• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.457-468
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• 2022

In aged society, it is important to prevent older people from being disability needing long-term care. The purpose of this study is to develop a prediction model to discover high-risk groups who are likely to be beneficiaries of Long-Term Care Insurance. This study is a retrospective study using database of National Health Insurance Service (NHIS) collected in the past of the study subjects. The study subjects are 7,724,101, the population over 65 years of age registered for medical insurance. To develop the prediction model, we used logistic regression, decision tree, random forest, and multi-layer perceptron neural network. Finally, random forest was selected as the prediction model based on the performances of models obtained through internal and external validation. Random forest could predict about 90% of the older people in need of long-term care using DB without any information from the assessment of eligibility for long-term care. The findings might be useful in evidencebased health management for prevention services and can contribute to preemptively discovering those who need preventive services in older people.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.51-58
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• 2023

In this paper, we propose an organ segmentation technique for the automatic extraction of medical diagnostic indicators from X-ray images. In order to calculate diagnostic indicators of heart disease and spinal disease such as VHS(vertebral heart scale) and Cobb angle, it is necessary to accurately segment the thoracic spine, carina, and heart in a chest X-ray image. A deep neural network model in which the high-resolution representation of the image for each layer and the structure converted into a low-resolution feature map are connected in parallel was adopted. This structure enables the relative position information in the image to be effectively reflected in the segmentation process. It is shown that learning performance can be improved by combining the OCR module, in which pixel information and object information are mutually interacted in a multi-step process, and the channel attention module, which allows each channel of the network to be reflected as different weight values. In addition, a method of augmenting learning data is presented in order to provide robust performance against changes in the position, shape, and size of the subject in the X-ray image. The effectiveness of the proposed theory was evaluated through an experiment using 145 human chest X-ray images and 118 animal X-ray images.

• Korean Journal of Construction Engineering and Management
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• v.23 no.3
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• pp.45-55
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• 2022

BIM models allow building spaces to be instantiated and recognized as unique objects independently of model elements. These instantiated spaces provide the required semantics that can be leveraged for building code checking, energy analysis, and evacuation route analysis. However, theses spaces or rooms need to be designated manually, which in practice, lead to errors and omissions. Thus, most BIM models today does not guarantee the semantic integrity of space designations, limiting their potential applicability. Recent studies have explored ways to automate space allocation in BIM models using artificial intelligence algorithms, but they are limited in their scope and relatively low classification accuracy. This study explored the use of Graph Convolutional Networks, an algorithm exclusively tailored for graph data structures. The goal was to utilize not only geometry information but also the semantic relational data between spaces and elements in the BIM model. Results of the study confirmed that the accuracy was improved by about 8% compared to algorithms that only used geometric distinctions of the individual spaces.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.147-161
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• 2022

This study examined changes in the ecological characteristics and design characteristics of Ban's exhibition space in three representative temporary exhibition halls and three permanent exhibition halls designed by Ban Shigeru since 2000. Through the investigation of the concepts and characteristics of ecological architecture, the design characteristics of exhibition space, the analysis framework of the design characteristics of exhibition space and the design elements of ecological architecture is obtained. The analysis results show that there are big changes between the temporary exhibition space and the permanent exhibition space in terms of building scale, space composition, function, materials and technology. On the one hand, the temporary exhibition space used recyclable materials, such as paper tubes, containers to be assembled on site into a single-layer space focused on display. The assembly method was simple and the construction period was short. After the exhibition, the exhibition space were dismantled. The materials were either transported to the next display site or recycled and reused. On the other hand, the permanent exhibition space used reinforced concrete as the main structure, and used a large amount of wood and glass materials to construct a multi-layered composite cultural space that separated the exhibition space and the leisure space. In terms of ecological characteristics, the building materials of the temporary exhibition space were recycled and no industrial wastes were generated after the demolition. The permanent exhibition hall uses eco-friendly wood for the roof and walls, so it is easy to replace and repair. Both types of exhibition halls are changing ecological architecture in a more sustainable direction by saving resources and energy through natural light and ventilation.

• Knowledge Management Research
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• v.23 no.2
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• pp.277-299
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• 2022

The massive card transaction data generated in the tourism industry has become an important resource that implies tourist consumption behaviors and patterns. Based on the transaction data, developing a smart service system becomes one of major goals in both tourism businesses and knowledge management system developer communities. However, the lack of rating scores, which is the basis of traditional recommendation techniques, makes it hard for system designers to evaluate a learning process. In addition, other auxiliary factors such as temporal, spatial, and demographic information are needed to increase the performance of a recommendation system; but, gathering those are not easy in the card transaction context. In this paper, we introduce CTDDTR, a novel approach using card transaction data to recommend tourism services. It consists of two main components: i) Temporal preference Embedding (TE) represents tourist groups and services into vectors through Doc2Vec. And ii) Deep tourism Recommendation (DR) integrates the vectors and the auxiliary factors from a tourism RDF (resource description framework) through MLP (multi-layer perceptron) to provide services to tourist groups. In addition, we adopt RFM analysis from the field of knowledge management to generate explicit feedback (i.e., rating scores) used in the DR part. To evaluate CTDDTR, the card transactions data that happened over eight years on Jeju island is used. Experimental results demonstrate that the proposed method is more positive in effectiveness and efficacies.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.123-131
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• 2009

Wireless networks have several unique features : node mobility, restricted bandwidth, time-variable bandwidth, large latency, and high bit error rates time due to channel fading. These features at wireless networks intend to decrease the performance TCP protocols are used in wireless networks. Lots of studies have been done for finding appropriate wireless transport protocols for current wireless communications. However, related studies have not provided good performance or some protocols have a good performance only in specific circumstances. Thus, these are not suitable for general wireless circumstance. Therefore, we propose a new wireless transport protocol which provides better performance than the previous ones. And we'd like to solve a problem that previous protocols cannot maintain their connections even though they have multiple paths until another path is successfully set up. To solve these problems, a new protocol ATP-M is proposed which is designed on already known TCP-M and ATP protocols. With NS-2 computer simulation, it is shown that this newly proposed protocol has better system throughput than TCP, TCP-M and ATP protocols.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.387-387
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• 2022

기존 홍수기시 댐 운영은 예측 강우와 실시간 관측 강우를 이용하여 댐 운영 모형을 수행하며, 예측 결과에 따라 의사결정 및 댐 운영을 실시하게 된다. 하지만 이 과정에서 반복적인 분석이 필요하며, 댐 운영 모형 수행자의 경험에 따라 예측 결과가 달라져서 반복작업에 대한 자동화, 모형 수행자에 따라 달라지지 않는 예측 결과의 일반화가 필요한 상황이다. 이에 댐 운영 모형에 AI 기법을 적용하여, 다양한 강우 상황에 따른 자동 예측 및 모형 결과의 일반화를 구현하고자 하였다. 이를 위해 수자원 분야에 적용된 국내외 129개 연구논문에서 사용된 딥러닝 기법의 활용성을 분석하였으며, 다양한 수자원 분야 AI 적용 사례 중에서 댐 운영 예측 모형에 적용한 사례는 없었지만 유사한 분야로는 장기 저수지 운영 예측과 댐 상·하류 수위, 유량 예측이 있었다. 수자원의 시계열 자료 활용을 위해서는 Long-Short Term Memory(LSTM) 기법의 적용 활용성이 높은 것으로 분석되었다. 댐 운영 모형에서 AI 적용은 2개 분야에서 진행하였다. 기존 강우관측소의 관측 강우를 활용하여 강우의 패턴분석을 수행하는 과정과, 강우에서 댐 유입량 산정시 매개변수 최적화 분야에 적용하였다. 강우 패턴분석에서는 유사한 표본끼리 묶음을 생성하는 K-means 클러스터링 알고리즘과 시계열 데이터의 유사도 분석 방법인 Dynamic Time Warping을 결합하여 적용하였다. 강우 패턴분석을 통해서 지점별로 월별, 태풍 및 장마기간에 가장 많이 관측되었던 강우 패턴을 제시하며, 이를 모형에서 직접적으로 활용할 수 있도록 구성하였다. 강우에서 댐 유입량을 산정시 활용되는 매개변수 최적화를 위해서는 3층의 Multi-Layer LSTM 기법과 경사하강법을 적용하였다. 매개변수 최적화에 적용되는 매개변수는 중권역별 8개이며, 매개변수 최적화 과정을 통해 산정되는 결과물은 실측값과 오차가 제일 적은 유량(유입량)이 된다. 댐 운영 모형에 AI 기법을 적용한 결과 기존 반복작업에 대한 자동화는 이뤘으며, 댐 운영에 따른 상·하류 제약사항 표출 기능을 추가하여 의사결정에 소요되는 시간도 많이 줄일 수 있었다. 하지만, 매개변수 최적화 부분에서 기존 댐운영 모형에 적용되어 있는 고전적인 매개변수 추정기법보다 추정시간이 오래 소요되며, 매개변수 추정결과의 일반화가 이뤄지지 않아 이 부분에 대한 추가적인 연구가 필요하다.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.343-350
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• 2022

It is worth verifying the effectiveness of data integration between data with different features. This study investigated whether the data integration affects the accuracy of deep neural network (DNN), and which integration method shows the best improvement. This study used two different public datasets. One public dataset was taken in an actual farm in India. And another was taken in a laboratory environment in Korea. Leaf images were selected from two different public datasets to have five classes which includes normal and four different types of plant diseases. DNN used pre-trained VGG16 as a feature extractor and multi-layer perceptron as a classifier. Data were integrated into three different ways to be used for the training process. DNN was trained in a supervised manner via the integrated data. The trained DNN was evaluated by using a test dataset taken in an actual farm. DNN shows the best accuracy for the test dataset when DNN was first trained by images taken in the laboratory environment and then trained by images taken in the actual farm. The results show that data integration between plant images taken in a different environment helps improve the performance of deep neural networks. And the results also confirmed that independent use of plant images taken in different environments during the training process is more effective in improving the performance of DNN.

• Analytical Science and Technology
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• v.19 no.3
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• pp.244-254
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• 2006

Flame retardants are added to prevent catching fire and to slow down the burning process. PBDEs are known to affect thyroid hormones and hormone disruption. The aim of this study was to propose a manual for determination of PBDEs, and investigate the accumulation of PBDEs(BDE-28, 47, 99, 100, 153, 154 and 183) in fish&shellfish and human milk samples. Pre-treatment for PBDEs determination, alkali digestion and L-L(Liquid-Liquid) extraction method could be applied to fish and shellfish. When Multi-layer column was used for cleaning up the sample, 50 mL of hexane and 100 mL of hexane:dichloromethane(9:1) solutions were used for pre- and post-elution, respectively. Activated-carbon column was optimized by a 100 mL of hexane:dichloromethane(3:1). The result of fish, highest concentration was detected in flatfish, 890 pg/g(wet weight). The other side, lowest concentration was detected in pollack, 40 pg/g(wet weight). The result of breast milk, PBDEs was detected 2,580 and 3,600 pg/g(lipid weight) from breast milk of Seoul and Juju, respectively. BDE-153 and 183 were not detected in all samples. There was no difference in PBDEs level was not difference between first and second delivery. In this study, we could find that PBDEs level in Korea is lower than other countries.

• Journal of Environmental Impact Assessment
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• v.32 no.6
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• pp.473-487
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• 2023

Korea's multi-purpose dams, which were constructed in the 1970s and 1980s, have a single outlet located near the bottom for hydropower generation. Problems such as freezing damage to crops due to cold water discharge and an increase the foggy days have been raised downstream of some dams. In this study, we analyzed the effect of water intake depth on the reservoir's water temperature stratification structure and outflow temperature targeting Hapcheon Reservoir, where hypolimnetic withdrawal is drawn via a fixed depth outlet. Using AEM3D, a three-dimensional hydrodynamic water quality model, the vertical water temperature distribution of Hapcheon Reservoir was reproduced and the seasonal water temperature stratification structure was analyzed. Simulation periods were wet and dry year to compare and analyze changes in water temperature stratification according to hydrological conditions. In addition, by applying the intake depth change scenario, the effect of water intake depth on the thermal structure was analyzed. As a result of the simulation, it was analyzed that if the hypolimnetic withdrawal is changed to epilimnetic withdrawal, the formation location of the thermocline will decrease by 6.5 m in the wet year and 6.8 m in the dry year, resulting in a shallower water depth. Additionally, the water stability indices, Schmidt Stability Index (SSI) and Buoyancy frequency (N²), were found to increase, resulting in an increase in thermal stratification strength. Changing higher withdrawal elevations, the annual average discharge water temperature increases by 3.5℃ in the wet year and by 5.0℃ in the dry year, which reduces the influence of the downstream river. However, the volume of the low-water temperature layer and the strength of the water temperature stratification within the lake increase, so the water intake depth is a major factor in dam operation for future water quality management.