• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1988년도 추계학술발표회 발표논문초록집; 서울시립대학교 사회과학관, 서울; 5 Nov. 1988
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• pp.5-5
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• 1988

• 한국관개배수논문집
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• 제13권2호
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• pp.293-302
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• 2006

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권12호
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• pp.4776-4798
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• 2015

The location selection of virtual machines in distributed cloud is difficult because of the physical resource distribution, allocation of multi-dimensional resources, and resource unit cost. In this study, we propose a multi-object virtual machine location selection algorithm (MOVMLSA) based on group information, doubly linked list structure and genetic algorithm. On the basis of the collaboration of multi-dimensional resources, a fitness function is designed using fuzzy logic control parameters, which can be used to optimize search space solutions. In the location selection process, an orderly information code based on group and resource information can be generated by adopting the memory mechanism of biological immune systems. This approach, along with the dominant elite strategy, enables the updating of the population. The tournament selection method is used to optimize the operator mechanisms of the single-point crossover and X-point mutation during the population selection. Such a method can be used to obtain an optimal solution for the rapid location selection of virtual machines. Experimental results show that the proposed algorithm is effective in reducing the number of used physical machines and in improving the resource utilization of physical machines. The algorithm improves the utilization degree of multi-dimensional resource synergy and reduces the comprehensive unit cost of resources.

• 유통과학연구
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• 제19권8호
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• pp.25-35
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• 2021

Purpose: This study aims to propose an integrated MCDM model to support the qualified personnel selection in the distribution science. Research design, data, and methodology: The integrated approach of AHP and TOPSIS was employed to address the personnel selection problem. The AHP method was used to define the weights of the selection criteria, whereas the TOPSIS was applied to rank alternatives. The proposed model was then applied into a leading logistics company to select the best alternatives to be the sales deputy manager. Results: The results showed that Candidate 3 is the most qualified personnel for the sales deputy manager position as he is ranked first in the order of preference for recruitment. Conclusions: The proposed model provides the decision makers with more effective and time-saving methods than conventional ones. Therefore, the model can be applied to personnel selection around the world. In terms of theoretical contribution, this study proposes a personnel selection model for choosing the most appropriate candidates. In addition, the study adds to the theory of human resources management and logistics management the full set of personnel selection criteria including education, experience, skills, health, personality traits and foreign language.

• 한국지능시스템학회논문지
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• 제7권5호
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• pp.8-13
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• 1997

다중 서버 환경이란 어떤 작업이 처리될 수 있는 서버가 다수 존재하는 환경을 할한다. 이러한 환경에서는 사용자의 요구를 처리하는 서버를 결정하는데 있어 시스템의 전체 성능을 극대화 시키고 요구들의 응답 시간을 최소화 하여야 한다. 이과정에서 기존에는 서버 부하량만을 기준하여 최소 부하를 지닌 서버를 선정하고 있다. 본 논문에서는 서버의 성능 정도와 부하 정도 그리고 서비스 수행 시간의 크기를 퍼지화하고 전문가 지식을 사용하는 새로운 서버 선정 방법을 제시한다. 퍼지 기법을 사용함으로써 기존 방법에 비하여 우수한 성능을 보임을 실험을 통해 보인다.

• Nuclear Engineering and Technology
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• 제56권2호
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• pp.558-567
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• 2024

Diagnostic support systems are being researched to assist operators in identifying and responding to abnormal events in a nuclear power plant. Most studies to date have considered single abnormal events only, for which it is relatively straightforward to obtain data to train the deep learning model of the diagnostic support system. However, cases in which multiple abnormal events occur must also be considered, for which obtaining training data becomes difficult due to the large number of combinations of possible abnormal events. This study proposes an approach to maintain diagnostic performance for multiple abnormal events by training a deep learning model with data on single abnormal events only. The proposed approach is applied to an existing algorithm that can perform feature selection and multi-label classification. We choose an extremely randomized trees classifier to select dedicated monitoring parameters for target abnormal events. In diagnosing each event occurrence independently, two-channel convolutional neural networks are employed as sub-models. The algorithm was tested in a case study with various scenarios, including single and multiple abnormal events. Results demonstrated that the proposed approach maintained diagnostic performance for 15 single abnormal events and significantly improved performance for 105 multiple abnormal events compared to the base model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권8호
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• pp.1890-1910
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• 2013

Routing overlay offers an ideal methodology to improve the end-to-end communication performance by deriving a backup path for any node pair. This paper focuses on a challenging issue of selecting a proper backup path to bypass the failures on the default path with high probability for any node pair. For existing backup path selection approaches, our trace-driven evaluation results demonstrate that the backup and default paths for any node pair overlap with high probability and hence usually fail simultaneously. Consequently, such approaches fail to derive a robust backup path that can take over in the presence of failure on the default path. In this paper, we propose a three-phase RBPS approach to identify a proper and robust backup path. It utilizes the traceroute probing approach to obtain the fine-grained topology information, and systematically employs the grid quorum system and the Bloom filter to reduce the resulting communication overhead. Two criteria, delay and fault-tolerant ability on average, of the backup path are proposed to evaluate the performance of our RBPS approach. Extensive trace-driven evaluations show that the fault-tolerant ability of the backup path can be improved by about 60%, while the delay gain ratio concentrated at 14% after replacing existing approaches with ours. Consequently, our approach can derive a more robust and available backup path for any node pair than existing approaches. This is more important than finding a backup path with the lowest delay compared to the default path for any node pair.

• 정보기술과데이타베이스저널
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• 제7권1호
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• pp.113-130
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• 2000

As software projects become larger and more complex, we need to take a more systematic approach to quality assurance. One plausible approach is the use of SQA (software quality assurance) methodology. Since this SQA methodology was not available, our study presents a SQA methodology. This methodology has a repository in which a set of quality assurance tasks with their related techniques and deliverables is defined and from which one can select only appropriate tasks based upon characteristics of project. This study further suggests a rule-based approach for supporting task selection process.

• Journal of Communications and Networks
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• 제17권3호
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• pp.306-320
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• 2015

Due to an explosive growth in services running in different datacenters, there is need for service selection and routing to deliver user requests to the best service instance. In current solutions, it is generally the client that must first select a datacenter to forward the request to before an internal load-balancer of the selected datacenter can select the optimal instance. An optimal selection requires knowledge of both network and server characteristics, making clients less suitable to make this decision. Information-Centric Networking (ICN) research solved a similar selection problem for static data retrieval by integrating content delivery as a native network feature. We address the selection problem for services by extending the ICN-principles for services. In this paper we present Queue and Latency, a network-driven service selection algorithm which maps user demand to service instances, taking into account both network and server metrics. To reduce the size of service router forwarding tables, we present a statistical method to approximate an optimal load distribution with minimized router state required. Simulation results show that our statistical routing approach approximates the average system response time of source-based routing with minimized state in forwarding tables.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.38-59
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• 2022

Web services instances can be classified into two categories, namely trusted and untrusted from users. A web service with high throughput (TP) and low response time (RT) instance values is a trusted web service. Web services are not trustworthy due to the mismatch in the guaranteed instance values and the actual values achieved by users. To perform web services selection from users' attained TP and RT values, we need to verify the correct prediction of trusted and untrusted instances from invoked web services. This accurate prediction of web services instances is used to perform the selection of web services. We propose to construct fuzzy rules to label web services instances correctly. This paper presents web services selection using a well-known machine learning algorithm, namely REPTree, for the correct prediction of trusted and untrusted instances. Performance comparison of REPTree with five machine learning models is conducted on web services datasets. We have performed experiments on web services datasets using a ten k-fold cross-validation method. To evaluate the performance of the REPTree classifier, we used accuracy metrics (Sensitivity and Specificity). Experimental results showed that web service (WS1) gained top selection score with the (47.0588%) trusted instances, and web service (WS2) was selected the least with (25.00%) trusted instances. Evaluation results of the proposed web services selection approach were found as (asymptotic sig. = 0.019), demonstrating the relationship between final selection and recommended trust score of web services.