• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.17-24
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• 2022

Hierarchical clustering methods have been proposed for more than sixty years and yet are used in various disciplines for relation observation and clustering purposes. In 1965, divisive hierarchical methods were proposed in biological sciences and have been used in various disciplines such as, and anthropology, ecology. Furthermore, recently hierarchical methods are being deployed in economy and energy studies. Unlike most clustering algorithms that require the number of clusters to be specified by the user, hierarchical clustering is well suited for situations where the number of clusters is unknown. This paper presents an overview of the hierarchical clustering algorithm. The dissimilarity measurements that can be utilized in hierarchical clustering algorithms are discussed. Further, the paper highlights the various and recent disciplines where the hierarchical clustering algorithms are employed.

• Journal of Information Processing Systems
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• 제11권1호
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• pp.39-56
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• 2015

Continuous multi-interval prediction (CMIP) is used to continuously predict the trend of a data stream based on various intervals simultaneously. The continuous integrated hierarchical temporal memory (CIHTM) network performs well in CMIP. However, it is not suitable for CMIP in real-time mode, especially when the number of prediction intervals is increased. In this paper, we propose a real-time integrated hierarchical temporal memory (RIHTM) network by introducing a new type of node, which is called a Zeta1FirstSpecializedQueueNode (ZFSQNode), for the real-time continuous multi-interval prediction (RCMIP) of data streams. The ZFSQNode is constructed by using a specialized circular queue (sQUEUE) together with the modules of original hierarchical temporal memory (HTM) nodes. By using a simple structure and the easy operation characteristics of the sQUEUE, entire prediction operations are integrated in the ZFSQNode. In particular, we employed only one ZFSQNode in each level of the RIHTM network during the prediction stage to generate different intervals of prediction results. The RIHTM network efficiently reduces the response time. Our performance evaluation showed that the RIHTM was satisfied to continuously predict the trend of data streams with multi-intervals in the real-time mode.

• 사물인터넷융복합논문지
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• 제3권1호
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• pp.9-11
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• 2017

최근에 인터넷과 모바일 장치가 널리 보급되면서 해커들이 네트워크를 이용해 공격하는 횟수 또한 증가하고 있다. 네트워크를 연결할 때 패킷을 주고받으며 통신을 하게 되는데, 여기에는 다양한 정보가 포함되어 있다. 이 패킷들의 정보를 Hierarchical Clustering 분석을 사용해 분석하고 정상적인 패킷과 비정상적인 패킷을 분류하여 공격자들의 공격을 탐지하였다. 이 분석 방법을 통해 새로운 패킷을 분석하여 공격을 탐지하는 것이 가능할 것이다.

• 한국IT서비스학회지
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• 제17권2호
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• pp.165-177
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• 2018

Recently, online commerces are becoming more common due to factors such as mobile technology development and smart device dissemination, and online review has a big influence on potential buyer's purchase decision. This study presents a set of analytical methodologies for understanding the meaning of customer reviews of products in online transaction. Using techniques currently developed in deep learning are implemented Hierarchical Attention Network for analyze meaning in online reviews. By using these techniques, we could solve time consuming pre-data analysis time problem and multiple topic problems. To this end, this study analyzes customer reviews of laptops sold in domestic online shopping malls. Our result successfully demonstrates over 90% classification accuracy. Therefore, this study classified the unstructured text data in the semantic analysis and confirmed the practical application possibility of the review analysis process.

• 전자공학회논문지B
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• 제32B권3호
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• pp.48-56
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• 1995

This paper presents a method of implementing efficient optical flow estimation for dynamic scene analysis using the hierarchical Hopfield neural networks. Given the two consequent inages, Zhou and Chellappa suggested the Hopfield neural network for computing the optical flow. The major problem of this algorithm is that Zhou and Chellappa's network accompanies self-feedback term, which forces them to check the energy change every iteration and only to accept the case where the lower the energy level is guaranteed. This is not only undesirable but also inefficient in implementing the Hopfield network. The another problem is that this model cannot allow the exact computation of optical flow in the case that the disparities of the moving objects are large. This paper improves the Zhou and Chellapa's problems by modifying the structure of the network to satisfy the convergence condition of the Hopfield model and suggesting the hierarchical algorithm, which enables the computation of the optical flow using the hierarchical structure even in the presence of large disparities.

• Management Science and Financial Engineering
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• 제12권2호
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• pp.87-110
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• 2006

In a hierarchical road network, all roads can be classified according to their attributes such as speed limit, number of lanes, etc. By splitting the whole road network into the subnetworks of the highlevel and low-level roads, we can reduce the size of the network to be calculated at once, and find a path in the way that drivers usually adopt when searching out a travel route. To exploit the hierarchical property of road networks, we define a convenient path and propose an algorithm for finding convenient paths. We introduce a parameter indicating the driver's tolerance to the difference between the length of a convenient path and that of a shortest convenient path. From this parameter, we can determine how far we have to search for the entering and exiting gateway. We also propose some techniques for reducing the number of pairs of entries and exits to be searched in a road network. A result of the computational experiment on a real road network is given to show the efficiency of the proposed algorithm.

• 한국ITS학회:학술대회논문집
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• 한국ITS학회 2003년도 제2회 정기총회 및 추계학술대회
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• pp.132-135
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• 2003

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 추계 학술대회 학술발표 논문집
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• pp.122-125
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• 2003

By advent of NNC(Neural Network Chip), it is possible that process in parallel and discern the importance of signal with learning oneself by experience in external signal. So, the design of general purpose operation unit using VHDL(VHSIC Hardware Description Language) on the existing FPGA(Field Programmable Gate Array) can replaced EN(Expert Network) and learning algorithm. Also, neural network operation unit is possible various operation using learning of NN(Neural Network). This paper present general purpose operation unit using hierarchical structure of EN EN of presented structure learn from logical gate which constitute a operation unit, it relocated several layer The overall structure is hierarchical using a module, it has generality more than FPGA operation unit.

• 디지털융복합연구
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• 제15권3호
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• pp.201-209
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• 2017

본 논문은 계층적 Convolutional Nerual Network(CNN)을 이용한 스마트폰용 객체 인식 시스템이다. 전체적인 구성은 스마트폰과 서버를 연결하여 서버에서 컨볼루셔널 뉴럴 네트워크로 객체 인식을 하고 수집된 데이터를 매칭시켜 스마트폰으로 객체의 상세정보를 전달하는 방법이다. 또한 계층적 컨볼루셔널 뉴럴 네트워크와 단편적 컨볼루셔널 뉴럴 네트워크와 비교하였다. 계층적 컨볼루셔널 뉴럴 네트워크는 88%, 단편적 컨볼루셔널 뉴럴 네트워크는 73%의 정확도를 가지며 15%p의 성능 향상을 보였다. 이를 기반으로 스마트폰과 방송매체와 연동한 T-Commerce 시장 확장의 가능성을 보여준다. 아울러 방송영상을 시청하면서 Information Retrieval, AR/VR 서비스도 제공 가능하다.

• 제어로봇시스템학회논문지
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• 제11권10호
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• pp.843-850
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• 2005

FBA(flux balance analysis) with Boolean rules for representing regulatory events has correctly predicted cellular behaviors, such as optimal flux distribution, maximal growth rate, metabolic by-product, and substrate concentration changes, with various environmental conditions. However, until now, since FBA has not taken into account a hierarchical regulatory network, it has limited the representation of the whole transcriptional regulation mechanism and interactions between specific regulatory proteins and genes. In this paper, in order to solve these problems, we describe the construction of hierarchical regulatory network with defined symbols and the introduction of a weight for representing interactions between symbols. Finally, the whole cellular behaviors with time were simulated through the linkage of a hierarchical regulatory network module and dynamic simulation module including FBA. The central metabolic network of E. coli was chosen as the basic model to identify our suggested modeling method.