• Journal of Chest Surgery
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• v.23 no.3
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• pp.423-429
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• 1990

Authors designed a computer-based article data management system using a 16 bit IBM personal computer and dBASE IV program and applied it to the management of article data of the Korean Journal of Thoracic and Cardiovascular Surgery. In this system, authors developed a coding system for systemic classification of form and subject of articles and this coding system made the storage and inquiry of data easy and convenient. Using the coding system in this article management system, total 1476 sheets of articles in the Korean Journal of Thoracic and Cardiovascular Surgery were analyzed and briefly described according to their forms and subjects. If this article data management system including the coding system is applied to other Journals related to the department of thoracic and cardiovascular surgery, we can utilize them easily and conveniently for the save of time and effort in the data inquiry or article preparation.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.163-166
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• 2000

In this paper, we will present a lossy data compression method for coding multispectral images. The proposed method uses both spatial and spectra] correlation inherent in multispectral images. First, band 2 and band 6 are vector quantized. Secondly, band 4 is estimated with the quantized band 2 using the predictive coding. Errors of band 4 are encoded at a second stage based on the magnitude of the errors. Thirdly, remaining bands are calculated with the quantized band 2 and band 4. Errors of residual bands are wavelet transformed and then we apply the SPIHT coding on the transformed coefficients. We classify classes without extra information transmitting and then use linear predictor. And errors can be encoded by SPIHT coding at any target rate we are want. It is shown that this method has better performance than FPVQ. Average PSNR rises 0.645 dB at the same bit rate.

• ETRI Journal
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• v.33 no.6
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• pp.871-879
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• 2011

Because a wide variety of multimedia services are provided through personal wireless communication devices, the demand for efficient bandwidth utilization becomes stronger. This demand naturally results in the introduction of the variable bitrate speech coding concept. One exemplary work is the selectable mode vocoder (SMV) that supports speech/music classification. However, because it has severe limitations in its classification performance, a couple of works to improve speech/music classification by introducing support vector machines (SVMs) have been proposed. While these approaches significantly improved classification accuracy, they did not consider correlations commonly found in speech and music frames. In this paper, we propose a novel and orthogonal approach to improve the speech/music classification of SMV codec by adaptively tuning SVMs based on interframe correlations. According to the experimental results, the proposed algorithm yields improved results in classifying speech and music within the SMV framework.

• Journal of the Korean Society for Precision Engineering
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• v.6 no.3
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• pp.60-67
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• 1989

An automated classification and coding system for structure of injection mold is developed based on the statistical analysis and the critical evaluation of the results for the sample survey of 200 assembly drawings of injection mold. The proposed system is a mixed code system consisting of 15 digits and each digit consists of 10 numerical codes. An interactive computer program is developed by using TURBO PASCAL on IBM PC/AT compatible system. A case study is discussed to show the procedure and the function of the system. The results for applications of the system to real problems show that the system works well and is useful for design, manufacturing and management of injection mold.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.735-743
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• 2020

Secure coding is a technique that detects malicious attack or unexpected errors to make software systems resilient against such circumstances. In many cases secure coding relies on static analysis tools to find vulnerable patterns and contaminated data in advance. However, secure coding has the disadvantage of being dependent on rule-sets, and accurate diagnosis is difficult as the complexity of static analysis tools increases. In order to support secure coding, we apply machine learning techniques, such as DNN, CNN and RNN to investigate into finding major weakness patterns shown in secure development coding guides and present machine learning models and experimental results. We believe that machine learning techniques can support detecting security weakness along with static analysis techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.385-405
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• 2019

High Efficiency Video Coding (HEVC) enables significantly improved compression performance relative to existing standards. However, the advance also requires high computational complexity. To accelerate the intra prediction mode decision, a minimum risk Bayesian classification framework is introduced. The classifier selects a small number of candidate modes to be evaluated by a rate-distortion optimization process using the sum of absolute Hadamard transformed difference (SATD). Moreover, the proposed method provides a loss factor that is a good trade-off model between computational complexity and coding efficiency. Experimental results show that the proposed method achieves a 31.54% average reduction in the encoding run time with a negligible coding loss of 0.93% BD-rate relative to HEVC test model 16.6 for the Intra_Main common test condition.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2240-2242
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• 1998

Image coding based on fractal theory presents highly compressed image. In this paper, we discuss about compression of Image using HV partition method. HV partition scheme devides the image adaptively in horizontal and vertical axis. And for reducing the enconding time for the domain-range comparison, we use classification scheme, which uses the order of brightness of the rectangular portion of the image. This paper focused on the technique to reduce coding time which is a problem in traditional fractal compression by adaptive selection of image and its classification method.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.437-447
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• 2006

Error Correcting Output Coding(ECOC) is used to solve multi-class problem. It is known that it improves the classification accuracy. In this paper, we compared various criteria to design code matrix while encoding. In addition. we prorpose an ensemble which uses the ability of each classifier while decoding. We investigate the justification of the proposed method through real data and synthetic data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.5074-5094
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• 2016

Screen splitting is one of the fundamental tasks in different methods including video and image compression, screen classification, screen content coding and the like. These methods in turn support various applications in data communications, remote screen sharing, remote desktop delivery to assist teaching-learning, telemedicine, Desktop as a Service etc. In the literature we find systems requiring splitting assumes a fixed size split that do not change dynamically, also there is no analysis why that split is chosen in terms of performance. By doing mathematical analysis this paper first finds the efficient splitting schemes that can be easily automated to make a system adaptive. Thereafter, taking the screen motion detection as a case study, it demonstrates the effects of various splitting methods on motion detection performance. The simulation results clearly shows how classification performances varies with different splitting which will facilitate to choose the best splitting for a specific application scenario as well as making the system adaptive by providing dynamic splitting.

• Archives of Plastic Surgery
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• v.45 no.5
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• pp.411-417
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• 2018

Background Accessory auricles (AAs) are common congenital anomalies. We present a new classification according to location and shape, and propose a system for coding the classifications. Methods This study was conducted by reviewing the records of 502 patients who underwent surgery for AA. AAs were classified into three anatomical types: intraauricular, preauricular, and buccal. Intraauricular AAs were divided into three subtypes: intracrural, intratragal, and intralobal. Preauricular AAs were divided into five subtypes: precrural, superior pretragal, middle pretragal, inferior pretragal, and prelobal. Buccal AAs were divided into two subtypes: anterior buccal and posterior buccal. AAs were also classified according to their protrusion pattern above the surrounding surface: pedunculated, sessile, areolar, remnant, and depressed. Pedunculated and sessile AAs were subclassified as spherical, ovoid, lobed, and nodular, according to their body shape. Cartilage root presence and family history of AA were reviewed. A coding system for these classifications was also proposed. Results The total number of AAs in the 502 patients was 1,003. Among the locations, the superior pretragal subtype (27.6%) was the most common. Among the protrusion patterns and shapes, pedunculated ovoid AAs were the most common in the preauricular (27.8%) and buccal areas (28.0%), and sessile lobed AAs were the most common in the intraauricular area (48.7%). The proportion of AAs with a cartilage root was 78.4%, and 11% of patients had a family history. The most common type of preauricular AA was the superior pretragal pedunculated ovoid AA (13.2%) with a cartilage root. Conclusions This new system will serve as a guideline for classifying and coding AAs.