• Journal of Korean Biological Nursing Science
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• 제21권3호
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• pp.217-223
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• 2019

Purpose: Caffeine is the most widely consumed psychostimulant of the methylxanthine class. Among adolescents, high-dose of caffeine consumption has increased rapidly over the last few decades due to the introduction of energy drinks. However, little is known about the time-dependent effect of high doses of caffeine consumption in adolescents. The present study aims to examine the short- and long-term influence of high-dose caffeine on behavior of adolescence. Methods: The animals were divided into three groups: a "vehicle" group, which was injected with 1 ml of phosphate-buffered saline for 14 days; a "Day 1" group, which was injected with caffeine (30 mg/kg), 2 h before the behavioral tests; and a "Day 14" group, which was infused with caffeine for 14 days. An open-field test, a Y-maze test, and a passive avoidance test were conducted to assess the rats'activity levels, anxiety, and cognitive function. Results: High-dose caffeine had similar effects in short-and long-term treatment groups. It increased the level of locomotor activity and anxiety-like behavior, as evidenced by the increase in the number of movements and incidences of rearing and grooming in the caffeine-treated groups. No significant differences were observed between the groups in the Y-maze test. However, in the passive avoidance test, the escape latency in the caffeine-treated group was decreased significantly, indicating impaired memory acquisition. Conclusion: These results indicate that high-dose caffeine in adolescents may increase locomotor activity and anxiety-like behavior and impair learning and memory, irrespective of the duration of administration. The findings will be valuable for both evidence-based education and clinical practice.

• Nutrition Research and Practice
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• 제13권5호
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• pp.399-409
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• 2019

BACKGROUND/OBJECTIVES: This study was designed to provide scientific evidence on the effectiveness of worksite-based dietary intervention to reduce obesity among overweight/obese employees. MATERIALS/METHODS: Electronic search was performed using Ovid Medline, Embase, Cochrane Library, and CINAHL databases. The keywords used were "obesity," "nutrition therapy," and "worksite." The internal validity of the randomized controlled trials (RCTs) was assessed using the Cochrane's Risk of Bias. Meta-analysis of selected studies was performed using Review Manager 5.3. RESULTS: A total of seven RCTs with 2,854 participants were identified. The effectiveness of dietary interventions was analyzed in terms of changes in weight, body mass index (BMI), total cholesterol, and blood pressure. The results showed that weight decreased with weighted mean difference (WMD) of -4.37 (95% confidence interval (CI): -6.54 to -2.20), but the effectiveness was statistically significant only in short-term programs < 6 months (P = 0.001). BMI also decreased with WMD of -1.26 (95% CI: -1.98 to -0.55), but the effectiveness was statistically significant only in short-term programs < 6 months (P = 0.001). Total cholesterol decreased with WMD of -5.57 (95% CI: -9.07 to -2.07) mg/dL, demonstrating significant effectiveness (P = 0.002). Both systolic (WMD: -4.90 mmHg) and diastolic (WMD: -2.88 mmHg) blood pressure decreased, demonstrating effectiveness, but with no statistical significance. CONCLUSIONS: The worksite-based dietary interventions for overweight/obese employees showed modest short-term effects. These interventions can be considered successful because weight loss was below approximately 5-10 kg of the initial body weight, which is the threshold for the management of obesity recommended by the Scottish Intercollegiate Guideline Network (SIGN).

• International Journal of Computer Science & Network Security
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• 제23권3호
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• pp.177-186
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• 2023

Hyperspectral imaging technology is one of the most efficient and fast-growing technologies in recent years. Hyperspectral image (HSI) comprises contiguous spectral bands for every pixel that is used to detect the object with significant accuracy and details. HSI contains high dimensionality of spectral information which is not easy to classify every pixel. To confront the problem, we propose a novel RGB channel Assimilation for classification methods. The color features are extracted by using chromaticity computation. Additionally, this work discusses the classification of hyperspectral image based on Domain Transform Interpolated Convolution Filter (DTICF) and 3D-CNN with Bi-directional-Long Short Term Memory (Bi-LSTM). There are three steps for the proposed techniques: First, HSI data is converted to RGB images with spatial features. Before using the DTICF, the RGB images of HSI and patch of the input image from raw HSI are integrated. Afterward, the pair features of spectral and spatial are excerpted using DTICF from integrated HSI. Those obtained spatial and spectral features are finally given into the designed 3D-CNN with Bi-LSTM framework. In the second step, the excerpted color features are classified by 2D-CNN. The probabilistic classification map of 3D-CNN-Bi-LSTM, and 2D-CNN are fused. In the last step, additionally, Markov Random Field (MRF) is utilized for improving the fused probabilistic classification map efficiently. Based on the experimental results, two different hyperspectral images prove that novel RGB channel assimilation of DTICF-3D-CNN-Bi-LSTM approach is more important and provides good classification results compared to other classification approaches.

• 스마트미디어저널
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• 제12권11호
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• pp.134-144
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• 2023

Nowadays, Deep Learning (DL) technology is being used in several government departments. South Korea imports a lot of seafood. If the demand for fishery products is not accurately predicted, then there will be a shortage of fishery products and the price of the fishery product may rise sharply. So, South Korea's Ministry of Ocean and Fisheries is attempting to accurately predict seafood imports using deep learning. This paper introduces the solution for the fish import prediction in South Korea using the Long Short-Term Memory (LSTM) method. It was found that there was a huge gap between the sum of consumption and export against the sum of production especially in the case of two species that are Hairtail and Pollock. An import prediction is suggested in this research to fill the gap with some advanced Deep Learning methods. This research focuses on import prediction using Machine Learning (ML) and Deep Learning methods to predict the import amount more precisely. For the prediction, two Deep Learning methods were chosen which are Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). Moreover, the Machine Learning method was also selected for the comparison between the DL and ML. Root Mean Square Error (RMSE) was selected for the error measurement which shows the difference between the predicted and actual values. The results obtained were compared with the average RMSE scores and in terms of percentage. It was found that the LSTM has the lowest RMSE score which showed the prediction with higher accuracy. Meanwhile, ML's RMSE score was higher which shows lower accuracy in prediction. Moreover, Google Trend Search data was used as a new feature to find its impact on prediction outcomes. It was found that it had a positive impact on results as the RMSE values were lowered, increasing the accuracy of the prediction.

• Communications for Statistical Applications and Methods
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• 제19권3호
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• pp.415-422
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• 2012

본 연구에서는 단기에 측정되는 트래픽 자료를 예측하기 위하여 Holt-Winters, Fractional Seasonal ARIMA, AR-GARCH, Seasonal AR-GARCH 모형을 사용하여 각 모형의 예측 성능을 비교하고자 한다. 예측에 이용된 시계열 모형에 대해 소개하고, 실제 트래픽 자료에 적용하여 트래픽 자료를 분석한 결과 Holt-Winters방법이 예측력 측면에서 가장 우수하였다.

• 아동학회지
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• 제29권6호
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• pp.73-86
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• 2008

The purpose of this study was to investigate the effects of learning-related skills in kindergarten on school adjustment in first-grade children. Subjects were 119 kindergarten children. Instruments were Learning-Related Skill (Park, 2008) and School Adjustment (Chi & Jung, 2006). Statistical methods were Pearson product moment correlation coefficients and multiple regressions. Results of this study showed that : (1) there were positive relationships between learning-related skill in kindergarten and school adjustment in first-grade children. (2) Cognitive, behavioral, and affective learning-related skills in kindergarten were significant predictors of school adjustment in elementary school Conclusions suggest the importance of learning-related skills in kindergarten.

• 조명전기설비학회논문지
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• 제29권7호
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• pp.54-61
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• 2015

This paper presents short-term wind farm power forecasting method using multivariate analysis and time series. Based on factor analysis, the proposed method makes new independent variables which newly composed by raw independent variables such as wind speed, ramp rate, wind power. Newly created variables are used in the time series model for forecasting wind farm power. To demonstrate the improved accuracy, the proposed method is compared with persistence model commonly used as reference in wind power forecasting using data from Jeju Island. The results of case studies are presented to show the effectiveness of the proposed forecasting method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 A
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• pp.810-812
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• 2005

In this paper, the short-term load forecasting of pole-transformer is performed by artificial neural networks. Input parameters of the Nosed algorithm are peak loads of pole-transformer of previous days and their temperatures. The proposed algorithm is tested for ore of the pole-transformers in seoul, korea. Test results show that the proposed algorithm improves the accuracy of the load forecasting of pole-transformer compared with the conventional algorithm.

• 제어로봇시스템학회논문지
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• 제15권10호
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• pp.1056-1061
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• 2009

This paper proposes a short-term water demand forecasting algorithm based on kalman filtering with data mining for sustainable water supply and effective energy saving. The proposed algorithm utilizes a mining method of water supply data and a decision tree method with special days like Chuseok. And the parameters of MLAR (Multi Linear Auto Regression) model are estimated by Kalman filtering algorithm. Thus, we can achieve the practicality of the proposed forecasting algorithm through the good results applied to actual operation data.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 A
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• pp.184-186
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• 2000

This paper proposes the introduction of TAR(Threshold Autoregressive) model for short-term load forecasting including temperature variable. TAR model is a piecewise linear autoregressive model. In the scatter diagram of daily peak load versus daily maximum or minimum temperature, we can find out that the load-temperature relationship has a negative slope in lower regime and a positive slope in upper regime due to the heating and cooling load, respectively. In this paper, daily peak load was forecasted by applying TAR model using this load-temperature characteristic in these regimes. The results are compared with those of linear and quadratic regression models.