• Journal of Auto-vehicle Safety Association
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• v.12 no.2
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• pp.33-39
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• 2020

This paper presents an Around View Monitoring (AVM) stop-line detection based longitudinal position correction algorithm for automated driving on urban roads. Poor positioning accuracy of low-cost GPS has many problems for precise path tracking. Therefore, this study aims to improve the longitudinal positioning accuracy of low-cost GPS. The algorithm has three main processes. The first process is a stop-line detection. In this process, the stop-line is detected using Hough Transform from the AVM camera. The second process is a map matching. In the map matching process, to find the corrected vehicle position, the detected line is matched to the stop-line of the HD map using the Iterative Closest Point (ICP) method. Third, longitudinal position of low-cost GPS is updated using a corrected vehicle position with Kalman Filter. The proposed algorithm is implemented in the Robot Operating System (ROS) environment and verified on the actual urban road driving data. Compared to low-cost GPS only, Test results show the longitudinal localization performance was improved.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.1
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• pp.22-28
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• 2021

Multivariate processes, such as chemical and mechanical process, power plants are operated in a state where several facilities are complexly connected, the fault of a particular system can also have fatal consequences for the entire process. In addition, since process data is measured in an unstable environment, outlier is likely to be include in the data. Therefore, monitoring technology is essential, which can remove outlier from measured data and detect failures in advance. In this paper, data obtained from dynamic and multivariate process models was used to detect fault in various type of processes. The dynamic process is a simulation of a process with autoregressive property, and the multivariate process is a model that describes a situation when a specific sensor fault. Mahalanobis distance was used to remove outlier contained in the data generated by dynamic process model and multivariate process model, and fault detection was performed using ICA. For comparison, we compared performance with and a conventional single ICA method. The proposed fault detection method improves performance by 0.84%p for bias data and 6.82%p for drift data in the dynamic process. In the case of the multivariate process, the performance was improves by 3.78%p, therefore, the proposed method showed better fault detection performance.

• Journal of Biomedical Engineering Research
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• v.8 no.2
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• pp.177-188
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• 1987

Detection of left ventricular boundary for the functional analysis of LV(left ventricle) is obtained using automatic boundary detection algorithm based on dynamic program ming method. This scheme reduces the edge searching time and ensures connective edge detection, since it does not require general edge operator, edge thresholding and linking process of other edge detection methods. The left ventricular diastolic volume and systolic volume were computed after this automatic boundary detection, and these volume data were applied to analyze LV ejection fraction.

• Journal of IKEEE
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• v.9 no.1 s.16
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• pp.40-46
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• 2005

A process variation compensation method called 'regulated drain detection' is proposed. This paper also shows the how this newly invented method is applied to a typical differential PLL. The proposed RDD(regulated drain detection) and its PLL application has been designed and tested in a $0.18{\mu}m$ 1-poly 3-metal plain digital process so that its stable performance and higher yield can be proven. The implemented PLL aimed to the operation range of 80MHz - 240MHz and the total die size is only $0.18{\mu}m$ including the internal loop filter. The tracking jitter characteristics is measured to less than 150 peak-to-peak under l.8V supply rail.

• Management & Information Systems Review
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• v.22
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• pp.211-229
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• 2007

The ISO/TS16949 APQP goal of defect prevention and decrease of spread waste, is the customer satisfaction which leads a continuous improvement and profit creation. The quality expense where the most is caused by but with increase of production initial quality problem occurrence is increasing to is actuality. Like this confirmation amendment. with the problem which is forecast in the place development at the initial stage which it does completeness it does not confront not to be able, production phase to be imminent, the problem accumulates and it talks the development shedding of which occurs. In opposition, prediction confrontation. is forecast in development early stage to and it is a structure which does not occur a problem to production early stage. Like this development is a possibility of accomplishing competitive company from production phase. Which attains an goal of, chance cause it leads a APQP activity (common cause) with special cause prevention & detection the connection characteristic of the focus technique against a interaction is important. And the customer requirement satisfaction and must convert a APQP goal of attainment at the key characteristics action step. (1) The Prevention - with Design FMEA application prevention of the present design management/detection, (2) the Detection (prevention/detection) - with Process FMEA application prevention of the present process control/detection, (3) Special Cause - statistical process control (SPC) 4M cause spread removal, (4) Common Cause - statistical process control (SPC) the nothing zero defect which leads the continuous improvement back of spread with application it will be able to attain with application.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.155-156
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• 2007

This paper presents the vision-based camber and optimal cutting line detection algorithm for hot-rolling process. It is important to measure the camber of head and tail part of strips because many problems are caused by the camber in the hot-rolling process. The hot-rolling process has time constraints. The camber detection algorithm of head and tail parts requires fast and less complex for satisfying time constraints. The proposed algorithm consists of two parts: measurement of the camber in the head and tail part of strips and decision part of the optimal cutting line of hot-rolled strip. First, we obtain the camber value of the strip from the difference between the real center line and the center line of head, tail part. Second, the head and tail part of strips isn't suitable for strips connections. Therefore, the cutting process is needed in the hot-rolling process. The optimal cutting line is determined by the head and tail images obtained from cameras. The algorithm is applied into the vision system with two area cameras, Matrox image processing board and host PC for verification.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.11
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• pp.883-890
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• 2003

Two GOS (Generalized Observer Scheme) type Fault Detection Isolation Schemes (FDIS), employing the bank of unknown input functional observers (UIFO) as a residual generator, are proposed to make the practical use of the multiple observer based FDIS. The one is IFD (Instrument Fault Detection) scheme and the other is PFD (Process Fault Detection) scheme. A design method of UIFO is suggested for robust residual generation and reducing the size of the observer bank. Several design objectives that can be achieved by the FDI schemes and the design methods to meet the objectives are described. An IFD system is constructed for the Boeing 929 Jetfoil boat system to show the effectiveness of the propositions. Major contributions of this paper are two folds. Firstly, the proposed UIFO approaches considerably reduce the size of residual generator in the GOS type FDI systems. Secondly, the FDI schemes, in addition to the basic functions of the conventional observer-based FDI schemes, can reconstruct the failed signal or give the estimates of fault magnitude that can be used for compensating fault effects. The schemes are directly applicable to the design of a fault tolerant control systems.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.4
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• pp.353-358
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• 2012

Human detection is a challenging task in many fields because it is difficult to detect humans due to their variable appearance and posture. Furthermore, it is also hard to track the detected human because of their dynamic and unpredictable behavior. The evaluation speed of method is also important as well as its accuracy. In this paper, we propose unified detection and tracking method for humans using Gaussian-PSO (Gaussian Particle Swarm Optimization) with the HOG (Histograms of Oriented Gradients) features to achieve a fast and accurate performance. Keeping the robustness of HOG features on human detection, we raise the process speed in detection and tracking so that it can be used for real-time applications. These advantages are given by a simple process which needs just one linear-SVM classifier with HOG features and Gaussian-PSO procedure for the both of detection and tracking.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.1
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• pp.55-60
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• 2014

In this paper, we proposed a method for face region extraction by skin-color hue, saturation and facial feature extraction in natural images. The proposed algorithm is composed of lighting correction and face detection process. In the lighting correction step, performing correction function for a lighting change. The face detection process extracts the area of skin color by calculating Euclidian distances to the input images using as characteristic vectors color and chroma in 20 skin color sample images. Eye detection using C element in the CMY color model and mouth detection using Q element in the YIQ color model for extracted candidate areas. Face area detected based on human face knowledge for extracted candidate areas. When an experiment was conducted with 10 natural images of face as input images, the method showed a face detection rate of 100%.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.3
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• pp.461-469
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• 2010

In this paper, we have proposed an improved fusion method of detection features which can enhance the detection probability under the given false alarm rate in the prescreening stage of SAR ATR(Synthetic Aperture Radar Automatic Target Recognition) system. Since the detection features have the positive correlation, the detection performance can be improved if the joint probability distribution of detection features is considered in the fusion process. The detection region is designed as a simple piecewise linear function which can be represented by few parameters. The parameters for the detection region can be derived by training the sample SAR images to maximize the detection probability with the given false alarm rate. Simulation result shows that the detection performance of the proposed method is improved for all combinations of detection features.