• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.5
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• pp.291-295
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• 2014

This paper presents the implementation of industrial wireless network for real-time control system and the performance evaluation on the implemented system. We propose the hybrid network architecture of wired EtherCAT and wireless 802.15.4e. For performance evaluation, we use the reference model of inverse pendulum system. Through the performance evaluation on our testbed system, it is verified that our proposed system can be applied to industrial real-time control system.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.11-16
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• 2010

This paper compares and studies scheduling methods to reduce jitter in real-time control systems. While previous research has focused on dynamic-priority scheduling schemes, this paper focuses on fixed-priority scheduling which is more widely used. It is pointed out that previously defined jitter measures might not be useful in enhancing the control performance of a real-time task because the measures are relative values. We present a new jitter measure and a new scheduling scheme for fixed-priority tasks. The experimental results through simulation show that the new scheduling scheme reduces jitter and enhances control performance.

• 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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.455-461
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• 2000

A multichannel signal processing algorithm for generating real time virtual sound field was proposed. Evaluation of the system performance was done by an objective function that minimizes the difference between the real and generated signals at each control point. Since impulse responses at the surface of a rigid sphere show characteristics similar to those of real HRTF, a rigid sphere model was adequate to simulate the multichannel sound system. A two-channel system and two four-channel systems were studied with various combinations of source locations and speaker positions. The results show that a two-channel system has its best configuration when the angle spanned by the loudspeakers is less than $60^{\circ}$. In the case of four-channel systems, the overall performance was highly improved with one pair of speakers fixed at an optimal position. Left/right symmetry was a reasonable choice, but the additional front/back symmetry degraded the performance of system.

• Proceedings of the KIEE Conference
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• 2002.07a
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• pp.59-62
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• 2002

Before a developed digital Power System Stabilizer(PSS) is installed to real power system, an efficient trial test for installing PSS is needed. In this paper, the performance of developed digital PSS for a single hydro-turbine generator and infinite bus system has been investigated using Real Time Digital Simulator(RTDS) in order to install PSS to real power system practically. The test system was composed of RTDS, three phase voltage/current amplifier and the PSS and the test scheme provided a very efficient way to verify the design and control performance of a PSS to be applied to real power system. The trial installation test through AVR 3% step test and three fault analysis may be guaranteed to install PSS to real power system.

• Journal of Sensor Science and Technology
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• v.22 no.1
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• pp.18-27
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• 2013

Real-time monitoring is one of the prime necessities in a weapon flight test that is required for the efficient and timely collection of large amounts of high-rate sampled data acquired by an event-trigger. The wireless sensor network is a good candidate to resolve this requirement, especially considering the inhospitable environment of a weapon flight test. In this paper, we propose a priority based multi-channel MAC protocol with CSMA/CA over a single radio for a real-time monitoring of a weapon flight test. Multi-channel transmissions of nodes can improve the network performance in wireless sensor networks. Our proposed MAC protocol has two operation modes: Normal mode and Priority Mode. In the normal mode, the node exploits the normal CSMA/CA mechanism. In the priority mode, the node has one of three grades - Class A, B, and C. The node uses a different CSMA/CA mechanism according to its grade that is determined by a signal level. High grade nodes can exploit more channels and lower backoff exponents than low ones, which allow high grade nodes to obtain more transmission opportunities. In addition, it can guarantee successful transmission of important data generated by high grade nodes. Simulation results show that the proposed MAC exhibits excellent performance in an event-triggered real-time application.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.11
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• pp.1074-1081
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• 2007

This paper presents a method for real-time face detection and tracking which combined Adaboost and Camshift algorithm. Adaboost algorithm is a method which selects an important feature called weak classifier among many possible image features by tuning weight of each feature from learning candidates. Even though excellent performance extracting the object, computing time of the algorithm is very high with window size of multi-scale to search image region. So direct application of the method is not easy for real-time tasks such as multi-task OS, robot, and mobile environment. But CAMshift method is an improvement of Mean-shift algorithm for the video streaming environment and track the interesting object at high speed based on hue value of the target region. The detection efficiency of the method is not good for environment of dynamic illumination. We propose a combined method of Adaboost and CAMshift to improve the computing speed with good face detection performance. The method was proved for real image sequences including single and more faces.

• Corrosion Science and Technology
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• v.18 no.6
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• pp.253-257
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• 2019

A deep learning based real-time painting surface inspection algorithm is proposed herein, designed for developing an autonomous inspection drone. The painting surface inspection is usually conducted manually. However, the manual inspection has a limitation in obtaining accurate data for correct judgement on the surface because of human error and deviation of individual inspection experiences. The best method to replace manual surface inspection is the vision-based inspection method with a camera, using various image processing algorithms. Nevertheless, the visual inspection is difficult to apply to surface inspection due to diverse appearances of material, hue, and lightning effects. To overcome technical limitations, a deep learning-based pattern recognition algorithm is proposed, which is specialized for painting surface inspections. The proposed algorithm functions in real time on the embedded board mounted on an autonomous inspection drone. The inspection results data are stored in the database and used for training the deep learning algorithm to improve performance. The various experiments for pre-inspection of painting processes are performed to verify real-time performance of the proposed deep learning algorithm.

• Journal of Positioning, Navigation, and Timing
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• v.7 no.1
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• pp.43-52
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• 2018

In recent years, the need of high accuracy navigation for vehicles has increased due to the development of autonomous driving vehicles and increase in land transportation convenience. This study is performed for vehicle users to achieve a performance of centimeter-level positioning accuracy by utilizing Compact Network Real-time Kinematic (RTK) that is applicable as a national-level infrastructure. To this end, medium-baseline RTK was implemented in real time to estimate accurate integer ambiguities between reference stations for reliable generation of Network RTK correction using the linear combination of carrier-phase observations and L1/L2 pseudo-range measurements. The residual tropospheric error was estimated in real time to improve the accuracy of double-differenced integer ambiguity resolution between network configuration reference stations that have at least 30 km or longer baseline distance. In addition, C++ based software was developed to enable real-time generation and broadcasting of Compact Network RTK correction information by utilizing an accurately estimated double-differenced integer ambiguity values. As a result, the horizontal and vertical 95% accuracy was 2.5cm and 5.2cm, respectively, without performance degradation due to user's position change within the network.

• Journal of Mechanical Science and Technology
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• v.19 no.spc1
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• pp.388-394
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• 2005

Recently, the authors have developed a method for real-time dynamics of multibody systems, which combines a semi-recursive formulation to derive the equations of motion in dependent relative coordinates, along with an augmented Lagrangian technique to impose the loop closure conditions. The following numerical integration procedures, which can be grouped into the so-called structural integrators, were tested : trapezoidal rule, Newmark dissipative schemes, HHT rule, and the Generalized-${\alpha}$ family. It was shown that, for large multi body systems, Newmark dissipative was the best election since, provided that the adequate parameters were chosen, excellent behavior was achieved in terms of efficiency and robustness with acceptable levels of accuracy. In the present paper, the performance of the described method in combination with another group of integrators, the Implicit Runge-Kutta family (IRK), is analyzed. The purpose is to clarify which kind of IRK algorithms can be more suitable for real-time applications, and to see whether they can be competitive with the already tested structural family of integrators. The final objective of the work is to provide some practical criteria for those interested in achieving real-time performance for large and complex multibody systems.