• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.17-26
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• 2005

In this paper we propose three nonparametric tests such as Wilcoxon test, Median test and Van der Waerden test, based on linear rank statistics for detecting edges in images. The methods used herein are based on detecting changes in gray-levels obtained using an edge-height parameter between two sub-regions in a 5$\times$5 window We compare and analysis the performance of three statistical edge detectors in terms of qualitative measures with the edge maps and objective, quantitative measures.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.506-509
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• 2009

This paper studies about reducing the complexity of ML detection in MIMO/V-blast system, based on MMSE and ZF linear detectors. Beforehand, the receiver detects the signal using the linear detector such as ZF or MMSE. Moreover, the next step is to assess whether the signal is reliable or not by verifying the reliability condition, if the latter is reliable then it is the output if not it has to be detected by the advanced detector until the reliability condition is verified.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.1015-1018
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• 2005

In this paper, we introduce about a new class to analysis of partial discharge signal based on Fuzzy model. We can early diagnose life of power cable through detection of partial discharge signal. However, partial discharge signal detector is difficult because of partial discharge signal is very non-linear. Also, it is very difficult work that separate partial discharge signal from noise. We constructed partial discharge accumulation detection system that use Labview for detection of non-linear partial discharge signal. And analyzed partial discharge signal that is detected by Labview system utilizing Fuzzy model.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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• pp.1416-1423
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• 2017

Lane detection technology is one of the most important issues on car safety and self-driving capability of autonomous vehicle. This paper introduces an accurate lane detection scheme based on OCC(Optical Camera Communication) for moving vehicles. For lane detection of moving vehicles, the streetlights and the front camera of the vehicle were used for a transmitter and a receiver, respectively. Based on the angle information of multiple streetlights in a captured image, the distance from sidewalk can be calculated using non-linear regression analysis. Simulation results show that the proposed scheme shows robust performance of accurate lane detection.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.2
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• pp.77-82
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• 2001

This paper deals with a fault detection algorithm for linear descriptor systems via unknown input PI observer. An unknown input PI observer is presented and its realization conditions is proposed by using the rank condition of system matrices. From the characteristics of unknown input PI observer, the states of system with unknown inputs are estimated and the occurrences of fault are detected, and its magnitudes are estimated easily by using integrated output estimation error under the step faults. Finally, a numerical example is given to verify the effectiveness of the proposed fault detection algorithm.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.34-36
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• 2009

This paper proposes an effective lane detection algorithm using hugh transform and lane model. The proposed lane detection algorithm includes two major components, i.e., lane marks segmentation and an exact lane extraction using a novel postprocessing technique. The first step is to segment lane marks from background images using HSV color model. Then, a novel postprocessing is used to detect an exact lane using Hugh transform and lane models(linear and curved lane models). The postprocessing consists of three parts, i.e, thinning process, Hugh Transform and filtering process. We divide input image into three regions of interests(ROIs). Based on lane curve function(LCF), we can detect an exact lane from various extracted lane lines. The lane models(linear and curved lane mode]) are used in order to judge whether each lane segment is fit or not in each ROIs. Experimental results show that the proposed scheme is very effective in lane detection.

• Journal of information and communication convergence engineering
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• v.20 no.2
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• pp.73-78
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• 2022

Based on experimental measurements conducted on many different practical wireless communication systems, ambient noise has been shown to be decidedly non-Gaussian owing to impulsive phenomena. However, most multiuser detection techniques proposed thus far have considered Gaussian noise only. They may therefore suffer from a considerable performance loss in the presence of impulsive ambient noise. In this paper, we consider a large-scale multiuser multiple-input multiple-output system in the presence of non-Gaussian noise and propose a genetic algorithm (GA) based detector for large-dimensional multiuser signal detection. The proposed algorithm is more robust than linear multi-user detectors for non-Gaussian noise because it uses a multi-directional search to manipulate and maintain a population of potential solutions. Meanwhile, the proposed GA-based algorithm has a comparable complexity because it does not require any complicated computations (e.g., a matrix inverse or derivation). The simulation results show that the GA offers a performance gain over the linear minimum mean square error algorithm for both non-Gaussian and Gaussian noise.

• Journal of Sensor Science and Technology
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• v.33 no.3
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• pp.119-124
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• 2024

Effective fire sensing is important to protect lives and property from the disaster. In this paper, we present an intelligent visual sensing method for detecting fires based on machine learning techniques. The proposed method involves a two-step process. In the first step, fire and non-fire images are used to train a convolutional neural network (CNN), and in the next step, feature vectors consisting of 256 values obtained from the CNN are used for the learning of a support vector machine (SVM). Linear and nonlinear SVMs with different parameters are intensively tested. We found that the proposed hybrid method using an SVM with a linear kernel effectively increased the recall rate of fire image detection without compromising detection accuracy when an imbalanced dataset was used for learning. This is a major contribution of this study because recall is important, particularly in the sensing of disaster situations such as fires. In our experiments, the proposed system exhibited an accuracy of 96.9% and a recall rate of 92.9% for test image data.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2092-2095
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• 2003

In this paper, we propose an algorithm to extract rectangular object area such 3s Data Matrix two-dimensional barcode using edge tracing-based linear feature detection. Hough transform is usually employed to detect lines of edge map. However, it requires parametric image space, and does not find the location of end points of the detected lines. Our algorithm detects end points of the detected lines using edge tracing and extracts object area using its shape information.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.519-524
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• 2003

We suggest a method of identifying outliers, using local influence, in regression when linear constraints are imposed on the regression coefficients. An example is given for illustration.