• Journal of the Korea Society of Computer and Information
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• v.21 no.4
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• pp.1-8
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• 2016

In small embedded systems including IoT devices, memory size is very small and it is important to reduce memory amount for execution of application programs. For multi-threaded applications, stack may consume a large amount of memory because each thread has its own stack of sufficiently large size for worst case. This paper presents an implementation of single stack multi-threading, called SSThread (Single Stack Thread), by sharing a stack for all threads to reduce stack memory size. By using SSThread, multi-threaded applications can be programmed based on normal C language environment and there is no requirement of transporting multi-threading operating systems. It consists of several library functions and various C macro definitions. Even though some functional restrictions in comparison to operating systems supporting complete multi-thread functionalities, it is very useful for small embedded systems with tiny memory size and it is simple to setup programming environment for multi-thread applications.

• The KIPS Transactions:PartA
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• v.12A no.2 s.92
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• pp.145-150
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• 2005

Recently, many Processor manufacturers have implemented simultaneous multi treading technology, which can simultaneously execute independent threads in one processor cycle, as a way of increasing processor efficiency, ana one particular example is Hyper Threading. Hyper Threading technology, which enables many logical processors to reside a physical processor, differs from the current multiprocessing environment which has many independent processors, and calls for a particular work assignment method optimized for Hyper Threading environment Thus, in this paper, We have proposed a scheduling algorithm compatible with Hyper Threading technology and analyzed its performance using various methods. As a result, we shall expect its efficient performance by properly understanding and managing Hyper Threading system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.3
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• pp.1313-1318
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• 2011

This paper proposes a protocol integration system with multi-threading method for the ship electronic devices to solve problems with the compatibility. The proposed protocol integration system receives the different signals from each of the ships' devices through the RS-232 serial communication port and then divides the input signal into the field data. The required field data for standard signal composition are extracted from among these signal and these are combined in accordance with standard signal format. Thereafter, the protocol integration system transmits the processed standard signal to the auto pilot system through a single port.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.6
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• pp.430-439
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• 2016

Estimation and analysis of traffic jams plays a vital role in an intelligent transportation system and advances safety in the transportation system as well as mobility and optimization of environmental impact. For these reasons, many researchers currently mainly focus on the brilliant machine learning-based prediction approaches for traffic prediction systems. This paper primarily addresses the analysis and comparison of prediction accuracy between two machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). Based on the fact that optimized estimation accuracy of these methods mainly depends on a large amount of recounted data and that they require much time to compute the same function heuristically for each action, we propose an approach that applies multi-threading to these heuristic methods. It is obvious that the greater the amount of historical data, the more processing time is necessary. For a real-time system, operational response time is vital, and the proposed system also focuses on the time complexity cost as well as computational complexity. It is experimentally confirmed that K-NN does much better than Naïve Bayes, not only in prediction accuracy but also in processing time. Multi-threading-based K-NN could compute four times faster than classical K-NN, whereas multi-threading-based Naïve Bayes could process only twice as fast as classical Bayes.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.256-258
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• 2002

지금가지 인지과학적인 연구는 주로 인간의 지능이나 신경망, 그리고 언어를 주 연구 대상으로 다루어 왔다. 그런데 인공지능은 철학의 고유영역에 속하던 많은 문제를 다루게 되었고, 그 주제들을 다뤄온 철학적 방법들을 여러 측면에서 채용하고 있다. 따라서 인지과학과 철학이라는 두 분야가 접목되어야 할 필요성이 있을 것이다. 본 연구는 위에 바탕을 두어 1) 인간 사유에 대한 데카르트의 성찰(cogito ergo sum)을 소개하고, 2) 이를 MFC를 이용한 Multi-threading으로 구현하고 실험하여, 3) 인간의 철학적 사유체계와 사고 중 이성과 오성에 관한 부분은 인공적으로 구현 가능하다는 사실과 응용 가능성을 검토하도록 한다.

• Journal of Information Processing Systems
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• v.8 no.2
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• pp.331-346
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• 2012

Task-based programming is becoming the state-of-the-art method of choice for extracting the desired performance from multi-core chips. It expresses a program in terms of lightweight logical tasks rather than heavyweight threads. Intel Threading Building Blocks (TBB) is a task-based parallel programming paradigm for multi-core processors. The performance gain of this paradigm depends to a great extent on the efficiency of its parallel constructs. The parallel overheads incurred by parallel constructs determine the ability for creating large-scale parallel programs, especially in the case of fine-grain parallelism. This paper presents a study of TBB parallelization overheads. For this purpose, a TBB micro-benchmarks suite called TBBench has been developed. We use TBBench to evaluate the parallelization overheads of TBB on different multi-core machines and different compilers. We report in detail in this paper on the relative overheads and analyze the running results.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.11b
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• pp.767-770
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• 2002

본 논문에서는 RTSP 프로토콜을 제어하기 위한 서버 시스템을 IOCP 기반의 Multi-Thread 기법을 이용하여 구현하는 방법을 소개한다. 다수의 클라이언트에 대한 응답을 Thread로 구성하는 부분에서 Multi-Threading을 이용함으로써 수행 속도를 높이고 Winsock2에서 제공하는 IOCP(T/O Completion Port)를 이용하여 견고하고 확장이 용이한 RTSP(Real Time Streaming Protocol) 스트리밍 서버를 개발하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.10
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• pp.2479-2496
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• 2013

Increasing demand for Full High-Definition (FHD) video and Ultra High-Definition (UHD) video services has led to active research on high speed video processing. Widespread deployment of multi-core systems has accelerated studies on high resolution video processing based on parallelization of multimedia software. Even if parallelization of a specific decoding step may improve decoding performance partially, such partial parallelization may not result in sufficient performance improvement. Particularly, entropy decoding has often been considered separately from other decoding steps since the entropy decoding step could not be parallelized easily. In this paper, we propose a parallelization technique called Integrated Multi-Threaded Parallelization (IMTP) which takes parallelization of the entropy decoding step, with other decoding steps, into consideration in an integrated fashion. We used the Simultaneous Multi-Threading (SMT) technique with appropriate thread scheduling techniques to achieve the best performance for the entire decoding step. The speedup of the proposed IMTP method is up to 3.35 times faster with respect to the entire decoding time over a conventional decoding technique for H.264/AVC videos.

• Journal of Broadcast Engineering
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• v.17 no.6
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• pp.945-953
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• 2012

A depth map can be obtained by projecting/capturing patterns of stripes using a projector-camera system and analyzing the geometric relationship between the projected patterns and the captured patterns. This is usually called structured light technique. In this paper, we propose a new multi-threading scheme for accelerating a conventional structured light technique. On CPUs and GPUs, multi-threading can be implemented by using OpenMP and CUDA, respectively. However, the problem is that their performance changes according to the computational conditions of partial processes of a structured light technique. In other words, OpenMP (using multiple CPUs) outperformed CUDA (using multiple GPUs) in partial processes such as pattern decoding and depth estimation. In contrast, CUDA outperformed OpenMP in partial processes such as rectification and pattern segmentation. Therefore, we carefully analyze the computational conditions where each outperforms the other and do use the better one in the related conditions. As a result, the proposed method can estimate a depth map in a speed of over 25 fps on $1280{\times}800$ images.

• Journal of the Korean Society of Safety
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• v.37 no.6
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• pp.166-173
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• 2022

A variety of input parameters are taken into consideration while performing a Level 3 PSA. Some parameters related to plume segments, spatial grids, and particle size distribution have flexible input formats. Fine modeling performed by splitting a number of segments or grids may enhance the accuracy of analysis but is time-consuming. Analysis speed is highly important because a considerably large number of calculations is required to handle Level 2 PSA scenarios for a single-unit or multi-unit Level 3 PSA. This study developed a sensitivity analysis supporting interface called MACCSsense to compare the results of the trials of plume segmentation with the results of the base case to determine its impact (in terms of time and accuracy) and to support the development of a modeling approach, which saves calculation time and improves accuracy. MACCSense is an automation tool that uses a large amount of plume segmentation analysis results obtained from MUST Converter and Mr. Manager developed by KAERI to generate a sensitivity report that includes impact (time and accuracy) by comparing them with the base-case result. In this study, various plume segmentation approaches were investigated, and both the accuracy and speed of offsite consequence analysis were evaluated using MACCS as a consequence analysis tool. A simultaneous evaluation revealed that execution time can be reduced using multi-threading. In addition, this study can serve as a framework for the development of a modeling strategy for plume segmentation in order to perform accurate and fast offsite consequence analyses.