• Communications for Statistical Applications and Methods
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• 제28권4호
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• pp.329-338
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• 2021

Deep learning methods have been developed, used in various fields, and they have shown outstanding performances in many cases. Many studies predicted a daily stock return, a classic example of time-series data, using deep learning methods. We also tried to apply deep learning methods to Korea's stock market data. We used Korea's stock market index (KOSPI) and several individual stocks to forecast daily returns and directions. We compared several deep learning models with other machine learning methods, including random forest and XGBoost. In regression, long short term memory (LSTM) and gated recurrent unit (GRU) models are better than other prediction models. For the classification applications, there is no clear winner. However, even the best deep learning models cannot predict significantly better than the simple base model. We believe that it is challenging to predict daily stock return data even if we use the latest deep learning methods.

• Animal cells and systems
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• 제12권1호
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• pp.11-14
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• 2008

Finding a way to strengthen human cognitive functions, such as learning and memory, has been of great concern since the moment people realized that these functions can be affected and even altered by certain chemicals. Since then, plenty of endeavors have been made to look for safe ways of improving cognitive performances without adverse side-effects. Unfortunately, most of these efforts have turned out to be unsuccessful until now. In this study, we examine the effect of a natural compound, idesolide, on hippocampus-dependent recognition memory. We demonstrate that idesolide is effective in the enhancement of recognition memory, as measured by a novel object recognition task. Thus, idesolide might serve as a novel therapeutic medication for the treatment of memoryrelated brain anomalies such as mild cognitive impairment(MCI) and Alzheimer's disease.

• 한국항행학회논문지
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• 제25권5호
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• pp.409-414
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• 2021

미세먼지에 대한 심각성이 사회적으로 대두됨에 따라 대중들은 미세먼지 예보에 대한 정보의 높은 신뢰성을 요구하고 있다. 이에 따라 다양한 신경망 알고리즘을 이용하여 미세먼지 예측을 위한 연구가 활발히 진행되고 있다. 본 논문에서는 미세먼지 예측을 위해 다양한 알고리즘으로 연구되고 있는 신경망 알고리즘들 중 대표적인 알고리즘들의 예측 성능 비교를 진행하였다. 신경망 알고리즘 중 DNN(deep neural network), RNN(recurrent neural network), LSTM(long short-term memory)을 이용하였으며, 하이퍼 파라미터 탐색을 이용하여 최적의 예측 모델을 설계하였다. 각 모델의 예측 성능 비교 분석 결과, 실제 값과 예측 값의 변화 추이는 전반적으로 좋은 성능을 보였다. RMSE와 정확도를 기준으로 한 분석에서는 DNN 예측 모델이 다른 예측 모델에 비해 예측 오차에 대한 안정성을 갖는 것을 확인하였다.

• 농업과학연구
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• 제46권1호
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• pp.67-78
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• 2019

The purpose of this study was to predict the water quality using the RNN (recurrent neutral network) and LSTM (long short-term memory). These are advanced forms of machine learning algorithms that are better suited for time series learning compared to artificial neural networks; however, they have not been investigated before for water quality prediction. Three water quality indexes, the BOD (biochemical oxygen demand), COD (chemical oxygen demand), and SS (suspended solids) are predicted by the RNN and LSTM. TensorFlow, an open source library developed by Google, was used to implement the machine learning algorithm. The Okcheon observation point in the Geum River basin in the Republic of Korea was selected as the target point for the prediction of the water quality. Ten years of daily observed meteorological (daily temperature and daily wind speed) and hydrological (water level and flow discharge) data were used as the inputs, and irregularly observed water quality (BOD, COD, and SS) data were used as the learning materials. The irregularly observed water quality data were converted into daily data with the linear interpolation method. The water quality after one day was predicted by the machine learning algorithm, and it was found that a water quality prediction is possible with high accuracy compared to existing physical modeling results in the prediction of the BOD, COD, and SS, which are very non-linear. The sequence length and iteration were changed to compare the performances of the algorithms.

• 재활간호학회지
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• 제5권2호
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• pp.205-216
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• 2002

This quasi-experimental study was done to test the effects of meta-memory enhancing program for elderly women. Data were collected 12 to 30, August 2002 from 34elderly women over 65 years living in Busan city. Subjects were 15 of experimental group and 19 of control group. The metamemory enhancing program was developed by five sessions composing of 1.5-2.0 hours one session. In experiment group, this program was performed for three weeks, twice per week. The degrees of four memory performance tasks were measured using instrument of Elderly Verbal Learning Test(Choi Kyung Mi, 1988) and Face Recognition Instrument(Min Hye Sook, 1999) and the metamemory were measured using MIA questionnaire(Dixon et al., 1988). Research results are as following. 1. After participating in five times memory training programs, experimental group has the significant increase of metamemory in comparison with control group.(t=59.58, p< 0.0001). In particular, the concepts of strategy(t=20.44, p< 0.0001), achievement (t=21.94, p< 0.0001), and locus degree (t=59.58, p< 0.0001) among sub-concepts of the metamemory are increasing significantly. 2. After participating in five time memory training programs, the degree of immediate word recall(t=17.25, p< 0.0001) and face recognition(t=16.69, p< 0.0001) among four memory tasks in experimental group are increasing significantly compared with those measures of control group. Considering this results, this metamemory enhancing program was found as an effective nursing program for metamemory improvement of elderly women's memory.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• 제4권4호
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• pp.159-176
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• 2023

The conservation of the raccoon dog (Nyctereutes procyonoides) in South Korea requires the protection and preservation of natural habitats while additionally ensuring coexistence with human activities. Applying habitat map modeling techniques provides information regarding the distributional patterns of raccoon dogs and assists in the development of future conservation strategies. The purpose of this study is to generate potential habitat distribution maps for the raccoon dog in South Korea using geospatial technology-based models. These models include the frequency ratio (FR) as a bivariate statistical approach, the group method of data handling (GMDH) as a machine learning algorithm, and convolutional neural network (CNN) and long short-term memory (LSTM) as deep learning algorithms. Moreover, the imperialist competitive algorithm (ICA) is used to fine-tune the hyperparameters of the machine learning and deep learning models. Moreover, there are 14 habitat characteristics used for developing the models: elevation, slope, valley depth, topographic wetness index, terrain roughness index, slope height, surface area, slope length and steepness factor (LS factor), normalized difference vegetation index, normalized difference water index, distance to drainage, distance to roads, drainage density, and morphometric features. The accuracy of prediction is evaluated using the area under the receiver operating characteristic curve. The results indicate comparable performances of all models. However, the CNN demonstrates superior capacity for prediction, achieving accuracies of 76.3% and 75.7% for the training and validation processes, respectively. The maps of potential habitat distribution are generated for five different levels of potentiality: very low, low, moderate, high, and very high.

• 항공우주시스템공학회지
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• 제14권2호
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• pp.50-56
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• 2020

In this study, we investigated whether long short-term memory (LSTM) can be used in the future to predict F10.7 index data; the F10.7 index is a space environment factor affecting atomic oxygen erosion. Based on this, we compared the prediction performances of LSTM, the Autoregressive integrated moving average (ARIMA) model (which is a traditional statistical prediction model), and the similar pattern searching method used for long-term prediction. The LSTM model yielded superior results compared to the other techniques in the prediction period starting from the max/min points, but presented inferior results in the prediction period including the inflection points. It was found that efficient learning was not achieved, owing to the lack of currently available learning data in the prediction period including the maximum points. To overcome this, we proposed a method to increase the size of the learning samples using the sunspot data and to upgrade the LSTM model.

• 컴퓨터교육학회논문지
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• 제17권5호
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• pp.99-106
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• 2014

본 연구는 영어 단어 학습의 핵심 인지기능인 작업기억의 음운고리를 적극적으로 활용하게 하는 영어 단어 제시 방법이 무엇이고 이 방법이 외국어 학습 동기 정도에 따라 효과가 다르게 나타나는지를 두실험을 통해 검증하였다. 실험1에서 학습자에게 음운고리를 최소 3회 사용하게 하는 제시 방법과 1회 사용하게 하는 제시 방법 그리고 자신이 제시 횟수 및 제시 시간을 조정할 수 있는 조건을 제시하였다. 실험1결과 3회 제시 조건이 1회 제시 조건에 비해 학습효과가 더 높게 나타났다. 실험2에서 외국어 학습 동기가 높은 집단과 낮은 집단에게 3회 제시 조건과 자기 조절 조건을 제시하여 학습 효과를 검증하였다. 실험2결과 고-동기 집단의 경우 제시 방법에 따른 학습의 정도는 차이가 없었지만, 저-동기 집단의 경우 자기 조절 조건에서 더 좋은 성과를 보였다. 이에 본 연구는 논의에서 컴퓨터 및 디지털 환경에서 영어 단어를 어떻게 제시해야 학습효과가 증진될 수 있는지를 제안하였다.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• 제13권1호
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• pp.85-92
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• 2002

목 적：본 연구에서는 주의력 결핍/과잉운동 장애(ADHD)와 학습장애(LD), 그리고 이 두 장애의 공존질병(ADHD+LD)을 보이는 혼합형 장애 아동 집단을 대상으로 여러 가지 기억 기능을 평가하는 신경심리검 사상에서 세 장애 집단의 수행 차이를 비교하였고, 지능수준과 기억 책략의 사용 여부가 이들의 기억능력 향상에 어떤 영향을 미치는지를 알아보고자 하였다. 방 법：ADHD 아동 11명, LD 아동 5명, 두 장애를 함께 가진 혼합형 장애(ADHD+LD) 아동 9명, 그리고 정상 아동 8명에게 기억 기능과 인지능력을 평가하기 위해 개별적으로 기억력 평가 검사(MAS)와 웩슬러 아동용 지능검사를 실시한 후, 집단간 수행 차이를 비교하였다. 결 과：언어 및 시각 과제에 대한 재인검사를 제외한 제반 기억 검사상에서 통제집단에 비해서 세 장애집단군이 저조한 수행을 보였으며, 특히 ADHD+LD 집단이 가장 저조한 수행을 보였고, 그 다음으로 LD, ADHD 집단의 순으로 전반적인 기억 검사상에서 저조한 수행을 보였다. 이러한 수행 양상은 언어 기억 소검사를 제외한 MAS 검사의 다른 하위검사에서도 동일하게 나타났으며, 제반 기억 검사에서의 우수한 수행은 검사시 아동이 사용하였던 기억 책략 및 오류 반응의 사용정도 유의미한 상관이 있었다. 논 의：ADHD, LD의 공존질병을 가질 경우 기억 및 학습에 더 어려움이 있으며, ADHD, LD, 혼합형 집단을 변별하는데 있어 기억기능을 측정하는 것이 각 장애를 감별진단하는데 도움이 될 가능성이 시사되었다.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• 제2권1호
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• pp.1-14
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• 2021

The study has been carried out with an objective to prepare Siberian roe deer habitat potential maps in South Korea based on three geographic information system-based models including frequency ratio (FR) as a bivariate statistical approach as well as convolutional neural network (CNN) and long short-term memory (LSTM) as machine learning algorithms. According to field observations, 741 locations were reported as roe deer's habitat preferences. The dataset were divided with a proportion of 70:30 for constructing models and validation purposes. Through FR model, a total of 10 influential factors were opted for the modelling process, namely altitude, valley depth, slope height, topographic position index (TPI), topographic wetness index (TWI), normalized difference water index, drainage density, road density, radar intensity, and morphological feature. The results of variable importance analysis determined that TPI, TWI, altitude and valley depth have higher impact on predicting. Furthermore, the area under the receiver operating characteristic (ROC) curve was applied to assess the prediction accuracies of three models. The results showed that all the models almost have similar performances, but LSTM model had relatively higher prediction ability in comparison to FR and CNN models with the accuracy of 76% and 73% during the training and validation process. The obtained map of LSTM model was categorized into five classes of potentiality including very low, low, moderate, high and very high with proportions of 19.70%, 19.81%, 19.31%, 19.86%, and 21.31%, respectively. The resultant potential maps may be valuable to monitor and preserve the Siberian roe deer habitats.