• Journal of Broadcast Engineering
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• v.19 no.1
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• pp.24-30
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• 2014

This study investigates computing offloading effect of cloud in real-time video personal broadcast service, whose server is mobile device. Mobile device does not have enough computing resource for encoding video. The computing burden is offloaded to cloud, which has abundant resources in terms of computing, power, and storage compared to mobile device. By reducing computing burden, computation energy can be saved while transmission data amount increases because of decreasing compression efficiency. This study shows that the optimal operation point can be found adaptively to time-varying LTE communication condition result of tradeoff analysis between offloaded computation burden and increase in amount of transmitted data.

• KEPCO Journal on Electric Power and Energy
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• v.1 no.1
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• pp.85-91
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• 2015

We investigated the structure of inductive coupler and its magnetic coupling to increase the transmission coefficient for power line communication. A Rogowski coil, which is an air-cored inductive coupler, and a magnetic cored coupler were fabricated to analyze the transmission coefficient for different coupler parameters. This paper proposes the impedance matching method using lumped elements and an impedance transformer to increase the transmission coefficient. In the experiment, the transmission coefficient of the proposed system was increased in both narrowband and broadband cases, and a trade-off between the transmission coefficient and the bandwidth was shown. This method will be useful for the further study of impedance matching with the load variation.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.131-134
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• 2013

최근 클라우드 컴퓨팅의 발전과 상업적인 성공과 함께 클라우드 자원의 이용률을 최대로 유지하면서 에너지를 효율적으로 사용하기 위한 연구에 대한 관심이 커지고 있다. 자원의 사용률이 최대로 높아지게 되면 에너지 소비량이 급격하게 증가하여 많은 에너지를 사용하게 되므로 자원의 사용율과 에너지 사용은 트레이드오프 관계를 가지게 된다. 따라서 본 논문에서는 자원의 최대 사용 및 효율적인 에너지 사용을 위해 에너지 소비가 최적이 되는 자원 이용률의 임계값을 찾기 위한 연구를 수행하였다. 실험을 위해 자원 중 가장 많은 에너지를 소비하는 CPU를 이용하였고, 전력 측정을 위해 KEM2500 전력계와 ThrottleStop_500 프로그램을 사용하였다. 실험 결과 CPU 사용률이 약 90%일 때 에너지 사용량이 급격하게 증가하였으며, 기존의 평균 자원 이용률과 비교했을 때 12.3% 정도의 전기량이 더 소모됨을 확인하였다. 따라서 클라우드 컴퓨팅에서 CPU 자원의 이용률이 90%일 때 에너지가 최적이라고 할 수 있다.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.36 no.3
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• pp.257-265
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• 2000

Brushless DC(BLDC) motors are widely used as AC servo motors in factory automation fields because of their quick instantaneous mobility, good energy saving efficiency and easiness of design for control system comparing with induction motors. Recently, a Two-Degree-of-Freedom(2DOF) PI control law has been adopted to some application parts to accomplish an advanced speed control of BLDC motors. The method can treat the two conflicting performances, minimum tracking errors versus reference inputs without large overshoot and rejection of some disturbances including modeling errors, independently. However, the method can not design the optimal system which is able to minimize tracking errors and energy consumption simultaneously. In this paper, a 2DOF integral type optimal servo control method is investigated to promote the speed control performances of BLDC motors considering energy consumption. In order to applicate the method to the speed servo system of the BLDC motor, the motor is modeled in the state space using the vector control and decoupling technique. To verify the validity of the suggested method, some simulations and experiments are performed.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.3
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• pp.79-88
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• 1999

One of the most popular algorithm in adaptive signal processing is the least mean square(LMS) algorithm. The majority of these papers examine the LMS algorithm with a constant step size. The choice of the step size reflects a tradeoff between misadjustment and the speed of adaptation. Subsequent works have discussed the issue of optimization of the step size or methods of varying the step size to improve performance. However there is as yet no detailed analysis of a variable step size algorithm that is capable of giving both the speed of adaptation and convergence. In this paper we propose a new variable step size algorithm where the step size adjustment is controlled by square of the prediction error. The simulation results obtained using the new algorithm about noise canceller system and system identification are described. They are compared to the results obtained for other variable step size algorithm. function.

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.391-394
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• 2009

All-IP는 통신에 관련된 모든 개체가 IP를 사용하는 네트워크를 의미한다. All-IP 네트워크에서는 보안을 위해 통신 내용에 대한 암호화가 반드시 이루어져야 하며, 신뢰할 수 있는 3자 (trusted third party)는 서비스 중재 및 부가 서비스 제공을 위해 통신에 사용된 암호화 키를 획득할 수 있어야 한다. 이는 통신 개체가 다른 개체에게 암호화 키를 전달하는 메커니즘이 필요하다는 것을 의미한다. 우리는 본 논문을 통해, 통신 개체가 다른 개체에게 키를 전달하는 기법을 37에로 분류하고 각 기법에 대해 상세히 설명한다. 또한, 우리는 새로운 키 전달 프로토콜을 제안하고 3가지 운용 모드를 제시한다. 우리가 제안하는 프로토콜용 사용자의 필요에 따라 보안 기능을 선택적으로 운용할 수 있다. 성능평가 및 분석 결과에 따르면, 보안 가능과 성능은 뚜렷한 트레이드오프(trade-off) 관계가 있음을 알 수 있고, 기본적인 기능만을 운용할 경우 추가적인 오버헤드가 거의 없음을 알 수 있다.

• Korean Management Science Review
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• v.32 no.4
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• pp.69-80
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• 2015

The semiconductor industry is one of those in which the most intricate processes are involved and there are many critical factors that are controlled with precision in those processes. Naturally production scheduling in the semiconductor industry is also very complex and studied by the industry and academia for many years; however, still there are many issues left unclear in the problem. This paper proposes an multi-objective optimization-based scheduling method for semiconductor fabrication(fab). Two main objectives are throughput maximization and meeting target production quantities. The first objective aims to reduce production cost, especially the fixed cost incurred by a large investment constructing a new fab facility. The other is meeting customer orders on time and also helps a fab maintain stable throughput through controlled WIP balancing in the long run. The paper shows a trade-off structure between the two objectives through experimental studies, which provides industrial practitioners with useful references.

• Journal of Korea Game Society
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• v.19 no.6
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• pp.71-82
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• 2019

This paper analyzes performance difference between Unreal Engine's built-in network solution and IOCP server. To do this, we developed IOCP server and 3D game with Unreal Engine 4. Also we considered the game variable obfuscation program to prevent the modification of the memory of the code-modulated game hacking program. This paper used SCUE4 Anti-Cheat Solution, which is Unreal Engine's solution, to study preventing memory modification and to analyze performance trade-offs.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.355-358
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• 2009

센서 네트워크에 대한 연구가 활발히 이루어지면서 센서 네트워크 보안에 대한 문제점이 많이 야기되고 있다. 무선 센서 네트워크에서는 개방된 환경에서 제한적인 자원을 가지는 노드들로 구성되어 있다. 개방된 환경에 배치된 노드들은 공격자에게 쉽게 노출되어질 수 있다. 공격자는 노드를 물리적으로 포획하여 데이터 인증에 사용하는 인증키와 같은 보안 정보들을 획득할 수 있다. 공격자는 포획된 노드를 통하여 허위 보고서로 무선 센서 네트워크에 쉽게 삽입시킬 수 있다. 이는 허위 보고서로 인한 혼란 및 위조 정보의 전달과정에서 발생하는 에너지 고갈 등의 문제점을 유발시키게 된다. 이러한 허위 보고서를 조기에 탐지 및 폐기하기 위하여 동적 여과 프로토콜(DEF: Dynamic En-route Filtering scheme)이 제안되었다. DEF에서 인증키를 재배포 하는 주기는 보안 강도와 비용을 트레이드-오프 하는 관계에 놓여있으므로 매우 중요하다. 본 논문에서는 센서네트워크에서 동적 여과 프로토콜의 인증키 재배포 주기를 결정하는 기법을 제안한다. 배포된 노드들의 위상변화, BS까지 도달한 허위보고서 비율, 공격자에게 포획된 노드의 수 등을 고려하여 재배포 여부를 결정하고 재배포가 결정되면 각 클러스터 헤드들에게 재배포를 명령하게 된다.