• Journal of the Korea Society for Simulation
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• v.28 no.2
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• pp.15-22
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• 2019

Simulation methods are being studied for various service models that can be used in industries as the development of web technology. DEVS is being used as a tool to simulate through scenarios using various information. In addition, DEVS/WS integrates web-based DEVS models through a distributed computing environment and can be used as tools for high-performance computing and data distribution. This paper describes a method for simulating an intelligent transportation system in DEVS/WS environment. This paper propose dynamic interest management model(DIMM) applied the genetic algorithm, manage efficiently road information of moving node. And, this paper evaluates performance of the dynamic interest management model in comparing to the none interest management model(NIMM). The transmitted messages and simulation time of the DIMM is saved than that of the NIMM.

• Smart Media Journal
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• v.13 no.3
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• pp.18-26
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• 2024

Road surface monitoring is essential for maintaining road environment safety through managing risk factors like rutting and crack detection. Using autonomous driving-based UGVs with high-performance 2D laser sensors enables more precise measurements. However, the increased energy consumption of these sensors is limited by constrained battery capacity. In this paper, we propose a fusion sensor system for efficient surface monitoring with UGVs. The proposed system combines color information from cameras and depth information from line laser sensors to accurately detect surface displacement. Furthermore, a dynamic sampling algorithm is applied to control the scanning frequency of line laser sensors based on the detection status of monitoring targets using camera sensors, reducing unnecessary energy consumption. A power consumption model of the fusion sensor system analyzes its energy efficiency considering various crack distributions and sensor characteristics in different mission environments. Performance analysis demonstrates that setting the power consumption of the line laser sensor to twice that of the saving state when in the active state increases power consumption efficiency by 13.3% compared to fixed sampling under the condition of λ=10, µ=10.

• Journal of the Korean Society of Propulsion Engineers
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• v.11 no.4
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• pp.26-37
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• 2007

A virtual engine test based on "Numerical test cell" can extremely reduce the time and cost for the development of a hardware by coupling multidisciplinary analysis. This paper introduces the development activities of full engine simulation programs in U.S.A. and Europe, with the their related techniques(the engineering models, the simulation environment and high performance computing) based on the NPSS(Numerical Propulsion System Simulation). NASA Glenn research conte. leads the development efforts of NPSS by assembling the current codes and improving their Auctions. VIVACE(Value Improvement through a Virtual Aeronautical Collaborative Enterprise), a consortium of universities, research centers and companies in Europe, is developing the PROOSIS(PRopulsion Object Oriented Simulation Software). The capability for the domestic development is also estimated by surveying the current status.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.172-174
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• 2005

플래시 메모리는 오늘날 다양한 형태로 우리 생활의 일부를 차지하고 있다. 휴대 전화기, MP3 플레이에, PDA등과 같은 모바일제품, 이동식 저장매체, 유비쿼터스 컴퓨팅 환경 등에 광범위하게 활용되고 있다. 이처럼 많은 분야에서 사용되는 주된 이유는 플래시 메모리의 장점인 저전력 비휘발성, 고성능, 물리적 안정성, 휴대성을 갖기 때문이다. 더불어 최근에는 Gb급 플래시 메모리도 개발되어 하드디스크의 자리를 대체할 수 있는 상황에 이르렀다. 하지만, 플래시 메모리는 하드디스크와 달리 이미 데이터가 기록된 블록에 대해 덮어쓰기(overwrite)가 되지 않는다는 특성을 갖고 있다. 덮어쓰기 위해서는 해당 블록을 지우고(즉, 소거(erase)) 쓰기 작업을 수행해야 한다. 이로 인해 플래시 메모리의 데이터 읽기/쓰기/소거에 비용이 하드 디스크와 같이 동일한 것이 아니라 서로 다르다(읽기 비용을 1로 가정할 경우 쓰기와 소거는 각각 8, 65)[1][5][6]. 따라서 OS, DBMS 등과 같은 시스템 소프트웨어에서 사용된 기존 버퍼 교체 기법은 플래시 메모리의 특성이 고려되지 않았기 때문에 플래시 메모리의 특성을 고려한 효율적인 버퍼 교체기법이 필요하다. 본 논문에서는 플래시 메모리의 서로 다른 연산 비용 고려한 새로운 버퍼 교체 기법을 제안한다.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.2
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• pp.118-126
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• 2004

Grid is a new Information Technology (IT) concept of "super Internet" for high-performance computing: worldwide collections of high-end resources such as supercomputers, storage, advanced instruments and immerse environments. The Grid is expected to bring together geographically and organizationally dispersed computational resources, such as CPUs, storage systems, communication systems, real-time data sources and instruments, and human collaborators. The term "the Grid" was coined in the mid1990s to denote a proposed distributed computing infrastructure for advanced science and engineering. The term computational Grids refers to infrastructures aimed at allowing users to access and/or aggregate potentially large numbers of powerful and sophisticated resources. More formally, Grids are defined as infrastructure allowing flexible, secure, and coordinated resource sharing among dynamic collections of individuals, institutions and resources referred to as virtual Organizations. GRID is an emerging IT as a kind of next generation Internet technology which will fit very well with agrometeorological services in the future. I believe that it would contribute to the resource sharing in agrometeorology by providing super computing power, virtual storage, and efficient data exchanges, especially for developing countries that are suffering from the lack of resources for their agmet services at national level. Thus, the establishment of CAgM-GRID based on existing RADMINSII is proposed as a part of FWIS of WMO.part of FWIS of WMO.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.5
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• pp.301-308
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• 2021

It is important to develop a digital SOC (Social Overhead Capital) maintenance system for preemptive maintenance in response to the rapid aging of social infrastructures. Abnormal signals induced from structures can be detected quickly and optimal decisions can be made promptly using IoT sensors deployed on the structures. In this study, a digital SOC monitoring system incorporating a multimetric IoT sensor was developed for long-term monitoring, for use in cloud-computing server for automated and powerful data analysis, and for establishing databases to perform : (1) multimetric sensing, (2) long-term operation, and (3) LTE-based direct communication. The developed sensor had three axes of acceleration, and five axes of strain sensing channels for multimetric sensing, and had an event-driven power management system that activated the sensors only when vibration exceeded a predetermined limit, or the timer was triggered. The power management system could reduce power consumption, and an additional solar panel charging could enable long-term operation. Data from the sensors were transmitted to the server in real-time via low-power LTE-CAT M1 communication, which does not require an additional gateway device. Furthermore, the cloud server was developed to receive multi-variable data from the sensor, and perform a displacement fusion algorithm to obtain reference-free structural displacement for ambient structural assessment. The proposed digital SOC system was experimentally validated on a steel railroad and concrete girder bridge.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.9
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• pp.898-903
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• 2010

Popularity of smartphones such as Google Android phones and Apple iphones, is increasing the demand on more reliable high performance file system for flash memory. In this paper, we propose two techniques to improve the performance of YAFFS (Yet Another Flash File System), while enhancing the reliability of the system. Specifically, we first propose to manage metadata and user data separately on segregated blocks and indexing information piggy-back technique for reducing mount time and also enhancing performance. Second, we tailor the wear-leveling to the segregated metadata and user data blocks. Performance evaluation results based on real hardware system with 1GB NAND flash memory show that the YAFFS with our proposed techniques realized outperforms the original YAFFS by six times in terms of mount speed and five times in terms of benchmark performance, while reducing the average erase count of blocks by 14%.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.333-344
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• 2022

In a container-based cloud environment, multiple containers can share a graphical processing unit (GPU), and GPU sharing can minimize idle time of GPU resources and improve resource utilization. However, in a cloud environment, GPUs, unlike CPU or memory, cannot logically multiplex computing resources to provide users with some of the resources in an isolated form. In addition, containers occupy GPU resources only when performing GPU operations, and resource usage is also unknown because the timing or size of each container's GPU operations is not known in advance. Containers unrestricted use of GPU resources at any given point in time makes managing resource contention very difficult owing to where multiple containers run GPU tasks simultaneously, and GPU tasks are handled in black box form inside the GPU. In this paper, we propose a container management technique to prevent performance degradation caused by resource competition when multiple containers execute GPU tasks simultaneously. Also, this paper demonstrates the efficiency of container management techniques that analyze and propose the problem of degradation due to resource competition when multiple containers execute GPU tasks simultaneously through experiments.

• KIISE Transactions on Computing Practices
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• v.23 no.7
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• pp.417-423
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• 2017

Recently, many embedded devices that have the computing capability required for deep learning have become available; hence, many new applications using these devices are emerging. However, these embedded devices have an architecture different from that of PCs and high-performance servers. In this paper, we propose a method that improves the performance of deep-learning framework by considering the architecture of an embedded device that shares memory between the CPU and the GPU. The proposed method is implemented in Caffe, an open-source deep-learning framework, and is evaluated on an NVIDIA Jetson TK1 embedded device. In the experiment, we investigate the image recognition performance of several state-of-the-art deep-learning networks, including AlexNet, VGGNet, and GoogLeNet. Our results show that the proposed method can achieve significant performance gain. For instance, in AlexNet, we could reduce image recognition latency by about 33% and energy consumption by about 50%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8C
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• pp.670-679
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• 2012

The recent dissemination of cloud computing makes the amount of data storage to be increased and the cost of storing the data grow rapidly. Accordingly, data and service requests from users also increases the load on the cloud storage. There have been many works that tries to provide low-cost and high-performance schemes on distributed file systems. However, most of them have some weaknesses on performing parallel and random data accesses as well as data accesses of frequent small workloads. Recently, improving the performance of distributed file system based on caching technology is getting much attention. In this paper, we propose a CHPC(Cloud storage High-Performance Caching) framework, providing parallel caching, distributed caching, and proxy caching in distributed file systems. This study compares the proposed framework with existing cloud systems in regard to the reduction of the server's disk I/O, prevention of the server-side bottleneck, deduplication of the page caches in each client, and improvement of overall IOPS. As a results, we show some optimization possibilities on the cloud storage systems based on some evaluations and comparisons with other conventional methods.