• Electronics and Telecommunications Trends
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• v.16 no.4 s.70
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• pp.67-76
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• 2001

본 논문은 ACE2000 시스템 소프트웨어의 Release 시점을 예측할 수 있는 최적 배포문제로, 시스템의 안정도를 평가해 볼 수 있는 측면에서 소프트웨어 최적 배포문제에 대해 살펴보고 평가기준을 제시하여 제품의 적기 공급 및 개발자원의 효율적 이용 측면을 분석한다. 즉, 신뢰성 평가척도와 개발 비용을 고려한 최적 배포문제를 기술하였다. 또 여러 가지 소프트웨어 신뢰도 성장모델 중 지수형 모델을 근거로 한 소프트웨어 개발비용과 신뢰성 평가기준을 고려한 배포시기를 결정하여 보았다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.2
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• pp.26-37
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• 2003

An optimal software release, which is related to the development cost, error detection and correction under the various operation systems, is a critical factor for managing project. This paper described optimal software release issues to predict the release time of large switching system with the system stability point of view and evaluated a timely supply of target system, proper utilization of resources under the software reliability valuation basis. Finally, Using initial failure data, based on the exponential reliability growth model methodology, optimal release time, and analysis of failure data during the system testing and managing methodologies were presented.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.7A
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• pp.649-658
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• 2006

Recently, Software Development was applied to new-approach methods as a various form : client-server system and web-programing, object-orient concept, distributed development with a network environments. On the other hand, it be concerned about the distributed development technology and increasing of object-oriented methodology. These technology is spread out the software quality and improve of software production, reduction of the software develop working. Futures, we considered about the distributed software development technique with a many workstation. In this paper, we discussed optimal release problem based on a stochastic differential equation model for the distributed Software development environments. In the past, the software reliability applied to quality a rough guess with a software development process and approach by the estimation of reliability for a test progress. But, in this paper, we decided to optimal release times two method: first, SRGM with an error counting model in fault detection phase by NHPP. Second, fault detection is change of continuous random variable by SDE(stochastic differential equation). Here, we decide to optimal release time as a minimum cost form the detected failure data and debugging fault data during the system test phase and operational phase. Especially, we discussed to limitation of reliability considering of total software cost probability distribution.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.588-591
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• 2009

In ubiquitous computing GIS services, it is possible to use the spatio-temporal data anytime through the mobile device. GIS services regularly update use the latest spatio-temporal data to provide the most suitable services. For this situation, update data is distributed to CD or wired networks update services. However, this method has problem to propagate update data to users as taking long time. In this paper, suggests a synchronization system which propagate update data to users for reducing processing time efficiently. This synchronization system collects update data in the field and synchronizes server with collected data to use mobile devices by real time. For this system, I design and materialize synchronization module which updates update data and wireless network module.

• Journal of Internet of Things and Convergence
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• v.6 no.2
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• pp.1-10
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• 2020

The mobile application based on the Android platform is simple to decompile, making it possible to create malicious applications similar to normal ones, and can easily distribute the created malicious apps through the Android third party app store. In this case, the Android malicious application in the smartphone causes several problems such as leakage of personal information in the device, transmission of premium SMS, and leakage of location information and call records. Therefore, it is necessary to select a optimal model that provides the best performance among the machine learning techniques that have published recently, and provide a technique to automatically identify malicious Android apps. Therefore, in this paper, after adopting the feature engineering to Android apps on official test set, a total of four performance evaluation experiments were conducted to select the machine learning model that provides the optimal performance for Android malicious app detection.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.4 s.310
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• pp.50-57
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• 2006

In Electronic Commerce, the latest most of the personalized recommender systems have applied to the collaborative filtering technique. This method calculates the weight of similarity among users who have a similar preference degree in order to predict and recommend the item which hits to propensity of users. In this case, we commonly use Pearson Correlation Coefficient. However, this method is feasible to calculate a correlation if only there are the items that two users evaluated a preference degree in common. Accordingly, the accuracy of prediction falls. The weight of similarity can affect not only the case which predicts the item which hits to propensity of users, but also the performance of the personalized recommender system. In this study, we verify the improvement of the prediction accuracy through an experiment after observing the rule of the weight of similarity applying Vector similarity, Entropy, Inverse user frequency, and Default voting of Information Retrieval field. The result shows that the method combining the weight of similarity using the Entropy with Default voting got the most efficient performance.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.1
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• pp.9-16
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• 2020

The k-anonymity scheme has been widely used to protect private information when Big Data are distributed to a third party for research purposes. When the scheme is applied, an optimal k value determination is one of difficult problems to be resolved because many factors should be considered. Currently, the determination has been done almost manually by human experts with their intuition. This leads to degrade performance of the anonymization, and it takes much time and cost for them to do a task. To overcome this problem, a simple idea has been proposed that is based on machine learning. This paper describes implementations and experiments to realize the proposed idea. In thi work, a deep neural network (DNN) is implemented using tensorflow libraries, and it is trained and tested using input dataset. The experiment results show that a trend of training errors follows a typical pattern in DNN, but for validation errors, our model represents a different pattern from one shown in typical training process. The advantage of the proposed approach is that it can reduce time and cost for experts to determine k value because it can be done semi-automatically.

• Journal of the Korea Society for Simulation
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• v.20 no.1
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• pp.1-8
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• 2011

Cloud computing emerges as a new paradigm for deploying, managing and offering IT resources as a service anytime, anywhere on any devices. Cloud computing data center stores many IT resources through resource integration. So cloud computing system has to be designed by technology and policy to make effective use of IT resources. In other words, cloud vendor has to provide high quality services to all user and mitigate the dissipation of IT resources. However, vendors need to predict the performance of cloud services and the use of IT resources before releasing cloud service. For solving the problem, this research presents cloud service modeling on network environment and evaluation index for availability optimization of cloud service. We also study how to optimize an amount of requested cloud service and performance of datacenter using CloudSim toolkit.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.343-353
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• 2022

This study examines which factors will act as important factors for the public in designing a platform in the future to raise public awareness of the horse industry, and through this, a study on platform design factors of the horse industry to present insights on optimal platform design. For this study, structured questionnaires were distributed to 300 domestic adults who were interested in the horse industry to collect data, and the research questions set by using the statistical processing program SPSS 22.0 Ver were verified. As a result of the study, the usefulness of information in the central route and the playfulness of the information source among the peripheral routes were the most influential factors for consumer attitudes, and the up-to-dateness of information in the central route on consumer behavioral intentions. It was found that the attractiveness of the information source among the surrounding routes was the most influential factor. In addition, it was found that the positive attitude of consumers toward the horse industry platform is a factor that has a positive effect on the purchase intention and positive word of mouth intention for the horse industry in the future. Based on these results, this researcher needs to design content that can unravel useful information related to the horse industry in an interesting way to raise public awareness of the horse industry, and to provide the latest trends related to the horse industry at all times to draw real demand It should be possible and suggested that a design configuration that can make the platform feel more attractive is needed.

• The Transactions of the Korea Information Processing Society
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• v.13 no.11
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• pp.604-618
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• 2024

The main challenge facing recent complex neural network models, which have shown competitive accuracy, is their efficient deployment in multi-GPU systems. The complex inter-layer dependences of the neural network models combined with the variable data communication overhead of multi-GPU systems make it almost impossible to achieve a fair performance gain under manual scheduling. To address this problem, we propose a new layer-scheduling approach called NN Maestro, which generates an efficient parallel execution strategy for multi-GPU systems that minimizes the data communication overhead, thereby improving the inference latency for complex neural network models. NN Maestro evaluates the advantages of multi-GPU scheduling using a pre-trained SVM classifier and calculates the scheduling order of layers based on Topological Sort and Significance Cost. Then, NN Maestro selects the optimal GPU by comparing Placement Costs and generates the final scheduling result by grouping layers for parallel execution. On various multi-GPU configurations (2 2080Ti, 4 V100, and 4 A6000 GPUs), NN Maestro achieves up to 1.67x of performance improvement over the baseline.