• Journal of Communications and Networks
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• 제14권4호
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• pp.434-442
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• 2012

Several gateway selection schemes have been proposed that select gateway nodes based on a single Quality of Service (QoS) path parameter, for instance path availability period, link capacity or end-to-end delay, etc. or on multiple non-QoS parameters, for instance the combination of gateway node speed, residual energy, and number of hops, for Mobile Ad hoc NETworks (MANETs). Each scheme just focuses on the ment of improve only a single network performance, i.e., network throughput, packet delivery ratio, end-to-end delay, or packet drop ratio. However, none of these schemes improves the overall network performance because they focus on a single QoS path parameter or on set of non-QoS parameters. To improve the overall network performance, it is necessary to select a gateway with stable path, a path with themaximum residual load capacity and the minimum latency. In this paper, we propose a gateway selection scheme that considers multiple QoS path parameters such as path availability period, available capacity and latency, to select a potential gateway node. We improve the path availability computation accuracy, we introduce a feedback system to updated path dynamics to the traffic source node and we propose an efficient method to propagate QoS parameters in our scheme. Computer simulations show that our gateway selection scheme improves throughput and packet delivery ratio with less per node energy consumption. It also improves the end-to-end delay compared to single QoS path parameter gateway selection schemes. In addition, we simulate the proposed scheme by considering weighting factors to gateway selection parameters and results show that the weighting factors improve the throughput and end-to-end delay compared to the conventional schemes.

• 한국통신학회논문지
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• 제37B권12호
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• pp.1102-1112
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• 2012

스마트폰의 급격한 보급에 따른 무선 접속망의 과부하 문제가 네트워크에서 중요한 문제로 부각되고 있다. 이 논문에서는 매크로 셀, 펨토 셀, 와이파이 접속망으로 다양하게 구성되어 있는 현재 이종 네트워크에서 접속망 과부하 문제를 해결하기 위한 최적의 셀 선정 기법과 리소스 할당 기법을 제안한다. 주어진 현재 서비스 부하 상태에서 네트워크가 동시에 추가 수용할 수 있는 사용자 수를 최대화할 수 있는 사용자-셀 간의 선정 기법을 제공한다. 이를 위해 이종 무선 접속망에서의 셀 선정 문제를 이진 정수계획 모형으로 최적화 문제를 수립하고, 이를 최적화 해법 도구를 이용하여 접속망 과부하를 억제할 수 있는 최적의 셀 선정 기법을 도출한다. 네트워크 레벨 시뮬레이션을 통해 이 논문에서 제안된 기법이 현재 무선 접속망에서 주로 사용되고 있는 국소적 셀 선정기법에 비해, 과부하가 걸린 무선 접속망에서 주어진 여러 셀들을 최대한 균등하게 효율적으로 활용함으로써 현저하게 네트워크 접속 장애율을 감소시킬 수 있음을 보인다. 또한 논문에서 사용된 이진 정수계획 모형의 최적화 문제를 푸는 데 소요되는 계산 복잡도에 대한 실험을 통해 제안된 알고리즘의 실용 가능성에 대해서 검증한다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제10권2호
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• pp.65-72
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• 2021

최근 네트워크 환경에 대한 공격이 급속도로 고도화 및 지능화 되고 있기에, 기존의 시그니처 기반 침입탐지 시스템은 한계점이 명확해지고 있다. 이러한 문제를 해결하기 위해서 기계학습 기반의 침입 탐지 시스템에 대한 연구가 활발히 진행되고 있다. 하지만 기계학습을 침입 탐지에 이용하기 위해서는 두 가지 문제에 직면한다. 첫 번째는 실시간 탐지를 위한 학습과 연관된 중요 특징들을 선별하는 문제이며, 두 번째는 학습에 사용되는 데이터의 불균형 문제로, 기계학습 알고리즘들은 데이터에 의존적이기에 이러한 문제는 치명적이다. 본 논문에서는 위 제시된 문제들을 해결하기 위해서 Hybrid Feature Selection과 Data Balancing을 통한 심층 신경망 기반의 네트워크 침입 탐지 모델인 HFS-DNN을 제안한다. NSL-KDD 데이터 셋을 통해 학습을 진행하였으며, 기존 분류 모델들과 성능 비교를 수행한다. 본 연구에서 제안된 Hybrid Feature Selection 알고리즘이 학습 모델의 성능을 왜곡 시키지 않는 것을 확인하였으며, 불균형을 해소한 학습 모델들간 실험에서 본 논문에서 제안한 학습 모델이 가장 좋은 성능을 보였다.

• Human Ecology Research
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• 제60권4호
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• pp.625-643
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• 2022

The purpose of this study was to explore the trends and core keywords of the secondary school home economics teacher selection test using content analysis and text network analysis. The sample comprised texts of the secondary school home economics teacher 1st selection test for the 2017-2022 school years. Determination of frequency of occurrence, generation of word clouds, centrality analysis, and topic modeling were performed using NetMiner 4.4. The key results were as follows. First, content analysis revealed that the number of questions and scores for each subject (field) has remained constant since 2020, unlike before 2020. In terms of subjects, most questions focused on 'theory of home economics education', and among the evaluation content elements, the highest percentage of questions asked was for 'home economics teaching·learning methods and practice'. Second, the network of the secondary school home economics teacher selection test covering the 2017-2022 school years has an extremely weak density. For the 2017-2019 school years, 'learning', 'evaluation', 'instruction', and 'method' appeared as important keywords, and 7 topics were extracted. For the 2020-2022 school years, 'evaluation', 'class', 'learning', 'cycle', and 'model' were influential keywords, and five topics were extracted. This study is meaningful in that it attempted a new research method combining content analysis and text network analysis and prepared basic data for the revision of the evaluation area and evaluation content elements of the secondary school home economics teacher selection test.

• 산업공학
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• 제18권3호
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• pp.253-267
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• 2005

Customer satisfaction becomes one of the important managerial concerns associated with corporate competency in current competitive environment for Internet communication service companies. Hence, it is demanding to improve a company's customer satisfaction through the total quality management perspective. In practice, engineers as well as the management hope to find major quality characteristics with Internet communication network that is closely related to customer satisfaction, consequently aiming to the raise of their company's customer satisfaction. This paper presents an empirical relationship analysis between network quality characteristics and customer satisfaction on Internet communication. Methodologically, the relationship analysis framework is based on the regression variable selection procedures. In this framework, it is implemented that; 1) iterative model building; and 2) consistent criteria application to statistical tests for selecting significant variables. A case study shows that; 1) the customer satisfaction on the network connection seems to be more closely related to the network quality characteristics compared with the customer satisfaction on the network speed; and 2) the download disconnection rate has relatively evident relationship with the customer satisfaction on the network connection.

• Asian Pacific Journal of Cancer Prevention
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• 제15권23호
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• pp.10539-10542
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• 2015

A method to predict the risk of lung cancer is proposed, based on two feature selection algorithms: Fisher and ReliefF, and BP Neural Networks. An appropriate quantity of risk factors was chosen for lung cancer risk prediction. The process featured two steps, firstly choosing the risk factors by combining two feature selection algorithms, then providing the predictive value by neural network. Based on the method framework, an algorithm LCRP (lung cancer risk prediction) is presented, to reduce the amount of risk factors collected in practical applications. The proposed method is suitable for health monitoring and self-testing. Experiments showed it can actually provide satisfactory accuracy under low dimensions of risk factors.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.299-302
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• 2000

Various types of measuring devices are used for reverse engineering and inspection in different fields of industry such as automotive, aerospace, computer graphics, and home appliance. In order to measure a part easily and efficiently, it is important to select appropriate measuring device considering the characteristics of each measuring machine and part information. In this research, an optimal measuring device selection system using neural networks is proposed. There are two major steps: Firstly, the measuring information such as curvature, normal, type of surface, edge, and facet approximation is extracted from the CAD model. Second, the best suitable measuring device is proposed using the neural network system based on the knowledge of the measuring parameters and the measuring resources. An example of machine selection is implemented to evaluate the performance of the system.

• International journal of advanced smart convergence
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• 제7권4호
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• pp.57-65
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• 2018

Artificial neural network is inspired by the biological neural network. For simplicity, in computer science, it is represented as a set of layers. Many research has been made in evaluating the number of neurons in the hidden layer but still, none was accurate. Several methods are used until now which do not provide the exact formula for calculating the number of thehidden layer as well as the number of neurons in each hidden layer. In this paper model selection approach was presented. Proposed model is based on geographical analysis of decision boundary. Proposed model selection method is useful when we know the distribution of the training data set. To evaluate the performance of the proposed method we compare it to the traditional architecture on IRIS classification problem. According to the experimental result on Iris data proposed method is turned out to be a powerful one.

• 한국IT서비스학회지
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• 제10권1호
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• pp.47-64
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• 2011

In the IT(Information Technology) service, which supply the solutions related to business management and IT, network construction and application trends, the related service business are increasing according to the enlargement of project scope and the diversity of project types as the need of service customers. In this paper, I propose the significant effect factors on the network management of IT service firms. The key findings are from the analysis result about 94 IT service firms as follows. For implementation the high performance of network management in the IT service firms, the strategic elements in the process of network construction are more conceived highly than the basic element in them. Also the perspective of project objectives are considered than the nominal perspectives in the partner selection process. The competency of partner firms', the cooperation process between the partner firms', network relation operation management and network relation structure management are the significant effect factors of network management.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2027-2032
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• 2003

For action selection as well as learning, simple associations between stimulus and response have been employed in most of literatures. But, for a successful task accomplishment, it is required that an animat can learn and express behavioral sequences. In this paper, we propose a novel action-selection-mechanism to deal with sequential behaviors. For this, we define behavioral motivation as a primitive node for action selection, and then hierarchically construct a network with behavioral motivations. The vertical path of the network represents behavioral sequences. Here, such a tree for our proposed ASM can be newly generated and/or updated, whenever a new sequential behaviors is learned. To show the validity of our proposed ASM, three 2-D grid world simulations will be illustrated.