• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.199-209
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• 2022

Typical anomaly detection algorithms are trained by using prior data. Thus the batch learning based algorithms cause inevitable performance degradation when characteristics of newly incoming normal data change over time. We propose an online anomaly detection algorithm which can consider the gradual characteristic changes of incoming normal data. The proposed algorithm based on one-class classification model includes both offline and online learning procedures. In offline learning procedure, the algorithm learns the prior data to be close to centroid of the latent space and then updates the centroid of the latent space incrementally by new incoming data. In the online learning, the algorithm continues learning by using the updated centroid. Through experiments using public underwater acoustic data, the proposed online anomaly detection algorithm takes only approximately 2 % additional learning time for the incremental centroid update and learning. Nevertheless, the proposed algorithm shows 19.10 % improvement in Area Under the receiver operating characteristic Curve (AUC) performance compared to the offline learning model when new incoming normal data comes.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.7
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• pp.125-131
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• 1999

In this paper, the algorithms which can track the two dimensional moving circular object using simple vision system are described. In order to track the moving object, the process of finding the object feature points - such as centroid of the object, corner points, area - is indispensable. With the assumption of two-dimensional circular moving object, the centroid of the circular object is computed from three points on the object circumference. Different kinds of algorithms for computing three edge points - simple x directional detection method, stick method. T-shape method are suggested. Through the computer simulation and experiments, three algorithms are compared from the viewpoint of detection accuracy and computational time efficiency.

• Journal of KIISE:Information Networking
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• v.32 no.5
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• pp.574-582
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• 2005

Localization in wireless sensor networks is essential to important network functions such as event detection, geographic routing, and information tracking. Localization is to determine the locations of nodes when node connectivities are given. In this paper, centroid approach known as a distributed algorithm is extended to a centralized algorithm. The centralized algorithm has the advantage of simplicity. but does not have the disadvantage that each unknown node should be in transmission ranges of three fixed nodes at least. The algorithm shows that localization can be formulated to a linear system of equations. We mathematically show that the linear system have a unique solution. The unique solution indicates the locations of unknown nodes are capable of being uniquely determined.

• Journal of Electrical Engineering and information Science
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• v.3 no.1
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• pp.73-79
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• 1998

In this paper, a new traffic sign detection algorithm.. and a symbol recognition algorithm are proposed. For traffic sign detection, a dominant color transform is introduced, which serves as a tool of highlighting a dominant primary color, while discarding the other two primary colors. For symbol recognition, the curvilinear shape distribution on a circle centered on the centroid of symbol, called a circular pattern vector, is used as a spatial feature of symbol. The circular pattern vector is invariant to scaling, translation, and rotation. As simulation results, the effectiveness of traffic sign detection and recognition algorithms are confirmed, and it is shown that group of circular patter vectors based on concentric circles is more effective than circular pattern vector of a single circle for a given equivalent number of elements of vectors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1778-1784
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• 2016

we improved the performance by parallelizing lane detection algorithms. Lane detection, as a intellectual assisting system, helps drivers make an alarm sound or revise the handle in response of lane departure. Four kinds of algorithms are implemented in order as following, Gaussian filtering algorithm so as to remove the interferences, gray conversion algorithm to simplify images, sobel edge detection algorithm to find out the regions of lanes, and hough transform algorithm to detect straight lines. Among parallelized methods, the data level parallelism algorithm is easy to design, yet still problem with the bottleneck. The high-speed data level parallelism is suggested to reduce this bottleneck, which resulted in noticeable performance improvement. In the result of applying actual road video of black-box on our parallel algorithm, the measurement, in the case of single-core, is approximately 30 Frames/sec. Furthermore, in the case of octa-core parallelism, the data level performance is approximately 100 Frames/sec and the highest performance comes close to 150 Frames/sec.

• Journal of Biosystems Engineering
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• v.24 no.3
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• pp.253-258
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• 1999

This study was conducted to develop an automatic egg inspection system for detecting creaked eggs based on acoustic impulse response. This system includes a sound generator, a sound sensor with signal conditioner, and a computer. The sound generator that hit the sharp of the dull edges of an egg was constructed with a ceramic ball pendulum attached to a rotary type solenoid. The signal conditioner included a pre-amplifier and a digital signal processing (DSP) board. The parameters for distinguishing cracked and normal eggs were the area, the geometric centroid and the resonance frequency of power spectrum of the acoustic signal generated. An algorithm for on-line detection of the continuous transferring eggs was developed. The performance tests resulted with 91% success rate to separate cracked and normal eggs at the rate of 1 second per an egg.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1166-1179
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• 2013

In wireless sensor network (WSN) environments, environmental noises are generated by, for example, small passing animals, crickets chirping or foliage blowing and will interfere target detection if the noises are higher than the sensor threshold value. For accurate tracking by acoustic WSNs, these environmental noises should be filtered out before initiating track. This paper presents the effect of environmental noises on target tracking and proposes a new algorithm for the noise mitigation in acoustic WSNs. We find that our noise mitigation algorithm works well even for targets with sensing range shorter than the sensor separation as well as with longer sensing ranges. It is also found that noise duration at each sensor affects the performance of the algorithm. A detection algorithm is also presented to account for the Doppler effect which is an important consideration for tracking higher-speed ground targets. For tracking, we use the weighted sensor position centroid to represent the target position measurement and use the Kalman filter (KF) for tracking.

• Journal of Information Processing Systems
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• v.8 no.3
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• pp.421-436
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• 2012

Different types of fingerprint detection algorithms that are based on extraction of minutiae points are prevalent in recent literature. In this paper, we propose a new algorithm to locate the virtual core point/centroid of an image. The Euclidean distance between the virtual core point and the minutiae points is taken as a random variable. The mean, variance, skewness, and kurtosis of the random variable are taken as the statistical parameters of the image to observe the similarities or dissimilarities among fingerprints from the same or different persons. Finally, we verified our observations with a moment parameter-based analysis of some previous works.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.293-297
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• 2007

This thesis suggests a new algorithm to detects multiple moving objects using a CMODE(Correct Multiple Object DEtection) method in the color images acquired in real-time and to track the interested pedestrian using motion and hue information. The multiple objects are detected, and then shaking trees or moving cars are removed using structural characteristics and shape information of the man , the interested pedestrian can be detected, The first similarity judgment for tracking an interested pedestrian is to use the distance between the previous interested pedestrian's centroid and the present pedestrian's centroid. For the area where the first similarity is detected, three feature points are calculated using k-mean algorithm, and the second similarity is judged and tracked using the average hue value for the $3{\times}3$ area of each feature point. The zooming of camera is adjusted to track an interested pedestrian at a long distance easily and the FOV(Field of View) of camera is adjusted in case the pedestrian is not situated in the fixed range of the screen. As a experiment results, comparing the suggested CMODE method with the labeling method, an average approach rate is one fourth of labeling method, and an average detecting time is faster three times than labeling method. Even in a complex background, such as the areas where trees are shaking or cars are moving, or the area of shadows, interested pedestrian detection is showed a high detection rate of average 96.5%. The tracking of an interested pedestrian is showed high tracking rate of average 95% using the information of situation and hue, and interested pedestrian can be tracked successively through a camera FOV and zooming adjustment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2754-2768
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• 2013

Video fingerprints generated from global features are usually vulnerable against general geometric transformations. In this paper, a novel video fingerprinting algorithm is proposed, in which a new spatio-temporal gradient is designed to represent the spatial and temporal information for each frame, and a new partition scheme, based on concentric circle and rings, is developed to resist the attacks efficiently. The centroids of spatio-temporal gradient orientations (CSTGO) within the circle and rings are then calculated to generate a robust fingerprint. Our experiments with different attacks have demonstrated that the proposed approach outperforms the state-of-the-art methods in terms of robustness and discrimination.