• 한국통신학회논문지
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• 제34권6B호
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• pp.559-567
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• 2009

본 논문에서는 콘텐츠 재분배 기능을 갖는 CDN (RDCDN)의 구조를 제안하고, 제안된 구조에 대한 특성과 콘텐츠 재분배 알고리즘에 대해 설명한다. 본 논문에서 제안한 방식은 대리시스템의 메모리 공간을 효율적으로 관리하며, 부하조절을 위한 특별한 알고리즘 없이 능동적인 부하조절 기능을 수행할 수 있다. 또한, 주 대리시스템(Main surrogate) 기능을 통해, 각 대리시스템에서 임의적인 콘텐츠 삭제가 일어나는 경우, 발생할 수 있는 신뢰성 저하를 방지할 수 있다. 제안한 방식의 특성을 평가하기 위해, 기존 DCDN과 비교하여 대리시스템의 메모리 사용량, 대리시스템 신뢰성의 우수함을 확인하였으며, 콘텐츠 재분배 비율에 관한 시뮬레이션을 통해 RDCDN의 콘텐츠 재분배 기능의 특성을 파악하였다. 또한, 기존 상업용 CDN 모델과 DCDN의 성능을 비교하여 RDCDN의 성능의 우수성을 분명히 확인하였다.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권3호
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• pp.156-165
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• 2005

Human being recognizes the physical world by integrating a great variety of sensory inputs, the information acquired by their own action, and their knowledge of the world using hierarchically parallel-distributed mechanism. In this paper, authors propose the sensor fusion system that can recognize multiple 3D objects from 2D projection images and tactile informations. The proposed system focuses on improving recognition performance of 3D objects. Unlike the conventional object recognition system that uses image sensor alone, the proposed method uses tactual sensors in addition to visual sensor. Neural network is used to fuse the two sensory signals. Tactual signals are obtained from the reaction force of the pressure sensors at the fingertips when unknown objects are grasped by four-fingered robot hand. The experiment evaluates the recognition rate and the number of learning iterations of various objects. The merits of the proposed systems are not only the high performance of the learning ability but also the reliability of the system with tactual information for recognizing various objects even though the visual sensory signals get defects. The experimental results show that the proposed system can improve recognition rate and reduce teeming time. These results verify the effectiveness of the proposed sensor fusion system as recognition scheme for 3D objects.

• Journal of Electrical Engineering and Technology
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• 제9권6호
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• pp.1822-1831
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• 2014

Micro-grid is connected to the main power grid through a static switch. One of the critical issues in micro-grids is protection which must disconnect the micro-grid from the network in short-circuit contingencies. Protective methods of micro-grid mainly follow the model of distribution system protection. This protection scheme suffers from improper operation due to the presence of single-phase loads, imbalance of three-phase loads and occurrence of power swings in micro-grid. In this paper, a new method which prevents from improper performance of static micro-grid protection is proposed. This method works based on artificial neural network (ANN) and able to differentiate short circuit from power swings by measuring impedance and the rate of impedance variations in PCC bus. This new technique provides a protective system with higher reliability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권4호
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• pp.1224-1248
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• 2022

In various sensor network applications, such as climate observation organizations, sensor nodes need to collect information from time to time and pass it on to the recipient of information through multiple bounces. According to field tests, this information corresponds to most of the energy use of the sensor hub. Decreasing the measurement of information transmission in sensor networks becomes an important issue.Compression sensing (CS) can reduce the amount of information delivered to the network and reduce traffic load. However, the total number of classification of information delivered using pure CS is still enormous. The hybrid technique for utilizing CS was proposed to diminish the quantity of transmissions in sensor networks.Further the energy productivity is a test task for the sensor nodes. However, in previous studies, a clustering approach using hybrid CS for a sensor network and an explanatory model was used to investigate the relationship between beam size and number of transmissions of hybrid CS technology. It uses efficient data integration techniques for large networks, but leads to clone attacks or attacks. Here, a new algorithm called SBEA (Snowball Endurance Algorithm) was proposed and tested with a bow. Thus, you can extend the battery life of your WSN by running effective copy detection. Often, multiple nodes, called observers, are selected to verify the reliability of the nodes within the network. Personal data from the source centre (e.g. personality and geographical data) is provided to the observer at the optional witness stage. The trust and reputation system is used to find the reliability of data aggregation across the cluster head and cluster nodes. It is also possible to obtain a mechanism to perform sleep and standby procedures to improve the life of the sensor node. The sniffers have been implemented to monitor the energy of the sensor nodes periodically in the sink. The proposed algorithm SBEA (Snowball Endurance Algorithm) is a combination of ERCD protocol and a combined mobility and routing algorithm that can identify the cluster head and adjacent cluster head nodes.This algorithm is used to yield the network life time and the performance of the sensor nodes can be increased.

• International Journal of Internet, Broadcasting and Communication
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• 제16권3호
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• pp.221-227
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• 2024

In autonomous navigation systems, the need for fast and accurate image processing using deep learning and advanced sensor technologies is paramount. These systems rely heavily on the ability to process and interpret visual data swiftly and precisely to ensure safe and efficient navigation. Despite the critical importance of such capabilities, there has been a noticeable lack of research specifically focused on ship image classification for maritime applications. This gap highlights the necessity for more in-depth studies in this domain. In this paper, we aim to address this gap by presenting a comprehensive comparative study of ship image classification using two distinct neural network models: the Feedforward Neural Network (FNN) and the Convolutional Neural Network (CNN). Our study involves the application of both models to the task of classifying ship images, utilizing a dataset specifically prepared for this purpose. Through our analysis, we found that the Convolutional Neural Network demonstrates significantly more effective performance in accurately classifying ship images compared to the Feedforward Neural Network. The findings from this research are significant as they can contribute to the advancement of core source technologies for maritime autonomous navigation systems. By leveraging the superior image classification capabilities of convolutional neural networks, we can enhance the accuracy and reliability of these systems. This improvement is crucial for the development of more efficient and safer autonomous maritime operations, ultimately contributing to the broader field of autonomous transportation technology.

• 한국정보통신학회논문지
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• 제15권3호
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• pp.753-758
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• 2011

군의 요구에 의해서 장비 성능에 대한 안정성과 긴박한 시간에 그 성능을 유지할 수 있는 생존성을 증대시키기 위한 방법이 대두되고 있으며, 그 방법 중의 하나로 시스템에서의 이중화 설계에 대한 이슈가 늘어나고 있는 추세이다. 일반적으로 시스템의 생존성을 증대시키기 위한 방법으로써 적용하는 이중화 기법은 두 개의 프로세스 상호간에 두 개의 네트워크망을 구성하여 이중화를 구현하는 것이다. 그러나 프로세스의 고장이나 물리적 네트워크망이 손실되었을 경우 기능을 제대로 수행하지 못할 수 있다. 이에 본 논문에서는 VMEbus의 master와 slave 간의 공유 메모리 영역, 인터럽트 방식 적용, 이중화를 담당하는 전용 task와 통신 이상 시 이를 처리하는 이벤트를 발생시키는 프로토콜을 직접 구현하고, 실험을 통하여 이 방안의 타당성을 확인한다.

• International Journal of Computer Science & Network Security
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• 제23권12호
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• pp.1-12
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• 2023

Ensuring the security of Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS) is paramount to safeguarding the reliability and safety of critical infrastructure. This paper addresses the significant threat posed by reconnaissance attacks on SCADA/ICS networks and presents an innovative methodology for enhancing their protection. The proposed approach strategically employs imbalance dataset handling techniques, ensemble methods, and feature engineering to enhance the resilience of SCADA/ICS systems. Experimentation and analysis demonstrate the compelling efficacy of our strategy, as evidenced by excellent model performance characterized by good precision, recall, and a commendably low false negative (FN). The practical utility of our approach is underscored through the evaluation of real-world SCADA/ICS datasets, showcasing superior performance compared to existing methods in a comparative analysis. Moreover, the integration of feature augmentation is revealed to significantly enhance detection capabilities. This research contributes to advancing the security posture of SCADA/ICS environments, addressing a critical imperative in the face of evolving cyber threats.

• 정보처리학회논문지B
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• 제15B권2호
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• pp.137-146
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• 2008

결함 지역화는 관찰된 결함의 근본 원인을 자동 인식 하는 것이 가능하기 때문에 규모가 큰 분산시스템에서 중요 역할 수행하며 시스템의 신뢰성 개선을 위해 시스템의 관리와 제어가 가능한 자가 관리를 지원한다. 결함 지역화를 지원하는 기존 연구들은 유비쿼터스 환경에서 베이지안 네트워크와 같은 인공지능 기술들을 주로 사용하여 진단과 예측 기능 중 하나만을 고려하고 있다. 따라서, 본 논문에서는 시스템의 신뢰성 개선을 위해 실시간 시스템 성능 스트림에 대한 학습을 통해 자가관리를 위한 확률적 의존 분석을 기반으로 하는 결함 지역화 방법을 제안하여 진단과 예측기능을 동시 제공한다. 학습 방법으로 베이지안 네트워크 알고리즘을 사용하여 각종 관련된 요소들을 연결함으로써 네트워크를 생성하고 확률적 의존 관계를 통해 귀납적과 연역적 추론기능을 제공한다. 베이지안 네트워크의 구성은 노드들간의 연관성을 찾아내는 것이 중요하기 때문에 그것을 구성하는 인자의 개수가 많은 경우 노드 순서 리스트를 추출하는 사전처리 과정이 필요하다. 따라서 전체 모델링 프로세스에 대한 개선이 요구된다. 이러한 문제를 해결하기 위해 발생한 문제와 관련성이 높은 노드 순서 리스트를 추출하는 방법을 제공한다. 구조 학습을 지원 하는 사전처리 방법을 통해 다양한 문제 영역에서의 학습 효율성을 높이며 학습에 필요로 되는 시간을 줄인다. 제안 방법론을 통해서 시스템의 자원 문제를 신속하고 정확하게 진단하는 것이 가능하며, 관찰된 정보를 기반으로 실행 중에 발생되는 잠재적인 문제를 예측하는 것이 가능하다. 시스템 성능 평가 영역에서 제안 방법론을 적용한 시스템 성능 분석을 기반으로 진단, 예측의 효율성과 정확성을 평가하여 제안 방법론의 유효성을 입증하였다.

• Journal of Communications and Networks
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• 제14권3호
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• pp.319-335
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• 2012

In this study, we design an applicable model enabling internet protocol television (IPTV) service providers to use a virtual network (VN) for IPTV service delivery. The model addresses the guaranteed service delivery, cost effectiveness, flexible control, and scalable network infrastructure limitations of backbone or IP overlay-based content networks. There are two major challenges involved in this research: i) The design of an efficient, cost effective, and reliable virtual network topology (VNT) for IPTV service delivery and the handling of a VN allocation failure by infrastructure providers (InPs) and ii) the proper approach to reduce the cost of VNT recontruction and reallocation caused by VNT allocation failure. Therefore, in this study, we design a more reliable virtual network topology for solving a single virtual node, virtual link, or video server failure. We develop a novel optimization objective and an efficient VN construction algorithm for building the proposed topology. In addition, we address the VN allocation failure problem by proposing VNT decomposition and reconstruction algorithms. Various simulations are conducted to verify the effectiveness of the proposed VNT, as well as that of the associated construction, decomposition, and reconstruction algorithms in terms of reliability and efficiency. The simulation results are compared with the findings of existing works, and an improvement in performance is observed.

• 한국멀티미디어학회논문지
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• 제24권3호
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• pp.431-439
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• 2021

Since the sensor nodes are randomly arranged in the region of interest, it may happen that the sensor network area is separated or there is no sensor node in some area. In addition, after the sensor nodes are deployed in the sensor network, a coverage hole may occur due to the exhaustion of energy or physical destruction of the sensor nodes. The coverage hole can greatly affect the overall performance of the sensor network, such as reducing the data reliability of the sensor network, changing the network topology, disconnecting the data link, and worsening the transmission load. Therefore, sensor network coverage hole recovery has been studied. Existing coverage hole recovery studies present very complex geometric methods and procedures in the two-step process of finding a coverage hole and recovering a coverage hole. This study proposes a method for discovering and recovering a coverage hole in a sensor network, discovering that the sensor node is a boundary node by itself, and determining the location of a mobile node to be added. The proposed method is expected to have better efficiency in terms of complexity and message transmission compared to previous methods.