• Journal of Digital Convergence
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• v.15 no.2
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• pp.173-181
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• 2017

In this paper, we have studied tracking as a training stage of considering the position and the scale of a person given its previous position, scale, as well as next and forward image fraction. Unlike other learning methods, CNN is thereby learning combines both time and spatial features from the image for the two consecutive frames. We introduce multiple path ways in CNN to better fuse local and global information. A creative shift-variant CNN architecture is designed so as to alleviate the drift problem when the distracting objects are similar to the target in cluttered environment. Furthermore, we employ CNNs to estimate the scale through the accurate localization of some key points. These techniques are object-independent so that the proposed method can be applied to track other types of object. The capability of the tracker of handling complex situations is demonstrated in many testing sequences. The accuracy of the SVM classifier using the features learnt by the CNN is equivalent to the accuracy of the CNN. This fact confirms the importance of automatically optimized features. However, the computation time for the classification of a person using the convolutional neural network classifier is less than approximately 1/40 of the SVM computation time, regardless of the type of the used features.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.109-117
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• 2020

An edutainment system aims to help learners to recognize problems effectively, grasp and classify important information needed to solve the problems and convey the contents of what they have learned. Edutainment contents can be usefully applied to education and training in the both scientific and industrial areas. Our present work proposes an edutainment system that can be applied to a drug discovery process including virtual screening by using intuitive multi-modal interfaces. In this system, a stereoscopic monitor is used to make three-dimensional (3D) macro-molecular images, with supporting multi-modal interfaces to manipulate 3D models of molecular structures effectively. In this paper, our system can easily solve a docking simulation function, which is one of important virtual drug screening methods, by applying gaming factors. The level-up concept is implemented to realize a bio-game approach, in which the gaming factor depends on number of objects and users. The quality of the proposed system is evaluated with performance comparison in terms of a finishing time of a drug docking process to screen new inhibitors against target proteins of human immunodeficiency virus (HIV) in an e-drug discovery process.

• Journal of Korean Library and Information Science Society
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• v.49 no.4
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• pp.167-185
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• 2018

The purpose of this study is to analyse the cases related with PBL and to suggest the activation plan of LAI based on the derived results. The analysis objects were the PBL cases which have been performed from 2000 to 2018 in Korean schools. The PBL cases were analysed in the aspects of school levels, target grades, subject clusters, teaching patterns, information sources, cooperative teaching status, library cooperative teaching status, and educational effects. The results were like these; 1)The PBL was applied at the elementary schools the most, the next middle schools, and high schools. 2)The teaching pattern was the exclusive teaching the most, and the next pattern was team teaching with other teacher, and the lowest pattern was with librarian teacher. 3)The subject cluster was natural science cluster the most and the next was the human & social science cluster and the art, music and physical education cluster. 4)The PBL performed with school library was low. 5)The main effects of PBL were the improvements of academic achievement, learning satisfaction degree, creative ability, problem-solving ability, and self-directed learning capability. As the results, it's proposed that it's important for librarian teachers to have the aggressive attitude for the activation of LAI as well as the cooperation with PBL subject teachers from the planning step to the evaluation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.175-182
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• 2020

The non-contact eye tracking is a nonintrusive human-computer interface providing hands-free communications for people with severe disabilities. Recently. it is expected to do an important role in non-contact systems due to the recent coronavirus COVID-19, etc. This paper proposes a novel approach for an eye mouse using an eye tracking method based on a context-aware based AdaBoost multi-region classifier and ASSL algorithm. The conventional AdaBoost algorithm, however, cannot provide sufficiently reliable performance in face tracking for eye cursor pointing estimation, because it cannot take advantage of the spatial context relations among facial features. Therefore, we propose the eye-region context based AdaBoost multiple classifier for the efficient non-contact gaze tracking and mouse implementation. The proposed method detects, tracks, and aggregates various eye features to evaluate the gaze and adjusts active and semi-supervised learning based on the on-screen cursor. The proposed system has been successfully employed in eye location, and it can also be used to detect and track eye features. This system controls the computer cursor along the user's gaze and it was postprocessing by applying Gaussian modeling to prevent shaking during the real-time tracking using Kalman filter. In this system, target objects were randomly generated and the eye tracking performance was analyzed according to the Fits law in real time. It is expected that the utilization of non-contact interfaces.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.913-924
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• 2022

In this paper, we presented a database construction of undersea images for the Habitats of Ecklonia cava and Sargassum and conducted an experiment for semantic segmentation using state-of-the-art (SOTA) models such as High Resolution Network-Object Contextual Representation (HRNet-OCR) and Shifted Windows-L (Swin-L). The result showed that our segmentation models were superior to the existing experiments in terms of the 29% increased mean intersection over union (mIOU). Swin-L model produced better performance for every class. In particular, the information of the Ecklonia cava class that had small data were also appropriately extracted by Swin-L model. Target objects and the backgrounds were well distinguished owing to the Transformer backbone better than the legacy models. A bigger database under construction will ensure more accuracy improvement and can be utilized as deep learning database for undersea images.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.4
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• pp.431-453
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• 2022

Hate is a collective expression of exclusivity toward others and it is fostered and reproduced through false public perception. This study aims to explore the objects and issues of hate discussed in our society using text mining techniques. To this end, we collected 17,867 news data published from 1990 to 2020 and constructed a co-word network and cluster analysis. In order to derive an explicit co-word network highly related to hate, we carried out sentence split and extracted a total of 52,520 sentences containing the words 'hate', 'prejudice' and 'discrimination' in the preprocessing phase. As a result of analyzing the frequency of words in the collected news data, the subjects that appeared most frequently in relation to hate in our society were women, race, and sexual minorities, and the related issues were related laws and crimes. As a result of cluster analysis based on the co-word network, we found a total of six hate-related clusters. The largest cluster was 'genderphobic', accounting for 41.4% of the total, followed by 'sexual minority hatred' at 28.7%, 'racial hatred' at 15.1%, 'selective hatred' at 8.5%, 'political hatred' accounted for 5.7% and 'environmental hatred' accounted for 0.3%. In the discussion, we comprehensively extracted all specific hate target names from the collected news data, which were not specifically revealed as a result of the cluster analysis.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.248-252
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• 2022

The environment refers to what is surrounded by something during human life. This environment is related to the way humans live, and presents various problems on how to perceive the surrounding environment and how the behaviors that constitute the environment support the elements necessary for human life. Humans have an interest in the supportability of the environment as the interrelationship increases as humans perceive and understand the environment and accept the factors supported by the environment. In space, human movement starts from one space to the next and exchanges stimuli and reactions with the environment until reaching a target point. These human movements start with subjective judgment and during gait movement, the spatial environment surrounding humans becomes a collection of information necessary for humans and gives stimulation. will do. In this process, in particular, humans move along the movement path through movement in space and go through displacement perception and psychological changes, and recognize a series of spatial continuity. An image of thinking is formed[1]. In this process, spatial experience is perceived through the process of filtering by the senses in the real space, and the result of cognition is added through the process of subjective change accompanied by memory and knowledge, resulting in human movement. As such, the spatial search behavior begins with a series of perceptual and cognitive behaviors that arise in the process of human beings trying to read meaning from objects in the environment. Here, cognition includes the psychological process of sorting out and judging what the information is in the process of reading the meaning of the external environment, conditions, and material composition, and perception is the process of accepting information as the first step. It can be said to be the cognitive ability to read the meaning of the environment given to humans. Therefore, if we can grasp the perception of space while moving and human behavior as a response to perception, it will be possible to predict how to grasp it from a human point of view in a space that does not exist. Modern people have the theme of reminiscing dog-friendly hotels for the healing of petloss syndrome, and this thesis attempts to approach the life of companions.

• Journal of the Microelectronics and Packaging Society
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• v.31 no.1
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• pp.29-34
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• 2024

The heating element utilizing the Joule heating generated when current flows through a conductor is widely researched and developed for various industrial applications such as moisture removal in automotive windshield, high-speed train windows, and solar panels. Recently, research utilizing heating elements with various nanostructures has been actively conducted to develop flexible heating elements capable of maintaining stable heating even under mechanical deformation conditions. In this study, flexible polyurethane possessing excellent flexibility was selected as the substrate, and silver (Ag) thin films with low electrical resistivity (1.6 μΩ-cm) were fabricated as the heating layer using magnetron sputtering. The 2D heating structure of the Ag thin films demonstrated excellent heating reproducibility, reaching 95% of the target temperature within 20 seconds. Furthermore, excellent heating characteristics were maintained even under mechanically deforming environments, exhibiting outstanding flexibility with less than a 3% increase in electrical resistance observed in repetitive bending tests (10,000 cycles, based on a curvature radius of 5 mm). This demonstrates that polyurethane/Ag planar heating structure bears promising potential as a flexible/wearable heating element for curved-shaped appliances and objects subjected to diverse stresses such as human body parts.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.57-71
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• 2013

Over a billion people in the world generate new news minute by minute. People forecasts some news but most news are from unexpected events such as natural disasters, accidents, crimes. People spend much time to watch a huge amount of news delivered from many media because they want to understand what is happening now, to predict what might happen in the near future, and to share and discuss on the news. People make better daily decisions through watching and obtaining useful information from news they saw. However, it is difficult that people choose news suitable to them and obtain useful information from the news because there are so many news media such as portal sites, broadcasters, and most news articles consist of gossipy news and breaking news. User interest changes over time and many people have no interest in outdated news. From this fact, applying users' recent interest to personalized news service is also required in news service. It means that personalized news service should dynamically manage user profiles. In this paper, a content-based news recommendation system is proposed to provide the personalized news service. For a personalized service, user's personal information is requisitely required. Social network service is used to extract user information for personalization service. The proposed system constructs dynamic user profile based on recent user information of Facebook, which is one of social network services. User information contains personal information, recent articles, and Facebook Page information. Facebook Pages are used for businesses, organizations and brands to share their contents and connect with people. Facebook users can add Facebook Page to specify their interest in the Page. The proposed system uses this Page information to create user profile, and to match user preferences to news topics. However, some Pages are not directly matched to news topic because Page deals with individual objects and do not provide topic information suitable to news. Freebase, which is a large collaborative database of well-known people, places, things, is used to match Page to news topic by using hierarchy information of its objects. By using recent Page information and articles of Facebook users, the proposed systems can own dynamic user profile. The generated user profile is used to measure user preferences on news. To generate news profile, news category predefined by news media is used and keywords of news articles are extracted after analysis of news contents including title, category, and scripts. TF-IDF technique, which reflects how important a word is to a document in a corpus, is used to identify keywords of each news article. For user profile and news profile, same format is used to efficiently measure similarity between user preferences and news. The proposed system calculates all similarity values between user profiles and news profiles. Existing methods of similarity calculation in vector space model do not cover synonym, hypernym and hyponym because they only handle given words in vector space model. The proposed system applies WordNet to similarity calculation to overcome the limitation. Top-N news articles, which have high similarity value for a target user, are recommended to the user. To evaluate the proposed news recommendation system, user profiles are generated using Facebook account with participants consent, and we implement a Web crawler to extract news information from PBS, which is non-profit public broadcasting television network in the United States, and construct news profiles. We compare the performance of the proposed method with that of benchmark algorithms. One is a traditional method based on TF-IDF. Another is 6Sub-Vectors method that divides the points to get keywords into six parts. Experimental results demonstrate that the proposed system provide useful news to users by applying user's social network information and WordNet functions, in terms of prediction error of recommended news.

• Radiation Oncology Journal
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• v.17 no.1
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• pp.84-88
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• 1999

Purpose :To develop a method for verifying a treatment setup in stereotactic radiotherapy by ma- tching portal images to DRRs. Materials and Methods : Four pairs of orthogonal portal images of one patient immobilized by a thermoplastic mask frame for fractionated stereotactic radiotherapy were compared with DRRs. Portal images are obtained in AP (anteriorfposterior) and lateral directions with a target localizer box containing fiducial markers attached to a stereotactic frame. DRRs superimposed over a planned iso-center and fiducial markers are printed out on transparent films. And then, they were overlaid over onhogonal penal images by matching anatomical structures. From three different kind of objects (isgcenter, fiducial markers, anatomical structure) on DRRs and portal images, the displacement error between anatomical structure and isocenters (overall setup error), the displacement error between anatomical structure and fiducial markers (irnrnobiliBation error), and the displacement error between fiducial markers and isocenters (localization error) were measured. Results : Localization error were 1.5$\pm$0.3 mm (AP), 0.9$\pm$0.3 mm (lateral), and immobilization errors were 1.9$\pm$0.5 mm (AP), 1.9$\pm$0.4 mm (lateral). In addition, overall setup errors were 1.0$\pm$0.9 mm (AP), 1.3$\pm$0.4 mm (lateral). From these orthogonal displacement errors, maximum 3D displacement errors($\sqrt{(\DeltaAP)^{2}+(\DeltaLat)^{2}$)) were found to be 1.7$\pm$0.4 mm for localization, 2.0$\pm$0.6 mm for immobilization, and 2.3$\pm$0.7 mm for overall treatment setup. Conclusion : By comparing orthogonal portal images with DRRs, we find out that it is possible to verify treatment setup directly in stereotactic radiotherapy.