• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.723-726
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• 2000

In this paper, we present an abstract object-oriented plat-form model .suitable for the real-time distributed telecommunication system. The proposed platform is constructed upon the extended version of the real-time, distributed operating system, SROS(Scalable Real-time Operating System), that is developed at ETRI and successfully operated in the ATM switching system for several years. The object-oriented software development and maintenance methodology will resolve the current software crisis in the area of telecommunication and switching systems due to the everlasting maintenance about the huge amount of the existing software and the ever increasing needs for the better and new communication services. In general, an object-oriented software platform realizes the object-oriented methodology and possesses many good aspects like high productivity, better reusability, easy maintenance, et at. The platform is also designed to present the distributed multimedia service in addition to real-time event delivery. Recently, we have been implementing a couple of prototype bated on the proposed platform. Reflecting the evaluation results from these prototypes, the final platform specification will be determined.

• The Journal of the Korea institute of electronic communication sciences
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• v.1 no.1
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• pp.20-26
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• 2006

One of the computer application fields which started showing noticeable new growth trends in recent years is the real time communication distributed computing application field. Object -oriented(OO) real time(RT) distributed computing is a form of real-time distributed computing realized with a distributed computer system structured in the form of an object network. In this paper, we describes the application environment as the DHS (distributed high-precision simulation) with TMO structure. The TMO scheme is aimed for enabling a great reduction of the designer's effort in guaranteeing timely service capabilities of distributed computing application systems. It has been formulated from the beginning with the objective of enabling design-time guaranteeing of timely action. In the real time simulation techniques based on TMO object modeling, we have observed several advantages to the TMO structuring scheme. TMO object modeling has a strong traceability between requirement specification and design, cost-effective high-coverage validation, autonomous subsystems, easy maintenance and flexible framework for requirement specification.

• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.1-7
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• 2019

Recently, smart factories have attracted much attention as a result of the 4th Industrial Revolution. Existing factory automation technologies are generally designed for simple repetition without using vision sensors. Even small object assemblies are still dependent on manual work. To satisfy the needs for replacing the existing system with new technology such as bin picking and visual servoing, precision and real-time application should be core. Therefore in our work we focused on the core elements by using deep learning algorithm to detect and classify the target object for real-time and analyzing the object features. We chose YOLO CNN which is capable of real-time working and combining the two tasks as mentioned above though there are lots of good deep learning algorithms such as Mask R-CNN and Fast R-CNN. Then through the line and inside features extracted from target object, we can obtain final outline and estimate object posture.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1243-1248
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• 2006

Object tracking in a real time image is one of interesting subjects in computer vision and many practical application fields past couple of years. But sometimes existing systems cannot find object by recognize background noise as object. This paper proposes a method of object detection and tracking using adaptive background image in real time. To detect object which does not influenced by illumination and remove noise in background image, this system generates adaptive background image by real time background image updating. This system detects object using the difference between background image and input image from camera. After setting up MBR(minimum bounding rectangle) using the internal point of detected object, the system tracks object through this MBR. In addition, this paper evaluates the test result about performance of proposed method as compared with existing tracking algorithm.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.76-85
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• 2019

We introduce a vision-based object detection method for real-time video surveillance system in low-end edge computing environments. Recently, the accuracy of object detection has been improved due to the performance of approaches based on deep learning algorithm such as Region Convolutional Neural Network(R-CNN) which has two stage for inferencing. On the other hand, one stage detection algorithms such as single-shot detection (SSD) and you only look once (YOLO) have been developed at the expense of some accuracy and can be used for real-time systems. However, high-performance hardware such as General-Purpose computing on Graphics Processing Unit(GPGPU) is required to still achieve excellent object detection performance and speed. To address hardware requirement that is burdensome to low-end edge computing environments, We propose sub-frame analysis method for the object detection. In specific, We divide a whole image frame into smaller ones then inference them on Convolutional Neural Network (CNN) based image detection network, which is much faster than conventional network designed forfull frame image. We reduced its computationalrequirementsignificantly without losing throughput and object detection accuracy with the proposed method.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.4
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• pp.85-94
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• 2004

Object tracking in a real time image is one of interesting subjects in computer vision and many Practical application fields during the past couple of years. But sometimes existing systems cannot find all objects by recognizing background noise as object. This paper proposes a method of object detection and tracking using adaptive background image in real time. To detect object which is not influenced by illumination and to remove noise in background image, this system generates adaptive background image by real time background image updating. This system detects object using the difference between background image and input image from camera. After setting up Minimum Bounding Rectangle(MBR) using the internal point of detected object, the system tracks object through this MBR In addition, this paper evaluates the test result about performance of proposed method as compared with existing tracking algorithm.

• 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.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.2
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• pp.127-136
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• 2016

The camera based object detection systems should satisfy the recognition performance as well as real-time constraints. Particularly, in safety-critical systems such as Autonomous Emergency Braking (AEB), the real-time constraints significantly affects the system performance. Recently, multi-core processors and system-on-chip technologies are widely used to accelerate the object detection algorithm by distributing computational loads. However, due to the advanced hardware, the complexity of system architecture is increased even though additional hardwares improve the real-time performance. The increased complexity also cause difficulty in migration of existing algorithms and development of new algorithms. In this paper, to improve real-time performance and design complexity, a task scheduling strategy is proposed for visual object tracking systems. The real-time performance of the vision algorithm is increased by applying pipelining to task scheduling in a multi-core processor. Finally, the proposed task scheduling algorithm is applied to crosswalk detection and tracking system to prove the effectiveness of the proposed strategy.

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.707-714
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• 2002

This paper presents a real-time Hausdorff matching algorithm for tracking of moving object acquired from an active camera. The proposed method uses the edge image of object as its model and uses Hausdorff distance as the cost function to identify hypothesis with the model. To enable real-time processing, a high speed approach to calculate Hausdorff distance and half cross matching method to improve performance of existing search methods are also presented. the experimental results demonstrate that the proposed method can accurately track moving object in real-time.

• 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.