• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.3 s.303
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• pp.51-64
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• 2005

The proposed algorithm is to achieve the robust optical flow detection which is applicable for the case that the outlier rate is over $80\%$. If the outlier rate of optical flows is over $30\%$, the discrimination between the inliers and outlier with the conventional algorithm is very difficult. The proposed algorithm is to overcome such difficulty withthree steps of grouping algorithm; 1) constructing the 2 D histogram with two axies of the lengths and the directions of optical flows. 2) sorting the number of optical flows in each bin of the two-dimensional histogram in the descendingorder and removing some bins with lower number of optical flows than threshold 3) increasing the resolution of the two-dimensional histogram if the number of optical flows in a specific bin is over $20\%$ and decreasing theresolution if the number of optical flows is less than $10\%$. Such processing is repeated until the the number of optical flows falls into the range of $10\%-20\%$ in all the bins. The proposed algorithm works well on the different kinds of images with many of wrong optical flows. Experimental results are included.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.175-181
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• 2014

With rapid growth of the wireless mobile computing network technologies, various mobile ad hoc network applications converged with other related technologies are rapidly disseminated nowadays. Vehicular Ad Hoc Networks are self-organizing mobile ad hoc networks that typically have moving vehicle nodes with high speeds and maintaining its topology very short with unstable communication links. Therefore, VANETs are very vulnerable for the malicious noise of sensors and anomalies of the nodes in the network system. In this paper, we propose an anomaly detection method by using big data techniques that efficiently identify malicious behaviors or noises of sensors and anomalies of vehicle node activities in these VANETs, and the performance of the proposed scheme is evaluated by a simulation study in terms of anomaly detection rate and false alarm rate for the threshold ${\epsilon}$.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.4
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• pp.45-52
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• 2009

We proposed a new pixel circuit and driving method for the large-area, high-luminance AMOLED applications in this study. We designed with the low-temperature poly-silicon(LTPS) thin film transistors(TFTs) that has poor uniformity but stable characteristic. To improve the uniformity of an image, the threshold voltage($V_{TH}$) and the mobility of the TFTs can be compensated together. The proposed method overcomes the previous methods for mobility compensation, and that is profitable for large-area applications. Black data insertion was introduced to improve the characteristics for moving images. AMOLED panel can operate in two compensation mode, so the luminance degradation by mobility compensation can be released. The scan driver for controlling the pixel circuits were optimized, and the compensation mode can be controlled simply by that. Final driving signal has large timing margin, and the panel operates stably. The pixel circuit was designed for 14.1" WXGA top-emission ANGLED panel. The non-uniformity of the designed panel was estimated under 5% for the mobility compensation time of 1us.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.23-31
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• 2012

This paper proposes a motion estimation-based super resolution algorithm to restore input low-resolution images of large movement into a super-resolution image. It is difficult to find the sub-pixel motion estimation in images of large movement compared to typical experimental images. Also, it has disadvantage which have high computational complexity to find reference images and candidate images using general motion estimation method. In order to solve these problems for the traditional two-dimensional motion estimation using the proposed registration threshold that satisfy the conditions based on the reference image is determined. Candidate image with minimum weight among the best candidates for super resolution images, the restoration process to proceed with to find a new image registration algorithm is proposed. According to experimental results, the average PSNR of the proposed algorithm is 31.89dB and this is better than PSNR of traditional super-resolution algorithm and it also shows improvement of computational complexity.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.7
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• pp.106-115
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• 2002

Growth characteristics of micro carbon structures fabricated by laser-assisted chemical vapor deposition are studied. Argon ion laser and ethylene were used as the energy source and reaction gas, respectively, to grow micro carbon rod through pyrolytic decomposition of the reaction gas. Experiments were performed at various conditions to investigate the influence of process parameters on growth characteristics such as the diameter or growth rate of the micro carbon rod with respect to reaction gas pressure and incident laser power. Reaction gas pressure in experiments ranges from 200 to 600Torr and the incident laser power from 0.3 to 3.8W. For these conditions, the diameter of the rod increases linearly with respect to the laser power but is almost independent of the reaction gas pressure. Growth rate of the rod changes little with gas pressure when the laser power remains below IW. For a constant reaction gas pressure, the growth rate increase with Increasing laser power, but the rate of increase decreases gradually, implying that the chemical vapor deposition condition changes from a kinetically-limited regime to a mass-transport-limited regime. When the carbon rod was grown at near threshold laser power, a very smooth surface is obtained on the rod. By continuously moving the focusing lens in the direction of growth, a micro carbon rod with a diameter of 287${\mu}{\textrm}{m}$ and aspect ratio of 100 was fabricated..

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.18-25
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• 2011

In a manufacturing process of metal coins, surface defects of coins are manually detected. This paper describes an new method for detecting surface defects of metal coins on a moving conveyor belt using image processing. This method consists of multiple procedures: segmentation of a coin from the background, alignment of the coin to the model, projection of the aligned coin to the best eigen image space, and detection of defects by comparison of the projection error with an adaptive threshold. In these procedures, the alignement and the projection are newly developed in this paper for the detection of coin surface defects. For alignment, we use the histogram of the segmented coin, which converts two-dimensional image alignment to one-dimensional alignment. The projection reduces the intensity variation of the coin image caused by illumination and coin rotation change. For projection, we build multiple eigen image spaces and choose the best eigen space using estimated coin direction. Since each eigen space consists of a small number of eigen image vectors, we can implement the projection in real- time.

• Journal of Biosystems Engineering
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• v.39 no.2
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• pp.142-149
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• 2014

Purpose: A multispectral algorithm for detection and differentiation of defective (defects on apple skin) and normal Red Delicious apples was developed from analysis of a series of hyperspectral line-scan images. Methods: A fast line-scan hyperspectral imaging system mounted on a conventional apple sorting machine was used to capture hyperspectral images of apples moving approximately 4 apples per second on a conveyor belt. The detection algorithm included an apple segmentation method and a threshold function, and was developed using three wavebands at 676 nm, 714 nm and 779 nm. The algorithm was executed on line-by-line image analysis, simulating online real-time line-scan imaging inspection during fruit processing. Results: The rapid multispectral algorithm detected over 95% of defective apples and 91% of normal apples investigated. Conclusions: The multispectral defect detection algorithm can potentially be used in commercial apple processing lines.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.5
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• pp.103-110
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• 2008

A reliable extraction of welded area is the precedent task before the detection of weld defects in industrial radiography. This paper describes an attempt to detect and extract the welded features of steel tubes from the computed radiography(CR) images. The statistical properties are first analyzed on over 160 sample radiographic images which represent either weld or non-weld area to identify the differences between them. The analysis is then proceeded by pattern classification to determine the clustering parameters. These parameters are the width, the functional match, and continuity. The observed weld image is processed line by line to calculate these parameters for each flexible moving window in line image pixel set. The local statistic line-clustering method is used as the classifier to recognize each window data as weld or non-weld cluster. The sequential procedure is to track the edge lines between two distinct regions by iterative calculation of threshold, and it results in extracting the weld feature. Our methodology is concluded to be effective after experiment with CR weld images.

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.214-223
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• 2006

Recently there is a need to store and process enormous spatial data in spatio-temporal databases. For effective query processing in spatio-temporal databases, selectivity estimation in query optimization techniques, which approximate query results when the precise answer is not necessary or early feedback is helpful, has been studied. There have been selectivity estimation techniques such as sampling-based techniques, histogram-based techniques, and wavelet-based techniques. However, existing techniques in spatio-temporal databases focused on selectivity estimation for future extent of moving objects. In this paper, we construct a new histogram, named T-Minskew, for query optimization of past spatio-temporal data. We also propose an effective selectivity estimation method using T-Minskew histogram and effective histogram maintenance technique to prevent frequent histogram reconstruction using threshold.

• The KIPS Transactions:PartB
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• v.14B no.6
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• pp.461-472
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• 2007

The emphasis in genome projects has Been moving towards the sequence analysis in order to extract biological "meaning"(e.g., evolutionary history of particular molecules or their functions) from the sequence. Especially. palindromic or direct repeats that appear in a sequence have a biophysical meaning and the problem is to recognize interesting patterns and configurations of words(strings of characters) over complementary alphabets. In this paper, we propose an algorithm to identify variable length palindromic pairs(longer than a threshold), where we can allow gaps(distance between words). The algorithm is called palindrome algorithm(PA) and has O(N) time complexity. A palindromic pair consists of a hairpin structure. By composing collected palindromic pairs we build n-pair palindromic patterns. In addition, we dot some of the longest pairs in a circle to represent the structure of a DNA sequence. We run the algorithm over several selected genomes and the results of E.coli K12 are presented. There existed very long palindromic pair patterns in the genomes, which hardly occur in a random sequence.