• 대한기계학회논문집A
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• 제24권7호
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• pp.1878-1884
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• 2000

In multibody dynamic analysis, one of the most important problems is to reduce computation times for real time simulation. In this paper, a symbolic computation method is implemented and tested for each dynamic analysis step. Applying symbolic formulations to the vehicle dynamics program AutoDyn7, the effectiveness of the symbolic computation method is verified.

• 대한기계학회논문집
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• 제13권3호
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• pp.500-507
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• 1989

본 논문에서는 흡배기 변동압을 실측하여 이것을 계산의 입력수치로 사용 하는 전산프로그램을 개발하여 간단하면서도 정확한 사이클 시뮬레이션이 가능하도록 하여 체적효율을 예측하였다.

• 소성∙가공
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• 제7권5호
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• pp.474-480
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• 1998

In the present study a domain decomposition scheme using the substructuring method is developed for the computational efficiency of the finite element analysis of metal forming processes. in order to avoid calculation of an inverse matrix during the substructuring procedure, the modified Cholesky decomposition method is implemented. As obtaining the data independence by the substructuring method the program is easily paralleized using the Parallel Virtual machine(PVM) library on a work-station cluster connected on networks. A numerical example for a simple upsetting is calculated and the speed-up ratio with respect to various number of subdomains and number of processors. The efficiency of the parallel computation is discussed by comparing the results.

• 한국통신학회논문지
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• 제23권4호
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• pp.1024-1034
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• 1998

정보통신망에서 사용하는 공개키 암호시스템은 대부분 지수 연산을 사용한다. 하지만, 암호시스템은 안전성을 고려한 큰 수의 지수 연산을 사용하기 때문에 많은 계산 량과 준비시간을 요구한다. 이러한 문제점을 해결하기 위하여 모듈러 감소 연산에서 Montgomery, Yang, Kawamura 등이 사전계산 방법, 중간계산, 그리고 테이블을 사용하는 방법을 제안하였으며, 지수 연산에서 Coster, Brickel, Lee 등이 addition chain, window, 그리고 고정된 수를 사용하는 경우 사전 계산을 하는 방법을 제안하였다. 본 논문에서는 sparse 소수를 사용한 모듈러 감소 연산 방법을 제안하고 지수연산시 계산 량을 줄이는 방법을 제안한다. 이는 이산대수 방식의 암호시스템에서 매우 효과적으로 적용할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.4081-4098
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• 2022

With the aim of tackling the contradiction between computation intensive industrial applications and resource-weak Edge Devices (EDs) in Industrial Internet of Things (IIoT), a novel computation task offloading scheme in SDIN-enabled MEC based IIoT is proposed in this paper. With the aim of reducing the task accomplished latency and energy consumption of EDs, a joint optimization method is proposed for optimizing the local CPU-cycle frequency, offloading decision, and wireless and computation resources allocation jointly. Based on the optimization, the task offloading problem is formulated into a Mixed Integer Nonlinear Programming (MINLP) problem which is a large-scale NP-hard problem. In order to solve this problem in an accessible time complexity, a sub-optimal algorithm GPCOA, which is based on hybrid evolutionary computation, is proposed. Outcomes of emulation revel that the proposed method outperforms other baseline methods, and the optimization result shows that the latency-related weight is efficient for reducing the task execution delay and improving the energy efficiency.

• Journal of Information Processing Systems
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• 제18권2호
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• pp.188-196
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• 2022

Recently, neural architecture search (NAS) has received increasing attention as it can replace human experts in designing the architecture of neural networks for different tasks and has achieved remarkable results in many challenging tasks. In this study, a path-based computation neural architecture encoder (PCE) was proposed. Our PCE first encodes the computation of information on each path in a neural network, and then aggregates the encodings on all paths together through an attention mechanism, simulating the process of information computation along paths in a neural network and encoding the computation on the neural network instead of the structure of the graph, which is more consistent with the computational properties of neural networks. We performed an extensive comparison with eight encoding methods on two commonly used NAS search spaces (NAS-Bench-101 and NAS-Bench-201), which included a comparison of the predictive capabilities of performance predictors and search capabilities based on two search strategies (reinforcement learning-based and Bayesian optimization-based) when equipped with different encoders. Experimental evaluation shows that PCE is an efficient encoding method that effectively ranks and predicts neural architecture performance, thereby improving the search efficiency of neural architectures.

• 한국소음진동공학회논문집
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• 제12권9호
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• pp.674-684
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• 2002

In order to decrease remarkably the computation time and storage used in the direct integration method without the loss of accuracy, authors suggest a new transient analysis algorithm. This algorithm is derived from the combination of three techniques, that is, the transfer technique of the transfer stiffness coefficient method, the modeling technique of the finite element method, and the numerical integration technique of the Newmark method. In this paper, the transient analysis algorithm of a frame structure is formulated by the proposed method. The accuracy and computation efficiency of the proposed method are demonstrated through the comparing with the computation results by the direct integration method for three computation models under various excitations.

• 스마트미디어저널
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• 제12권3호
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• pp.68-76
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• 2023

Emerging mobile edge computing (MEC) can be used in battery-constrained Internet of things (IoT). The execution latency of IoT applications can be improved by offloading computation-intensive tasks to an MEC server. Recently, the popularity of unmanned aerial vehicles (UAVs) has increased rapidly, and UAV-based MEC systems are receiving considerable attention. In this paper, we propose a dynamic computation offloading paradigm for UAV-based MEC systems, in which a UAV flies over an urban environment and provides edge services to IoT devices on the ground. Since most IoT devices are energy-constrained, we formulate our problem as a Markov decision process considering the energy level of the battery of each IoT device. We also use model-free Q-learning for time-critical tasks to maximize the system utility. According to our performance study, the proposed scheme can achieve desirable convergence properties and make intelligent offloading decisions.

• 정보과학회 논문지
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• 제42권6호
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• pp.707-712
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• 2015

모바일 클라우드 컴퓨팅은 모바일 기기의 자원제약적인 한계를 극복하기 위해 클라우드 서비스를 활용하는 기술로 모바일 기기에서 실행해야 할 일부 작업을 클라우드에서 수행하게 하는 컴퓨테이션 오프로딩 기법이 사용된다. 오프로딩에 필요한 통신 비용보다 모바일 기기 내에서의 연산 비용이 클 경우 모바일 기기는 클라우드에게 작업 수행을 위탁한다. 모바일 기기에서 수행할 작업과 클라우드에서 수행할 작업을 분할하기 위한 기존의 비용 분석 모델은 함수 호출에 필요한 데이터 전송과 응답 시간만을 오프로딩 비용으로 산정하였다. 본 논문에서는 컴퓨테이션 오프로딩 비용 산출 시 함수의 호출 및 응용 프로그램의 동기화 빈도를 고려한 작업 분할 기법을 제안하였고 실험을 통해 기존의 기법들에 비해 에너지 효율성을 높일 수 있음을 확인하였다.

• 대한산업공학회지
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• 제1권1호
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• pp.67-71
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• 1975

This paper studies the efficiency of four algorithms in determining the shortest-route length between two specified nodes of a network in which every pair of nodes is connected by two nonnegative length arcs. The efficiency is measured in terms of number of additions and comparisons in computation of the shortest-route length. Also, each algorithm is programmed on the IBM 1130 for solving for example problems, and the computing time is measured for further efficiency comparisons.