• Journal of Internet Computing and Services
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• v.14 no.2
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• pp.15-24
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• 2013

In recent years, the number of social network system has grown rapidly. Among them, social bookmarking system(SBS) is one of the most popular systems. SBS provides network platform which users can share and manage various types of online resources by using tags. In SBS, it can be possible to reflect tag and time in order to enhance the quality of personalized recommendation. In this paper, we proposed recommender system which reflect tag and time at weight generation and similarity calculation. Also we adapted proposed method to real dataset and the result of experiment showed that the our method offers better performance when such information is integrated.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.457-460
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• 2014

클라우드 컴퓨팅은 IT 자원의 효율적인 관리와 비용 대비 양질의 서비스 제공을 위한 새로운 패러다임으로써, 국내외의 기업뿐만 아니라 많은 사용자들에게 주목 받고 있다. 하지만 관련 시장의 빠른 성장과 함께 다양한 사이버 범죄에 노출될 수 있는 위험이 높아졌음에도 불구하고, 클라우드 컴퓨팅에 대한 디지털 포렌식은 실질적인 역할을 수행하기에 아직 미비한 실정이다. 클라우드 컴퓨팅은 증거 데이터가 물리적으로 분산되어 있고, 자원이 가상공간에 존재할 수 있기 때문에 기존의 디지털 포렌식 수사와는 다르게 접근해야 한다. 이에, 본 논문에서는 추상화된 클라우드 계층에 따른 기존 포렌식 절차 상의 데이터 수집 방법에 관한 한계를 분석하고, 확보한 증거 데이터의 신뢰성 보장 및 다양한 클라우드 환경에 보다 유연하게 적용할 수 있는 디지털 증거 수집 절차를 제안한다. 해당 절차는 클라우드 구성 요소들 중 물리적인 자원들을 가상화하여 논리적으로 구성할 수 있도록 하며, 가상화된 자원들을 서비스 목적에 따라 폭넓게 활용할 수 있도록 관리 체계를 제공해주는 클라우드 플랫폼을 기반으로 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.1410-1413
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• 2012

최근 스마트 시대에 디지털 컨버젼스(digital convergence)의 대표기기로 대두되고 있는 태블릿 PC는 휴대전화와 컴퓨터의 기능을 바탕으로 장소의 제한 없이 네트워크에 접속할 수 있다. 이는, 개인의 일상생활에서 큰 영향을 미치고 있는 실정이다. 10년 이상 e러닝이 주도해 온 IT교육시장에서 스마트러닝으로의 변화는 새로운 플랫폼을 구축하는 그 이상의 의미를 가진다. 스마트 러닝은 기존의 수직적인 학습방식을 수평적, 참여적, 지능적, 그리고 상호작용적인 방식으로 전환하여 학습의 효과를 높였다. 이러한 트랜드를 반영하여 스마트러닝의 장점을 극대화 시킬 수 있는 학습자 중심의 컨버젼스 러닝시스템(learning system)을 구현하고자 하였다. 또한, 영어의 중요성이 대두되면서 영어 인증시험에 대한 관심이 날로 커지고 있다. 그리하여 바쁜 일상생활 중에서 시간과 장소에 구애 받지 않고 태블릿 PC를 통하여 영어 인증시험을 공부할 수 있는 어플리케이션을 기획하였다. 본 LEMON(Learn English Mobile ON-air) 앱(application)은 영어 학습 시간이 충분하지 않은 대학생 및 직장인 등을 대상으로 TOEIC, TOEFL, TOEIC SPEAKING 영어 인증시험에 대한 학습이 가능하도록 구현하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.287-288
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• 2019

본 논문에서는 대용량 화물 DTG 데이터 분석을 위한 빅데이터 플랫폼을 설계 및 구현한다. DTG(digital tacho graph)는 차량운행기록을 실시간으로 저장하는 장치로서, 차량의 GPS, 속도, RPM, 제동유무, 이동거리 등 차량운행 관련 데이터가 1초 단위로 기록된다. 차량 운행 패턴 및 분석을 하기 위해서는 DTG 데이터의 빠른 처리가 필수적이며, 특히 대용량 DTG 데이터를 가공 및 변환하기 위해서는 별도의 플랫폼이 필요하다. 본 논문에서는 오픈소스 기반의 빅데이터 프레임워크인 스파크(Spark)를 이용하여 대용량 화물 DTG 데이터의 전처리 플랫폼을 구현하였다. 실제 대용량 화물 DTG 데이터를 대상으로 데이터를 변환 및 지도상에 표현해 보인다.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.95-105
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• 2019

A great deal needs to be learned about why and how users participate and consume information on various online sites. The design of socio-technical systems especially for promoting engagement in terms of maximum user participation is both a theoretical and real-world challenge that researchers strive to understand. At present, most of the research on the motives of Internet video users' behavior focuses on the user's "viewing motivation" and "sharing motivation", and lacks the analysis of the factors affecting users' "subscription motivation". This study will attempt to compensate for this gap. Based on the YouTube platform, we take Chinese video users as the research object and uses the "Censydiam user motivation analysis model" to make assumptions about user subscription motivation from the two levels of social needs and personal needs, using regression analysis. Validate the hypothesis and get the influencing factors that may be available in the user's subscription motivation based on the assumptions. Built on survey data from 215 respondents, the study found that Enjoyment, Vitality, Power, and Conviviality are four factors that influence user motivation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2069-2085
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• 2021

A Social Networking Service (SNS) platform provides digital footprints to discover users' interests and track the social diffusion of product adoptions. How to identify a small set of seed users in a SNS who is potential to adopt a new promoting product with high probability, is a key question in social networks. Existing works approached this as a social influence maximization problem. However, these approaches relied heavily on text information for topic modeling and neglected the impact of seed users' relation in the model. To this end, in this paper, we first develop a general product adoption function integrating both users' interest and social influence, where the user interest model relies on historical user behavior and the seed users' evaluations without any text information. Accordingly, we formulate a product adoption maximization problem and prove NP-hardness of this problem. We then design an efficient algorithm to solve this problem. We further devise a method to automatically learn the parameter in the proposed adoption function from users' past behaviors. Finally, experimental results show the soundness of our proposed adoption decision function and the effectiveness of the proposed seed selection method for product adoption maximization.

• 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.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.2
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• pp.177-186
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• 2022

A network QoS model for the joint integrated C4I structure was proposed for the integration of network infrastructure and network operations(NetOps) for NCOE. Detailed QoS requirements process of the joint integrated C4I systems are needs in the Multi-Domain Operation Environment(MDOE). A process is proposed for identifying QoS requirements and establishing in the MDOE using JMT(Joint Mission Thread) reference architecture and solution architecture. Mission analysis identify JCOAs(Joint Critical Operational Activities) and related activities based on JMT & System architecture's OVs, and Information analysis identify QoS attributes using System architecture's SVs. Identifying QoS attributes will be registered at PPS Registry by pre-regulated process, and will be set-up by NetOps. MDOE QoS requirement Process will support efficiently MUM-T and smart defense platform users under the future uncertain battlefield circumstances.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.5
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• pp.723-740
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• 2022

This study aimed to develop an Arduino-based garment pressure device (APD) on the basis of using Single-Tact sensor by suggesting the reliable clothing pressure range and coefficient of selected sensors through the APD calibration process. Once the APD was validated, the pressure of the experimental men's lower body compression wears was measured using the APD and was compared to the pressure measured using the existing air-pack type pressure meter. The subjects were one mannequin and eight men in their 20's, and the trial compression wears were calf sleeves and pants. Clothing pressures were measured in hip, mid-thigh, calf, and ankle. In terms of the 99% confidence level, the experimental clothing pressure measured at the designated measuring points using the APD was considered identical to the one measured using an existing clothing pressure meter. Therefore, on the basis of the experiment results, this study demonstrated that the APD is as reliable as the existing clothing pressure meter within the pressure ranges of 0.54-16.79 kPa and 0.18-25.47 kPa as provided by the SingleTact sensor supplier's data on the basis of using an external ADC (Analog to Digital Converter) module.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.129-135
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• 2022

Real human digital models appear more and more frequently in VR/AR application scenarios, in which real-time markerless face capture animation of personalized virtual human faces is an important research topic. The traditional way to achieve personalized real human facial animation requires multiple mature animation staff, and in practice, the complex process and difficult technology may bring obstacles to inexperienced users. This paper proposes a new process to solve this kind of work, which has the advantages of low cost and less time than the traditional production method. For the personalized real human face model obtained by 3D reconstruction technology, first, use R3ds Wrap to topology the model, then use Avatary to make 52 Blend-Shape model files suitable for AR-Kit, and finally realize real-time markerless face capture 3D real human on the UE4 platform facial motion capture, this study makes rational use of the advantages of software and proposes a more efficient workflow for real-time markerless facial motion capture of personalized 3D real human models, The process ideas proposed in this paper can be helpful for other scholars who study this kind of work.