• Journal of applied mathematics & informatics
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• 제33권1_2호
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• pp.185-191
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• 2015

This paper considers a discrete-time buffer system with session-based arrivals, an infinite storage capacity and one unreliable output line. There are multiple different types of sessions and the output line is governed by a finite state Markov chain. Based on a generating functions approach, we obtain an exact expression for the mean buffer content.

• 한국멀티미디어학회논문지
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• 제7권9호
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• pp.1273-1281
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• 2004

실시간 트래픽 전송에서 종단간 지연시간 한도를 보장하기 위한 방법으로 EDF 알고리즘을 이용한 실시간 패킷 스케줄링에 대한 많은 연구들이 있어왔다. 그러나, EDF기반 패킷 스케줄러는 비실시간 트래픽이 존재하는 경우 실시간 트래픽의 실시간 요구조건을 보장할 수 없게 된다. 본 논문에서 EDF 알고리즘을 사용하는 실시간 패킷 스케줄러에서 비실시간 트래픽을 고려하는 패킷 스케줄러 기법과 수락 제어 알고리즘을 연구하였다. 제안하는 수락 제어 알고리즘은 유사 다항 시간(pseudo-polynomial time)의 시간 복잡도를 가지지만 실험을 통해 적은 수행 시간 부담으로 사용할 수 있음을 보였다.

• 대한교통학회지
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• 제24권5호
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• pp.77-88
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• 2006

본 연구는 복합교통망의 실시간적 운영전략에 활용 가능한 동적 K최소시간경로탐색알고리즘을 제안하는 것을 목적으로 한다. 이를 위해 정적 K경로탐색에 적용되었던 전체경로삭제방안을 동적 최적경로탐색알고리즘에 확장 적용함으로써 복합교통망에서 시간종속적으로 변화하는 수단-링크 통행시간과 수단-링크간의 환승비용에 기초하여 경로를 순차적으로 탐색하는 K경로알고리즘을 제시하였다. 특히, 링크기반동적표지를 적용함으로써 수단간 환승시 발생되는 환승이동, 환승대기 및 기타 환승행태를 용이하게 모사하면서 최적해를 도출하도록 하였다. 최적식과 함께 제시된 알고리즘은 복잡도계산을 통해 효율성을 살펴보았으며. 버스와 지하철로 구성된 소규모 가상네트워크에 대해 적용해 봄으로써 알고리즘의 검증 및 활용성을 검토해 보았다.

• 한국CDE학회논문집
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• 제12권1호
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• pp.1-7
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• 2007

In this paper, we present a machining time estimation algorithm for 5-axis high-speed machining. Estimation of machining time plays an important role in process planning and production scheduling of a shop. In contrast to the rapid evolution of machine tools and controllers, machining time calculation is still based on simple algorithms of tool path length divided by input feedrates of NC data, with some additional factors from experience. We propose an algorithm based on 5-axis machine behavior in order to predict machining time more exactly. For this purpose, we first investigated the operational characteristics of 5-axis machines. Then, we defined some dominant factors, including feed angle that is an independent variable for machining speed. With these factors, we have developed a machining time calculation algorithm that has a good accuracy not only in 3-axis machining, but also in 5-axis high-speed machining.

• 한국위성정보통신학회논문지
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• 제12권3호
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• pp.69-73
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• 2017

본 논문에서는 기계학습 기반의 실시간 이미지 인식 알고리즘을 개발하고 개발한 알고리즘의 성능을 테스트 하였다. 실시간 이미지 인식 알고리즘은 기계 학습된 이미지 데이터를 바탕으로 실시간으로 입력되는 이미지를 인식한다. 개발한 실시간 이미지 인식 알고리즘의 성능을 테스트하기 위해 자율주행 자동차 분야에 적용해보았고 이를 통해 개발한 실시간 이미지 인식 알고리즘의 성능을 확인해보았다.

• 한국정보통신학회논문지
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• 제5권6호
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• pp.1104-1109
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• 2001

본 논문은 비디오 스트림을 실시간에 전송하기 위하여 RTP(Real-Time Protocol)을 이용한 전송 기법에 관한 연구이다. 먼저, 비디오 스트림의 연속적인 전송을 보장하기 위해 네트워크의 종단간 지연 특성을 분석하여 네트워크 상황을 파악하는 방법과 불규칙한 대역폭을 극복하기 위한 버퍼링 기법과 RTCP(Real-Time Control Protocol)를 이용하여 데이터의 전송 상태를 분석하여 실시간으로 멀티미디어 데이터를 전송할 경우 QoS를 보장하고 연속성을 유지할 수 있는 알고리즘을 과 성능 분석 결과를 제시하였다.

• 한국정보전자통신기술학회논문지
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• 제11권1호
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• pp.26-33
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• 2018

A web application running on the Internet is causing many difficulties for a program developer, and it requires to process multiple sessions at the same time due to the occurrence of excessive traffic. Web applications should be able to process concurrent requests efficiently and in real time. Node.js is a single-threaded server-side JavaScript environment implemented in C and C ++ as one of the latest frameworks to implement event models across the entire stack. Nodes implement JavaScript quickly and robust to achieve the best performance using a JavaScript V8 engine developed by Google. In this paper, it will be explained the operation principle of Node.js, which is a lightweight real-time web server that can be implemented in JavaScript for real-time responsive web applications. In addition, this application was practically implemented through automatic alignment system for group scheduling to demonstrate event-based real-time response web processing.

• Journal of information and communication convergence engineering
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• 제21권1호
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• pp.82-89
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• 2023

Various machine-learning models may yield high predictive power for massive time series for time series prediction. However, these models are prone to instability in terms of computational cost because of the high dimensionality of the feature space and nonoptimized hyperparameter settings. Considering the potential risk that model training with a high-dimensional feature set can be time-consuming, we evaluate a feature-importance-based feature selection method to derive a tradeoff between predictive power and computational cost for time series prediction. We used two machine learning techniques for performance evaluation to generate prediction models from a retail sales dataset. First, we ranked the features using impurity- and Local Interpretable Model-agnostic Explanations (LIME) -based feature importance measures in the prediction models. Then, the recursive feature elimination method was applied to eliminate unimportant features sequentially. Consequently, we obtained a subset of features that could lead to reduced model training time while preserving acceptable model performance.

• 한국멀티미디어학회논문지
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• 제18권11호
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• pp.1289-1301
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• 2015

Pedestrian detection (PD) is an essential task in various applications and sliding window-based methods utilizing HOG (Histogram of Oriented Gradients) or HOG-like descriptors have been shown to be very effective for accurate PD. However, due to exhaustive search across images, PD methods based on sliding window usually require heavy computational time. In this paper, we propose a real-time PD method for embedded visual surveillance with fixed backgrounds. The proposed PD method employs HOG descriptors as many PD methods does, but utilizes selective search so that it can save processing time significantly. The proposed selective search is guided by restricting searching to candidate regions extracted from Adaptive Gaussian Mixture Model (AGMM)-based background subtraction technique. Moreover, approximate computation of HOG descriptor and implementation in fixed-point arithmetic mode contributes to reduction of processing time further. Possible accuracy degradation due to approximate computation is compensated by applying an appropriate one among three offline trained SVM classifiers according to sizes of candidate regions. The experimental results show that the proposed PD method significantly improves processing speed without noticeable accuracy degradation compared to the original HOG-based PD and HOG with cascade SVM so that it is a suitable real-time PD implementation for embedded surveillance systems.

• International Journal of Aeronautical and Space Sciences
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• 제17권4호
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• pp.467-475
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• 2016

Ultrasonic propagation imaging (UPI) has shown great potential for detection of impairments in complex structures and can be used in wide range of non-destructive evaluation and structural health monitoring applications. The software implementation of such algorithms showed a tendency in time-consumption with increment in scan area because the processor shares its resources with a number of programs running at the same time. This issue was addressed by using field programmable gate arrays (FPGA) that is a dedicated processing solution and used for high speed signal processing algorithms. For this purpose, we need an independent and flexible block of logic which can be used with continuously evolvable hardware based on FPGA. In this paper, we developed an FPGA-based ultrasonic propagation imaging system, where FPGA functions for both data acquisition system and real-time ultrasonic signal processing. The developed UPI system using FPGA board provides better cost-effectiveness and resolution than digitizers, and much faster signal processing time than CPU which was tested using basic ultrasonic propagation algorithms such as ultrasonic wave propagation imaging and multi-directional adjacent wave subtraction. Finally, a comparison of results for processing time between a CPU-based UPI system and the novel FPGA-based system were presented to justify the objective of this research.