• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1667-1671
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• 2015

Multi-level holographic data storage is a candidate for the next generation data storage system, since it can store more than one bit per pixel. It is possible to increase the number of codewords if the number of levels is increased, and the code with an appropriate selection of codewords can also increase the minimum distance. In this paper, we propose three multi-level modulation codes of the code rate 1 bit/pixel and compare the performance according to the minimum distance. The result shows that the code with small number of levels is better than that of large number of levels because it is hard to detect threshold value.

• Journal of Animal Environmental Science
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• v.21 no.1
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• pp.1-8
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• 2015

This study was conducted to investigate the dispersion prediction of odor from Hanwoo and dairy farms. Gaussian Plume model used in considering of farm size, wind velocity, atmospheric stability and threshold odor unit to prediction of odor dispersion based on the survey on current state of odor emission and control from 9 site of Hanwoo and 9 site of dairy farms. Farm size, wind velocity and atmospheric stability were affected the distance of odor dispersion, showed longer distance in cases of large farm, low wind velocity and stable atmospheric condition. We will suggestion the adjusted distance of odor dispersion according to farm size was estimated to 50~100 m in Hanwoo farm and 50~150 m in dairy farm when apply the 3OU, 5 m/s wind velocity and stable atmospheric condition.

• 한국지형공간정보학회:학술대회논문집
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• 1993.10a
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• pp.141-145
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• 1993

A new digital change detection algorithm, Euclidean Distance Analysis, was developed in an attempt to utilize the multi-band information in a selected band-comination, as an alternative to the conventional single-band analysis methods. To evaluate the relative performance of this new method, image differencing was applied. The better performance in change detection between the two algorithms investigated was provided by the Euclidean distance analysis. The new technique of Euclidean distance analysis holds promise for change detection, since it summarizes the multiple-band information on the cover-type changes and reduces the data dimensionality. It is suggested to further evaluate this new method, quantitatively, in the different environments. The use of different accuracy indices was also examined in the determining the optimal threshold level for each change image. As the standard measure for classification accuracy, the Kappa coefficient of agreement was used for evaluation.

• Journal of Sensor Science and Technology
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• v.21 no.1
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• pp.39-45
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• 2012

Recently the convergence of healthcare technology is used for daily life healthcare monitoring. Cardiac arrhythmia is presented by the state of the heart irregularity. Abnormal heart's electrical signal pathway or heart's tissue disorder could be the cause of cardiac arrhythmia. Fatal arrhythmia could put patient's life at risk. Therefore arrhythmia detection is very important. Previous studies on the detection of arrhythmia in various ECG analysis and classification methods had been carried out. In this paper, an ECG signal processing techniques to detect abnormal ECG based on DTW minimum accumulation distance through the template matching for normalized data and variable threshold method for ECG R-peak detection. Signal processing techniques able to determine the occurrence of normal ECG and abnormal ECG. Abnormal ECG detection algorithm using DTW minimum accumulation distance method is performed using MITBIH database for performance evaluation. Experiment result shows the average percentage accuracy of using the propose method for Rpeak detection is 99.63 % and abnormal detection is 99.60 %.

• Journal of Animal Environmental Science
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• v.20 no.2
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• pp.41-48
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• 2014

This study was conducted to investigate the dispersion prediction of odor from swine farms in Korea. Gaussian Plume model used in considering of farm size, wind velocity, atmospheric stability and threshold odor unit to prediction of odor dispersion based on the survey on current state of odor emission and control from 48 site of swine farms. Farm size, wind velocity and atmospheric stability were affected the distance of odor dispersion, showed longer distance in cases of large farm, low wind velocity and stable atmospheric condition. We will suggestion the adjusted distance of odor dispersion according to farm size was estimated to 180 m in small farm and 320 m in large farm when apply the 3 OU, 5 m/s wind velocity and stable atmospheric condition.

• Current Optics and Photonics
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• v.2 no.4
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• pp.348-355
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• 2018

How to measure the creepage distance of an insulator quickly and accurately is a problem for the power industry at present, and the noticeable concern is that the high voltage insulation equipment cannot be measured online in the charged state. In view of this situation, we develop an on-line measurement system of creepage distance for high voltage insulators based on binocular stereo vision. We have proposed a method of generating linear structured light using a conical off-axis mirror. The feasibility and effect of two ways to solve the interference problem of strong sunlight have been discussed, one way is to use bandpass filters to enhance the contrast ratio of linear structured light in the images, and the other way is to process the images with adaptive threshold segmentation and feature point extraction. After the system is calibrated, we tested the measurement error of the on-line measurement system with a composite insulator sample. Experimental results show that the maximum relative error is 1.45% and the average relative error is 0.69%, which satisfies the task requirement of not more than 5% of the maximum relative error.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.106-116
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• 2024

Ensuring robust 3D object detection is a core challenge for autonomous driving systems operating in urban environments. To tackle this issue, various 3D representation, including point cloud, voxels, and pillars, have been widely adopted, making use of LiDAR, Camera, and Radar sensors. These representations improved 3D object detection performance, but real-world urban scenarios with unexpected situations can still lead to numerous false positives, posing a challenge for robust 3D models. This paper presents a post-processing algorithm that dynamically adjusts object detection thresholds based on the distance from the ego-vehicle. While conventional perception algorithms typically employ a single threshold in post-processing, 3D models perform well in detecting nearby objects but may exhibit suboptimal performance for distant ones. The proposed algorithm tackles this issue by employing adaptive thresholds based on the distance from the ego-vehicle, minimizing false negatives and reducing false positives in the 3D model. The results show performance enhancements in the 3D model across a range of scenarios, encompassing not only typical urban road conditions but also scenarios involving adverse weather conditions.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.4
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• pp.1-10
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• 2000

The error diffusion technique is frequently utilized for the digital Imaging output devices to convert continuous level Image into bi-level Image It Yields the binary image with the high frequency emphasis that gives a pleasing perception to human eyes But, due to the non-homogeneous distribution of dots, It exhibits undesirable patterns that degenerate the perceived quality Various techniques have been proposed to Improve the Image quality by the error diffusion techniques In this paper, the cause of non-homogeneity of dot distribution is analyzed first. A threshold modulation technique that employs a simple sinusoidal function is proposed in this paper The proposed method achieves the homogeneous dot distribution by forcing the minor pixels on the binary Image to maintain the principal distance defined according to their gray levels. It also minimizes the void and clusters of minor pixels.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.216-224
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• 2013

The within-class variance of Otsu's method is moderate but improper in expressing class statistical distributions. Otsu's method uses a variance to represent the distribution of each class. The variance utilizes a distance square from the mean to a data. This process is not proper in denoting a real class statistical distribution because of the distance square. In this paper, to express more exact class statistical distributions, the within-class standard deviation as a criterion for threshold selection is proposed and then the optimal threshold is determined by minimizing it. In order to have validity, it is shown through the experimental results that the proposed method was more superior to the counterparts.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.438-441
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• 2007

Recently, due to molecular biology and engineering technology, DNA microarray makes people watch thousands of genes and the state of variation from the tissue samples of living body. With DNA Microarray, it is possible to construct a genetic group that has similar expression patterns and grasp the progress and variation of gene. This paper practices Cluster Analysis which purposes the discovery of biological subgroup or class by using gene expression information. Hence, the purpose of this paper is to predict a new class which is unknown, open leukaemia data are used for the experiment, and MCL (Markov CLustering) algorithm is applied as an analysis method. The MCL algorithm is based on probability and graph flow theory. MCL simulates random walks on a graph using Markov matrices to determine the transition probabilities among nodes of the graph. If you look at closely to the method, first, MCL algorithm should be applied after getting the distance by using Euclidean distance, then inflation and diagonal factors which are tuning modulus should be tuned, and finally the threshold using the average of each column should be gotten to distinguish one class from another class. Our method has improved the accuracy through using the threshold, namely the average of each column. Our experimental result shows about 70% of accuracy in average compared to the class that is known before. Also, for the comparison evaluation to other algorithm, the proposed method compared to and analyzed SOM (Self-Organizing Map) clustering algorithm which is divided into neural network and hierarchical clustering. The method shows the better result when compared to hierarchical clustering. In further study, it should be studied whether there will be a similar result when the parameter of inflation gotten from our experiment is applied to other gene expression data. We are also trying to make a systematic method to improve the accuracy by regulating the factors mentioned above.