사 비행훈련 성패예측모형 구축을 위한 중요변수 선정)

  • Lee, Sang-Heon;Lee, Sun-Doo
    • IE interfaces
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    • v.20 no.1
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    • pp.41-48
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    • 2007
  • The main purpose of this paper is cost reduction in absurd pilot positive expense and human accident prevention which is caused by in the pilot selection process. We use classification models such as logistic regression, decision tree, and neural network based on aptitude test results of 505 ROK Air Force applicants in 2001~2004. First, we determine the reliability and propriety against the aptitude test system which has been improved. Based on this conference flight simulator test item was compared to the new aptitude test item in order to make additional yes or no decision from different models in terms of classification accuracy, ROC and Response Threshold side. Decision tree was selected as the most efficient for each sequential flight training result and the last flight training results predict excellent. Therefore, we propose that the standard of pilot selection be adopted by the decision tree and it presents in the aptitude test item which is new a conference flight simulator test.