• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2004년도 춘계종합학술대회
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• pp.416-419
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• 2004

최근에는 컴퓨터 시뮬레이션을 통해서 실제 전투 자산을 가동하지 않고 실전과 같은 전투경험을 부여하고 있다. 이러한 시뮬레이션이 실제와 똑같은 환경을 구축하기 위해서는 현재 사용하는 워게임 모델을 운용하기 위한 데이터베이스가 잘 구축되어야 하고, 그 데이터베이스를 포함한 페더레이트(federate)간의 연동(federation)이 네트워크 상에서 잘 수행되어야 한다. 이에 본 논문에서는 전장 데이터(이하 액티브 패킷)의 신속한 전달을 필요로 하는 긴급한 실제상황과 유사한 전장공간을 구축할 수 있도록 액티브 네트워크 상에서 페더레이트(혹은 액티브 노드) 간의 효율적인 트래픽 처리가 가능한 가상 전장 환경을 구성하고, 이에 대한 유효성을 모의 실험을 통하여 검증하였다.

• 정보처리학회논문지A
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• 제12A권4호
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• pp.297-304
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• 2005

본 논문은 적응적 망 경로 선택 기능을 최대한 활용하기 위하여 두 가지 새로운 큐 구조 DAMQWR와 VCDAMQ를 제안하였다. DAMQWR은 리쿠르트 레지스터를 사용하여 정체된 채널의 메시지를 비 정체 채널로 라우팅을 유도할 수 있게 하여주며 VCDAMQ는 가상 채널상의 교통량을 동적으로 지원하도록 함으로써 망의 흐름을 보다 원활하게 하여 준다. 시뮬레이션과 분석을 통하여 제안된 큐 구조의 특성과 성능을 평가하였고 그 결과 제안되어진 큐 구조들인 VCDAMQ와 DAMQWR 구조가 메모리 및 망의 자원을 효과적으로 사용하여 적응적 라우터에 가장 적합함을 알 수 있었으며, 실험결과에서 기존의 DAMQ에 비하여 최대 $20\%$까지 망의 통신 성능이 향상됨을 보였다.

• 한국추진공학회지
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• 제23권2호
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• pp.38-45
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• 2019

본 연구에서는 항공용 가스터빈의 연소실에서의 연소불안정 해석을 위한 고유값 도출을 목적으로 하는 1D 네트워크 모델을 개발하였다. 모델은 면적 변화가 있는 음향 네트워크 요소들 사이의 각종 지배 방정식을 통하여 개발되었고, 이를 이용하여 현재 개발 중인 복잡한 유로 형상을 갖는 실제 항공용 가스터빈 연소기에서의 음향장 해석에 적용되었다. 본 모델을 통하여 도출된 음향장 해석 결과는 3차원 유한요소해석 기반의 헬름홀츠 솔버의 계산 결과와 비교하였다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2014년도 추계학술대회
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• pp.96-98
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• 2014

무선 센서 네트워크(WSN)는 컴퓨팅 능력과 무선 통신 능력을 갖추고 있는 센서 노드로부터 획득한 정보를 무선으로 실시간 수집하며, 처리, 활용하는 기술로서 현재 그 응용 분야는 환경 모니터링, 헬스 케어, 보안, 스마트 홈, 스마트 그리드 등 매우 다양하다. 하지만 무선 센서 네트워크는 저가의 센서 노드를 구성하기 위해 저전력과 저용량이라는 제약조건을 갖고 있다. 그러므로 무선 센서 네트워크에서는 제한된 에너지와 용량을 효율적으로 사용하는 알고리즘이 요구된다. 본 논문에서는 노드간의 연결 상태와 남아있는 에너지의 양을 비교함으로써 하이브리드 형식의 클러스터 헤드 노드를 선정하고 클러스터링하는 알고리즘을 제안함으로서 무선 센서 네트워크의 효율성과 정확성 증대를 목표로 한다.

• 융합경영연구
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• 제7권4호
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• pp.20-32
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• 2019

Purpose - There have been many studies regarding the preceding factors required for success of start-up. The purpose of this study is to verify one of the paths by which the individual characteristics of founders and governmental support lead to enhanced start-up performance, via entrepreneurship and network building. Research design, data, methodology - Data for this study was collected from surveys of 332 founders throughout South Korea, and statistical analyses of this data were performed using SPSS 22.0 and Smart PLS 2.0. To verify our hypothesis, path analysis was performed using a structural equation model. Results -The variables, entrepreneurial self-esteem and experience were found to have positive effects on the entrepreneurship and networks. Secondly, governmental support did not have significant positive effect on entrepreneurship, but it did have positive effect on networking. Thirdly, entrepreneurship and networking were confirmed to have positive effects on both the utilization of opportunities and financial performance, which are variables indicating start-up performance. Conclusion - Although founder's characteristics are important for success of start-up, it is also critically important to actively utilize governmental support. Notably, many founders suffer from inadequate networks. They would be able to enhance their start-up performance by utilizing various governmental support programs to reinforce their network capabilities.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.184-188
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• 2022

The article considers the improved method and model of automated air situation assessment using a decision support system based on fuzzy networks of target installations. The advanced method of automated assessment of the air situation using the decision support system is based on the methodology of reflexive control of the first rank. With this approach, the process of assessing the air situation in the framework of the formulated task can be reduced to determining the purpose, probabilistic nature of actions and capabilities of the air target. The use of a homogeneous functional network for the formal presentation of air situation assessment processes will formally describe the process of determining classes of events during air situation assessment and the process of determining quantitative and qualitative characteristics of recognized air situation situations. To formalize the patterns of manifestation of the values of quantitative and symbolic information, it is proposed to use the mathematical apparatus of fuzzy sets.

• International Journal of Computer Science & Network Security
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• 제23권10호
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• pp.89-96
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• 2023

Intrusion detection has been widely studied in both industry and academia, but cybersecurity analysts always want more accuracy and global threat analysis to secure their systems in cyberspace. Big data represent the great challenge of intrusion detection systems, making it hard to monitor and analyze this large volume of data using traditional techniques. Recently, deep learning has been emerged as a new approach which enables the use of Big Data with a low training time and high accuracy rate. In this paper, we propose an approach of an IDS based on cloud computing and the integration of big data and deep learning techniques to detect different attacks as early as possible. To demonstrate the efficacy of this system, we implement the proposed system within Microsoft Azure Cloud, as it provides both processing power and storage capabilities, using a convolutional neural network (CNN-IDS) with the distributed computing environment Apache Spark, integrated with Keras Deep Learning Library. We study the performance of the model in two categories of classification (binary and multiclass) using CSE-CIC-IDS2018 dataset. Our system showed a great performance due to the integration of deep learning technique and Apache Spark engine.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.158-161
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• 2003

A cascade structured neural network called Sigma-Pi$_{t}$ Cascaded Hybrid Neural Network ($\sigma$$\pi$$_{t}$-CHNN) is Proposed. It is an extended version of the Sigma-Pi Cascaded extended Hybrid Neural Network ($\sigma$$\pi$-CHNN), where the classical multiplicative neuron ($\pi$-neuron) is replaced by the translated multiplicative ($\pi$$_{t}$-neuron) model. The learning algorithm of $\sigma$$\pi$$_{t}$-CHNN is composed of an evolutionary programming method, responsible for determining the network architecture, and of a Levenberg-Marquadt algorithm, responsible for tuning the weights of the network. The $\sigma$$\pi$$_{t}$-CHNN is evaluated in 2 pattern classification problems: the 2 spirals and the sonar problems. In the 2 spirals problem, $\sigma$$\pi$$_{t}$-CHNN can generate neural networks with 10％ less hidden neurons than that in previous neural models. In the sonar problem, $\sigma$$\pi$$_{t}$-CHNN can find the optimal solution for the problem i.e., a network with no hidden neurons. These results confirm the expanded information processing capabilities of $\sigma$$\pi$$_{t}$-CHNN, when compared to previous neural network models. network models.

• Journal of Ginseng Research
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• 제48권4호
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• pp.373-383
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• 2024

Background: Network pharmacology has emerged as a powerful tool to understand the therapeutic effects and mechanisms of natural products. However, there is a lack of comprehensive evaluations of network-based approaches for natural products on identifying therapeutic effects and key mechanisms. Purpose: We systematically explore the capabilities of network-based approaches on natural products, using Panax ginseng as a case study. P. ginseng is a widely used herb with a variety of therapeutic benefits, but its active ingredients and mechanisms of action on chronic diseases are not yet fully understood. Methods: Our study compiled and constructed a network focusing on P. ginseng by collecting and integrating data on ingredients, protein targets, and known indications. We then evaluated the performance of different network-based methods for summarizing known and unknown disease associations. The predicted results were validated in the hepatic stellate cell model. Results: We find that our multiscale interaction-based approach achieved an AUROC of 0.697 and an AUPR of 0.026, which outperforms other network-based approaches. As a case study, we further tested the ability of multiscale interactome-based approaches to identify active ingredients and their plausible mechanisms for breast cancer and liver cirrhosis. We also validated the beneficial effects of unreported and top-predicted ingredients, in cases of liver cirrhosis and gastrointestinal neoplasms. Conclusion: our study provides a promising framework to systematically explore the therapeutic effects and key mechanisms of natural products, and highlights the potential of network-based approaches in natural product research.

• 로봇학회논문지
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• 제3권4호
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• pp.278-286
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• 2008

Reliable functionalities for autonomous navigation and object recognition/handling are key technologies to service robots for executing useful services in human environments. A considerable amount of research has been conducted to make the service robot perform these operations with its own sensors, actuators and a knowledge database. With all heavy sensors, actuators and a database, the robot could have performed the given tasks in a limited environment or showed the limited capabilities in a natural environment. With the new paradigms on robot technologies, we attempted to apply smart environments technologies-such as RFID, sensor network and wireless network- to robot functionalities for executing reliable services. In this paper, we introduce concepts of proposed smart environments based robot navigation and object recognition/handling method and present results on robot services. Even though our methods are different from existing robot technologies, successful implementation result on real applications shows the effectiveness of our approaches.