• Journal of Ocean Engineering and Technology
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• v.22 no.1
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• pp.30-36
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• 2008

Underwater ambient noise originating from geophysical, biological, and man-made acoustic sources contains information on the source and the ocean environment. Such noise affectsthe performance of sonar equipment. In this paper, three steps are used to identify the ambient noise source, detection, feature extraction, and similarity measurement. First, we use the zero-crossing rate to detect the ambient noisesource from background noise. Then, a set of feature vectors is proposed forthe ambient noise source using the Hilbert-Huang transform and the Karhunen-Loeve transform. Finally, the Euclidean distance is used to measure the similarity between the standard feature vector and the feature vector of the unknown ambient noise source. The developed algorithm is applied to the observed ocean data, and the results are presented and discussed.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.2
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• pp.71-77
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• 2017

This paper proposes a low complexity frequency modulated continuous wave (FMCW) surveillance radar algorithm. In the conventional surveillance radar systems, the two dimensional (2D) fast Fourier transform (FFT) method is usually employed in order to detect the distance and velocity of the targets. However, in a surveillance radar systems, it is more important to immediately detect the presence or absence of the targets, rather than accurately detecting the distance or speed information of the target. In the proposed algorithm, in order to immediately detect the presence or absence of targets, 1D FFT is performed on the first and M-th bit signals among a total of M beat signals and then a phase change between two FFT outputs is observed. The range of target is estimated only when the phase change occurs. By doing so, the proposed algorithm achieves a significantly lower complexity compared to the conventional surveillance scheme using 2D FFT. In addition, show in order to verify the performance of the proposed algorithm, the simulation and the experiment results are performed using 24GHz FMCW radar module.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.12
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• pp.583-595
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• 2002

Homing operation can be defined as a series of actions which are necessary for a mobile robot to move from the current position with any arbitrary orientation to a desired position with a specified orientation, while avoiding possible obstacles. In this paper, a homing and obstacle avoidance algorithm for nonholonomic mobile robots is proposed. The proposed algorithm consists of a local goal generator, a discrete state controller, and local path tracking controller based on Aicardi's path following algorithm. In the discrete state controller, 4 states are defined according to the environmental conditions and 4 desired high-level command for the states are given as follows: avoid, wander, home and homing zones. The proposed local goal generator is designed to generate the desired local path by using weighted distance transforms which are newly made to satisfy the nonholonomic constraints of mobile robots. Here, subgoals are also found as vertices of the desired local path. To demonstrate result effectiveness and applicability of the proposed algorithm, computer simulations are illustrated and experimental results for a real mobile robot system are also provided.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.583-586
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• 1999

In this paper, the wavelet transform is performed in the input 256$\times$256 color image and decomposes a image into low-pass and high-pass components. Since the high-pass band contains the components of three directions, edges are detected by combining three parts. After finding the position of face using the histogram of the edge component, a face region in low-pass band is cut off. Since RGB color image is sensitively affected by luminances, the image of low pass component is normalized, and a facial region is detected using face color informations. As the wavelet transform decomposes the detected face region into three layer, the dimension of input image is reduced. In this paper, we use the 3000 images of 10 persons, and KL transform is applied in order to classify face vectors effectively. FCM(Fuzzy C-Means) algorithm classifies face vectors with similar features into the same cluster. In this case, the number of cluster is equal to that of person, and the mean vector of each cluster is used as a codebook. We verify the system performance of the proposed algorithm by the experiments. The recognition rates of learning images and testing image is computed using correlation coefficient and Euclidean distance.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.471-475
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• 1995

This paper deals with the aliasing problem minimized by using an analog low-pass prefilter have a sampling frequency of 18000 Hz is designed and describes to extract their fundamental frequency components by AFT filter. And them distance relaying algorithm based AFT filter is computational simple, good frequency response and fast convergence in calculation of system apparant impedance. We performed off-line simulation using data from EMTP.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2000.11a
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• pp.207-217
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• 2000

In this paper, we describe an image processing algorithm which is able to recognize the road lane. This algorithm performs to recognize the interrelation between AGV and the other vehicle. We experimented on AGV driving test with color CCD camera which is setup on the top of vehicle and acquires the digital signal. This paper is composed of two parts. One is image preprocessing part to measure the condition of the lane and vehicle. This finds the information of lines using RGB ratio cutting algorithm, the edge detection and Hough transform. The other obtains the situation of other vehicles using the image processing and viewport. At first, 2 dimension image information derived from vision sensor is interpreted to the 3 dimension information by the angle and position of the CCD camera. Through these processes, if vehicle knows the driving conditions which are angle, distance error and real position of other vehicles, we should calculate the reference steering angle.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2363-2371
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• 2015

In this paper, we propose a new feature matching algorithm which is robust to the viewpoint change by using the FAST(Features from Accelerated Segment Test) feature detector and the SIFT(Scale Invariant Feature Transform) feature descriptor. The original FAST algorithm unnecessarily results in many feature points along the edges in the image. To solve this problem, we apply the principal curvatures for refining it. We use the SIFT descriptor to describe the extracted feature points and calculate the homography matrix through the RANSAC(RANdom SAmple Consensus) with the matching pairs obtained from the two different viewpoint images. To make feature matching robust to the viewpoint change, we classify the matching pairs by calculating the Euclidean distance between the transformed coordinates by the homography transformation with feature points in the reference image and the coordinates of the feature points in the different viewpoint image. Through the experimental results, it is shown that the proposed algorithm has better performance than the conventional feature matching algorithms even though it has much less computational load.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.35-46
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• 2009

Lane extraction and lane departure warning algorithms using the image sensor attached in the vehicle are addressed. With the research about intelligent automobile, there have been many algorithms about lane recognition and lane departure warning system. However, since these algorithms require to detect 2 lanes, the high time complexity and the low recognition rate under various driving circumstances are critical problems. In this paper, we present a lane departure warning algorithm using single lane extraction and center point analysis that achieves the fast processing time and high detection rate. From the geometry between camera and objects, the region of interest (ROI) is determined and splitted into two parts. Hough transform detects the part of the lane. After the detected lane is restored to have a pre-determined size, lane departure is estimated by calculating the distance from the center point. On real driving environments, the presented algorithm is compared with previous algorithms. Experiment results support that the presented algorithm is fast and accurate.

• Korean Journal of Remote Sensing
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• v.14 no.2
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• pp.175-195
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• 1998

Automatic lineament extraction algorithms had been developed by various researches for geological purpose using remotely sensed data. However, most of them are designed for a certain topographic model, for instance rugged mountainous region or flat basin. Most of common topographic characteristic in Korea is a mountainous region along with alluvial plain, and consequently it is difficult to apply previous algorithms directly to this area. A new algorithm of automatic lineament extraction from remotely sensed images is developed in this study specifically for geological applications. An algorithm, named as DSTA(Dynamic Segment Tracing Algorithm), is developed to produce binary image composed of linear component and non-linear component. The proposed algorithm effectively reduces the look direction bias associated with sun's azimuth angle and the noise in the low contrast region by utilizing a dynamic sub window. This algorithm can successfully accomodate lineaments in the alluvial plain as well as mountainous region. Two additional algorithms for estimating the individual lineament vector, named as ALEHHT(Automatic Lineament Extraction by Hierarchical Hough Transform) and ALEGHT(Automatic Lineament Extraction by Generalized Hough Transform) which are merging operation steps through the Hierarchical Hough transform and Generalized Hough transform respectively, are also developed to generate geological lineaments. The merging operation proposed in this study is consisted of three parameters: the angle between two lines($\delta$$\beta$), the perpendicular distance($(d_ij)$), and the distance between midpoints of lines(dn). The test result of the developed algorithm using Landsat TM image demonstrates that lineaments in alluvial plain as well as in rugged mountain is extremely well extracted. Even the lineaments parallel to sun's azimuth angle are also well detected by this approach. Further study is, however, required to accommodate the effect of quantization interval(droh) parameter in ALEGHT for optimization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3104-3120
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• 2017

The intelligent monitoring system has been successfully applied in many fields such as: monitoring of production lines, transportation, etc. Smart surveillance systems have been developed and proven effective in some specific areas such as monitoring of human activity, traffic, etc. Most of critical application monitoring systems involve object tracking as one of the key steps. However, task of tracking of moving object is not easy. In this paper, the authors propose a method to implement human object tracking in outdoor environment based on human features in shearlet domain. The proposed method uses shearlet transform which combines the human features with context-sensitiveness in order to improve the accuracy of human tracking. The proposed algorithm not only improves the edge accuracy, but also reduces wrong positions of the object between the frames. The authors validated the proposed method by calculating Euclidean distance and Mahalanobis distance values between centre of actual object and centre of tracked object, and it has been found that the proposed method gives better result than the other recent available methods.