• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Korean Journal of Child Education & Care
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• v.18 no.4
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• pp.123-143
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• 2018

Objective: The purpose of this study was to investigate daycare center director's awareness of male teacher recruitment and need for effective male teacher recruitment. Methods: To this end, eight directors of child care centers with male teachers were selected as subjects of study. The data collection method was applied to the Focus Group Interview method, and a four interviews were conducted for two to two and a half hours. Results: After the interview data was analyzed, the contents were categorized into two major themes and six sub themes in awareness of male teacher recruitment by director of daycare center. The two major themes were (1) A vague fear of upcoming difficulties (2) The light and darkness of male teachers in the organization culture of childcare. Looking at the results, in a vague fear of upcoming difficulties theme includes administrative disadvantages, gender-related social atmosphere, and uncertainty about their role performance. Second, in the light and darkness theme includes women-centered organizational culture and adaptation, the vision of child care sites, and the role of male teachers at childcare sites. Next the contents were categorized into one major theme and four sub themes in need for effective male teacher recruitment by director of daycare center. The major theme was a male teacher's way into the daycare site, and sub five themes were expanding opportunities for child care experience and practices, a shift in the perception that it's not a man, it's an individual problem, maximizing the strengths of men, and improving the system. Conclusion/Implications: Based on these results, several specific implications of need for effective male teacher recruitment were suggested.

• Journal of Industrial Convergence
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• v.17 no.2
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• pp.29-34
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• 2019

The purpose of this study is to analyze the achievements of employment support by the government support specialization project. The data used in this study are based on the comparison of 5 - year employment support field and the operation results of the program until 2014-2018. The results of the study are as follows. First, the overall employment rate of the university has been continuously increased. Especially, the employment rate of the specialization department has been higher than the employment rate of the non - specialization department. Second, as a result of the analysis of the employment capacity strengthening index and the learning capacity strengthening index, it showed a steady increase in each year and contributed to the cultivation of customized talents required by the local society and the national industry. Third, as a result of analyzing the satisfaction of students who are business users, it was confirmed that the business reflecting the demands of the consumers was realized. Fourth, the continuous improvement of the business and the reflux have made the infrastructure of the employment support project more advanced and the system of supporting employment of the university systematically established. In conclusion, the result of the employment support project according to the specialization program showed excellent results and it is necessary to complement theses results when establishing related business plan in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.272-278
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• 2018

Traffic volume has been greatly increasing due to urban development and the improvement of living standards, and many complaints are being raised due to the increasing road noise. As a countermeasure against these problems, highly soundproof walls are installed on the sides of roads. However, the ability to bear wind loads is a major design requirement for soundproof walls, which contributes to the exponential increases in construction costs and restricts the height of the walls. The aim of this study is to improve the performance of soundproof walls and to dramatically reduce wind loads while maintaining excellent price competitiveness. Based on Helmholz's resonator theory, a new concept is proposed for a ventilation-type soundproofing plate that can pass through a fluid like air and reduce noise. A full-scale metal soundproofing plate was produced to satisfy the quality standards of highways by conducting a sound-pressure transmission-loss test, wind tunnel test, and material quality test. To verify the reliability, the wall was manufactured and installed, and the sound insulation effect was examined by measuring the noise over time. In the future, ventilated soundproof walls on roads could create a pleasant living environment due to the high noise-insulation effect.

• Korean Journal of Construction Engineering and Management
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• v.22 no.5
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• pp.73-85
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• 2021

This study proposes an efficient management direction for Korean construction accident cases through a deep learning-based text data classification model. A deep learning model was developed, which categorizes five categories of construction accidents: fall, electric shock, flying object, collapse, and narrowness, which are representative accident types of KOSHA. After initial model tests, the classification accuracy of fall disasters was relatively high, while other types were classified as fall disasters. Through these results, it was analyzed that 1) specific accident-causing behavior, 2) similar sentence structure, and 3) complex accidents corresponding to multiple types affect the results. Two accuracy improvement experiments were then conducted: 1) reclassification, 2) elimination. As a result, the classification performance improved with 185.7% when eliminating complex accidents. Through this, the multicollinearity of complex accidents, including the contents of multiple accident types, was resolved. In conclusion, this study suggests the necessity to independently manage complex accidents while preparing a system to describe the situation of future accidents in detail.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.99-108
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• 2021

There are unrestricted conditions on the driver's face inside the vehicle, such as changes in lighting, partial occlusion and various changes in the driver's condition. In this paper, we propose a face identification system in an unrestricted vehicle environment. The proposed method uses a near-infrared (NIR) camera to minimize the changes in facial images that occur according to the illumination changes inside and outside the vehicle. In order to process a face exposed to extreme light, the normal face image is changed to a simulated overexposed image using mean and variance for training. Thus, facial classifiers are simultaneously generated under both normal and extreme illumination conditions. Our method identifies a face by detecting facial landmarks and aggregating the confidence score of each landmark for the final decision. In particular, the performance improvement is the highest in the class where the driver wears glasses or sunglasses, owing to the robustness to partial occlusions by recognizing each landmark. We can recognize the driver by using the scores of remaining visible landmarks. We also propose a novel robust rejection and a new evaluation method, which considers the relations between registered and unregistered drivers. The experimental results on our dataset, PolyU and ORL datasets demonstrate the effectiveness of the proposed method.

• Journal of Food Hygiene and Safety
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• v.36 no.1
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• pp.34-41
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• 2021

The purpose of this research is to improve analysis methods of determining the contents of fatty acids in infant formulas and follow-up formulas. A gas chromatography (GC) method was performed on a GC system coupled to flame ionization detector, with a fused silica capillary column (SP2560, 100 m×0.25 mm, 0.20 ㎛). The method was validated using standard reference material (SRM, NIST 1849a). Performance parameters for method validation such as specificity, linearity, limits of detection (LOD) and quantification (LOQ), accuracy and precision were examined. The linearity of standard solution with correlation coefficient was higher than 0.999 in the range of 0.1-5 mg/mL. The LOD and LOQ were 0.01-0.06 mg/mL and 0.03-0.2 mg/mL, respectively. The recovery using standard reference material was confirmed and the precision was found to be between 0.8% and 2.9% relative standard deviation (RSD). Optimized methods were applied in sample analysis to verify the reliability. All the tested products had acceptable contents of fatty acids compared with component specification for nutrition labeling. The result of this research will provide efficient experimental information and strengthen the management of nutrients in infant formula and follow-up formula.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.782-789
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• 2020

The demand for the development of rolling stock technology to maintain the best performance in various climatic environments has increased to expand the overseas market of rolling stock. In this study, international and domestic standards that must be applied to build a harsh climatic environment test system were investigated and compared. The way of improvement for domestic standards is proposed. The wind velocities and temperatures are specified in the UIC, EN, and IEC standards for climatic wind tunnel, and EN 50125-1 provides the velocity test up to 180km/h, the largest wind speed. UIC and EN provide the lowest temperature of -45℃, and IEC 62498-1 provides the highest temperature 55℃. The solar radiation test was specified up to 1200W/m2 in the UIC, EN, and IEC. The IEC, EN, and KS R 9145 provide the water tightness standards, which are different from each other in water capacity, pressure, and methods. The snow test method was not well specified. KRTS-VE-Part 31 provides pressurization test methods. The airtightness standards for high-speed rolling stock are defined and regulated for internal pressure change rate in UIC 660 and 779-11. The domestic standard for the wind tunnel test was not well prepared, and the solar radiation test and snow test do not exist in Korea. Therefore, it is necessary to improve domestic standards to an international level for the climatic wind tunnel test of rolling stock.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1493-1507
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• 2020

The LEOP Cal/Val (Launch and Early Operation Phase Calibration/Validation) was carried out during 6 months after KOMPSAT-3A (KOMPSAT-3A Korea Multi-Purpose Satellite-3A) was launched in March 2015. After LEOP Cal/Val was successfully completed, high resolution KOMPSAT-3A has been successfully distributing to users over the past 8 years. The sub-meter high-resolution satellite image data obtained from KOMPSAT-3A is used as basic data for qualitative and quantitative information extraction in various fields such as mapping, GIS (Geographic Information System), and national land management, etc. The KARI (Korea Aerospace Research Institute) periodically checks and manages the quality of KOMPSAT-3A's product and the characteristics of satellite hardware to ensure the accuracy and reliability of information extracted from satellite data of KOMPSAT-3A. To minimize the deterioration of image quality due to aging of satellite hardware, payload and attitude sensors of KOMPSAT-3A, continuous improvement of image quality has been carried out. In this paper, the Cal/Val work-flow defined in the KOMPSAT-3A development phase was illustrated for the period of before and after the launch. The MTF, SNR, and location accuracy are the key parameters to estimate image quality and the methods of the measurements of each parameter are also described in this work. On the basis of defined quality parameters, the performance was evaluated and measured during the period of after LEOP Cal/Val. The current status and characteristics of MTF, SNR, and location accuracy of KOMPSAT-3A from 2016 to May 2020 were described as well.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.101-125
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• 2022

Recently, with the development of computing technology and the improvement of the cloud environment, deep learning technology has developed, and attempts to apply deep learning to various fields are increasing. A typical example is anomaly detection, which is a technique for identifying values or patterns that deviate from normal data. Among the representative types of anomaly detection, it is very difficult to detect a contextual anomaly that requires understanding of the overall situation. In general, detection of anomalies in image data is performed using a pre-trained model trained on large data. However, since this pre-trained model was created by focusing on object classification of images, there is a limit to be applied to anomaly detection that needs to understand complex situations created by various objects. Therefore, in this study, we newly propose a two-step pre-trained model for detecting abnormal situation. Our methodology performs additional learning from image captioning to understand not only mere objects but also the complicated situation created by them. Specifically, the proposed methodology transfers knowledge of the pre-trained model that has learned object classification with ImageNet data to the image captioning model, and uses the caption that describes the situation represented by the image. Afterwards, the weight obtained by learning the situational characteristics through images and captions is extracted and fine-tuning is performed to generate an anomaly detection model. To evaluate the performance of the proposed methodology, an anomaly detection experiment was performed on 400 situational images and the experimental results showed that the proposed methodology was superior in terms of anomaly detection accuracy and F1-score compared to the existing traditional pre-trained model.