• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.1109-1114
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• 1992

Modern chemical processes are becoming more complicated. The sophisticated chemical processes have needed the fault diagnosis pxpert systems that can detect and diagnose the fault diagnosis expert systems that can detect and diagnose the faults of some processes and give and advice to the operator in the event of process faults. We present the Time-Delay Neural Network(TDNN) approach for on-line fautl diagnosis. The on-line fault diagnosis system finds the exact origin of the fault of which the symptom is propagated continuously with time. The proposed method has been applied to a pilot distillation column to show the merits and applicability of the TDNN.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.733-738
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• 2014

We propose a new approach called as a peak time approach for faster detection of step initiation for the lower limb exoskeleton. As faster detection of step initiation is an important criterion in evaluating the lower limb exoskeleton, many studies have investigated approaches to detect step initiation faster, including using electromyography, the center of pressure, the heel-off time and the toe-off time. In this study, we will utilize vertical ground reaction force events to detect step initiation, and compare our approach with prior approaches. Additionally, we will predict the first step's heel strike time with vertical ground reaction force events from multiple regression equations to support our approach. The lower limb exoskeleton should assist the operator's movement much faster and more reliably with our approach.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.3
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• pp.273-278
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• 2017

This paper is to study a technique to detect the real-time route aberrance on the passage route using bumper area of the ship domain theory. In order to evaluate the risk of route aberrance, a quarter line was created between the center line and the outer line, and a passage route with the image line outside the outer line was designed. It calculated the real-time route aberrance with the vessel bumper area to measure the risk level on the passage route. The route aberrance using overlap bumper area was simulated through three kinds of scenario vessel at the designed passage route. In this paper, we proposed Ratio to Aberrance Risk as one of the evaluation parameter to detect the route aberrance risk at each sector in the passage route and to give the evaluation criteria of 5 levels for seafarer's navigation safety. The purpose of this work is to provide the information of the route aberrance to seafarer automatically, to make it possible to prevent the human errors of seafarer on the high risk aberrance route. As the real-time risk of route aberrance on the passage route is automatically evaluated, it was well thought that seafarer can have only a little workload in order to know the risk of route aberrance at early-time. Following the further development of this work, the techniques for detecting the real-time route aberrance will be able to use the unmanned vessel.

• JSTS:Journal of Semiconductor Technology and Science
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• v.12 no.2
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• pp.150-161
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• 2012

Eye detection is widely used in applications, such as face recognition, driver behavior analysis, and human-computer interaction. However, it is difficult to achieve real-time performance with software-based eye detection in an embedded environment. In this paper, we propose a parallel hardware architecture for real-time eye detection. We use the AdaBoost algorithm with modified census transform(MCT) to detect eyes on a face image. We parallelize part of the algorithm to speed up processing. Several downscaled pyramid images of the eye candidate region are generated in parallel using the input face image. We can detect the left and the right eye simultaneously using these downscaled images. The sequential data processing bottleneck caused by repetitive operation is removed by employing a pipelined parallel architecture. The proposed architecture is designed using Verilog HDL and implemented on a Virtex-5 FPGA for prototyping and evaluation. The proposed system can detect eyes within 0.15 ms in a VGA image.

• The Transactions of the Korea Information Processing Society
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• v.2 no.1
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• pp.119-129
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• 1995

In this paper, we propose a schedulatility analysis method for real-time programs. Several approaches to anlayzing schedulability have been developed, but since these approaches use a fixed priority scheduling method and/or traverse all possible state spaces, there take place exponential time and space complexity of these methods, Therefore it is necessary to reduce the state space and detect schedulability at earlier time. Our schedulability analysis method uses a minimum unit time taken to terminate synchronization action, a minimum unit time taken to terminate actions after synchronization, and a deadline of processes to detect unschedulability at earlier time and dynamic scheduling scheme to reduce state space. We conclude that our method can detected unschedulability earlier 50 percent unit time than Fredette's method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.1
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• pp.209-221
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• 1999

Several approaches to anlayzing real-time schedulability have been presented, but since these used a fixed priority scheduling scheme and/or traverse all possible state spaces, there take place exponential time and space complexity of these methods. Therefore it is necessary to reduce the state space and detect schedulability at earlier time. This paper proposes and implements an advanced schedulability analysis algorithm to determine that is satisfied a given deadlines for real-time processes. These use a minimum execution time of process, periodic, deadline, and a synchronization time of processes to detect schedulability at earlier time and dynamic scheduling scheme to reduce state space using the transition rules of process algebra. From a result of implementation, we demonstrated the effective performance to determine schedulability analysis.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.7
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• pp.720-725
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• 2008

This paper proposes a system architecture for USN with a service robot to provide more active assisted living services for elderly persons by monitoring their mental and physical well-being with USN environments at home, hospital, or silver town. Sensors embedded in USN are used to detect preventive measures for chronic disease. Logged data are transferred to main controller of a service robot via wireless channel in which the analysis of data is performed. For the purpose of handling emergency situations, it needs real-time processing on gathering variety sensor data, routing algorithms for sensor nodes to a moving sink node and processing of logged data. This paper realized multi-hop sensor network to detect user movements with biometric data transmission and performed algorithms on Xenomai, a real-time embedded Linux. To leverage active sensing, a mobile robot is used of which task was implemented with a priority to process urgent data came from the sink-node. This software architecture is anticipated to integrate sensing, communication and computing with real-time manner. In order to verify the usefulness of a proposed system, the performance of data transferring and processing on a real-time OS with non real-time OS is also evaluated.

• Journal of Information Processing Systems
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• v.10 no.3
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• pp.429-442
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• 2014

In this paper, we investigated the use of seasonal autoregressive integrated moving average (SARIMA) time series models for fault detection in semiconductor etch equipment data. The derivative dynamic time warping algorithm was employed for the synchronization of data. The models were generated using a set of data from healthy runs, and the established models were compared with the experimental runs to find the faulty runs. It has been shown that the SARIMA modeling for this data can detect faults in the etch tool data from the semiconductor industry with an accuracy of 80% and 90% using the parameter-wise error computation and the step-wise error computation, respectively. We found that SARIMA is useful to detect incipient faults in semiconductor fabrication.

• The Journal of Information Technology
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• v.10 no.4
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• pp.27-39
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• 2007

Object tracking in a real time environment is one of challenging subjects in computer vision area during past couple of years. This paper proposes a method of object detection and tracking using adaptive background estimation in real time environment. To obtain a stable and adaptive background, we combine 3-frame differential method and running average single gaussian background model. Using this background model, we can successfully detect moving objects while minimizing false moving objects caused by noise. In the tracking phase, we propose a matching criteria where the weight of position and inner brightness distribution can be controlled by the size of objects. Also, we adopt a Kalman Filter to overcome the occlusion of tracked objects. By experiments, we can successfully detect and track objects in real time environment.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.889-893
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• 2000

In this paper, a method to detect lanes and obstacles from the images captured by a CCD camera fitted in an automobile is proposed, and a new terminology “Moving Window” is defined. Processing the input dynamic images in real time can cause quite a few constraints in terms of hardware. In order to overcome these problems and detect lanes and obstacles in real time using the images, the optimal size of “Moving Window” is determined, based upon road conditions and automobile states. The real time detection is made possible through the technique. For each image frame, the moving window is moved in a predicted direction, the accuracy of which is improved by the Kalman filter estimation. The feasibility of the proposed algorithm is demonstrated through the simulated experiments of freeway driving.