• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3652-3661
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• 1999

To support multimedia applications such as a multimedia communication systems and multimedia broadcasting, an operating system need to predict their timing-constraints. So, In this real-time systems, we must solve the priority inversion problem that may make the behavior of unpredictable systems and need a real-time server model that provides a better preemptability and minimizes a system overhead. In current real-time systems, the single thread server model, the worker model and the dynamic server model are being used for synchronization but they cannot propose an effective structure for managing shared resources. In this paper, the priority inheritance protocol is used to solve the priority inversion problem and the hybrid prioritized worker model is proposed, which can provide a more effective structure and a faster response time minimizing a system overhead. The hybrid prioritized worker model is to combine the static and the dynamic prioritized worker model, and have a better performance than other models in response time which is an important factor in a real-time system.

• Journal of Power System Engineering
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• v.13 no.2
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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.

• The Transactions of the Korea Information Processing Society
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• v.7 no.4
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.2C
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• pp.147-161
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• 2003

Video object segmentation is one of the core technologies for content-based real-time MPEG-4 encoding system. For real-time requirement, the segmentation algorithm should be fast and accurate but almost all existing algorithms are computationally intensive and not suitable for real-time applications. The MPEG-4 VM(Verification Model) has provided basic algorithms for MPEG-4 encoding but it has many limitations in practical software development, real-time camera input system and compression efficiency. In this paper, we implemented the preprocessing system for real-time camera input and VOP extraction for content-based video coding and also implemented motion detection to achieve the 180 : 1 compression rate for real-time and high compression MPEG-4 encoding.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.2
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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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.

• The KIPS Transactions:PartA
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• v.11A no.7 s.91
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• pp.483-488
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• 2004

For real-time applications, the underlying operating system (0S) should support timeliness guarantees of real-time tasks. However, most of current operating systems do not provide timely management facilities in an efficient way. There could be two approaches to support timely management facilities for real-time applications: (1) by modifying 0S kernel and (2) by Providing a middleware without modifying 0S. In our approach, we adopted the middleware approach based on the TMO (Time-triggerred Message-triggered Object) model which is a well-known real-tine object model. The middleware, named TMSOM (TMO Support Middleware) has been implemented on various OSes such as Linux and Windows XP/NT/98. In this paper, we mainly consider TMOSM implemented on Linux(TMOS/Linux). Although the real-time schedul-ing aIgorithm used in current TMOSM/Linux can produce an efficient real-time schedule, it can be improved for periodic real-time tasks by considering several factors. In this paper, we discuss those factors and propose an improved real-time scheduling algorithm for periodic real-time tasks, In order to simulate the performance of our algorithm, we measure timeliness guarantee rate for periodic real-time tasks. The result shows that the performance of our algorithm is superior to that of existing algorithm. Additionally, the proposed algorithm can improve system performance by making the structure of real-time middleware simpler.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.3
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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.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.3
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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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.5
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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.