• 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.

• Journal of agriculture & life science
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• v.52 no.6
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• pp.27-36
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• 2018

Recently, the demand for new cultivar of oriental mustard(Brassica juncea) is increased as the consumption of oriental mustard has increased dramatically in the market due to Kimchi is attracting the world's attention. However, absence of seed supply system can cause many problems including inbreeding depression due to self seed production, deterioration of the seed purity and heterogeneity of commercial seed. To establish the $F_1$ variety breeding system of oriental mustard, pure line and inbred lines were screened from inbreeding genetic resources. Male-sterile line was also selected from breeding combinations for the quality improvement of mustard. The combining ability from(Indojasai ${\times}$ Goheungdamyang) combinations and isolation line of(MS910 ${\times}$ Japan red mustard 8 ${\times}$ Ganghwa mustard 9) was highest, thus these lines were selected as parental lines. PCR(Polymerase Chain Reaction) and gene sequence analysis revealed that the genes related to CMS(orf263, orf220, and orf288) were distributed in mitochondria. The isolated lines from this study also showed good performance in yield test and farmhouse prove test.

• Journal of Korean Society of Rural Planning
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• v.24 no.4
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• pp.135-146
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• 2018

A Visual landscape planning and management system has been introduced and implemented by each ministry so as to solve the problems of visual landscape destruction due to recognition on the value of natural landscape of beautiful territory and various development projects. At present, this system emphasizes the importance of the visual and perceptual aspect of the landscape however, there is a lack of techniques required for comprehensively predicting, evaluating, and managing it. Furthermore, sustainable landscape management after the completion of development projects has been inadequately carried out, as the focus has been only on consultation in the planning process of the development project in institutional performance. To this end, we presented objective and standardized criteria to predict and judge the effects of development projects on landscapes before project implementation. During the implementation of the development project, the influence of the visual landscape becomes accumulated in the construction progress stage. There is a need to identify the main viewpoints and to examine the continuous changes in the landscape-influencing factors, owing to the remarkable influences on the landscape, such as the change in the topography and the change caused by the artificial structure. During the stage of managing the influence on the visual landscape after the completion of the project, the influence on landscape should be monitored by measuring the change in the continuous landscape-influencing factors and determining the extent to which the actual reduction plan has been implemented. These processes should be performed continuously to maintain the quality of the visual landscape. The change in the landscape caused by the development project is shown to cause relatively greater visual damage than other factors composing the landscape owing to the influence of the artificial factors including the structure or the building. This shows that not only detailed examination of the visual impact before the development project but also continuous management is required during and after the development project. For this purpose, we derived eight landscape-influencing factors including form/shape, line, color, texture, scale/volume, height, skyline, and landscape control point. The proposed considering to be of high utilization in that it has a clear target of the landscape influencing factors.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.543-559
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• 2019

Data often include missing values due to various reasons. If the missing data mechanism is not MCAR, analysis based on fully observed cases may an estimation cause bias and decrease the precision of the estimate since partially observed cases are excluded. Especially when data include many variables, missing values cause more serious problems. Many imputation techniques are suggested to overcome this difficulty. However, imputation methods using parametric models may not fit well with real data which do not satisfy model assumptions. In this study, we review imputation methods using nonlinear models such as kernel, resampling, and spline methods which are robust on model assumptions. In addition, we suggest utilizing imputation classes to improve imputation accuracy or adding random errors to correctly estimate the variance of the estimates in nonlinear imputation models. Performances of imputation methods using nonlinear models are compared under various simulated data settings. Simulation results indicate that the performances of imputation methods are different as data settings change. However, imputation based on the kernel regression or the penalized spline performs better in most situations. Utilizing imputation classes or adding random errors improves the performance of imputation methods using nonlinear models.

• Tunnel and Underground Space
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• v.30 no.6
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• pp.540-550
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• 2020

Machine learning has been actively used in the field of automation due to the development and establishment of AI technology. The important thing in utilizing machine learning is that appropriate algorithms exist depending on data characteristics, and it is needed to analysis the datasets for applying machine learning techniques. In this study, advance rate is predicted using geotechnical and machine data of TBM tunnel section passing through the soil ground below the stream. Although there were no problems of application of statistical technology in the linear regression model, the coefficient of determination was 0.76. While, the ensemble model and support vector machine showed the predicted performance of 0.88 or higher. it is indicating that the model suitable for predicting advance rate of the EPB Shield TBM was the support vector machine in the analyzed dataset. As a result, it is judged that the suitability of the prediction model using data including mechanical data and ground information is high. In addition, research is needed to increase the diversity of ground conditions and the amount of data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.104-112
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• 2021

For extrusion processes in the process industry, the need to build smart factories is increasing. However, in most extrusion production sites, the production method is continuous, and because the properties of the data are undeed, it is difficult to process the data. In order to solve this problem, we present a methodology utilizing a near field communication (NFC) sensor rather than water-based data entry. To this end, a wireless network environment was built, and a data management method was designed. A non-contact NFC method was studied for the production performance-data input method, and an analysis method was implemented using the pivot function of the Excel program. As a result, data input using NFC was automated, obtaining a quantitative effect from reducing the operator's data processing time. In addition, using the input data, we present a case where a bottleneck is improved due to quality problems.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.1
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• pp.13-20
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• 2022

Aircraft weight is an important factor affecting performance and fuel efficiency. In the conceptual design stage of the aircraft, the process of balancing cost and weight is performed using empirical formulas such as fuel consumption cost per weight in estimating element weight. In addition, when an airline operates an aircraft, it promotes fuel efficiency improvement, fuel saving and carbon reduction through weight management activities. The relationship between changes in aircraft weight and changes in fuel consumption is called the cost of weight, and the cost of weight is used to evaluate the effect of adding or reducing weight to an aircraft on fuel consumption. In this study, the problems of the existing cost of weight calculation method are identified, and a new cost of weight calculation method is introduced to solve the problem. Using Breguet's Range Formula and actual flight data of the A350-900 aircraft, two weight costs are calculated based on take-off weight and landing weight. In conclusion, it was suggested that it is reasonable to use the cost of weight based on the take-off weight and the landing weight for other purposes. In particular, the cost of weight based on the landing weight can be used as an empirical formula for estimating element weight and optimizing cost and weight in the conceptual design stage of similar aircraft.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.4
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• pp.537-548
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• 2022

It is important to design the pavement thickness considering heavy-duty traffic loads, which can cause excessive stress and strain in the pavement. Port-rear roads and industrial roads have many problems due to early stress in pavement because these have a higher ratio of heavy loads than general roads such as national roads and expressways. Internationally, composite pavement has been widely applied in pavement designs in heavy-duty areas. Composite pavement is established as an economic pavement type that can increase the design life by nearly double compared to that of existing pavement while also decreasing maintenance and user costs. This study suggests a thickness design method for composite pavement using roller-compacted concrete as a base material to ensure long-term serviceability in heavy-duty areas such as port-rear roads and industrial roads. A three-dimensional finite element analysis was conducted to investigate the mechanical behavior and the long-term pavement performance ultimately to suggest a thickness design method that considers changes in the material properties of the roller-compacted concrete (RCC) base layer. In addition, this study presents a user-friendly catalog design method for RCC-base composite pavement considering the concept of linear damage accumulation for each container trailer depending on the season.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.403-421
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• 2021

Most of the urban tunnels in Korea, which are represented by the 1st to 3rd subways, use the drainage tunnel by NATM. Recently, when a construction project that actively utilizes large-scale urban space is promoted, negative effects that do not conform to the existing empirical rules of urban tunnels may occur. In particular, there is a high possibility that groundwater fluctuations and hydrodynamic behavior will occur owing to the practice of tunnel technology in Korea, which has mainly applied the drainage tunnel. In order to solve the problem of the drainage tunnel, attempts are being made to control groundwater fluctuations. For this, the establishment of tunnel groundwater management standard concept and the analysis of the tunnel hydraulic behavior were performed. To prevent the problem of groundwater fluctuations caused by the construction of large-scale tunnels in urban areas, it was suggested that the conceptual transformation of the empirical technical practice, which is applied only in the underground safety impact assessment stage, to the direction of controlling the inflow in the tunnel, is required. And the relationship between the groundwater level and the inflow of the tunnel required for setting the allowable inflow when planning the tunnel was derived. The introduction of a tunnel groundwater management concept is expected to help solve problems such as groundwater fluctuations, ground settlement, depletion of groundwater resources, and decline of maintenance performance in various urban deep tunnel construction projects to be promoted in the future.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.19-36
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• 2023

As advances in information and communication technology have made it easier for anyone to produce and disseminate information, a new problem has emerged: fake news, which is false information intentionally shared to mislead people. Initially spread mainly through text, fake news has gradually evolved and is now distributed in multimedia formats. Since its founding in 2005, YouTube has become the world's leading video platform and is used by most people worldwide. However, it has also become a primary source of fake news, causing social problems. Various researchers have been working on detecting fake news on YouTube. There are content-based and background information-based approaches to fake news detection. Still, content-based approaches are dominant when looking at conventional fake news research and YouTube fake news detection research. This study proposes a fake news detection method based on background information rather than content-based fake news detection. In detail, we suggest detecting fake news by utilizing related video information from YouTube. Specifically, the method detects fake news through CNN, a deep learning network, from the vectorized information obtained from related videos and the original video using Doc2vec, an embedding technique. The empirical analysis shows that the proposed method has better prediction performance than the existing content-based approach to detecting fake news on YouTube. The proposed method in this study contributes to making our society safer and more reliable by preventing the spread of fake news on YouTube, which is highly contagious.