• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.1545-1548
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• 2003

In rigid-plastic finite element method, there is a heavy computation time and convergence problem. In this study. static-explicit rigid-plastic finite element method will be introduced. This method is the way that restrict the convergence interval. In result, convergence problem and computation time due to large non-linearity in the existing numerical analysis method were no longer a critical problem. Also, we investigated the effect of punch stroke and the strain increment this method. It is expected that various results from the numerical analysis will give very useful information for the design of tools in sheet metal forming process.

• 한국기계가공학회지
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• 제3권3호
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• pp.39-45
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• 2004

In rigid-plastic finite element method, there is a heavy computation time and convergence problem. In this study, static-explicit rigid-plastic finite element method will be introduced. This method is the way that restrict the convergence interval. In result, convergence problem and computation time due to large non-linearity in the existing numerical analysis method were no longer a critical problem. It is expected that various results from the numerical analysis will give very useful information for the design of tools in sheet metal forming process.

• 한국기계가공학회지
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• 제2권2호
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• pp.91-97
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• 2003

In rigid-plastic finite element method, there is a heavy computation time and convergence problem. In this study, revised rigid-plastic finite element method Will be introduced. This method is the way that restrict the convergence interval. In result, convergence problem and computation time due to large non-linearity in the existing numerical analysis method were no longer a critical problem. It is expected that various results from the numerical analysis will give very useful information for the design of tools in sheet metal forming process.

• 전기학회논문지
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• 제65권12호
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• pp.2094-2102
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• 2016

In this paper, we propose an extended Kalman filter(EKF)-based simultaneous localization and mapping(SLAM) method using laser corner pattern matching for mobile robots. SLAM is one of the most important problems of mobile robot. However, existing method has the disadvantage of increasing the computation time, depending on the number of landmarks. To improve computation time, we produce the corner pattern using classified and detected corner points. After producing the corner patterns, it is estimated that mobile robot's global position by matching them. The estimated position is used as measurement model in the EKF. To evaluated proposed method, we preformed the experiments in the indoor environments. Experimental results of proposed method are shown to maintain an accuracy and decrease the computation time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권11호
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• pp.4347-4366
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• 2015

Distinguished with traditional strategies which offload an application's computation to a single server, parallel computation offloading can promote the performance by simultaneously delivering the computation to multiple computing resources around the mobile terminal. However, due to the variability of communication and computation environments, static application component multi-partitioning algorithms are difficult to maintain the optimality of their solutions in time-varying scenarios, whereas, over-frequent algorithm executions triggered by changes of environments may bring excessive algorithm costs. To this end, an adaptive application component mapping algorithm for parallel computation offloading in variable environments is proposed in this paper, which aims at minimizing computation costs and inter-resource communication costs. It can provide the terminal a suitable solution for the current environment with a low incremental algorithm cost. We represent the application component multi-partitioning problem as a graph mapping model, then convert it into a pathfinding problem. A genetic algorithm enhanced by an elite-based immigrants mechanism is designed to obtain the solution adaptively, which can dynamically adjust the precision of the solution and boost the searching speed as transmission and processing speeds change. Simulation results demonstrate that our algorithm can promote the performance efficiently, and it is superior to the traditional approaches under variable environments to a large extent.

• 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.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(2)
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• pp.250-253
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• 2000

In this paper, we Implement a new arithmetic computation for MPEG audio data to overcome the limitations of real number processing in the fixed-point arithmetics, such as: overheads in processing time and power consumption. We aims at efficiently dealing with real numbers by extending the fixed-point arithmetic manipulation for floating-point numbers in MPEG audio data, and implementing the DSP libraries to support the manipulation and computation of real numbers with the fixed-point resources.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2018년도 춘계학술대회
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• pp.419-421
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• 2018

Cloud vehicular networks are a promising paradigm to improve vehicular through distributing computation tasks between remote clouds and local vehicular terminals. Software-Defined Network(SDN) can bring advantages to Intelligent Transportation System(ITS) through its ability to provide flexibility and programmability through a logically centralized controlled cluster that has a full comprehension of view of the network. However, as the SDN paradigm is currently studied in vehicular ad hoc networks(VANETs), adapting it to work on cloud-based vehicular network requires some changes to address particular computation features such as task computation of applications of cloud-based vehicular networks. There has been initial work on briging SDN concepts to vehicular networks to reduce the latency by using the fog computing technology, but most of these studies do not directly tackle the issue of task computation. This paper proposes a Software-Defined Cloud-based vehicular Network called SDCVN framework. In this framework, we study the effectiveness of task computation of applications of cloud-based vehicular networks with vehicular cloud and roadside edge cloud. Considering the edge cloud service migration due to the vehicle mobility, we present an efficient roadside cloud based controller entity scheme where the tasks are adaptively computed through vehicular cloud mode or roadside computing predictive trajectory decision mode. Simulation results show that our proposal demonstrates a stable and low route setup time in case of installing the forwarding rules of the routing applications because the source node needs to contact the controller once to setup the route.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2018년도 춘계학술대회
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• pp.238-240
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• 2018

Cloud vehicular networks are a promising paradigm to improve vehicular through distributing computation tasks between remote clouds and local vehicular terminals. Software-Defined Network(SDN) can bring advantages to Intelligent Transportation System(ITS) through its ability to provide flexibility and programmability through a logically centralized controlled cluster that has a full comprehension of view of the network. However, as the SDN paradigm is currently studied in vehicular ad hoc networks(VANETs), adapting it to work on cloud-based vehicular network requires some changes to address particular computation features such as task computation of applications of cloud-based vehicular networks. There has been initial work on briging SDN concepts to vehicular networks to reduce the latency by using the fog computing technology, but most of these studies do not directly tackle the issue of task computation. This paper proposes a Software-Defined Cloud-based vehicular Network called SDCVN framework. In this framework, we study the effectiveness of task computation of applications of cloud-based vehicular networks with vehicular cloud and roadside edge cloud. Considering the edge cloud service migration due to the vehicle mobility, we present an efficient roadside cloud based controller entity scheme where the tasks are adaptively computed through vehicular cloud mode or roadside computing predictive trajectory decision mode. Simulation results show that our proposal demonstrates a stable and low route setup time in case of installing the forwarding rules of the routing applications because the source node needs to contact the controller once to setup the route.

• 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.