• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.470-472
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• 2006

Sensor networks are the results of convergence of very important technologies such as wireless communication and micro electromechanical systems. In recent years, sensor networks found a wide applicability in various fields such as environment and health, industry scene system monitoring, etc. A very important step for these many applications is pattern classification and recognition of data collected by sensors installed or deployed in different ways. But, pattern classification and recognition are sometimes difficult to perform. Systematic approach to pattern classification based on modem learning techniques like Multivariate Gaussian mixture models, can greatly simplify the process of developing and implementing real-time classification models. This paper proposes a new recognition system which is hierarchically composed of many sensor nodes having the capability of simple processing and wireless communication. The proposed system is able to perform context classification of sensed data using the Multivariate Gaussian function. In order to verify the usefulness of the proposed system, it was applied to intelligent dust collecting system.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.767-772
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• 2005

The Internet is a packet switched network which offers best-effort service, but current IP network provide enhanced services such Quality of Services, Virtual Private Network (VPN) services, Distribute Firewall and IP Security Gateways. All such services need packet classification for determining the flow. The problem is performing scalable packet classification at wire speeds even as rule databases increase in size. Therefore, this research offer packet classification algorithm that increase classifier performance when working with enlarge rules database by rearrange rule structure into Bitmap Intersection Lookup (BIL) tables. It will use packet's header field for looking up BIL tables and take the result with intersection operation by logical AND. This approach will use simple algorithm and rule structure, it make classifier have high search speed and fast updates.

• Journal of the Korea Fashion and Costume Design Association
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• v.17 no.4
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• pp.173-189
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• 2015

The purpose of this study is to present the classification and operation systems of fashion offline stores. This research analyzed fashion literatures, articles and papers published by fashion-related companies and interviewed fashion practitioners. This research can be used as information for practitioners of the domestic fashion brand and students of fashion majors. The classification and operation systems of fashion offline stores are as follows. 1. The types of fashion offline store is classified as a form of road shop, department store, complex shopping center, select shop, outlet, and fashion wholesale retail specialty store. 2. The road shop is classified flagship store, franchise store, direct sales store, and street brand store. 3. The department store is recently using strategy to improve the profit rate, as setting up the select shop, expand the import contemporary brand stores, the men's brand stores, SPA brand stores, the street brand stores, and the soho internet shopping mall brands instead of reducing the national brands. 4. Most forms of fashion offline stores enhanced the functions to combine the catering, cultural activities and purchasing the lifestyle-related products, as well as fashion items. 5. The types of the operation system in fashion offline stores is classified as direct operations, franchise operations, middle management operations, and fully insert operations. 6. Franchise operations are tended to decline, however middle manager operations are overwhelming.

• Journal of Korean Library and Information Science Society
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• v.18
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• pp.139-192
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• 1991

Though the computer has become deeply entrenched as the major tool in information processing(library works), it may be obvious that automatic book classification techniques ate still under experimentation, and the techniques have not yet been tested against the criterion of usefulness. The purpose of this study is to design of knowledge base for automatic book classification which can be put to use in library operation, and to present a methodology of application of the automatic classification into the library. Since the enumerative classification schemes which are existing are manual systems, it cannot be applied to the automatic classification, the principle of faceted classification based on concept analysis is brought in and studied. The result of this study are summarized as follows : 1. The design of knowledge base confined the field of agriculture and medicine. 2. If title is entered by the computer keyboard it will be searched in knowledge base, and then be classified by the principle of automatic classification. 3. Program flowcharts are designed as a bases of classification procedures for automatic subject recognition and classification. 4. 283 books in agriculture, 196 books in medicine were drawn at random from Taegu University Library and Young-Nal Medical Center Library respectively. 5. The experiment of automatic classification is performed 143 books in agriculture 166 books in medicine except for other subject books. 6. It was proved that automatic book classification is possible by design of knowledge base. In addition the expected values from design of knowledge base for automatic book classification are as follows : 1. The prompt and accurate process of classification is possible. 2. Though some title is classified in any library, it can be classified the some classification number by a program. 3. The user can retrieve the classification codes of books for which he or she wants to search through the computer. 4. Since the concept coordination method is employed the representing of a multisubject concept is make simple. 5. By performing automatic book classification the automation of total system can be achieved. 6. The efficient international information transfer will be advanced since all the institution maintain unified classification number.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.2
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• pp.47-56
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• 2014

Internet traffic has rapidly increased due to the supplying wireless devices and the appearance of various applications and services. By increasing internet traffic rapidly, the need of Internet traffic classification becomes important for the effective use of network resource. However, the traffic classification scheme is not much studied comparing to the study for classification method. This paper proposes novel classification scheme for multilateral and hierarchical traffic identification. The proposed scheme can support multilateral identification with 4 classification criteria such as service, application, protocol, and function. In addition, the proposed scheme can support hierarchical analysis based on roll-up and drill-down operation. We prove the applicability and advantages of the proposed scheme by applying it to real campus network traffic.

• Kyungpook Mathematical Journal
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• v.63 no.4
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• pp.709-718
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• 2023

The inner automorphism groups of quandles are related to the classification problem of quandles. The inner automorphism group of a quandle is generated by inner automorphisms which are presented by columns in the operation table of the quandle. In this paper, we describe inner automorphisms of an abelian extension of a quandle by expressing columns of the operation table of the extended quandle as columns of the operation table of the original quandle. Such a description will be helpful in studying inner automorphism groups of abelian extensions of quandles.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.12
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• pp.1-7
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• 2021

The deep learning image processing technique was used to prevent accidents in lathe work caused by worker negligence. During lathe operation, when the chuck is rotated, it is very dangerous if the operator's hand is near the chuck. However, if the chuck is stopped during operation, it is not dangerous for the operator's hand to be in close proximity to the chuck for workpiece measurement, chip removal or tool change. We used YOLO (You Only Look Once), a deep learning image processing program for object detection and classification. Lathe work images such as hand, chuck rotation and chuck stop are used for learning, object detection and classification. As a result of the experiment, object detection and class classification were performed with a success probability of over 80% at a confidence score 0.5. Thus, we conclude that the artificial intelligence deep learning image processing technique can be effective in preventing incidents resulting from worker negligence in future manufacturing systems.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.29 no.4
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• pp.297-317
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• 2018

The purpose of this study is to propose a classification system that can classify various industrial and craft materials consistently and systematically by comparing and analyzing the classification system of materials libraries in domestic and foreign. For this study, it was investigated the operation cases and classification system of domestic and foreign material libraries, and then proposed a method to classify consistently and systematically by comparing and analyzing the classification items of KDC and DDC. The classification system of material library was not classified by subject, but classified into the name of material and type, or the classification system of a material library was divided by Arabic numeral as a general classification system. It is difficult to access and share information because most of material libraries use the classification system developed by the library itself. Therefore, it is necessary to develop a standard classification system to share the information stored in the material libraries.

• Archives of Plastic Surgery
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• v.33 no.4
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• pp.449-453
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• 2006

Purpose: Perceiving the experience that $MRD_1$ is increased after small-incision double-eyelid operation, it has been statistically verified the improvement of $MRD_1$ retrospectively. Therefore, we suggest new interpretation for Korean-specific small eye and more definite classification for ptosis. Methods: In operation, we made 3 small incisions according to the line designed on upper eyelid and removed some soft tissues including connective tissues. Then, figure-of-8 continuous buried ligation was performed through the incision windows. This study is a retrospective study, measures $MRD_1$ changes and excursion distance change of eyelid margine and eyebrow using pre-and post-operative photos of 14 patients. Results: Postoperative photos were taken on the 4th day and all of 14 patients had an improvement in $MRD_1$ statistically. The excursion distance of upper eyelid was improved in half of patients, though not in the range of statistical verification. It was statistically confirmed that the movement of eyebrow was reduced after operation. Conclusion: It is thought that $MRD_1$ improvement without any operation on levator complex means that soft tissue including connective tissue eliminated in operation restricts the movement of upper eyelid. It is defined such factors as burden factors and such phenomenon as burdened eyelid. It is thougt that Korean's small eye is mainly burdened eyelid and cosmetic improvement may be obtaind even by removing such burden factors.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.216-226
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• 2020

In this paper, a stacked convolutional autoencoder model is proposed in order to classify Alzheimer's disease with high accuracy in PET/CT images. The proposed model makes use of the latent space representation - which is also called the bottleneck, of the encoder-decoder architecture: The input image is sent through the pipeline and the encoder part, using stacked convolutional filters, extracts the most useful information. This information is in the bottleneck, which then uses Softmax classification operation to classify between Alzheimer's disease, Mild Cognitive Impairment, and Normal Control. Using the data from Dong-A University, the model performs classification in detecting Alzheimer's disease up to 98.54% accuracy.