• Proceedings of the ESK Conference
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• 1992.10a
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• pp.26-32
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• 1992

세계적으로 항공기 사고의 통계적 분석에 의하면 항공기 운용자의 인적과실(Human error)로 인한 항공기 사고가 약 70% 이상으로 보고되고 있다. 항공기 운용자의 인적과실에 기인한 요인들 중에서 운용자의 작업량, 작업공간, 작업환경, 인체크기, 인체 생리, 인간 심리 및 습관 등을 항공기 설계단계에서 고려하지 못한 요인이 대부분이다. 일반적으로 항공기 비행품질(Flying qualities)의 영향을 주는 설계분야는 크게 세가지로 항공기 형상(Configuration), 조종체계(Control system)및 조종실 배치(Cockpit layout)로 분류된다. 이들 세가지 설계분야 중에서 조종실의 운용자 인간공학적인 요구 사항을 고려하지 않으면 항공기 운용성 품질중에서 삼분의 일이 감소될 수 있다. 그리고 항공기 개발시에 전담하는 항공기 설계 분야별로 구분하고 그 전담설계 부서들과 인간공학적 조종실 설계 전담 부서가 항공기 비행 품질 및 운용자 인적과실(Human error)에 미치는 영향을 분석하고 인간공학의 중요성을 강조한다. 항공기를 개발할때에 개발자는 그 항공기를 운용하는 운용자의 인체, 생리, 심리, 습관 등을 고려 하여 항공기 조종실의 인간공학적 최적화 설계 및 배치 (Design and layout)를 개발초기단계부터 항공기를 설계할때에, 그 항공기의 조종실 품질은 조종사가 항공기 비행 임무를 수행할때에 항공기 비행을 위한 용이한 정보 인식(Sencing), 용이한 정보 결심(Deciding) 및 용이한 조종(Manipulating)의 특성을 조종사에게 제공할 때 항공기 비행 품질이 좋아질 것이다.

• Journal of The Korean Association of Information Education
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• v.16 no.1
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• pp.81-89
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• 2012

Quality in smart learning is composed of many factors, and it is more complicated than the traditional education. This study put emphasis on three aspects of the smart learning quality(contents, systems, services). This study depended mostly on literature review, supplemented by FGI(Focus Group Interview) for classification of the smart learning quality factors. On a 5 point Likert scale, the survey enables the users to rate the relative importance of factors, followed by another factor performance rating. The questionnaire were composed of 39 questions. 8 questionnaire sheets were excluded which were not properly filled in or unsuitable for the analysis, and therefore, a total of 112 questionnaires were used for the final analysis. Collected data was statistically analyzed using the SPSS 18.0 for Windows statistical package. Importance-performance analysis(IPA; gap between importance and performance) is used for the empirical test.

• The Journal of Society for e-Business Studies
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• v.15 no.4
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• pp.79-99
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• 2010

As data exchange between business partners in e-business becomes more active, obtaining and managing reliable data is emerging as a pressing issue for corporations and organizations. For the resolution of data quality, this paper proposes a framework for data quality management with its scenario. The data quality management framework consists of three phases: data quality monitoring, data quality improvement and data application, each of which has three processes. In each process, necessity, functions, roles, and relationships among processes are specified. In order for users to directly apply the framework to the business field, a business scenario is given with examples of product identification and classification code systems widely used in e-business.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.80-85
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• 2023

기계 번역에서 품질 검증은 정답 문장 없이 기계 번역 시스템에서 생성된 번역의 품질을 자동으로 추정하는 것을 목표로 한다. 일반적으로 이 작업은 상용화된 기계 번역 시스템에서 후처리 모듈 역할을 하여 사용자에게 잠재적인 번역 오류를 경고한다. 품질 검증의 하위 작업인 치명적인 오류 탐지는 번역의 오류 중에서도 정치, 경제, 사회적으로 문제를 일으킬 수 있을 만큼 심각한 오류를 찾는 것을 목표로 한다. 본 논문은 치명적인 오류의 유무를 분류하는 것을 넘어 문장에서 치명적인 오류가 존재하는 부분을 제시하기 위한 새로운 데이터셋과 모델을 제안한다. 이 데이터셋은 거대 언어 모델을 활용하는 구축 방식을 채택하여 오류의 구체적인 범위를 표시한다. 또한, 우리는 우리의 데이터를 효과적으로 활용할 수 있는 다중 작업 학습 모델을 제시하여 오류 범위 탐지에서 뛰어난 성능을 입증한다. 추가적으로 언어 모델을 활용하여 번역 오류를 삽입하는 데이터 증강 방법을 통해 보다 향상된 성능을 제시한다. 우리의 연구는 기계 번역의 품질을 향상시키고 치명적인 오류를 줄이는 실질적인 해결책을 제공할 것이다.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.653-662
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• 2022

Recently, various researches have been made on a fingerprint classification method using Convolutional Neural Networks (CNN), which is widely used for multidimensional and complex pattern recognition such as images. The CNN-based fingerprint classification method can be executed by integrating the two-step process, which is generally divided into feature extraction and classification steps. Therefore, since the CNN-based methods can automatically extract features of fingerprint images, they have an advantage of shortening the process. In addition, since they can learn various features of incomplete or low-quality fingerprints, they have flexibility for feature extraction in exceptional situations. In this paper, we intend to identify the research trends of CNN-based fingerprint classification and discuss future direction of research through the analysis of experimental methods and results.

• Industry Promotion Research
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• v.2 no.2
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• pp.45-52
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• 2017

this study, the problem of the quality is analyzed by questionnaire analysis of the current quality managers, and the German meister system, the Japanese name deduction system, the functional manager, and the quality manager system. First, we set up a quality guide selection classification guide model. Secondly, we set up a model for combining experience and expertise with theory. Third, we set up a quality brand application model to enhance competitiveness of SMEs. Fourth, The basic model is presented. The expected effects of these models are that, in the knowledge-based economy, in terms of identifying, fostering, and utilizing superior talents, quality experts combine academic theories and experience in each field to provide core expertise and knowledge, As a transformative leader and expert, we will become a leader of innovation activities by enhancing corporate competitiveness, developing younger leaders, and cooperating with suppliers. In order to strengthen competitiveness of SMEs, It will be possible to scale and cultivate the technology of SMEs.

• Journal of Cadastre & Land InformatiX
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• v.52 no.1
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• pp.37-55
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• 2022

This study presents the plans to derive quality items for develop the data quality standard for ensuring the quality of 3D building geospatial data in NDT(National Digital Twin). This paper is organized as follows. The first section briefly examines various factors that impact the quality of 3D geospatial data, and proposes the role and necessity of the data quality standard as a means of addressing the data errors properly and also meeting the minimum requirements of stakeholders. The second section analyzes the relationship between the standards - building data model for NDT and ISO 19157: Geospatial data quality - in order to consider directly relevant standards. Finally, we suggest three plans on developing NDT data quality standard: (1) the scope for evaluating data quality, (2) additional quality elements(geometric integrity, geometric fidelity, positional accuracy and semantic classification accuracy), and (3) NDT data quality items model based on ISO 19157. The plans reveled through the study would contribute to establish a way for the national standard on NDT data quality as well as the other standards associated with NDT over the coming years.

• The Journal of the Korea Contents Association
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• v.17 no.9
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• pp.132-144
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• 2017

The fingerprint classification of categorizing fingerprints by classes should be used in order to improve the processing speed and accuracy in a fingerprint recognition system using a large database. The fingerprint classification methods extract features from the fingerprint ridges of a fingerprint and classify the fingerprint using learning and reasoning techniques based on the classes defined according to the flow and shape of the fingerprint ridges. In earlier days, many researches have been conducted using NIST database acquired by pressing or rolling finger against a paper. However, as automated systems using live-scan scanners for fingerprint recognition have become popular, researches using fingerprint images obtained by live-scan scanners, such as fingerprint data provided by FVC, are increasing. And these days the methods of fingerprint classification using Deep Learning have proposed. In this paper, we investigate the trends of fingerprint classification technology and compare the classification performance of the technology. We desire to assist fingerprint classification research with increasing large fingerprint database in improving the performance by mentioning the necessity of fingerprint classification research with consideration for fingerprint images based on live-scan scanners and analyzing fingerprint classification using deep learning.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9B
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• pp.577-588
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• 2007

The rapid growth of the Internet has stimulated the development of various new applications and services, and the service providers and the Internet users now require different levels of service qualities rather than current best-effort service which treats all incoming packet equally. Therefore, next generation routers should provide the various levels of services. In order to provide the quality of services, incoming packets should be classified into flows according to pre-defined rules, and this should be performed for all incoming packets in wire-speed. Packet classification not only involves multi-dimensional search but also finds the highest priority rule among all matching rules. Area-based quad-trie is a very good algorithm that constructs a two-dimensional trie using source and destination prefix fields. However, it performs the linear search for the prefix length, and hence it does not show very good search performance. In this paper, we propose to apply binary search on length to the area-based quad-trie algorithm. In improving the search performance, we also propose two new algorithms considering the priority of rules in building the trie.

• Journal of the Korean Society for information Management
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• v.39 no.3
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• pp.293-310
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• 2022

In this study, we analyzed the performance of the BERT-based document classification model by automatically classifying documents in the field of library and information science based on the KoBERT. For this purpose, abstract data of 5,357 papers in 7 journals in the field of library and information science were analyzed and evaluated for any difference in the performance of automatic classification according to the size of the learned data. As performance evaluation scales, precision, recall, and F scale were used. As a result of the evaluation, subject areas with large amounts of data and high quality showed a high level of performance with an F scale of 90% or more. On the other hand, if the data quality was low, the similarity with other subject areas was high, and there were few features that were clearly distinguished thematically, a meaningful high-level performance evaluation could not be derived. This study is expected to be used as basic data to suggest the possibility of using a pre-trained learning model to automatically classify the academic documents.