• 한국컴퓨터정보학회논문지
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• 제16권12호
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• pp.247-255
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• 2011

무선 센서 네트워크에서 클러스터 기반의 계층적 라우팅 프로토콜들은 모든 노드들의 수명을 균등하게 유지하여, 센서 네트워크의 수명을 최대로 연장하는 것을 목표로 하고 있다. 본 논문에서는 싱크의 위치 변화를 고려한 적응적 클러스터링 프로토콜을 제안한다. 본 논문에서 제안하는 클러스터링 프로토콜의 특징은 클러스터 트리의 레벨에 따라 클러스터의 크기를 제한하는 대칭형 계층적 클러스터를 구성함으로써 싱크의 위치 변화에 적응적으로 대응 가능하며, 모든 클러스터의 생존 시간을 향상시킴과 동시에 균등한 생존 시간을 보장할 수 있다. 이 기법의 효율성을 입증하기 위해 기존의 대표적인 클러스터링 프로토콜들인 LEACH, EEUC와 본 논문에서 제안하는 적응적 클러스터링 프로토콜의 에너지 소비 정도를 시뮬레이션을 통해 비교하였으며, 그 결과 에너지 소비와 네트워크 수명의 균형에 대해 더 나은 성능을 얻어낼 수 있었다.

• IEIE Transactions on Smart Processing and Computing
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• 제5권4호
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• pp.256-266
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• 2016

Concurrently detected and annotated abnormal events can have a significant impact on surveillance systems. By considering the specific domain of pedestrian trajectories, this paper presents two main contributions. First, as introduced in much of the work on trajectory-based anomaly detection in the literature, only information about pedestrian paths, such as direction and speed, is considered. Differing from previous work, this paper proposes a framework that deals with additional types of trajectory-based anomalies. These abnormal events take places when a person enters prohibited areas. Those restricted regions are constructed by an online learning algorithm that uses surrounding information, including detected pedestrians and background scenes. Second, a simple data-boosting technique is introduced to overcome a lack of training data; such a problem particularly challenges all previous work, owing to the significantly low frequency of abnormal events. This technique only requires normal trajectories and fundamental information about scenes to increase the amount of training data for both normal and abnormal trajectories. With the increased amount of training data, the conventional abnormal trajectory classifier is able to achieve better prediction accuracy without falling into the over-fitting problem caused by complex learning models. Finally, the proposed framework (which annotates tracks that enter prohibited areas) and a conventional abnormal trajectory detector (using the data-boosting technique) are integrated to form a united detector. Such a detector deals with different types of anomalous trajectories in a hierarchical order. The experimental results show that all proposed detectors can effectively detect anomalous trajectories in the test phase.

• 한국정보통신학회논문지
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• 제9권5호
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• pp.1002-1011
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• 2005

본 논문에서는 방송 프로그램에 시청자가 대화 채널을 이용하여 참여할 수 있도록 하기 위한 양방향 방송 시스템 즉, 리턴채널서버를 설계, 구현한다. 방송 프로그램에 시청자의 의견이나 응답을 실시간 반영하는 서비스를 제공하기 위한 리턴채널서버는 제어 모듈, 네트웍 인터페이스 모듈, 데이터베이스 관리 모듈, 그리고 실시간 콘텐츠 저작 모듈 등으로 구성된다. 이때 방송 서비스에 무관한 형태로 리턴채널서버를 구성하기 위하여, 본 논문에서는 리턴채널서버의 각 모듈들을 리턴채널서버 응용과 리턴채널서버 응용 실행 환경 등으로 계층적으로 구성한다. 리턴채널서버 응용은 실행코드와 실행코드의 실행에 필요한 파라메터로 구성되어 있으며, 리턴채널서버가 특정 방송 프로그램을 제공하기 위해 처리해야 하는 절차들을 정의한다. 한편 리턴채널서버응용 실행환경은 리턴채널서버 응용이 실행되기 위한 환경을 제공한다. 리턴채널서버를 리턴채널서버 응용 실행환경과 리턴채널서버 응용으로 계층적으로 구성하면, 방송 서비스 제공자가 제공하고자 하는 방송 서비스에 무관하게 리턴채널서버가 동작하도록 리턴채널서버를 구성할 수 있으며 이는 실험을 통해 확인되었다.

• 한국콘텐츠학회논문지
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• 제6권1호
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• pp.72-79
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• 2006

최근의 MMORPG 게임은 심리스 월드로 분할하는 분산 서버를 구축하고 있다. 본 연구는 이러한 분산서버 간의 공유영역에 해당되는 지역에 대한 충돌처리를 다룬다. 분산 서버간의 공유 영역에 대한 경계영역을 동적으로 조정하기 위해 DLS을 사용하고, 광선과 단말 노드 간의 충돌 위치 관계를 통하여 이웃 노드를 빠르게 탐색한다. 이렇게 구해진 노드의 값을 통하여 객체 간의 충돌처리를 판별한다. 이것은 각 서버가 공유 영역에 대한 정보를 계속 보유할 필요가 없고, 서버간의 경계 영역을 포인터를 이용하여 빠르게 탐색할 수 있게 한다. 충돌은 계층적 경계상자를 이용하여 인접한 개체의 값들을 그룹으로 이진트리로 구축한다. 이러한 처리 방법은 처리량을 이분화 시켜 효과적으로 처리량을 줄일 수 있다. 또한 실시간적으로 발생되는 개체 정보의 변경 시 공유영역에 대한 복사가 필요하지 않으므로 네트워크 트래픽에 대한 처리량도 효과적으로 줄일 수 있다.

• 대한원격탐사학회지
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• 제27권3호
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• pp.379-388
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• 2011

At present, GEOBIA (Geographic Object-based Image Analysis), heir of OBIA (Object-based Image Analysis), is regarded as an important methodology by object-oriented paradigm for remote sensing, dealing with geo-objects related to image segmentation and classification in the different view point of pixel-based processing. This also helps to directly link to GIS applications. Thus, GEOBIA software is on the booming. The main theme of this study is to look into the applicability of geo-spatial processing open source to GEOBIA. However, there is no few fully featured open source for GEOBIA which needs complicated schemes and algorithms, till It was carried out to implement a preliminary system for GEOBIA running an integrated and user-oriented environment. This work was performed by using various open sources such as OTB or PostgreSQL/PostGIS. Some points are different from the widely-used proprietary GEOBIA software. In this system, geo-objects are not file-based ones, but tightly linked with GIS layers in spatial database management system. The mean shift algorithm with parameters associated with spatial similarities or homogeneities is used for image segmentation. For classification process in this work, tree-based model of hierarchical network composing parent and child nodes is implemented by attribute join in the semi-automatic mode, unlike traditional image-based classification. Of course, this integrated GEOBIA system is on the progressing stage, and further works are necessary. It is expected that this approach helps to develop and to extend new applications such as urban mapping or change detection linked to GIS data sets using GEOBIA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권9호
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• pp.2285-2301
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• 2012

Peer-to-Peer (P2P) networks are becoming more and more popular in video content delivery services, such as Video on Demand (VoD). Scalability feature of P2P allows a higher number of simultaneous users at a given server load and bandwidth to use stream service. However, the quality of service (QoS) in these networks is difficult to be guaranteed because of the free-riding problem that nodes download the recourses while never uploading recourses, which degrades the performance of P2P VoD networks. In this paper, a distributed trust model is designed to reduce node's free-riding phenomenon in P2P VoD networks. In this model, the P2P network is abstracted to be a super node hierarchical structure to monitor the reputation of nodes. In order to calculate the reputation of nodes, the Hidden Markov Model (HMM) is introduced in this paper. Besides, a distinction algorithm is proposed to distinguish the free-riders and malicious nodes. The free-riders are the nodes which have a low frequency to free-ride. And the malicious nodes have a high frequency to free-ride. The distinction algorithm takes different measures to response to the request of these two kinds of free-riders. The simulation results demonstrate that this proposed trust model can improve QoS effectively in P2P VoD networks.

• 분석과학
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• 제33권2호
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• pp.98-107
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• 2020

Methamphetamine (MA) is currently the most abused illicit drug in Korea. MA is produced by chemical synthesis, and the final target drug that is produced contains small amounts of the precursor chemicals, intermediates, and by-products. To identify and quantify these trace compounds in MA seizures, a practical and feasible approach for conducting chromatographic fingerprinting with a suite of traditional chemometric methods and recently introduced machine learning approaches was examined. This was achieved using gas chromatography (GC) coupled with a flame ionization detector (FID) and mass spectrometry (MS). Following appropriate examination of all the peaks in 71 samples, 166 impurities were selected as the characteristic components. Unsupervised (principal component analysis (PCA), hierarchical cluster analysis (HCA), and K-means clustering) and supervised (partial least squares-discriminant analysis (PLS-DA), orthogonal partial least squares-discriminant analysis (OPLS-DA), support vector machines (SVM), and deep neural network (DNN) with Keras) chemometric techniques were employed for classifying the 71 MA seizures. The results of the PCA, HCA, K-means clustering, PLS-DA, OPLS-DA, SVM, and DNN methods for quality evaluation were in good agreement. However, the tested MA seizures possessed distinct features, such as chirality, cutting agents, and boiling points. The study indicated that the established qualitative and semi-quantitative methods will be practical and useful analytical tools for characterizing trace compounds in illicit MA seizures. Moreover, they will provide a statistical basis for identifying the synthesis route, sources of supply, trafficking routes, and connections between seizures, which will support drug law enforcement agencies in their effort to eliminate organized MA crime.

• 방송공학회논문지
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• 제18권6호
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• pp.872-883
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• 2013

본 논문에서는 패킷 오류가 발생하는 IP망을 통해 SVC 비디오 전송 서비스를 제공하기 위한 Exclusive-OR 기반의 FEC (forward error correction) 오류제어 시스템을 설계하고 성능을 분석한다. 설계된 시스템에서는 계산적으로 복잡도가 낮은 표준 Exclusive-OR 연산에 기반한 FEC 방법을 활용하고, SVC 비디오의 계층적 구조에 적합하도록 FEC 기법을 적용 한다. 설계된 Exclusive-OR 기반의 오류 제어 시스템의 성능을 검증하기 위하여 NIST-NET 기반의 전송 시뮬레이터를 활용한다. NIST-NET 기반의 시뮬레이터를 통한 SVC 비디오 패킷 전송 실험에 의해 설계된 Exclusive-OR 기반의 FEC 시스템의 오류 강인 전송 성능을 확인한다.

• Genomics & Informatics
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• 제7권2호
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• pp.122-130
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• 2009

A systems biology approach for the identification of perturbed molecular functions is required to understand the complex progressive disease such as breast cancer. In this study, we analyze the microarray data with Gene Ontology terms of molecular functions to select perturbed molecular functional modules in breast cancer tissues based on the definition of Gene ontology Functional Code. The Gene Ontology is three structured vocabularies describing genes and its products in terms of their associated biological processes, cellular components and molecular functions. The Gene Ontology is hierarchically classified as a directed acyclic graph. However, it is difficult to visualize Gene Ontology as a directed tree since a Gene Ontology term may have more than one parent by providing multiple paths from the root. Therefore, we applied the definition of Gene Ontology codes by defining one or more GO code(s) to each GO term to visualize the hierarchical classification of GO terms as a network. The selected molecular functions could be considered as perturbed molecular functional modules that putatively contributes to the progression of disease. We evaluated the method by analyzing microarray dataset of breast cancer tissues; i.e., normal and invasive breast cancer tissues. Based on the integration approach, we selected several interesting perturbed molecular functions that are implicated in the progression of breast cancers. Moreover, these selected molecular functions include several known breast cancer-related genes. It is concluded from this study that the present strategy is capable of selecting perturbed molecular functions that putatively play roles in the progression of diseases and provides an improved interpretability of GO terms based on the definition of Gene Ontology codes.

• 디지털산업정보학회논문지
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• 제15권4호
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• pp.197-212
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• 2019

The purpose of this study is to identify the maturity stages of venture firms through classification analysis, which is widely used as a big data technique. Venture companies should develop a competitive advantage in the market. And the maturity stage of a company can be classified into five stages. I will analyze a difference in the growth stage of venture firms between the survey response and the statistical classification methods. The firm growth level distinguished five stages and was divided into the period of start-up and declines. A classification method of big data uses popularly k-mean cluster analysis, hierarchical cluster analysis, artificial neural network, and decision tree analysis. I used variables that asset increase, capital increase, sales increase, operating profit increase, R&D investment increase, operation period and retirement number. The research results, each big data analysis technique showed a large difference of samples sized in the group. In particular, the decision tree and neural networks' methods were classified as three groups rather than five groups. The groups size of all classification analysis was all different by the big data analysis methods. Furthermore, according to the variables' selection and the sample size may be dissimilar results. Also, each classed group showed a number of competitive differences. The research implication is that an analysts need to interpret statistics through management theory in order to interpret classification of big data results correctly. In addition, the choice of classification analysis should be determined by considering not only management theory but also practical experience. Finally, the growth of venture firms needs to be examined by time-series analysis and closely monitored by individual firms. And, future research will need to include significant variables of the company's maturity stages.