• Journal of Information Processing Systems
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• v.16 no.4
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• pp.859-869
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• 2020

This paper introduces page-level rewrite interval prediction (PRWIP). By recording and analyzing the memory access history at page-level, we are able to predict the future memory accesses to any pages. Leveraging this information, this paper proposes a faster incremental checkpoint design by overlapping checkpoint phase with computing phase and thus achieves higher performance. Experimental results show that our new incremental checkpoint design can achieve averagely 22% speedup over traditional incremental checkpoint and 14% over the previous state-of-the-art work.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.169-175
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• 2004

This paper develops a dynamic travel time prediction model for ATIS in a national highway. While there have been many research on travel time prediction, none of them is for national highway in Korea. The study uses AVI data installed on the national highway No.1 with 10km interval for travel time prediction model, and probe vehicle data for evaluating the model. The study area has many access points, so there are many outlying observations in the raw AVI data. Therefore, this study uses the algorithm proposed by the author for removing the outliers, and then Kalman filtering algorithm is applied for the travel time prediction. The prediction model is performed for 5, 10, 15 and 30 minute-aggregating interval and the results are $0.061{\sim}0.066$ for 5, 10 and 15 interval and 0.078 for 30 minute one with a little low performance as MAREs.

• Physical Therapy Korea
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• v.17 no.3
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• pp.59-67
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• 2010

Mortality rates from stroke have been declining. Because of this, more people are living with residual disability. Rehabilitation plays an important role in functional recovery of stroke survivors. In stroke rehabilitation, early prediction of the obtainable level of functional recovery is desirable to deliver efficient care, set realistic goals, and provide appropriate discharge planning. The purpose of this study was to identify predictors of functional outcome after stroke using inpatient rehabilitation as measured by Functional Independence Measure (FIM) total scores. Correlation and stepwise multiple regression analyses were performed on data collected retrospectively from two-hundred thirty-five patients. More than moderate correlation was found between FIM total scores at the time of hospital admission and FIM total scores at the time of discharge from the hospital. Significant predictors of FIM at the time of discharge were FIM total scores at the time of hospital admission, age, and onset-admission interval. The equation was as follows: expected discharge FIM total score = $76.12+.62{\times}$(admission FIM total score)-$.38{\times}(age)-.15{\times}$(onset-admission interval). These findings suggest that FIM total scores at the time of hospital admission, age, and onset-admission interval are important determinants of functional outcome.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.38-50
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• 2018

Spatio-temporal load forecasting (STLF) is a foundation for building the prediction-based power map, which could be a useful tool for the visualization and tendency assessment of urban energy application. Constructing one point-forecasting model for each electricity cell in the geographic space is possible; however, it is unadvisable and insufficient, considering the aggregation features of electricity cells and uncertainties in input variables. This paper presents a new STLF method, with a data-driven framework consisting of 3 subroutines: multi-level clustering of cells considering their aggregation features, load regression for each category of cells based on SLS-SVRNs (sparse least squares support vector regression networks), and interval forecasting of spatio-temporal load with sampled blind number. Take some area in Pudong, Shanghai as the region of study. Results of multi-level clustering show that electricity cells in the same category are clustered in geographic space to some extent, which reveals the spatial aggregation feature of cells. For cellular load regression, a comparison has been made with 3 other forecasting methods, indicating the higher accuracy of the proposed method in point-forecasting of spatio-temporal load. Furthermore, results of interval load forecasting demonstrate that the proposed prediction-interval construction method can effectively convey the uncertainties in input variables.

• Asian-Australasian Journal of Animal Sciences
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• v.14 no.11
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• pp.1511-1515
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• 2001

A prediction of 305 d milk production from early records using an empirical Bayes method (EBM) was performed. The EBM was compared with the best predicted estimation (BPE), test interval method (TIM), and the linearized Wood's model (LWM). Daily milk yields were obtained from 606 first lactation Japanese Holstein cows in three herds. From each file of 305 daily records, 10 random test day records with an interval of approximately one month were taken. The accuracies of these methods were compared using the absolute difference (AD) and the standard deviation (SD) of the differences between the actual and the estimated 305 d milk production. The results showed that in the early stage of the lactation, EBM was superior in obtaining the prediction with high accuracy. When all the herds were analyzed jointly, the AD during the first 5 test day records were on average 373, 590, 917 and 1,042 kg for EBM, BPE, TIM, and LWM, respectively. Corresponding SD for EBM, BPE, TIM, and LWM were on average 488, 733, 747 and 1,605 kg. When the herds were analyzed separately, the EBM predictions retained high accuracy. When more information on the actual lactation was added to the prediction, TIM and LWM gradually achieved better accuracies. Finally, in the last period of the lactation, the accuracy of both of the methods exceeded EBM and BPM. The AD for the last 2 samples analyzing all the herds jointly were on average 141, 142, 164, and 214 kg for LWM, TIM, EBM, and BPE, respectively. In the current practices of collecting monthly records, early prediction of future milk production may be more accurate using EBM. Alternatively, if enough information of the actual lactation is accumulated, TIM may obtain better accuracy in the latter stage of lactation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.31-36
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• 2008

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the enviromuent. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.213-222
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• 2002

The 3-hour-interval prediction of ground-level temperature up to +45 hours in Seoul area is performed using dynamic linear models(DLM). Numerical outputs and observations we used as input values of DLM. According to compare DLM forecasts to RDAPS forecasts using RMSE, DLM improve the accuracy of prediction and systematic error of numerical model outputs are eliminated by DLM.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.920-924
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• 2005

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the environment. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• Journal of Industrial Technology
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• v.28 no.B
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• pp.101-109
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• 2008

In general, non-stationary or chaos time series forecasting is very difficult since there exists a drift and/or nonlinearities in them. To overcome this situation, we suggest a new prediction method based on multiple model TS fuzzy predictors combined with preprocessing of time series data, where, instead of time series data, the differences of them are applied to predictors as input. In preprocessing procedure, the candidates of optimal difference interval are determined by using con-elation analysis and corresponding difference data are generated. And then, for each of them, TS fuzzy predictor is constructed by using k-means clustering algorithm and least squares method. Finally, the best predictor which minimizes the performance index is selected and it works on hereafter for prediction. Computer simulation is performed to show the effectiveness and usefulness of our method.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.125-132
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• 2023

In this study, we compared the performance of machine learning models for predicting Vapor Pressure Deficits (VPD) in greenhouses that affect pore function and photosynthesis as well as plant growth due to nutrient absorption of plants. For VPD prediction, the correlation between the environmental elements in and outside the greenhouse and the temporal elements of the time series data was confirmed, and how the highly correlated elements affect VPD was confirmed. Before analyzing the performance of the prediction model, the amount and interval of analysis time series data (1 day, 3 days, 7 days) and interval (20 minutes, 1 hour) were checked to adjust the amount and interval of data. Finally, four machine learning prediction models (XGB Regressor, LGBM Regressor, Random Forest Regressor, etc.) were applied to compare the prediction performance by model. As a result of the prediction of the model, when data of 1 day at 20 minute intervals were used, the highest prediction performance was 0.008 for MAE and 0.011 for RMSE in LGBM. In addition, it was confirmed that the factor that most influences VPD prediction after 20 minutes was VPD (VPD_y__71) from the past 20 minutes rather than environmental factors. Using the results of this study, it is possible to increase crop productivity through VPD prediction, condensation of greenhouses, and prevention of disease occurrence. In the future, it can be used not only in predicting environmental data of greenhouses, but also in various fields such as production prediction and smart farm control models.