• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.615-616
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• 2009

휴대폰을 이용한 무선데이터의 전송환경에서 사용자는 무선Web을 통해 받은 데이터들을 개인의 휴대폰에 저장한다. 그리고 각각의 콘텐츠들은 DRM기술을 통해 저작권을 보호한다[1]. 이러한 DRM기술은 콘텐츠와 저작권을 가진 회사의 영업이익을 보호하는 반면, 사용자의 금전적인 부담을 안겨주는 양면성을 가진다. 본 제안서에서는 통합 환경의 DATA 메신저를 제안하여 저작권을 보호하고 불법 복제를 방지하면서 다수의 사용자에게 무선데이터의 활용도를 높일 수 있는 메커니즘을 제안하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.69-71
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• 2020

Principal Component Analysis (PCA) is an effective data analysis technique which is commonly used for fault detection on collected data of Wireless Sensor Networks (WSN), However, applying PCA on the whole data make the detection performance low. In this paper, we propose Joint PCA and Adaptive Threshold for Fault Detection (JPATAD). Experimental results on a real dataset show a remarkably higher performance of JPATAD comparing to conventional PCA model in detection of noise which is a popular fault in collected data of sensors.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.14-15
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• 2007

Now-a-days, data and task migration in collaborative work provides enormous facilities to users. Here, we propose an ontology-based information management scheme to facilitate data and task migration in collaborative work. This ontologybased model will help us to organize huge information (e.g. device status, runtime state etc.) efficiently.

• Proceedings of the Korean Society of Computer Information Conference
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• 2008.06a
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• pp.189-192
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• 2008

최근에 제한된 데이터 셋보다 센서 데이터 처리, 웹 서버 로그나 전화 기록과 같은 다양한 트랜잭션 로그 분석등과 관련된 대용량 데이터 스트림을 실시간으로 처리하는 것에 많은 관심이 집중되고 있으며, 특히 데이터 스트림의 조인 처리에 대한 관심이 증가하고 있다. 본 논문에서는 조인 연산을 빠르게 처리하기 위한 효율적인 해시 구조와 조인 방법에 대해서 연구하고 다양한 환경에서 제안 방법을 검증한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.04a
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• pp.692-697
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• 2000

멀티미디어 개인 휴대 통신(MPCS)에서 다중 클래스호에 대한 효율적인 채널할당은 매우 중요하다고 할 수 있다. 본 논문에서는 Image/Voice/Data 호에 대하여 가변적 보호 채널을 할당하는 새로운 방식을 제안하였다. 이러한 방식은 3차원 상태 천이도로 모델링 되며 보호 채널의 크기를 가변적으로 조절함으로써 보다 융통성있는 서비스를 제공하게 되며, 또한 수학적 분석과 시뮬레이션을 통해 비교분석을 수행하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.179-182
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• 2013

We develop Framework architecture of Online Shopping Services based on M2M and IoT for Handheld Devices in Cloud Computing. MapReduce model will be used as a method to simplify large scale data processing when user search for purchasing products online which provide efficient, and fast respond time. Therefore, providing user with a enhanced Quality of Experience (QoE) as well as Quality of Service (QoS) when purchasing/searching products Online from big data.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.88-90
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• 2013

With the continuous growth of a number of high-quality multimedia services for handheld devices, the lack of power resources becomes an increasingly critical issue. One of the ways to overcome existing problem is to make multimedia data processing more efficient. In order to do that this paper introduces a video streaming application for Android platform which, while being used along with offloading technique, may provide an efficient progressive download service for user devices along with relief of media servers.

• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.40-46
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• 2020

As interest in traffic safety increases, research on autonomous driving, which reduces the incidence of traffic accidents, is increased. Object recognition and detection are essential for autonomous driving. Therefore, research on object recognition and detection through traffic image big data is being actively conducted to determine the road conditions. However, because most existing studies use only daytime data, it is difficult to recognize objects on night roads. Particularly, in the case of a light source object, it is difficult to use the features of the daytime as it is due to light smudging and whitening. Therefore, this study proposes Yolo based light source object detection for traffic image big data processing. The proposed method performs image processing by applying color model transitions to night traffic image. The object group is determined by extracting the characteristics of the object through image processing. It is possible to increase the recognition rate of light source object detection on a night road through a deep learning model using candidate group data.

• Journal of Information Processing Systems
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• v.3 no.1
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• pp.38-42
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• 2007

Microarray data includes tens of thousands of gene expressions simultaneously, so it can be effectively used in identifying the phenotypes of diseases. However, the retrieval of functional information from a large corpus of gene expression data is still a time-consuming task. In this paper, we propose an efficient method for identifying functional categories of differentially expressed genes from a micro-array experiment by using Gene Ontology (GO). Our method is as follows: (1) The expression data set is first filtered to include only genes with mean expression values that differ by at least 3-fold between the two groups. (2) The genes are then ranked based on the t-statistics. The 100 most highly ranked genes are selected as informative genes. (3) The t-value of each informative gene is imposed as a score on the associated GO terms. High-scoring GO terms are then listed with their associated genes and represent the functional category information of the micro-array experiment. A system called HMDA (Hallym Micro-array Data analysis) is implemented on publicly available micro-array data sets and validated. Our results were also compared with the original analysis.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.386-395
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• 2016

In trajectory-based navigation systems, a huge amount of trajectory data is needed for efficient route explorations. However, it would be very hard to collect trajectories from all the possible start and destination combinations. To provide a practical solution to this problem, we suggest a method combining collected GPS trajectories data into additional generated trajectories with new start and destination combinations without road information. We present a trajectory combination algorithm and its implementation with Scala programming language on Spark platform for big data processing. The experimental results proved that the proposed method can effectively populate the collected trajectories into valid trajectory paths more than three hundred times.