• KIPS Transactions on Software and Data Engineering
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• v.4 no.4
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• pp.195-200
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• 2015

Recently, most people use android based smartphones and we can find music players in any smartphones. However, it's hard to find a personalized music player which applies user's preference. In this paper, we propose an emotion-based music player, which analyses and classifies the music with user's emotion, recommends the music, applies the user's preference, and visualizes the music by color. Through the proposed music player, user could be able to select musics easily and use an optimized application.

• Proceedings of the KSRS Conference
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• v.1
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• pp.260-262
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• 2006

One of future remote sensing techniques for transportation application is vehicle detection from the space, which could be the basis of measuring traffic volume and recognizing traffic condition in the future. This paper introduces an approach to vehicle detection using image object segmentation approach. The object-oriented image processing is particularly beneficial to high-resolution image classification of urban area, which suffers from noisy components in general. The project site was Dae-Jeon metropolitan area and a set of true color aerial images at 10cm resolution was used for the test. Authors investigated a variety of parameters such as scale, color, and shape and produced a customized solution for vehicle detection, which is based on a knowledge-based hierarchical model in the environment of eCognition. The highest tumbling block of the vehicle detection in the given data sets was to discriminate vehicles in dark color from new black asphalt pavement. Except for the cases, the overall accuracy was over 90%.

• Journal of KIBIM
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• v.9 no.4
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• pp.31-40
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• 2019

With Building Information Modeling(BIM) becoming the de facto standard for data sharing in the AEC industry, additional needs have increased to ensure the data integrity of BIM models themselves. Although the Industry Foundation Classes provide an open and neutral data format, its generalized schema leaves it open to data loss and misclassifications This research applied deep learning to automatically classify BIM elements and thus check the integrity of BIM-to-IFC mappings. Multi-view CNN(MVCC) and PointNet, which are two deep learning models customized to learn and classify in 3 dimensional non-euclidean spaces, were used. The analysis was restricted to classifying subtypes of architectural walls. MVCNN resulted in the highest performance, with ACC and F1 score of 0.95 and 0.94. MVCNN unitizes images from multiple perspectives of an element, and was thus able to learn the nuanced differences of wall subtypes. PointNet, on the other hand, lost many of the detailed features as it uses a sample of the point clouds and perceived only the 'skeleton' of the given walls.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.229-230
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• 2022

Smart AtoN(Aids to Navigation) that meet the future environment will generate variety of information and be provided in variety. In order to provide a customized service to marine users, the managers of AtoN should be able to check the any time and data in the desired format. In addition, in order to properly manage the AtoN in the future, it is necessary to identify the cause of the AtoN accidents and make efforts to prevent accident. In this study, 7 types of causes and 11 types of accidents were presented for AtoN accidents.

• The Journal of Information Systems
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• v.11 no.1
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• pp.129-149
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• 2002

Contents are the critical factors for website success. It is important to give the right information to each customer. For this, not only systematic classification but also management of contents is necessary. With regard In the former, some studies are found, but not for the latter. This paper proposes some methods of efficient contents management, which include customized service, push service of technology information, and real-time offering service. For each of them, the process of management is defined focusing on the B2B under make-to-order environment. The methodology is applied to the case of a marine engine manufacturing company.

• Journal of Information Technology Applications and Management
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• v.26 no.6
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• pp.89-101
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• 2019

Many researchers have been focused on designing beauty product recommendation system for a long time because of increased need of customers for personalized and customized recommendation in beauty product domain. In addition, as the application of the deep neural network technique becomes active recently, various collaborative filtering techniques based on the deep neural network have been introduced. In this context, this study proposes a deep neural network model suitable for beauty product recommendation by applying Neural Collaborative Filtering and Generalized Matrix Factorization (NCF + GMF) to beauty product recommendation. This study also provides an implementation of web API system to commercialize the proposed recommendation model. The overall performance of the NCF + GMF model was the best when the beauty product recommendation problem was defined as the estimation rating score problem and the binary classification problem. The NCF + GMF model showed also high performance in the top N recommendation.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.360-376
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• 2020

Many heuristic attribute reduction algorithms have been proposed to find a single reduct that functions as the entire set of original attributes without loss of classification capability; however, the proposed reducts are not always perfect for these multiclass datasets. In this study, based on a probabilistic rough set model, we propose the class-oriented attribute reduction (COAR) algorithm, which separately finds a reduct for each target class. Thus, there is a strong dependence between a reduct and its target class. Consequently, we propose a type of ensemble constructed on a group of classifiers based on class-oriented reducts with a customized weighted majority voting strategy. We evaluated the performance of our proposed algorithm based on five real multiclass datasets. Experimental results confirm the superiority of the proposed method in terms of four general evaluation metrics.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.134-136
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• 2012

u-City 환경에서 사용자 맞춤형 국토정보를 제공하기 위해 대용량의 데이터를 효과적으로 분석할 수 있는 데이터마이닝 기법이 적용되고 있다. 따라서 이 논문에서는 데이터마이닝 기법 중 연관분류기법을 적용하여 사용자 맞춤형 관광명소 추천 시스템을 개발하였다. 특히, CPFP-tree를 이용하여 빈발항목집합 탐사에 대한 시간을 단축하였으며, 연관분류를 통해 보다 높은 정확도로 결과를 예측 및 분류할 수 있게 하였다. 제시한 시스템은 공간정보에 대해 사용자 맞춤 서비스를 제공할 수 있음을 보였으며, 다양한 시나리오 적용을 통해 맞춤형 국토정보화 기술의 기반이 될 수 있다.

• Journal of Genetic Medicine
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• v.18 no.2
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• pp.83-93
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• 2021

Charcot-Marie-Tooth disease (CMT) is the most common hereditary motor and sensory peripheral neuropathy. CMT is usually classified into two categories based on pathology: demyelinating CMT type 1 (CMT1) and axonal CMT type 2 (CMT2) neuropathy. CMT1 can be distinguished by assessing the median motor nerve conduction velocity as greater than 38 m/s. The main clinical features of axonal CMT2 neuropathy are distal muscle weakness and loss of sensory and areflexia. In addition, they showed unusual clinical features, including delayed development, hearing loss, pyramidal signs, vocal cord paralysis, optic atrophy, and abnormal pupillary reactions. Recently, customized treatments for genetic diseases have been developed, and pregnancy diagnosis can enable the birth of a normal child when the causative gene mutation is found in CMT2. Therefore, accurate diagnosis based on genotype/phenotypic correlations is becoming more important. In this review, we describe the latest findings on the phenotypic characteristics of axonal CMT2 neuropathy. We hope that this review will be useful for clinicians in regard to the diagnosis and treatment of CMT.

• Journal of the Korean Society for information Management
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• v.33 no.3
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• pp.85-106
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• 2016

With a purpose of investigating the current status of resource organization for infants and young children's resources, this study examined 8 municipal libraries in J city and suggested recommendations for improvements. To do that, interviews were conducted with 8 children's librarians and 25 users, who were parents visiting the libraries. All libraries examined were using KDC to classify young children's resources, but books were shelved by the alphabetic order of publishers' names. This arrangement strategy was regarded very convenient in re-shelving materials from the perspective of librarians, but users had difficulties in finding books because of the separation of the classification system and the arrangement system. Also, the online public cataloging system did not provide accurate and sufficient information to locate a book. Based on the results, this study suggested two ways for improvements: (1) classifying and arranging books by KDC, (2) developing a new classification system customized to infants and young children.