• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.127-130
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• 2004

For the quality control of the industrial products, an automatic hole measuring system has been developed. The measurement device allows X-Y movement due to contact forces between a hole and its own circular cone and the device is attached to an industrial robot. Its measurement accuracy is about 0.04mm. This movement of the plate is measured by two LVDT sensor system. But this system using the LVDT sensors is restricted by high cost and precision of measurement and correspondence of environment so particularly, a vision system with CCD-Camera is discussed in this paper for the above mentioned purpose. The device consists of two of two links jointed with hinge pins basically and, they guarantee free movement of the touch prove attached on the second link in the same plane. These links are returned to home position by the spring plungers automatically after each process for the next one. On the surface of the touch prove, it has a circular white mark for camera recognition. The system detect and notify the center coordinate of capture mark image through the image processing. Its measuring accuracy has been proved to be about $\pm$0.01mm through the repeated implementation over 200 times. This technique will shows the advantage of touch-indirect image capture idea using cone-shaped touch prove in various symmetrical shaped holes particulary, like tapped holes, chamfered holes, etc As a result, we attained our object in a view of the accuracy, economical efficiency, and functionality

• Journal of KIISE
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• v.44 no.1
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• pp.78-83
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• 2017

Finding the densest subgraphs from social networks, such that people in the subgraph are in a particular community or have common interests, has been a recurring problem in numerous studies undertaken. However, these algorithms focused only on finding the single densest subgraph. We suggest a heuristic algorithm of the bottom-up type, which finds the densest subgraph by increasing its size from a given starting node, with the repeated addition of adjacent nodes with the maximum degree. Furthermore, since this approach matches well with parallel processing, we further implement a parallel algorithm on the MapReduce framework. In experiments using various graph data, we confirmed that the proposed algorithm finds the densest subgraphs in fewer steps, as compared to other related studies. It also scales efficiently for many given starting nodes.

• BMB Reports
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• v.39 no.4
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• pp.441-447
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• 2006

Abuse of drugs can elicit compulsive drug seeking behaviors upon repeated administration, and ultimately leads to the phenomenon of addiction. We developed a procedure for the standardization of microarray gene expression data of rat brain in drug addiction and stored them in a single integrated database system, focusing on more effective data processing and interpretation. Another characteristic of the present database is that it has a systematic flexibility for statistical analysis and linking with other databases. Basically, we adopt an intelligent SQL querying system, as the foundation of our DB, in order to set up an interactive module which can automatically read the raw gene expression data in the standardized format. We maximize the usability of this DB, helping users study significant gene expression and identify biological function of the genes through integrated up-to-date gene information such as GO annotation and metabolic pathway. For collecting the latest information of selected gene from the database, we also set up the local BLAST search engine and non-redundant sequence database updated by NCBI server on a daily basis. We find that the present database is a useful query interface and data-mining tool, specifically for finding out the genes related to drug addiction. We apply this system to the identification and characterization of methamphetamine-induced genes' behavior in rat brain.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.1
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• pp.49-57
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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 with three steps of grouping algorithm; 1) constructing the 2D 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 descending order 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 the resolution if the number of optical flows is less than 10%. Such processing is repeated until 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.

• Journal of Korea Multimedia Society
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• v.13 no.2
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• pp.216-224
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• 2010

A RAM-based Neural Network(RNN) has the advantages of processing speed and hardware implementation. In spite of these advantages, it has a saturation problem, weakness of repeated learning and extract of a generalized pattern. To resolve these problems of RNN, the 3DNS model using cumulative multi discriminator was proposed. But that model does not solve the saturation problem yet. In this paper, we proposed a adaptive weight cumulative neural net(AWCNN) using the adaptive weight neuron (AWN) for solving the saturation problem. The proposed nets improved a recognition rate and the saturation problem of 3DNS. We experimented with the MNIST database of NIST without preprocessing. As a result of experimentations, the AWCNN was 1.5% higher than 3DNS in a recognition rate when all input patterns were used. The recognition rate using generalized patterns was similar to that using all input patterns.

• The KIPS Transactions:PartA
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• v.10A no.4
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• pp.375-380
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• 2003

Although vocabulary is one of the most important aspects in learning English, it is not dealt with as extensively as the grammar and reading comprehension in the classes due to time limitation. Furthermore, it is also dealt with in only a limited way at most of the English learning web sites compared to the other aspects such as grammar and reading comprehension. In this study. a web-based learning system for English vocabulary which allows a student to study the vocabulary before or after the classes by herself in order to supplement the English classes provided at school. Especially, it allows the students to learn the vocabulary within the context of sentences. It also provides an efficient structure for a repeated study of vocabulary that is new or difficult to the student.

• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.473-480
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• 2007

Feature tracking in video frame sequences has suffered from the instability and the frequent failure of feature matching between two successive frames. In this paper, we propose a robust 2D feature tracking method that is stable to long video sequences. To improve the stability of feature tracking, we predict the spatial movement in the current image frame using the state variables. The predicted current movement is used for the initialization of the search window. By computing the feature similarities in the search window, we refine the current feature positions. Then, the current feature states are updated. This tracking process is repeated for each input frame. To reduce false matches, the outlier rejection stage is also introduced. Experimental results from real video sequences showed that the proposed method performs stable feature tracking for long frame sequences.

• Journal of Haehwa Medicine
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• v.14 no.2
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• pp.93-105
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• 2005

Objective : We investigated the actual condition of oriental medical office in a public health center. Methods : We classified patients who visited an oriental medical office iin a public pealth center of Dalseong-gun, Daegu Metropolitan City during recent 3 years by KCPJMAIN(data processing system), according to location, age, time(month, year), sex, disease, insurance, etc. Results : As a local category, the residents in Hyeunpung-Myeon higher than 80% of the patients who visited the public health center. The patients aged over 60 occupied 80% of the patients. The frequency of the treatment was more than 10 in those patients aged over 60. The number of the patients was the highest in May when there was a change for the past 36months, whereafter the number has sustained decline for 3-5 months and repeated decreasing. As a gender category, the female inpatients were 10732(82%). This statistics shows that these musculoskeletal system disease occupied large part in them. As a heath insurance category, the patients who were insured by health care were 12454(96.30%). Conclusion : It should need to enable the rural residents who have difficulty benefitting from medical service to reach the service by making their access to the oriental medical office in public health center easier. Most of all, the support from both government and municipality should be urged to accomplish it. plus, it should be included not only boosting doctors' reliance but also improving the capability and services of doctors in public heath center. In conclusion, the treatment service in public health center should be diversified beyond musculoskeletal system disease and the identity transform of public health center should be needed to appeal to young generation.

• 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.

• Journal of IKEEE
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• v.20 no.4
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• pp.398-411
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• 2016

SVM, a machine learning method, is widely used in image processing for it's excellent generalization performance. However, to add other data to the pre-trained data of the system, we need to train the entire system again. This procedure takes a lot of time, especially in embedded environment, and results in low performance of SVM. In this paper, we implemented an SVM trainer and classifier in an FPGA to solve this problem. We parlallelized the repeated operations inside SVM and modified the exponential operations of the kernel function to perform fixed point modelling. We implemented the proposed hardware on Xilinx ZC 706 evaluation board and used TSR algorithm to verify the FPGA result. It takes about 5 seconds for the proposed hardware to train 2,000 data samples and 16.54ms for classification for $1360{\times}800$ resolution in 100MHz frequency, respectively.