• Journal of the Korean Home Economics Association
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• v.40 no.6
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• pp.159-171
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• 2002

Today, many people seek for their own personal character which is distinguished from another people and they utilize fashion coordination as the was of expression their own image. In addition, interest in electronic commerce and cuber shopping mall on the internet is increasing. For this reason, visual and interesting virtual fashion coordination system is needed. The purpose of this study is to propose possibility of fashion coordination by virtual 3D model. For this study, 1. We make a 3D standard body model by automatic generation. 2. We make 3D fashion item (sleeveless top and flare skirt) by automatic generation. 3. We combine 3D body model with fashion item by special point, grouping and gap being between body and clothes. 4. We make textile palettes and textile DB for texture mapping and rendering. As a effect of this study, 1. It can give the chance to coordinate clothes suitable for their own character and bodyshape on the cuber space more speedily and variously. 2. It can help fashion internet shopping mall company can save a time, expenses and tries to advertise their new products, offer service for customers and lead customers to purchasing. 3. It can accumulate a database of design and textile for using by fashion and textile industry.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1814-1829
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• 2011

In this paper, we present a model-based video bit rate estimation scheme for reducing the bit rate while maintaining a given target quality in many video streaming services limited by network bandwidth, such as IPTV services. Each item of video content can be stored on a video streaming server and delivered with the estimated bit rate using the proposed scheme, which consists of the following two steps: 1) In the first step, the complexity of each intra-frame in a given item of video content is computed as a frame feature to extract a group of candidate frames with a lot of bits. 2) In the second step, the bit rate of the video content is determined by applying statistical analysis and hypothesis testing to that group. The experimental results show that our scheme can reduce the bit rate by up to 78% with negligible degradation of subjective quality, especially with the low-complexity videos commonly used in IPTV services.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3192-3212
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• 2016

With the advent and popularity of e-commerce, an increasing number of consumers prefer to order tourism products online. A recommender system can help these users contend with information overload; however, such a system is affected by the cold start problem. Online tourism destination searching is a more difficult task than others on account of its more restrictive factors. In this paper, we therefore propose a tourism destination recommender system that employs opinion-mining technology to refine user preferences and item opinion reputations. These elements are then fused into a hybrid collaborative filtering method by combining user- and item-based collaborative filtering approaches. Meanwhile, we embed an artificial interactive module in our recommender system to alleviate the cold start problem. Compared with several well-known cold start recommendation approaches, our method provides improved recommendation accuracy and quality. A series of experimental evaluations using a publicly available dataset demonstrate that the proposed recommender system outperforms existing recommender systems in addressing the cold start problem.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.127-133
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• 2019

Since frequent item extraction so far requires searching for patterns and traversal for the FP-Tree, it is more likely to store the mining data in a tree and thus CPU time is required for its searching. In order to overcome these drawbacks, in this paper, we provide each item with its location identification of transaction data without relying on conditional FP-Tree and convert transaction data into 2-dimensional position information look-up table, resulting in the facilitation of time and spatial accessibility. We propose an algorithm that considers the mapping scheme between the location of items and items that guarantees the linear time complexity. Experimental results show that the proposed method can reduce many execution time and memory usage based on the data set obtained from the FIMI repository website.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2399-2413
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• 2021

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.33-39
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• 2020

The paper is to build recommendation systems leveraging Deep Learning and Big Data platform, Spark to predict item ratings of the Amazon e-commerce site. Recommendation system in e-commerce has become extremely popular in recent years and it is very important for both customers and sellers in daily life. It means providing the users with products and services they are interested in. Therecommendation systems need users' previous shopping activities and digital footprints to make best recommendation purpose for next item shopping. We developed the recommendation models in Amazon AWS Cloud services to predict the users' ratings for the items with the massive data set of Amazon customer reviews. We also present Big Data architecture to afford the large scale data set for storing and computation. And, we adopted deep learning for machine learning community as it is known that it has higher accuracy for the massive data set. In the end, a comparative conclusion in terms of the accuracy as well as the performance is illustrated with the Deep Learning architecture with Spark ML and the traditional Big Data architecture, Spark ML alone.

• Smart Structures and Systems
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• v.30 no.2
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• pp.145-157
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• 2022

The design of safe and economical structures depends on the reliable live load from load survey. Live load surveys are traditionally conducted by randomly selecting rooms and weighing each item on-site, a method that has problems of low efficiency, high cost, and long cycle time. This paper proposes a deep learning-based method combined with Internet big data to perform live load surveys. The proposed survey method utilizes multi-source heterogeneous data, such as images, voice, and product identification, to obtain the live load without weighing each item through object detection, web crawler, and speech recognition. The indoor objects and face detection models are first developed based on fine-tuning the YOLOv3 algorithm to detect target objects and obtain the number of people in a room, respectively. Each detection model is evaluated using the independent testing set. Then web crawler frameworks with keyword and image retrieval are established to extract the weight information of detected objects from Internet big data. The live load in a room is derived by combining the weight and number of items and people. To verify the feasibility of the proposed survey method, a live load survey is carried out for a meeting room. The results show that, compared with the traditional method of sampling and weighing, the proposed method could perform efficient and convenient live load surveys and represents a new load research paradigm.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.131-140
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• 2018

This study aims to analyze various discussions for influences on employment by the technologies related to the frequently mentioned Fourth Industrial Revolution and to conduct an exploratory research. For this aim, this paper analyzes and extends the survey related to realization possibility for managements and professionals of ICT sector in Global Agenda Council of World Economic Forum (WEF) in September 2015. Based upon these results, this study further conducts an empirical survey not only over realization possibility but also over its employment impact. For each 23 item of realization possibility, all the respondents (n=169) responded positively to each item to be actualized in 2025. In addition, for each 23 item of the strength of employment impact, most items were responded as decrease of employment but a few items were predicted as expansion of employment. This research has a meaning in providing a clue of empirical survey for employment impact by the Fourth Industrial Revolution in the future.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.27 no.3
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• pp.1-25
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• 2014

Objective : For activation of study on filed of odontology in Korean medicine academia, we analyzed the trend of articles that published in journals of Korean medicine. Methods : Using search words of odontology, in internet reference sites we collected papers and classified those into three categories like as review article, original article, case report. Observation points of each item are as follows. Inclusive item of papers; publication year, journal, number of authors, disease. Item of review article; subject of paper, Item of original article; number of patients, period of research, remedy. Item of case report; remedy, valuation of criteria, number of cases. Results : The total number of articles searched was 88, consisting of 23 review articles, 46 original articles and 19 case reports. The percentage of 'TMJ diseases' ranked highest(47.7%) in classification by disease. The most reviewed subject was 'Remedy' (16 times). '25 or less' person ranked highest(50%) in the number of patient in original articles. Acupuncture, chuna manual medicine and herbal medicine were mostly applied in case reports. The number of 'one case' reported case reports was highest(52.6%).

• The Research Journal of the Costume Culture
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• v.19 no.3
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• pp.473-487
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• 2011

The purpose of this study is to analyze the body type and fashion of posthuman in SF movies. The method of this study was to analyze documentaries, internet web site, fashion books and so forth. The results were as follows: The body types of posthuman were expressed as mutation type, prosthetic type, clone type by biological hybrid or renovation and digital type by computer simulation. The mutation type was expressed as reinforcement of masculinity or feminity and reinforcement of body functions. The fashion item was expressed as a black tailored suit, leather jacket, cat suit, whip, black sunglass, garter belt, high heel shoes, short pants, black one piece dress and functional body suit. The prosthetic type was expressed as reinforcement of body functions and reinforcement of masculinity or feminity. The fashion item was expressed as a military item, high-tech power suit and ergonomic armor suit. The clone type was expressed as the plural ego with reinforcement of body functions. The fashion item was expressed as a power shoulder jacket, fake fur coat, vinyl, black see-through look and functional suit. The digital type was expressed as reinforcement of masculinity or feminity and the plural ego with reinforcement of body functions. The fashion item was expressed as a data suit, leather jacket, black over coat, boots, black sun glass, ethnic items and military items. The meanings of posthuman fashion in SF movies were impurity of posthuman, display of superhuman's power by sexuality, metaphor of power and fantasy of superhero in opposition futuristic dystopia. As mentioned above, posthuman body type and fashion in SF movies become the conversational topic in the real world. The fact that we think about utopia and identity of posthuman in the future is of great significance.