• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.15 no.2
• /
• pp.124-133
• /
• 2022

With the development of hardware technology and the advancement of numerical model methods, more precise weather forecasts can be carried out. In this paper, we propose a Unicon Optimization scheme combining Loop Vectorization, Dependency Vectorization, and Code Modernization to optimize and increase Maintainability the Unicon source contained in SCAM, a simplified version of CESM, and present an overall SCAM structure. This paper tested the unicorn optimization scheme in the SCAM structure, and compared to the existing source code, the loop vectorization resulted in a performance improvement of 3.086% and the dependency vectorization of 0.4572%. And in the case of Unicorn Optimization, which applied all of these, the performance improvement was 3.457% compared to the existing source code. This proves that the Unicorn Optimization technique proposed in this paper provides excellent performance.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.12
• /
• pp.5614-5633
• /
• 2018

Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) are both recognized as promising techniques, one is for solving the resource insufficient of mobile devices and the other is for powering the mobile device. Naturally, by integrating the two techniques, task will be capable of being executed by the harvested energy which makes it possible that less intrinsic energy consumption for task execution. However, this innovative integration is facing several challenges inevitably. In this paper, we aim at prolonging the battery life of mobile device for which we need to maximize the harvested energy and minimize the consumed energy simultaneously, which is formulated as residual energy maximization (REM) problem where the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device are all considered as key factors. To this end, we jointly optimize the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device to solve the REM problem. Furthermore, we propose an efficient convex optimization and sequential unconstrained minimization technique based combining method to solve the formulated multi-constrained nonlinear optimization problem. The result shows that our joint optimization outperforms the single optimization on REM problem. Besides, the proposed algorithm is more efficiency.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2000.10b
• /
• pp.1079-1082
• /
• 2000

인터넷에서 호스트의 이동성을 지원해주기 위한 프로토콜인 Mobile IP 의 가장 근 문제점의 하나는 Triangle Routing Problem이며 이를 해결하기 위한 방안으로서 Route Optimization이 있다. 그러나, 이 방식은 Route Optimization 을 위해서 기존의 인터넷 호스트, 즉 Correspondent Node 가 Binding Cache를 유지하고, Encapsulation의 기능을 가져야 하고, Home Agent와 Security Association을 갖도록 변경이 불가피하다. 본 논문에서는 기존 인터넷 호스트에서의 변경을 필요로 하지 않는 새로운 Route Optimization 방안인 Backward-Compatible Route Optimization을 제시한다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2020.07a
• /
• pp.83-84
• /
• 2020

Sum Utility를 최적화하는 Convex Optimization Algorithm을 제안한다. 일반적으로, Sum Utility 최적화 문제는 Non Convex Optimization Problem이다. 하지만, '상대간섭'과 '간섭주요화'를 활용하여 Non Convex Optimization Problem이 간섭구간에 따라 Convex Optimization으로 해결할 수 있음을 확인하였다. 특히, 유틸리티 함수는 상대간섭 0.1 이하에서는 오목함수임을 확인하였다. 실험결과 상대간섭이 작아질수록 제안하는 알고리즘에 의한 Sum Utility는 증가함을 확인하였다.

• International Journal of Computer Science & Network Security
• /
• v.23 no.10
• /
• pp.49-56
• /
• 2023

Computer architecture serves as a link between application requirements and underlying technology capabilities such as technical, mathematical, medical, and business applications' computational and storage demands are constantly increasing. Machine learning these days grown and used in many fields and it performed better than traditional computing in applications that need to be implemented by using mathematical algorithms. A mathematical algorithm requires more extensive and quicker calculations, higher computer architecture specification, and takes longer execution time. Therefore, there is a need to improve the use of computer hardware such as CPU, memory, etc. optimization has a main role to reduce the execution time and improve the utilization of computer recourses. And for the importance of execution time in implementing machine learning supervised module linear regression, in this paper we focus on optimizing machine learning algorithms, for this purpose we write a (Diabetes prediction program) and applying on it a Practical Swarm Optimization (PSO) to reduce the execution time and improve the utilization of computer resources. Finally, a massive improvement in execution time were observed.

• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.786_787
• /
• 2009

This paper proposes a novel multimodal optimization method, Coupling particles swarm optimization (PSO), to find all optima in design space. This method based on the conventional Particle Swarm Optimization with modifications. The Coupling method is applied to make a couple from main particle and then each couple of particles searches its own optimum by using non-stop-moving PSO. We tested out our method and other one, such as ClusteringParticle Swarm Optimization and Niche Particle Swarm Optimization, on three analytic functions. The Coupling Particle Swarm Optimization is also applied to solve a significant benchmark problem, the TEAM workshop benchmark problem 22

• Journal of the Korean Operations Research and Management Science Society
• /
• v.9 no.2
• /
• pp.46-55
• /
• 1984

The purpose of this parer is to provide a survey of the state of the art of routing methods in store-and-forward computer networks. The survey is carried out in line with a new taxonomy： heuristic methods, user-optimization methods, and system-optimization methods. This taxonomy on routing algorithms is based on two viewpoints： the level of optimization and the relative difficulty for the implementation in real computer networks. Some actual methods implemented in real computer networks are surveyed as well as the theoretical studies in the literature. This paper concludes with some points in need of further researches.

• International Journal of Control, Automation, and Systems
• /
• v.5 no.2
• /
• pp.192-199
• /
• 2007

This paper proposes a new printed circuit board (PCB) assembly planning method for multi-head surface mounters. We present an integer programming formulation for the optimization problem, and propose a heuristic method to solve the large NP-complete problem within a reasonable time. A dynamic programming technique is then applied to the feeder arrangement optimization and placement sequence optimization to reduce the overall assembly time. Comparative simulation results are finally presented to verify the usefulness of the proposed method.

• Proceedings of the KIEE Conference
• /
• 2008.10c
• /
• pp.86-89
• /
• 2008

There are some modified methods such as K-means Clustering Particle Swarm Optimization and Niching Particle Swarm Optimization based on PSO which aim to locate all optima in multimodal functions. K-means Clustering Particle Optimization could locate all optima of functions with finite number of optima. Niching Particle Swarm Optimization is able to locate all of optima but high computing time. Because of those disadvantages, we proposed a new method that could locate all of optima with reasonal time. We applied our method and others as well to analytic functions. By comparing the outcomes, it is shown that our method is significantly more effective than the two others.

• IEIE Transactions on Smart Processing and Computing
• /
• v.4 no.6
• /
• pp.450-454
• /
• 2015

Short execution time is the major performance factor for computer systems. This performance factor is directly determined by code quality, which is influenced by the compiler's optimizations. However, a compiler has limitations when optimizing source code due to insufficient information. Thus, if programmers can learn the reasons why a compiler fails to apply optimizations, they can rewrite code that is more easily understood by the compiler, and thus improve performance. In this paper, we propose a compiler that provides a programmer with reasons for failed optimization and recognizes programmer's additional information to obtain better optimization. As a result, we obtain performance improvement, i.e., reducing execution time and code size, by taking advantage of additional optimization opportunities.