• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.285-290
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• 2021

In the medical field, disease diagnosis and prediction research using artificial intelligence technology is being actively conducted. It is being released as a variety of products for disease diagnosis and prediction, which are most widely used in the application of artificial intelligence technology based on medical images. Artificial intelligence is being applied to diagnose diseases, to classify diseases into benign and malignant, and to separate disease regions for use in identification or reading according to the risk of disease. Recently, in connection with cloud technology, its utility as a service product is increasing. Among the diseases dealt with in this paper, liver disease is a disease with very high risk because it is difficult to diagnose early due to the lack of pain. Artificial intelligence technology was introduced based on medical images as a non-invasive diagnostic method for diagnosing these diseases. We describe the development of a web service to help the most meaningful clinical reading of liver cirrhosis patients. Then, it shows the web service process and shows the operation screen of each process and the final result screen. It is expected that the proposed service will be able to diagnose liver cirrhosis at an early stage and help patients recover through rapid treatment.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.25-34
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• 2020

In this paper, we empirically compare the effectiveness of training models to recognize beauty-related skin disease using supervised deep learning algorithms. Recently, deep learning algorithms are being actively applied for various fields such as industry, education, and medical. For instance, in the medical field, the ability to diagnose cutaneous cancer using deep learning based artificial intelligence has improved to the experts level. However, there are still insufficient cases applied to disease related to skin beauty. This study experimentally compares the effectiveness of identifying beauty-related skin disease by applying deep learning algorithms, considering CNN, ResNet, and SE-ResNet. The experimental results using these training models show that the accuracy of CNN is 71.5% on average, ResNet is 90.6% on average, and SE-ResNet is 95.3% on average. In particular, the SE-ResNet-50 model, which is a SE-ResNet algorithm with 50 hierarchical structures, showed the most effective result for identifying beauty-related skin diseases with an average accuracy of 96.2%. The purpose of this paper is to study effective training and methods of deep learning algorithms in consideration of the identification for beauty-related skin disease. Thus, it will be able to contribute to the development of services used to treat and easy the skin disease.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.4
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• pp.274-284
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• 2022

To use kenaf (Hibiscus cannabinus L.) as a fiber and livestock feed, a high-yielding variety needs to be identified. For this, accurate phenotyping of plant height is required for this breeding purpose due to the strong relationship between plant height and yield. Plant height can be estimated using RGB images from unmanned aerial vehicles (UAV-RGB) and photogrammetry based on Structure from Motion (SfM) algorithms. In kenaf, accurate measurement of height is limited because kenaf stems have high flexibility and its height is easily affected by wind, growing up to 3 ~ 4 m. Therefore, we aimed to identify a method suitable for the accurate estimation of plant height of kenaf and investigate the feasibility of using the UAV-RGB-derived plant height map. Height estimation derived from UAV-RGB was improved using multi-point calibration against the five different wooden structures with known heights (30, 60, 90, 120, and 150 cm). Using the proposed method, we analyzed the variation in temporal height of 23 kenaf cultivars. Our results demontrated that the actual and estimated heights were reliably comparable with the coefficient of determination (R²) of 0.80 and a slope of 0.94. This method enabled the effective identification of cultivars with significantly different heights at each growth stages.

• Journal of Venture Innovation
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• v.6 no.3
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• pp.165-184
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• 2023

Since municipal welfare institutions operate for different purposes from general companies or public enterprises, ESG practice items and model construction should be conducted through various and comprehensive social welfare studies. Since there are not many studies available in domestic welfare institutions yet and there are no suitable ESG management utilization indicators, the Cheonan Welfare Foundation's strategy and management strategy system were established to spread the model to other welfare institutions and become a leading foundation through education and training. The foundation and front-line welfare institutions selected issues identification and key issues through the foundation's empirical analysis and criticality analysis, focusing on understanding ESG management and ways to establish a practice model that positively affects institutional image and business performance. Based on this, the promotion system was examined by establishing a performance management plan after deriving appropriate strategies and establishing a strategic system for social welfare institutions. Environmental and social responsibility, transparent management, safety management system establishment, emergency and prevention, user (customer) satisfaction system establishment, anti-corruption prevention and integrity ethics monitoring and evaluation, responsible supply chains, and community contribution programs. This study attempted to specifically present efforts to settle ESG management through the consideration of the Cheonan Welfare Foundation. Therefore, it is considered to be useful data for developing ESG management by referring to the systematic development process of the Cheonan City Restoration Foundation to develop ESG measurement indicators.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.873-881
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• 2023

The evolution of civil engineering technology, exemplified by recent milestones like the completion of the Gangnam Global Business Center (GBC), has fostered the construction of expansive civil and architectural structures both above and below the earth's surface. This surge in construction necessitates a commensurate advancement in research and technology pertaining to safety protocols applicable to these vast edifices. Such protocols encompass a spectrum of concerns, ranging from the preemptive mitigation of accidents to the effective management of exigencies such as fires. As the trajectory of construction endeavors continues unabated, encompassing both subterranean and elevated domains, a concomitant imperative emerges to refine the methodologies underpinning precise indoor positioning. To address this need, an innovative web-based simulator has been devised to emulate indoor positioning scenarios for rigorous testing. This research further entails the development of an indoor positioning data Application Programming Interface (API) fortified by Geographic Information System (GIS) spatial operation techniques. This API is anchored in the construction of intricate test data, centered on the spatial layout of building 13 at the Electronics and Telecommunications Research Institute (ETRI). Consequently, the study renders feasible the expeditious provisioning of diverse signal-based and image-based spatial information, pivotal for enhancing the navigational acumen of mobile devices. Path delineation, cellular signal mapping, landmark identification, and ancillary navigational aids are among the manifold datasets promptly furnished by the indoor positioning data API. In summation, this study engenders a crucial leap towards the fortification of safety protocols and navigational precision within the expansive confines of modern architectural wonders.

• Geophysics and Geophysical Exploration
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• v.8 no.4
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• pp.270-279
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• 2005

Under the research project supported by Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT), we have conducted the development of GPR systems for landmine detection. Until 2005, we have finished development of two prototype GPR systems, namely ALIS (Advanced Landmine Imaging System) and SAR-GPR (Synthetic Aperture Radar-Ground Penetrating Radar). ALIS is a novel landmine detection sensor system combined with a metal detector and GPR. This is a hand-held equipment, which has a sensor position tracking system, and can visualize the sensor output in real time. In order to achieve the sensor tracking system, ALIS needs only one CCD camera attached on the sensor handle. The CCD image is superimposed with the GPR and metal detector signal, and the detection and identification of buried targets is quite easy and reliable. Field evaluation test of ALIS was conducted in December 2004 in Afghanistan, and we demonstrated that it can detect buried antipersonnel landmines, and can also discriminate metal fragments from landmines. SAR-GPR (Synthetic Aperture Radar-Ground Penetrating Radar) is a machine mounted sensor system composed of B GPR and a metal detector. The GPR employs an array antenna for advanced signal processing for better subsurface imaging. SAR-GPR combined with synthetic aperture radar algorithm, can suppress clutter and can image buried objects in strongly inhomogeneous material. SAR-GPR is a stepped frequency radar system, whose RF component is a newly developed compact vector network analyzers. The size of the system is 30cm x 30cm x 30 cm, composed from six Vivaldi antennas and three vector network analyzers. The weight of the system is 17 kg, and it can be mounted on a robotic arm on a small unmanned vehicle. The field test of this system was carried out in March 2005 in Japan.

• Cartoon and Animation Studies
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• s.33
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• pp.99-126
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• 2013

This study is meant to be seeing how fun of animation works by reviewing it theoretically and coordinating it to suggest the structure which integrates fun of animation and validates the proposed fun model. After reviewing fun theoretically, the fun of animation could be able to coordinate that fun of animation is consist of perceptive fun, cognitive fun, and psychological fun. Perceptive fun is induced by visual, auditory and other sensory information and it is directly affected the image, sound, and movement. Cognitive fun can be obtained by reasoning and interpretation to mobilize their knowledge with sensuously perceived stimulation and it is directly affected the story. Psychological fun occurs when the audience see the animation. The psychological fun is the psychological emotional state when the audience watches animation by relieving psychological congestion. It consists of fun of unfamiliarity or identification. By suggesting research model and validating it how the perceptive fun, cognitive fun, and psychological fun affects each other, perceptive fun enhances cognitive fun and psychological fun. Although cognitive fun enhances psychological fun, cognitive fun enhances psychological fun twice than perceptive fun. Also when perceptive fun affects psychological fun, cognitive fun shows the indirect effect as a parameter. In conclusion, perceptive fun affects psychological fun directly and be enhanced through cognitive fun. Fun of animation can be experienced when perceptive fun caused by accepting sensory information of animation instantly, cognitive fun caused by interpretation and understanding sensory information of animation, and psychological fun caused by relieving psychological identity through recognition fuses and acts as one. An animation emphasized a certain element is difficult to be loved by the audience. In this reason, an harmonical combination among the elements of story, image, sound and movement are important to combinate harmoniously for a successful animation to make the audiences fun by arising funny emotions.

• Journal of the Korean Institute of Landscape Architecture
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• v.42 no.1
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• pp.104-114
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• 2014

Nowadays, forests have been recognized as valuable resources for biological diversity and tourism/recreation. Temple forests occupy 1.3% of all the Korean forest and are under weak management although their ecological states are very good. Currently in the Buddhist society, the concern for the Buddhist arboretum has been raised as a good alternative for the practical use of temple forests to secure the sustainability of the temple forests as well as to actively meet the demand of the times for forests. This study aims to suggest establishment measures of Buddhist arboretum. This survey was performed on 105 temple forest managers and 130 laypersons. To summarize the results, the two groups differ in opinion. The temple forest managers more concerned for advertizing Buddhist culture and enhancing the image of the temple, while the laypersons had higher expectations for relaxation and education. However, they are similar in putting more emphasis on the conserving the heritage value of the temple and managing the temple forest. Above all, both groups evaluated the needs higher than the urgency and perceived managing temple forest as the most crucial function of a Buddhist arboretum. They also thought that a Buddhist arboretum should be planned to respond to the ecological characteristics of the temple area as well as to be non-exclusive to its users. Based on the important findings, five suggestions for a Buddhist arboretum were proposed. First, a Buddhist arboretum should be carried forward from a long-term point of view, developing a bond of sympathy between members of Buddhist society as well as conducting promotion and education to the general public. Second, the most significant function of a Buddhist arboretum should be preserving the temple forest, with the emphasis on relaxation and education. Third, in order to provide nonexclusive use, a Buddhist arboretum should provide mixed programs applicable to diverse user groups for high user satisfaction and educational effects. Fourth, the Buddhistic identification could be obtained through variety of plants closely associated with Buddhist culture. Lastly, in the process of collecting plants, it is also crucial to reflect the image of the temple and resource property so as to contribute itself in conservation and management of original temple forests. Thereby all Buddhist Arboretum can be classified into two types; preservation/collection and display/education/rest.

• The Korean Journal of Nuclear Medicine
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• v.34 no.6
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• pp.478-486
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• 2000

Purpose: The sentinel lymph node is defined as the first draining node from a primary tumor and reflects the histologic feature of the remainder of the lymphatic basin status. The aim of this study was to evaluate the usefulness of lymphoscintigraphy and intraoperative radioguided gamma probe for identification and removal of sentinel lymph node in breast cancer. Materials and Methods: Lymphoscintigraphy was performed preoperatively in 15 patients with biopsy proven primary breast cancer. Tc-99m antimony sulfide colloid was injected intradermally at four points around the tumor. Imaging acquisition included dynamic imaging, followed by early and late static images at 2 hours. The sentinel lymph node criteria on lymphoscintigraphy is the first node of the highest uptake in early and late static images. We tagged the node emitting the highest activity both in vivo and ex vivo. Histologic study for sentinel and axillary lymph node investigation was done by Hematoxylin-Eosin staining. Results: On lymphoscintigraphy, three of 15 patients had clear lymphatic vessels in dynamic images, and 11 of 15 patients showed sentinel lymph node in early static image and three in late static 2 hours image. Mean detection time of sentinel lymph node on lymphoscintigraphy was $33.5{\pm}48.4$ minutes. The sentinel lymph node localization and removal by lymphoscintigraphy and intraoperative gamma probe were successful in 14 of 15 patients (detection rate: 93.3%). On lymphoscintigraphy, 14 of 15 patients showed $2.47{\pm}2.00$ sentinel lymph nodes. On intraoperative gamma probe, $2.36{\pm}1.96$ sentinel lymph nodes were detected. In 7 patients with positive results of sentinel lymph node metastasis, 5 patients showed positive results of axillary lymph node (sensitivity: 72%) but two did not. In 7 patients with negative results of sentinel lymph node metastasis, all axillary nodes were free of disease (specificity: 100%). Conclusion: Sentinel lymph node biopsy with lymphoscintigraphy and intraoperative gamma probe is a reliable method to predict axillary lymph node metastasis in breast cancer, and unnecessary axillary lymph node dissection can be avoided.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.3
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• pp.247-262
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• 2022

In domestic tunnels, it is mandatory to install CCTVs in tunnels longer than 200 m which are also recommended by installation of a CCTV-based automatic accident detection system. In general, the CCTVs in the tunnel are installed at a low height as well as near by the moving vehicles due to the spatial limitation of tunnel structure, so a severe perspective effect takes place in the distance of installed CCTV and moving vehicles. Because of this effect, conventional CCTV-based accident detection systems in tunnel are known in general to be very hard to achieve the performance in detection of unexpected accidents such as stop or reversely moving vehicles, person on the road and fires, especially far from 100 m. Therefore, in this study, the region of interest is set up and a new concept of inverse perspective transformation technique is introduced. Since moving vehicles in the transformed image is enlarged proportionally to the distance from CCTV, it is possible to achieve consistency in object detection and identification of actual speed of moving vehicles in distance. To show this aspect, two datasets in the same conditions are composed with the original and the transformed images of CCTV in tunnel, respectively. A comparison of variation of appearance speed and size of moving vehicles in distance are made. Then, the performances of the object detection in distance are compared with respect to the both trained deep-learning models. As a result, the model case with the transformed images are able to achieve consistent performance in object and accident detections in distance even by 200 m.