• Journal of the Korean Society of Safety
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• v.15 no.2
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• pp.36-45
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• 2000

Automated production system is composed of many complicated techniques and it become a very difficult task to control, monitor and diagnose this compound system. Moreover, it is required to develop an effective diagnosing technique and reduce the diagnosing time while operating the system in parallel under many faults occurring concurrently. This study develops a Modular Artificial Neural Network(MANN) which can perform a diagnosing function of multiple faults with the following steps: 1) Modularizing a complicated system into subsystems. 2) Formulating a hierarchical structure by dividing the subsystem into many detailed elements. 3) Planting an artificial neural network into hierarchical module. The system developed is implemented on workstation platform with $X-Windows^{(r)}$ which provides multi-process, multi-tasking and IPC facilities for visualization of transaction, by applying the software written in $ANSI-C^{(r)}$ together with $MOTIF^{(r)}$ on the fault diagnosis of PI feedback controller reactor. It can be used as a simple stepping stone towards a perfect multiple diagnosing system covering with various industrial applications, and further provides an economical approach to prevent a disastrous failure of huge complicated systems.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06d
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• pp.287-288
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• 2011

In this paper, we propose a hierarchical P2P architecture using clustering mobile peers. The proposed scheme utilizes the maximum connection time of connected peers to form the mobile network, which makes the network topology relatively stable. The connection time of connected peers can be determined by the location, velocity vector and communication range of each mobile peer. Therefore, the update overhead of the network is decreased and the success rate of contents search is increased. Experiments have shown that our proposed scheme outperforms the existing schemes.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2008.10b
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• pp.507-511
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• 2008

Supporting energy efficiency and load balancing in wireless sensor network is the most important issue in devising the hierarchical routing protocols. Recently, the dual layered clustering scheme with GPS was proposed for the supporting of load balancing for cluster heads but there would be many collided messages in the overlapped area between two layers. Thereby, the purpose of this paper is to reduce the collision rate in the overlapped layer by concisely distinguish them with the same number of nodes in them. For the layer partition, this paper uses an equation $x^2+ y^2{\le}(\frac{R}{\sqrt{2\pi}})^2$ to distinguish layers. By using it, the scheme could efficiently distinguish two layers and gets the balanced number of elements in them. Therefore, the proposed routing scheme could prolong the overall network life cycle about 10% compared to the previous two layered clustering scheme.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.579-581
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• 1988

This paper proposes the logic simulation for hierarchical logic network with function descriptor base data structure and algorithm on which a macro cell is considered as a logic elements. Function descriptor base data structure is useful when many logic elements of which type is same exist in a network, for it lessens the computer memory size used during the simulation. And the proposed simulation algorithm may improve the simulation speed.

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.3
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• pp.427-440
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• 1996

Constraint networks are a major approach to knowledge representation in Concurrent Engineering (CE) systems. The networks model various factors in CE as constraints linked by shared variables. Many systems have been developed to assist constraint network processing. While these systems can be useful, their underlying assumption that a solution must simultaneously satisfy all the constraints is often unrealistic and hard to achieve. Proposed in this paper is a hierarchical representation of constraint networks using priorities, namely Prioritized Constraint Network (PCN). A mechanism to propagate priorities is developed, and a new satisfiability definition taking into account the priorities is described. Strength of constraint supporters can be derived from the propagated priorities. Several properties useful for investigating PCN's and finding effective solving strategies ore developed.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1188-1195
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• 2014

This paper presents an analytical technique for reliability evaluation of electrical distribution network (EDN) containing distributed generation (DG). Based on hierarchical levels of circuit breaker controlling zones and feeder sections, a directed-relation-graph (DRG) for an END is formed to describe the hierarchical structure of the EDN. The reliability indices of EDN and load points can be evaluated directly using the formed DRG, and the reliability evaluation of an EDN containing DGs can also be done without re-forming the DRG. The proposed technique incorporates multi-state models of photovoltaic and diesel generations, as well as weather factors. The IEEE-RBTS Bus 6 EDN is used to validate the proposed technique; and a practical campus EDN containing DG was also analyzed using the proposed technique.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.724-726
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• 2005

베이지안망(Bayesian network)은 다수의 변수들 사이의 확률적 관계(조건부독립성: conditional independence)를 그래프 구조로 표현하는 모델이다. 이러한 베이지안망은 비감독학습(unsupervised teaming)을 통한 데이터마이닝에 적합하다. 이를 위해 데이터로부터 베이지안망의 구조와 파라미터를 학습하게 된다. 주어진 데이터의 likelihood를 최대로 하는 베이지안망 구조를 찾는 문제는 NP-hard임이 알려져 있으므로, greedy search를 통한 근사해(approximate solution)를 구하는 방법이 주로 이용된다. 하지만 이러한 근사적 학습방법들도 데이터를 구성하는 변수들이 수천 - 수만에 이르는 경우, 방대한 계산량으로 인해 그 적용이 실질적으로 불가능하게 된다. 본 논문에서는 그러한 대규모 데이터에서 학습될 수 있는 계층적 베이지안망(hierarchical Bayesian network) 모델 및 그 학습방법을 제안하고, 그 가능성을 실험을 통해 보인다.

• IE interfaces
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• v.13 no.2
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• pp.234-245
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• 2000

In this paper, the conceptual framework of IBS(Intelligent Building Systems) is redefined, and we propose two ANP(Analytic Network Process) models for ranking IBS. Traditional models have ranked IBS according to technical features or the number of elements in IBS. But, we consider relative functional importance among IBS elements for efficient building operation. According to the structure of interactive-relationship among IBS elements, we present two types of model. The one is Model A that is composed of both hierarchical and network structures. It has $12{\times}12$ supermatrix consists of interdependent relationship between 6 benefit elements(Productivity, Saving, Safety, Convenience, Pleasantness, Environment Affinity) and 6 IBS elements(Building Operation, Security, Safety, Telecommunication(TC), Office Automation(OA), other elements). Each of 6 IBS elements has subelements in hierarchical structure. The other is Model B that has $25{\times}25$ supermatrix consists of interdependent relationship between 6 benefit elements and 19 IBS sub elements. Merits and demerits of each model are discussed in detail.

• ETRI Journal
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• v.41 no.5
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• pp.574-584
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• 2019

A new Mirai variant found recently was equipped with a dynamic update ability, which increases the level of difficulty for DDoS mitigation. Continuous development of 5G technology and an increasing number of Internet of Things (IoT) devices connected to the network pose serious threats to cyber security. Therefore, researchers have tried to develop better DDoS mitigation systems. However, the majority of the existing models provide centralized solutions either by deploying the system with additional servers at the host site, on the cloud, or at third party locations, which may cause latency. Since Internet service providers (ISP) are links between the internet and users, deploying the defense system within the ISP domain is the panacea for delivering an efficient solution. To cope with the dynamic nature of the new DDoS attacks, we utilized an unsupervised artificial neural network to develop a hierarchical two-layered self-organizing map equipped with a twofold feature selection for DDoS mitigation within the ISP domain.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.185-207
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• 2022

Recently, with the development of deep learning technology, researches to apply a deep learning algorithm to analyze unstructured data such as text and images are being actively conducted. Text classification has been studied for a long time in academia and industry, and various attempts are being performed to utilize data characteristics to improve classification performance. In particular, a hierarchical relationship of labels has been utilized for hierarchical classification. However, the top-down approach mainly used for hierarchical classification has a limitation that misclassification at a higher level blocks the opportunity for correct classification at a lower level. Therefore, in this study, we propose a methodology for classifying hierarchical data using the autoencoder-based deeply supervised network that high-level classification does not block the low-level classification while considering the hierarchical relationship of labels. The proposed methodology adds a main classifier that predicts a low-level label to the autoencoder's latent variable and an auxiliary classifier that predicts a high-level label to the hidden layer of the autoencoder. As a result of experiments on 22,512 academic papers to evaluate the performance of the proposed methodology, it was confirmed that the proposed model showed superior classification accuracy and F1-score compared to the traditional supervised autoencoder and DNN model.