• Smart Media Journal
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• v.12 no.7
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• pp.59-67
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• 2023

Recently, research on behavior analysis tracking human posture and movement has been actively conducted. In particular, OpenPose, an open-source software developed by CMU in 2017, is a representative method for estimating human appearance and behavior. OpenPose can detect and estimate various body parts of a person, such as height, face, and hands in real-time, making it applicable to various fields such as smart healthcare, exercise training, security systems, and medical fields. In this paper, we propose a method for classifying four exercise movements - Squat, Walk, Wave, and Fall-down - which are most commonly performed by users in the gym, using OpenPose-based deep learning models, DNN and CNN. The training data is collected by capturing the user's movements through recorded videos and real-time camera captures. The collected dataset undergoes preprocessing using OpenPose. The preprocessed dataset is then used to train the proposed DNN and CNN models for exercise movement classification. The performance errors of the proposed models are evaluated using MSE, RMSE, and MAE. The performance evaluation results showed that the proposed DNN model outperformed the proposed CNN model.

• Journal of the Korea Society for Simulation
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• v.30 no.4
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• pp.1-8
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• 2021

Deep learning is one of the most widely accepted methods for the forecasting of time series data which have the complexity and non-linear behavior. In this paper, we investigate the modification of a state-of-art WaveNet deep learning architecture and walk forward validation (WFV) in order to forecast electric power consumption data 24-hour-ahead. WaveNet originally designed for raw audio uses 1D dilated causal convolution for long-term information. First of all, we propose a modified version of WaveNet which activates real numbers instead of coded integers. Second, this paper provides with the training process with tuning of major hyper-parameters (i.e., input length, batch size, number of WaveNet blocks, dilation rates, and learning rate scheduler). Finally, performance evaluation results show that the prediction methodology based on WFV performs better than on the traditional holdout validation.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.137-143
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• 2019

Fake news is a form of misinformation that has the advantage of rapid spreading of information on media platforms that users interact with, such as social media. There has been a lot of social problems due to the recent increase in fake news. In this paper, we propose a method to detect such false news. Previous research on fake news detection mainly focused on text analysis. This research focuses on a network where social media news spreads, generates qualities with DeepWalk, a network embedding method, and classifies fake news using logistic regression analysis. We conducted an experiment on fake news detection using 211 news on the Internet and 1.2 million news diffusion network data. The results show that the accuracy of false network detection using network embedding is 10.6% higher than that of text analysis. In addition, fake news detection, which combines text analysis and network embedding, does not show an increase in accuracy over network embedding. The results of this study can be effectively applied to the detection of fake news that organizations spread online.

• Bulletin of the Korean Chemical Society
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• v.13 no.6
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• pp.665-669
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• 1992

We consider the problem of a random walker on a one-dimensional lattice (N sites) confronting a centrally-located deep trap (trapping probability, T=1) and N-1 adjacent sites at each of which there is a nonzero probability s(0 < s < 1) of the walker being trapped. Exact analytic expressions for < n > and the average number of steps required for trapping for arbitrary s are obtained for two types of finite boundary conditions (confining and reflecting) and for the infinite periodic chain. For the latter case of boundary condition, Montroll's exact result is recovered when s is set to zero.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.63-65
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• 2022

DDI(Drug-Drug Interaction)는 병원에서 발생하는 약물이상반응의 30%를 유발하는 부작용이지만, 현실적으로 모든 약물쌍의 DDI 를 기존 in vivo, in vitro 방식으로 예측하는 것은 불가능하다. 그렇기에, 다양한 in silico 방식의 DDI 예측 모델이 연구되고 있다. 본 연구에서는, 단백질 네트워크 상에서 RWR(Random Walk with Restart) 알고리즘을 통해 약물과 직접적으로 상호작용하는 단백질과 간접적으로 상호작용하는 단백질의 정보를 사용하여 DDI 를 예측하는 모델을 개발하였다. 이 모델을 통하여 기존에 발견하지 못한 DDI 를 새롭게 발견하고, 신약 개발 시에도, 신약과 함께 복용 시 문제를 일으킬 수 있는 약물을 예측하여 약물 이상반응을 방지하고자 한다.

• Journal of the Korean Society of Physical Medicine
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• v.11 no.4
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• pp.55-64
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• 2016

PURPOSE: The aim of this study was to examine the effect of air stacking exercise on lung capacity, activities of daily living, and walking ability in elderly adults. METHODS: A total of 27 subjects were randomly assigned to an experimental group (EG=13) or a control group (CG=14). Subjects in the experimental group participated in an active pulmonary rehabilitation program. 5 days a week for 4 weeks. The active pulmonary rehabilitation program was composed of an air stacking exercise with an oral nasal mask and manually assisted coughing. Conventional pulmonary rehabilitation exercises, such as, cough exercise, deep breathing, and abdominal muscle strengthening exercises were performed by both groups. Pulmonary function parameters, peak cough flow (PCF), and oxygen saturation were measured and the 6-minute walk test and Korean version of the modified Barthel index (K-MBI) scores were applied. RESULTS: Significant intergroup differences were observed for forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) results after intervention (p<.05), and for 6 minute walk test and PCF results after intervention and at 2-week follow-up visits (p<.05). Post hoc test results showed significant differences in K-MBI, 6-minute walk test, and FEV1 in the experimental group after intervention (p<.05). FVC values were significantly higher after intervention and at 2-week follow-up visits versus pre-intervention (p<.05). PCF values were also significantly higher after intervention and remained significantly higher at 2-week follow-up visits (p<.05). CONCLUSION: Air stacking exercise in elderly adults improves lung capacity and exercise tolerance.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.307-314
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• 2022

Recently, researches using deep learning-based models are being actively conducted to replace statistical-based time series forecast techniques to predict electric power demand. The result of analyzing the researches shows that the performance of the LSTM-based prediction model is acceptable, but it is not sufficient for long-term regional-wide power demand prediction. In this paper, we propose a WaveNet deep learning model to predict electric power demand 24-hour-ahead with temperature data in order to achieve the prediction accuracy better than MAPE value of 2% which statistical-based time series forecast techniques can present. First of all, we illustrate a delated causal one-dimensional convolutional neural network architecture of WaveNet and the preprocessing mechanism of the input data of electric power demand and temperature. Second, we present the training process and walk forward validation with the modified WaveNet. The performance comparison results show that the prediction model with temperature data achieves MAPE value of 1.33%, which is better than MAPE Value (2.33%) of the same model without temperature data.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.63-66
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• 2021

The technological advancement in computer vision has made system like grab-and-go grocery a reality. Now all the shoppers have to do now is to walk in grab the items and go out without having to wait in the long queues. This paper presents an intelligent retail environment system that is capable of monitoring and tracking customer's activity during shopping based on their interaction with the shelf. It aims to develop a system that is low cost, easy to mount and exhibit adequate performance in real environment.

• Journal of Korea Water Resources Association
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• v.31 no.3
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• pp.347-360
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• 1998

Numerical simulations for fluid flow and solute transport in a fracture rock masses are performed by using a transient flow model, which is based on the three-dimensional stochastic and discrete fracture network model (DFN model) and is coupled hydraulic model with mechanical model. In the numerical simulations of the solute transport, we used to the particle following algorithm which is similar to an advective biased random walk. The purpose of this study is to predict the response of the tracer test between two deep bore holes (GPK1 and GPK2) implanted at Soultz sous Foret in France, in the context of the geothermal researches.l The data sets used are obtained from in situcirculating experiments during 1995. As the result of the transport simulation, the mean transit time for the non reactive particles is about 5 days between two bore holes.

• KIEAE Journal
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• v.8 no.3
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• pp.51-59
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• 2008

response to the global city. It was developed to cope with the decline of the manufacturing industry, a rise in unemployment and a welfare state system in danger. In cities of the concept, there has been much change in existing urban space and accordingly wisdoms and knowledge has been accumulated. The purpose of this research is to scrutinize urban spatial modification of a regenerated model city "Bologna". The target area of analysis was a ex-factory ailing district. Field site visit, deep interview with professionals and citizens, walk through observation, and historial literature review on the site were employed. As results, its history, retrofit process and current change were systematically described. The results showed ways of thinking, attitude toward historic preservation, technology, and creativity of using existing buildings for contemporary functions. This has a significant implication on Korean urban development which mostly, has ignored the existing value of community and buildings.