• Journal of the Korean Applied Science and Technology
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• v.36 no.1
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• pp.189-199
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• 2019

The aim of this study was to investigate the effect of combine exercise on the fall injury related index and myokine in elderly women. Sarcopenia leads to a loss of strength, alter on to a decreased functional status, impaired mobility, a higher risk of falls, and eventually an increased risk of mortality. Aerobic exercise characterized by rhythmic and repetitive movements of large muscles, for sustained periods that depends primarily on the use of oxygen to meet energy demands through aerobic metabolism, and that is structured and intended to generate improvements in cardiopulmonary fitness, body composition, and cardiorespiratory health. Resistance training has performance in the elderly. As combined exercise therapy can be used to enhance muscle functions and cardiopulmonary functions, it is being highlighted as an effective health management methods for the aged. The myokine has been regarded an important factor of exercise how muscle communicate adipose tissue, bone and muscle to exert beneficial effects at the whole body level.

• Journal of IKEEE
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• v.24 no.4
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• pp.1122-1128
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• 2020

Bandwidth Extension refers to restoring and expanding a narrow band signal(NB) that is damaged or damaged in the encoding and decoding process due to the lack of channel capacity or the characteristics of the codec installed in the mobile communication device. It means converting to a wideband signal(WB). Bandwidth extension research mainly focuses on voice signals and converts high bands into frequency domains, such as SBR (Spectral Band Replication) and IGF (Intelligent Gap Filling), and restores disappeared or damaged high bands based on complex feature extraction processes. In this paper, we propose a model that outputs an bandwidth extended signal based on an autoencoder among deep learning models, using the residual connection of one-dimensional convolutional neural networks (CNN), the bandwidth is extended by inputting a time domain signal of a certain length without complicated pre-processing. In addition, it was confirmed that the damaged high band can be restored even by training on a dataset containing various types of sound sources including music that is not limited to the speech.

• Advances in concrete construction
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• v.10 no.6
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• pp.479-488
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• 2020

The experimental and numerical works were carried out on high performance fiber reinforced concrete (HPFRC) with w/cm ratios ranging from 0.25 to 0.40, fiber volume fraction (Vf)=0-1.5% and 10% silica fume replacement. Improvements in compressive and flexural strengths obtained for HPFRC are moderate and significant, respectively, Empirical equations developed for the compressive strength and flexural strength of HPFRC as a function of fiber volume fraction. A relation between flexural strength and compressive strength of HPFRC with R=0.78 was developed. Due to the complex mix proportions and non-linear relationship between the mix proportions and properties, models with reliable predictive capabilities are not developed and also research on HPFRC was empirical. In this paper due to the inadequacy of present method, a back propagation-neural network (BP-NN) was employed to estimate the 28-day compressive strength of HPFRC mixes. BP-NN model was built to implement the highly non-linear relationship between the mix proportions and their properties. This paper describes the data sets collected, training of ANNs and comparison of the experimental results obtained for various mixtures. On statistical analyses of collected data, a multiple linear regression (MLR) model with R2=0.78 was developed for the prediction of compressive strength of HPFRC mixes, and average absolute error (AAE) obtained is 6.5%. On validation of the data sets by NNs, the error range was within 2% of the actual values. ANN model has given the significant degree of accuracy and reliability compared to the MLR model. ANN approach can be effectively used to estimate the 28-day compressive strength of fibrous concrete mixes and is practical.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.49-62
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• 2022

Realized volatility is well known to have long memory, strong association with other global financial markets and interdependences among macroeconomic indices such as exchange rate, oil price and interest rates. This paper proposes autoencoder factor-augmented heterogeneous autoregressive (AE-FAHAR) model for realized volatility forecasting. AE-FAHAR incorporates long memory using HAR structure, and exogenous variables into few factors summarized by autoencoder. Autoencoder requires intensive calculation due to its nonlinear structure, however, it is more suitable to summarize complex, possibly nonstationary high-dimensional time series. Our AE-FAHAR model is shown to have smaller out-of-sample forecasting error in empirical analysis. We also discuss pre-training, ensemble in autoencoder to reduce computational cost and estimation errors.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• Journal of People, Plants, and Environment
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• v.23 no.1
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• pp.1-14
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• 2020

The purpose of this research is to strengthen rural community empowerment by creating a tourism farm as a plan to reduce relative poverty and to improve the quality of life by creating jobs and increasing the income of local residents in underdeveloped countries. To do so, literature review, stakeholder interviews, on-site investigations, focus-group interviews, a project meeting for residents and a questionnaire survey were performed and analyzed and the results were as follows. First, Uganda has the potential to increase agricultural production based on its warm climate, fertile land and abundant natural resources. The quality of life of local residents is expected to be improved by realizing high-added values through the convergence of the conventional existing agricultural industry and other industries if the agricultural technology is properly transferred based on abundant labor force and low labor expenses. Opportunities for the success of the project can be spread to other rural villages across the country. Second, since local residents are now cultivating sugar cane, cassava, matoke, banana, coffee and so on as a farm owner, tourism agriculture with high-added values can be promoted by vitalizing communities based on farming technology to be transferred and a cooperative farm. It is also necessary to implement a rural community empowerment project to do so. Third, the university that is the cooperative partner of the project is positively considering to train experts by establishing a community development department, and, if necessary, a technical training center to educate the general public, which is expected to create synergic effects through the convergence of education, agriculture and tourism.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.737-746
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• 2022

Utilizing weather independent SAR images along with machine learning based object detector is effective in robust vessel monitoring. While conventional SAR images often applied amplitude data from Single Look Complex, exploitation of polarimetric parameters acquired from multiple polarimetric SAR images was yet to be implemented to vessel detection utilizing machine learning. Hence, this study used four polarimetric parameters (H, p₁, DoP, DPRVI) retrieved from eigen-decomposition and two backscattering coefficients (γ₀, _VV, γ₀, _VH) from radiometric calibration; six bands in total were respectively exploited from 52 Sentinel-1 SAR images, accompanied by vessel training data extracted from AIS information which corresponds to acquisition time span of the SAR image. Evaluating different cases of combination, the use of polarimetric indexes along with amplitude values derived enhanced vessel detection performances than that of utilizing amplitude values exclusively.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.85-92
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• 2022

In this paper, we propose a technique that can express low-resolution hair and fur simulations in high-resolution without noise using ConvNet and geometric images of strands in the form of lines. Pairs between low-resolution and high-resolution data can be obtained through physics-based simulation, and a low-resolution-high-resolution data pair is established using the obtained data. The data used for training is used by converting the position of the hair strands into a geometric image. The hair and fur network proposed in this paper is used for an image synthesizer that upscales a low-resolution image to a high-resolution image. If the high-resolution geometry image obtained as a result of the test is converted back to high-resolution hair, it is possible to express the elastic movement of hair, which is difficult to express with a single mapping function. As for the performance of the synthesis result, it showed faster performance than the traditional physics-based simulation, and it can be easily executed without knowing complex numerical analysis.

• Smart Structures and Systems
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• v.30 no.6
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• pp.687-701
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• 2022

Inevitable response loss under complex operational conditions significantly affects the integrity and quality of measured data, leading the structural health monitoring (SHM) ineffective. To remedy the impact of data loss, a common way is to transfer the recorded response of available measure point to where the data loss occurred by establishing the response mapping from measured data. However, the current research has yet addressed the structural condition changes afterward and response mapping learning from a small sample. So, this paper proposes a novel data driven structural response reconstruction method based on a sophisticated designed generating adversarial network (UAGAN). Advanced deep learning techniques including U-shaped dense blocks, self-attention and a customized loss function are specialized and embedded in UAGAN to improve the universal and representative features extraction and generalized responses mapping establishment. In numerical validation, UAGAN efficiently and accurately captures the distinguished features of structural response from only 40 training samples of the intact structure. Besides, the established response mapping is universal, which effectively reconstructs responses of the structure suffered up to 10% random stiffness reduction or structural damage. In the experimental validation, UAGAN is trained with ambient response and applied to reconstruct response measured under earthquake. The reconstruction losses of response in the time and frequency domains reached 16% and 17%, that is better than the previous research, demonstrating the leading performance of the sophisticated designed network. In addition, the identified modal parameters from reconstructed and the corresponding true responses are highly consistent indicates that the proposed UAGAN is very potential to be applied to practical civil engineering.

• Journal of the Korean Society of Physical Medicine
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• v.17 no.4
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• pp.45-52
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• 2022

PURPOSE: This study examined the center of pressure (COP) and ground reaction force (GRF) characteristics during each task-oriented sit-to-stand in stroke patients. METHODS: Twenty stroke subjects were included in this study. The task consisted of sit-to-stand (SS), sit-to-stand for reaching (SR), and sit-to-stand for walking (SW). The response time, COP, and GRF were measured during each task. The COP and GRF data were obtained using a two-force plate. The force plates were placed on a chair (below the buttock) and floor (below the feet). RESULTS: Significant differences were observed between SS (1.48 ± .48 s) and SR (2.09 ± 0.82 s) and between SS and SW (2.27 ± .72 s) in the preparatory phase time during each sit-to-stand exercise (p = .002) and showed significant differences between SS (13.90 ± 6.44 cm) and SW (34.62 ± 39.38 cm) and between SR (16.14 ± 8.04 cm) and SW in the mediolateral COP range during each sit-to-stand exercise (p = .013). CONCLUSION: These findings suggest that more complex task-oriented sit-to-stand exercise requires a high-level motor programming process than a simple sit-to-stand task. Therefore, a variety of tasks-oriented sit-to-stand exercises will be useful training to achieve better ADL ability for stroke patients.