• Journal of the Korean Society for information Management
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• v.35 no.2
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• pp.37-62
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• 2018

This study examined the factors affecting the performance of automatic classification based on machine learning for domestic journal articles in the field of LIS. In particular, In view of the classification performance that assigning automatically the class labels to the articles in "Journal of the Korean Society for Information Management", I investigated the characteristics of the key factors(weighting schemes, training set size, classification algorithms, label assigning methods) through the diversified experiments. Consequently, It is effective to apply each element appropriately according to the classification environment and the characteristics of the document set, and a fairly good performance can be obtained by using a simpler model. In addition, the classification of domestic journals can be considered as a multi-label classification that assigns more than one category to a specific article. Therefore, I proposed an optimal classification model using simple and fast classification algorithm and small learning set considering this environment.

• Journal of the Korean Society for information Management
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• v.33 no.2
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• pp.33-59
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• 2016

This study examined the factors affecting the performance of automatic classification for the domestic conference papers based on machine learning techniques. In particular, In view of the classification performance that assigning automatically the class labels to the papers in Proceedings of the Conference of Korean Society for Information Management using Rocchio algorithm, I investigated the characteristics of the key factors (classifier formation methods, training set size, weighting schemes, label assigning methods) through the diversified experiments. Consequently, It is more effective that apply proper parameters (${\beta}$, ${\lambda}$) and training set size (more than 5 years) according to the classification environments and properties of the document set. and If the performance is equivalent, I discovered that the use of the more simple methods (single weighting schemes) is very efficient. Also, because the classification of domestic papers is corresponding with multi-label classification which assigning more than one label to an article, it is necessary to develop the optimum classification model based on the characteristics of the key factors in consideration of this environment.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.10
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• pp.839-846
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• 2016

Bin picking is a task of picking a small object from a bin. For accurate bin picking, the 3D pose information, position, and orientation of a small object is required because the object is mixed with other objects of the same type in the bin. Using this 3D pose information, a robotic gripper can pick an object using exact distance and orientation measurements. In this paper, we propose a 3D vision technique for accurate measurement of 3D position and orientation of small objects, on which a paper label is stuck to the surface. We use a maximally stable extremal regions (MSERs) algorithm to detect the label areas in a left bin image acquired from a stereo camera. In each label area, image features are detected and their correlation with a right image is determined by a stereo vision technique. Then, the 3D position and orientation of the objects are measured accurately using a transformation from the camera coordinate system to the new label coordinate system. For stable measurement during a bin picking task, the pose information is filtered by averaging at fixed time intervals. Our experimental results indicate that the proposed technique yields pose accuracy between 0.4~0.5mm in positional measurements and $0.2-0.6^{\circ}$ in angle measurements.

• Journal of Electrical Engineering and Technology
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• v.6 no.4
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• pp.551-555
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• 2011

The Crab label tag with a loop matching feed and a modified dipole antenna structure was proposed. The antenna impedance is conjugated easily to a radio frequency identification IC chip impedance by a loop matching feed. The reading range of the crab structure tag is 0.9-1.0 m from the upper side of the formula milk can lid. The fabricated label tag size is $44.0{\times}44.0mm^2$. The operating frequency at -3 dB return loss is 861.0-929.0 MHz, and the maximum reading range at the anechoic chamber is 1.5 m.

• Journal of Korea Multimedia Society
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• v.23 no.6
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• pp.756-764
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• 2020

A typical algorithm used for image analysis is the Convolutional Neural Network(CNN). R-CNN, Fast R-CNN, Faster R-CNN, etc. have been studied to improve the performance of the CNN, but they essentially require large amounts of data and high algorithmic complexity., making them inappropriate for small and medium-sized services. Therefore, in this paper, the image label prediction algorithm based on CNN with collaborative layer with low complexity, high accuracy, and small amount of data was proposed. The proposed algorithm was designed to replace the part of the neural network that is performed to predict the final label in the existing deep learning algorithm by implementing collaborative filtering as a layer. It is expected that the proposed algorithm can contribute greatly to small and medium-sized content services that is unsuitable to apply the existing deep learning algorithm with high complexity and high server cost.

• The KIPS Transactions:PartA
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• v.8A no.2
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• pp.179-188
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• 2001

We analyze the property of candidate node set in the network graph, and propose an algorithm to decrease shortest path-finding computation time by using multiple dynamic-range queue(MDRQ) structure. This MDRQ structure is newly created for effective management of the candidate node set. The MDRQ algorithm is the shortest path-finding algorithm that varies range and size of queue to be used in managing candidate node set, in considering the properties that distribution of candidate node set is constant and size of candidate node set rapidly change. This algorithm belongs to label-correcting algorithm class. Nevertheless, because re-entering of candidate node can be decreased, the shortest path-finding computation time is noticeably decreased. Through the experiment, the MDRQ algorithm is same or superior to the other label-correcting algorithms in the graph which re-entering of candidate node didn’t frequently happened. Moreover the MDRQ algorithm is superior to the other label-correcting algorithms and is about 20 percent superior to the other label-setting algorithms in the graph which re-entering of candidate node frequently happened.

• Journal of The Korean Association of Information Education
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• v.11 no.2
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• pp.185-194
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• 2007

With the advent of ubiquitous society, the paradigm of education is also changing. In u-learning in ubiquitous environment, mobile devices such as cellular phone, PDA, PMP, UMPC and TPC will become important learning tools. Particularly if RFID, a key technology in ubiquitous society, is utilized in teaching-learning along with mobile devices, we can expect much more meaningful learning. The present study proposed four types of application of RFID to teaching.learning, which were RFID-card, RFID-book, RFID-label and RFID-test, and examined their possibility and effectiveness. According to the results of this study, technology was insufficient to support the application of RFID-label to teaching-learning and continuous research was required for modeling the application of RFID to teaching-learning.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.5
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• pp.841-849
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• 2001

The pixel information of the object was obtained sequentially and pixels were clustered to a label by the line labeling method. Feature points were determined by finding the slope for edge pixels after selecting the fixed number of edge pixels. The slope was estimated by the least square method to reduce the detection error. Once a matching point was determined by comparing the feature information of the object and the pattern, the parameters for translation, scaling and rotation were obtained by selecting the longer line of the two which passed through the matching point from left and right sides. Finally, modified Hausdorff Distance has been used to identify the similarity between the object and the given pattern. The multi-label method was developed for recognizing the patterns with more than one label, which performs the modified Hausdorff Distance twice. Experiments have been performed to verify the performance of the proposed algorithm and method for simple target image, complex target image, simple pattern, and complex pattern as well as the partially hidden object. It was proved via experiments that the proposed image matching algorithm for recognizing the object had a good performance of matching.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.4 no.1
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• pp.85-102
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• 1980

Accepting the necessity for maintaining the objectives of the existing circulation system, the computer-based system could be designed by the system analyst and librarians to gain a variety of improvements in the maintenance, accessibility of circulation records and more meaningful statistical records. If the terminal can be operated on-line, then this circulation data is transmitted directly to the computer, where it may update to the circulation file immediately or alternatively be kept in direct access file for updating in batch mode. on-line system in the circulation operations is "data-collection system" and "Bar-coded label system" Bar-coded label system is simple, quick, and error-free input of data. Attached to CRT terminal is a "light pen" which is hand held and will read a bar-coded label as the pen is passed over the labels (one affixed to the book itself, other carried on the borrower cards). Instantaneously the data concerning transaction is stored in the central mini-computer. It is useful, economical for us to co-operate many libraries in Korea and design borrower's ID code, book no., classification code in the Bar-coded label system by the members of the computer center and the library staff at every stage. As for book loan, the borrowers ID code, book number and classification code are scanned by the bar-code scanner or light pen and the computer decides whether to loan and store the data. The visual display unit shows the present status of a borrowers borrowing and decides whether borrower can borrow.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.201-202
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• 2018

In this paper, a dog breed image provided by Dataset of existing ImageNet and Oxford-IIIT Pet Image is combined with a dog breed image obtained through data mining on Internet and a random-label is added. this paper introduces to recognize 122 classes of dog breeds and 1 class that is not dog breeds. The recognition rate of dog breeds using both conventional DB and collection DB was improved 1.5% over Top-1 compared to recognition rate of dog breeds using only existing DB. The image recognition rate about non-dog image, was 93% recognition rate in case of 10000 random DBs.