• 한국정보처리학회논문지
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• 제6권12호
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• pp.3652-3661
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• 1999

최근 들어 많이 사용되어지는 원격 전자회의 시스템이나 멀티미디어 브로드캐스팅과 같은 분산 멀티미디어 어플리케이션을 지원하기 위해서는 시스템이 어플리케이션의 시간제약성을 만족시켜주어야 한다. 따라서, 이와 같은 실시간 시스템에서는 시스템의 행위를 예측하고 분석하기 어렵게 하는 우선순위 반전 문제를 해결하여야 하며, 시스템의 오버헤드를 최소화하면서 공유자원에 대한 선점가능성을 높일 수 있는 실시간 서버모델을 사용할 필요가 있다. 현재 동기화에서 주로 사용되는 실시간 서버 모델에는 단일 스래드 서버모델, 작업자 모델 그리고 동적 서버 모델이 있으나 공유자원을 관리하기 위한 효율적인 구조를 제시하고 있지는 못하다. 본 논문에서는 우선순위 반전문제를 해결하기 위하여 우선순위 계승 프로토콜을 이용하고 있으며, 시스템의 오버헤드에 영향을 최소화하면서 서버에 대한 보다 나은 선점가능성을 제공할 수 있고 좀더 빠른 응답시간을 갖는 실시간 서버 모델로서 혼합형 우선순위 작업자 모델을 제안한다. 흔합형 우선순위 작업자 모델은 정적 우선순위 작업자 모델과 동적 우선순위 작업자 모델을 혼합한 형태로서 성능평가 결과 혼합형 우선순위 작업자 모델이 기존의 다른 모델들 보다 좀 더 나은 성능을 보이고 있음을 알 수 있다.

• 동력기계공학회지
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• 제13권2호
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• pp.71-82
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• 2009

This paper presents a real-time haptic rendering method for a realistic force feedback in a remote environment with varying communication time-delay. The remote environment is assumed as a virtual environment based on a computer graphics, for example, on-line shopping mall, internet game and cyber-education. The properties of a virtual object such as stiffness and viscosity are assumed to be unknown because they are changed according to the contact position and/or a penetrated depth into the object. The DARMAX model based output estimator is proposed to trace the correct impedance of the virtual object in real-time. The output estimator is developed on the input-output relationship. It can trace the varying impedance in real-time by virtue of P-matrix resetting algorithm. And the estimator can trace the correct impedance by using a white noise that prevents the biased input-output information. Realistic output forces are generated in real-time, by using the inputs and the estimated impedance, even though the communication time delay and the impedance of the virtual object are unknown and changed. The generated forces trace the analytical forces computed from the virtual model of the remote environment. Performance is demonstrated by experiments with a 1-dof haptic device and a spring-damper-based virtual model.

• 한국정보처리학회논문지
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• 제7권4호
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• pp.1062-1072
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• 2000

The applications developing in distributed object computing enviroments are faced with the difficulties for managing various lots of distributed objects. Also, because the most multimedia service, like video, audio, and so forth, must be satisfied itself with real-time constraints, the users also are feeling with necessary to apply real-time mechanisms to distributed multimedia services. The goal of this paper is to solve the problems for managing distributed objects, and to be easy to develop complex applications that can provide real-time services. To do this, we designed and implemented a real-time object group platform that can be placed between applications and CORBA. This platform is extended the existing object group model[13,14] added to the scheduler and timer object components for supporting real-time concept. We designed the components for platform by using James Rumbaugh object modeling technology that consists of object, function, and dynamic model. And then we described the detailed interfaces of the components by IDL, and implemented our real-time object group's platform using OrbixMT 22 which is the IONA Technologies' ORB product. Finally, we showed the execution procedures of the schduler object of each components in a real-time object group platform.

• 한국통신학회논문지
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• 제28권2C호
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• pp.147-161
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• 2003

비디오 객체 분할(Video Object Segmentation)은 MPEG-4 부호화의 핵심기술로 실시간 요구사항을 위해 빠르고 정확하여야 한다. 그러나 대부분의 존재하는 알고리즘은 계산량이 많으며 실시간 응용을 위해 적합하지 않다. 또한 이전 MPEG-4 VM(Verification Model) 기본 모델은 MPEG-4 부호화 처리를 위한 기본 알고리즘을 제공하였으나 실시간 요구사항을 위한 카메라 입력 시스템, 실용적인 소프트웨어 개발, 비디오 객체 분할 그리고 압축효율에 많은 제한이 있다. 이에 본 논문은 기본 MPEG-4 VM모델에 내용 기반 비디오 코딩의 핵심인 VOP 추출알고리즘, 실시간 카메라 입력 시스템, 압축율을 높일 수 있는 움직임 감지 알고리즘을 추가하여 최대 180:1의 압축율을 보여주는 실시간 고압축 MPEG-4 전처리 시스템을 개발하였다.

• 제어로봇시스템학회논문지
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• 제14권2호
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• pp.184-190
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• 2008

This paper describes a message model based on XML (eXtensible Markup Language) to present real-time data from sensors and instruments at manufacturing processes for web service. HTML (Hyper Text Markup Language) is inadequate for describing real-time data from process control plants while it is suitable for displaying non-real-time multimedia data on web. For XML-based web service of process data, XML format for the data presentation was proposed after investigating data of various instruments at steel-making plants. Considering transmission delay inevitably caused from increased message length and processing delay from transformation of raw data into defined format, which was critical for operation of a real-time system, its performance was evaluated by simulation. In the simulation, we assumed two implementation models for conducting the transformation function. In one model, transformation was done at an SCC (Supervisory Control Computer) after receiving real-time data from instruments. In the other model, transformation had been carried out at instruments before the data were transmitted to the SCC. Various tests had been conducted under different conditions of offered loads and data lengths and their results were described.

• 전기학회논문지
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• 제65권12호
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• pp.2084-2093
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• 2016

It is crucial to predict water temperature for aquatic ecosystem studies and management. In this paper, we first address challenging issues in predicting water temperature in a real time manner and propose a distributed computing model to address such issues. Then, we present an Artificial Neural Network (ANN)-based water temperature prediction model developed for the Soyang River and a cyberinfrastructure system called WT-Agabus to run such prediction models in an automated and real time manner. The ANN model is designed to use only weather forecast data (air temperature and rainfall) that can be obtained by invoking the weather forecasting system at Korea Meteorological Administration (KMA) and therefore can facilitate the automated and real time water temperature prediction. This paper also demonstrates how easily and efficiently the real time prediction can be implemented with the WT-Agabus prototype system.

• 정보처리학회논문지A
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• 제11A권7호
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• pp.483-488
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• 2004

실시간 응용 제품을 개발하기 위해 운영체제는 실시간 태스크의 시간 보장성(timeliness guarantee)이 지원되어야한다. 그러나 현재 대부분의 운영체제는 실시간 태스크의 시간적 제약조건(timing constraints)을 효율적으로 지원할 수 있는 방법을 제공해 주지 못하고 있다. 실시간 응용의 시간적 제약조건을 지원하기 위해서는 운영체제 커널 변경 방법과 미들웨어 방법이 있다. 본 논문에서는 운영체제 변경없이 잘 알려진 Real-time Object Model인 TMO에 근거한 미들웨어 접근 방식을 적용한다. 현재 TMO(Time-triggered Message-triggered Object) 모델을 기반으로 한 미들웨어로 다양한 운영체제 시스템 상에서 개발되어온 TMOSM(TMO Support Middleware)이 있다. 리눅스 기반의 TMOSM의 스케줄링 알고리즘은 효율적으로 실시간 스케줄링을 지원하지만 주기적인 실시간 태스크를 위해 몇 가지 고려해야할 사항들이 있다. 본 논문에서 는 주기적인 실시간 태스크를 효율적으로 처리할 수 있는 개선된 실시간 미들웨어 스케줄링 알고리즘을 제안하고 성능을 비교한다. 제안한 알고리즘은 실시간 미들웨어의 구조를 간단하게 함으로써 시스템 성능 향상과 주기적인 실시간 태스크의 적시성을 더욱더 보장함을 확인하였다.

• 제어로봇시스템학회논문지
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• 제2권3호
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• pp.209-219
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• 1996

Fieldbus provides a real-time data communication among field devices in the process control and manufacturing automation systems. In this paper, a Petri Net model of the 1993 draft of IEC/ISA fieldbus which is proposed as an international standard of fieldbus network is developed. Based on the Petri Net model, discrete-event simulation model of IEC/ISA fieldbus network is developed. This paper evaluates the network induced delay in the data link layer of IEC/ISA fieldbus using the simulation model. In addition, an integrated discrete-event/continuous-time simulation model of fieldbus system and distributed control system is developed. This paper investigates the real-time data processing capability of IEC/ISA fieldbus and the effect of network-induced delay to the performance of control system.

• 대한임베디드공학회논문지
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• 제19권3호
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• pp.151-158
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• 2024

Although on-device artificial intelligence (AI) has gained attention to diagnosing machine faults in real time, most previous studies did not consider the model retraining and redeployment processes that must be performed in real-world industrial environments. Our study addresses this challenge by proposing an on-device AI-based real-time machine fault diagnosis system that utilizes continual learning. Our proposed system includes a lightweight convolutional neural network (CNN) model, a continual learning algorithm, and a real-time monitoring service. First, we developed a lightweight 1D CNN model to reduce the cost of model deployment and enable real-time inference on the target edge device with limited computing resources. We then compared the performance of five continual learning algorithms with three public bearing fault datasets and selected the most effective algorithm for our system. Finally, we implemented a real-time monitoring service using an open-source data visualization framework. In the performance comparison results between continual learning algorithms, we found that the replay-based algorithms outperformed the regularization-based algorithms, and the experience replay (ER) algorithm had the best diagnostic accuracy. We further tuned the number and length of data samples used for a memory buffer of the ER algorithm to maximize its performance. We confirmed that the performance of the ER algorithm becomes higher when a longer data length is used. Consequently, the proposed system showed an accuracy of 98.7%, while only 16.5% of the previous data was stored in memory buffer. Our lightweight CNN model was also able to diagnose a fault type of one data sample within 3.76 ms on the Raspberry Pi 4B device.

• 대한기계학회논문집A
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• 제26권5호
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• pp.969-975
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• 2002

Visual effects are important cues for providing occupants with virtual reality in a vehicle simulator which imitates real driving. The viewpoint of an occupant is sensitively dependent upon the occupant's posture, therefore, the total human body motion must be considered in a graphic simulator. A real-time simulation is required for the dynamic analysis of complex human body motion. This study attempts to apply a neural network to the motion analysis in various driving situations. A full car of medium-sized vehicles was selected and modeled, and then analyzed using ADAMS in such driving conditions as bump-pass and lane-change for acquiring the accelerations of chassis of the vehicle model. A hybrid III 50%ile adult male dummy model was selected and modeled in an ellipsoid model. Multibody system analysis software, MADYMO, was used in the motion analysis of an occupant model in the seated position under the acceleration field of the vehicle model. Acceleration data of the head were collected as inputs to the viewpoint movement. Based on these data, a back-propagation neural network was composed to perform the real-time analysis of occupant motions under specified driving conditions and validated output of the composed neural network with MADYMO result in arbitrary driving scenario.