• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.28 no.10
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• pp.402-407
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• 2016

Ground-coupled heat pump systems have been widely used, as they are regarded as a renewable energy source and ensure a high annual efficiency. Among the system components, borehole heat exchangers (BHE) play an important role in decreasing the entering water temperature (EWT) to heat pumps in the cooling season, and consequently improve the COP. The optimal sizing of the BHEs is crucial for a successful project. Other than the existing sizing methods, a simulation-based design tool is more applicable for modern complex geothermal systems, and it may also be useful since design and engineering works operate on the same platform. A simulation-based sizing method is proposed in this study using the well-known Duct STorage (DST) model in Trnsys. TRNOPT, the Trnsys optimization tool, is used to search for an optimal value of the length of BHEs under given ground loads and ground properties. The result shows that a maximum EWT of BHEs during a design period (10 years) successfully approaches the design EWT while providing an optimal BHE length. Compared to the existing design tool, very similar lengths are calculated by both methods with a small error of 1.07%.

• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.105-111
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• 2020

In this paper, we propose a scheme that minimizes the energy imbalance problem of solar-powered wireless sensor network (SP-WSN) using both a mobile sink capable of wireless power transfer and an efficient clustering scheme (including cluster head election). The proposed scheme charges the cluster head using wireless power transfer from a mobile sink and mitigates the energy hotspot of the nodes nearby the head. SP-WSNs can continuously harvest energy, alleviating the energy constraints of battery-based WSN. However, if a fixed sink is used, the energy imbalance problem, which is energy consumption rate of nodes located near the sink is relatively increased, cannot be solved. Thus, recent research approaches the energy imbalance problem by using a mobile sink in SP-WSN. Meanwhile, with the development of wireless power transmission technology, a mobile sink may play a role of energy charging through wireless power transmission as well as data gathering in a WSN. Simulation results demonstrate that increase the amount of collected data by the sink using the proposed scheme.

• Journal of Practical Engineering Education
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• v.12 no.2
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• pp.309-319
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• 2020

Billiards is a fun and popular sport, but both route planning and cueing prevent beginners from becoming skillful. A beginner in billiards requires constant concentration and training to reach the right level, but without the right motivating factor, it is easy to lose interests. This study aims to induce interest in billiards and accelerate learning by utilizing billiard path prediction and visualization on a highly immersive augmented reality platform that combines a stereo camera and a VR headset. For implementation, the placement of billiard balls is recognized through the OpenCV image processing program, and physics simulation, path search, and visualization are performed in Unity Engine. As a result, accurate path prediction can be achieved. This made it possible for beginners to reduce the psychological burden of planning the path, focus only on accurate cueing, and gradually increase their billiard proficiency by getting used to the path suggested by the algorithm for a long time. We confirm that the proposed AR billiards is remarkably effective as a learning assistant tool.

• The Journal of Korean Medicine
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• v.43 no.4
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• pp.102-130
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• 2022

Objectives: The persistence and unpredictability of coronavirus disease (COVID-19) and new measures to prevent direct medical intervention (e.g., social distancing and quarantine) have induced various psychological symptoms and disorders that require self-treatment approaches and integrative treatment interventions. To address these issues, the Korean Medicine Mental Health (KMMH) center developed a field manual by reviewing previous literature and preexisting manuals. Methods: The working group of the KMMH center conducted a keyword search in PubMed in June 2021 using "COVID-19" and "SARS-CoV-2". Review articles were examined using the following filters: "review," "systematic review," and "meta-analysis." We conducted a narrative review of the retrieved articles and extracted content relevant to previous manuals. We then created a treatment algorithm and recommendations by referring to the results of the review. Results: During the initial assessment, subjective symptom severity was measured using a numerical rating scale, and patients were classified as low- or moderate-high risk. Moderate-high-risk patients should be classified as having either a psychiatric emergency or significant psychiatric condition. The developed manual presents appropriate psychological support for each group based on the following dominant symptoms: tension, anxiety-dominant, anger-dominant, depression-dominant, and somatization. Conclusions: We identified the characteristics of mental health problems during the COVID-19 pandemic and developed a clinical mental health support manual in the field of Korean medicine. When symptoms meet the diagnostic criteria for a mental disorder, doctors of Korean medicine can treat the patients according to the manual for the corresponding disorder.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.113-120
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• 2024

With the advancement of information and communication technology, we can easily generate various forms of data in our daily lives. To efficiently manage such a large amount of data, systematic classification into categories is essential. For effective search and navigation, data is organized into a tree-like hierarchical structure known as a category tree, which is commonly seen in news websites and Wikipedia. As a result, various techniques have been proposed to classify large volumes of documents into the terminal nodes of category trees. However, document classification methods using category trees face a problem: as the height of the tree increases, the number of terminal nodes multiplies exponentially, which increases the probability of misclassification and ultimately leads to a reduction in classification accuracy. Therefore, in this paper, we propose a new node expansion-based classification algorithm that satisfies the classification accuracy required by the application, while enabling detailed categorization. The proposed method uses a greedy approach to prioritize the expansion of nodes with high classification accuracy, thereby maximizing the overall classification accuracy of the category tree. Experimental results on real data show that the proposed technique provides improved performance over naive methods.

• Journal of the Korea Computer Graphics Society
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• v.8 no.2
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• pp.1-7
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• 2002

The fast extraction of the silhouettes of a model is very useful for many applications in computer graphics and animation. In this paper, we present an efficient algorithm to compute a sequence of perspective silhouettes for a polyhedral model from a moving viewpoint. The viewpoint is assumed to move along a trajectory q(t), which is a space curve of a time parameter t. Then, we can compute the time-intervals for each edge of the model to be contained in the silhouette by two major computations: (i) intersecting q(t) with two planes and (ii) a number of dot products. If q(t) is a curve of degree n, then there are at most of n + 1 time-intervals for an edge to be in a silhouette. For each time point $t_i$ we can extract silhouette edges by searching the intervals containing $t_i$ among the computed intervals. For the efficient search, we propose two kinds of data structures for storing the intervals: an interval tree and an array. Our algorithm can be easily extended to compute the parallel silhouettes with minor modifications.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.5
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• pp.285-295
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• 2020

In the case of small observation towers located at sea, it is necessary to confirm the change in dynamic characteristics due to the influence of environmental loads. In this study, the dynamic characteristics were analyzed and the numerical analysis model was designed through field dynamic response measurement on the Mangyeong Offshore Observation Tower (Mangyeong Tower) located near the Saemangeum Embankment. As a result of the measurement, the natural frequency was found to increase slowly as the tide level is lowered. In addition, it was confirmed that the same mode has two frequencies, which was judged to be a phenomenon in which the natural frequency was partially increased when the pile and the ground contacted by scouring. For numerical analysis, the upper mass, artificial fixity point, scour depth and fluid influences are reflected in the structural characteristics of the Mangyeong Tower. In addition, the model updating from the estimated natural frequency and pattern search algorithm was performed. From the model updating, it is expected that it can be applied to future studies on stability of Mangyeong Tower.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.5
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• pp.61-76
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• 2018

Everything is coming into the world of network where modern people are living. The Internet of Things that attach sensors to objects allows real-time data transfer to and from the network. Mobile devices, essential for modern humans, play an important role in keeping all traces of everyday life in real time. Through the social network services, information acquisition activities and communication activities are left in a huge network in real time. From the business point of view, customer needs analysis begins with SNS data. In this research, we want to build an automatic collection system of SNS contents of web environment in real time using Python. We want to help customers' needs analysis through the typical data collection system of Instagram, Twitter, and YouTube, which has a large number of users worldwide. It is stored in database through the exploitation process and NLP process by using the virtual web browser in the Python web server environment. According to the results of this study, we want to conduct service through the site, the desired data is automatically collected by the search function and the netizen's response can be confirmed in real time. Through time series data analysis. Also, since the search was performed within 5 seconds of the execution result, the advantage of the proposed algorithm is confirmed.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.13-23
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• 2022

In this paper, we propose a detailed component image classification algorithm by fashion item for unstructured data retrieval in the fashion field. Due to the COVID-19 environment, AI-based online shopping malls are increasing recently. However, there is a limit to accurate unstructured data search with existing keyword search and personalized style recommendations based on user surfing behavior. In this study, pre-processing using Mask R-CNN was conducted using images crawled from online shopping sites and then classified components for each fashion item through CNN. We obtain the accuaracy for collar of the shirt's as 93.28%, the pattern of the shirt as 98.10%, the 3 classese fit of the jeans as 91.73%, And, we further obtained one for the 4 classes fit of jeans as 81.59% and the color of the jeans as 93.91%. At the results for the decorated items, we also obtained the accuract of the washing of the jeans as 91.20% and the demage of jeans accuaracy as 92.96%.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.43-53
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• 2009

Knowledge retrieval service that uses collective intelligence which has special quality of open structure and can share the accumulative data is gaining popularity. However, acquiring the right needs for users from massive public knowledge is getting harder. Recently, search results from Google which is known for it's exquisite algorism, shows results for collective intelligence such as Wikipedia, Yahoo Q/A at the highest rank. Objective of this paper is to show that most answers come from human and to find the most influential people in on-line knowledge retrieval service. Hereupon, this paper suggest the influence value calculation algorism by analyzing user relation as centrality which social network is based on user activeness and reliance in Naver 지식iN. The influence value calculated by the suggested algorism will be an important index in distinguishing reliable and the right user for the question by ranking users with troubleshooting solutions in the knowledge retrieval service. This will contribute in search satisfaction by acquiring the right information and knowledge for the users which is the most important objective for knowledge retrieval service.