• Title/Summary/Keyword: Classifying system

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A Study on Effect of Organizational Performance of SME in Gumi by R&D Learning Organization (R&D 학습조직이 구미지역 중소기업의 조직성과에 미치는 영향에 대한 연구)

  • Ahn, Joong Min;Shin, Tae Shik;Kim, Tae Sung
    • Journal of Digital Convergence
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    • v.14 no.1
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    • pp.163-170
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    • 2016
  • The purpose of this study is to determine the effect of R&D learning organization activities of domestic small and medium-sized businesses on economic/technological results. This study, through investigation on preceding studies of domestic and foreign learning organization researchers, examined the definition, characteristics, and deteriorating factors of learning organization, and learned dependent variables, which are the definition of organizational performance, and relationship between learning organization and organizational performance. Then it performed a survey targeting small- and medium-sized businesses in Gumi and grasped the relationship between R&D capability (study planning, vision goal adequacy, project management, commercialization of technologies) and learning organization capability (creation of constant learning opportunities, knowledge sharing and utilizing system, strategic learning leadership) by classifying them to seven independent variables, using regression analysis. Because this study grasped the effect of R&D learning organization activities on organizational performance, it is expected to promote forming R&D learning organization for active R&D activities and contribute to enhancing small and medium-sized businesses' recognition on the need of R&D activities.

Identification of Psychrotrophic Lactic Acid Bacteria Isolated from Kimchi (김치에서 분리한 저온성 젖산균의 동정)

  • So, Myung-Hwan;Kim, Young-Bae
    • Korean Journal of Food Science and Technology
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    • v.27 no.4
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    • pp.495-505
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    • 1995
  • The purpose of this study was to identify the psychrotrophic lactic acid bacteria isolated from kimchi, a Korean traditional fermented vegetable food. Thirty isolates of psychrotrophic lactic acid bacteria were isolated randomly from kimchi-A and kimchi-B which were fermented at $5{\sim}7^{\circ}C$ for 20 days and 50 days, respectively. Among 30 isolates of lactic acid bacteria isolated from kimchi-A, 14 isolates were identified as Leuconostoc mesenteroides subsp. mesenteroides, 12 as Leuconostoc mesenteroides subsp. dextranicum and 4 as Lactobacillus bavaricus. Among 30 isolates isolated from kimchi-B, 20 isolates were identified as Lactobacillus bavaricus, 3 as Leuconostoc mesenteroides subsp. mesenteroides, 3 as Leuconostoc lactis, 2 as Leuconostoc paramesenteroides and 2 as Lactobacillus homohiochii. Though these strains were identified as above, there were many strains whose sugar fermenting patterns and $NH_3$ producing ability from arginine were inconsistent with those described in Bergey's Manual of Systematic Bacteriology, and some strains identified as Leuconostoc mesenteroides subsp. mesenteroides and Leuconostoc mesenteroides subsp. dextranicum even disclosed such contradictions as the comparisons of sugar fermenting patterns between the strains of different subspecies were much more coincident than those between the same subspecies. As there were difficulties in classifying these psychrotrophic lactic acid bacteria according to the current taxonomic system, further studies were needed to solve these problems.

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A Study on Strategic Groups of Program Providers(PP) and the Performance in Korea (국내 방송채널사용사업자(PP)의 전략집단과 성과에 관한 연구)

  • Ryo, Hyon-Chol;Kim, Jai-Beom;Lee, Sahang-Shik
    • Korean journal of communication and information
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    • v.46
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    • pp.387-419
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    • 2009
  • The concept of strategic group is defined as an aggregate of corporations utilizing similar strategies with similar resources. It becomes a kinds of contact point in the middle of corporation and industry between the industrial organization theory and the strategic management theory. This study tried to apply the strategic group model, which has been a main theory in the management studies, to program providing industry in Korea. This study shed lights upon research problems such as number of strategic groups, differences of strategic variables among the groups, finally differential performances according to strategic groups. 40 commercial broadcasting companies were analyzed to find answers. 9 strategic groups were drawn as a result of cluster analysis. Major variables which contribute to making groups were operating efficiency(4.05), pricing(3.83), size(number of system operator, 3.56), reliance on license revenue(2.58), horizontal integration(number of sister networks, 2.16) in order. An analysis of variance between performance variables has shown statistical significance regarding total net revenue per subscriber, however, insignificances statistically in regards to ratio of operating profit to net sales, cash Abstracts 687 flow ratio. Some studies in the past insisted that history variable played an important role to classifying strategic groups. However, this study found that the history didn't exert significant influence on either the group classification itself or performance.

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Development of Unique Naming Algorithm for 3D Straight Bridge Model Using Object Identification (3차원 직선교 모델 객체의 인식을 통한 고유 명칭부여 알고리즘 개발)

  • Park, Junwon;Park, Sang Il;Kim, Bong-Geun;Yoon, Young-Cheol;Lee, Sang-Ho
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.27 no.6
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    • pp.557-564
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    • 2014
  • In this study, we present an algorithm that conducts an unique naming process for the bridge object through the solid object identification focused on 3D straight bridge model. For the recognition of 3D objects, the numerical algorithm utilizes centroid point, and solid object on the local coordination system. It classifies the object feature set by classifying the objects and members based on the bridge direction. By doing so, unique names, which contain the information about span, members and order of the object, were determined and the suitability of this naming algorithm was examined through a truss bridge model and a bridge model with different coordinate systems. Also, the naming process based on the object feature set was carried out for the real 3D bridge model and then was applied to the module on local server and mobile device for real bridge inspection work. From the comparison of the developed naming algorithm based on object identification and the conventional one based on field inspection, it was shown that the conventional field inspection work can be effectively improved.

An Application of Artificial Intelligence System for Accuracy Improvement in Classification of Remotely Sensed Images (원격탐사 영상의 분류정확도 향상을 위한 인공지능형 시스템의 적용)

  • 양인태;한성만;박재국
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.20 no.1
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    • pp.21-31
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    • 2002
  • This study applied each Neural Networks theory and Fuzzy Set theory to improve accuracy in remotely sensed images. Remotely sensed data have been used to map land cover. The accuracy is dependent on a range of factors related to the data set and methods used. Thus, the accuracy of maps derived from conventional supervised image classification techniques is a function of factors related to the training, allocation, and testing stages of the classification. Conventional image classification techniques assume that all the pixels within the image are pure. That is, that they represent an area of homogeneous cover of a single land-cover class. But, this assumption is often untenable with pixels of mixed land-cover composition abundant in an image. Mixed pixels are a major problem in land-cover mapping applications. For each pixel, the strengths of class membership derived in the classification may be related to its land-cover composition. Fuzzy classification techniques are the concept of a pixel having a degree of membership to all classes is fundamental to fuzzy-sets-based techniques. A major problem with the fuzzy-sets and probabilistic methods is that they are slow and computational demanding. For analyzing large data sets and rapid processing, alterative techniques are required. One particularly attractive approach is the use of artificial neural networks. These are non-parametric techniques which have been shown to generally be capable of classifying data as or more accurately than conventional classifiers. An artificial neural networks, once trained, may classify data extremely rapidly as the classification process may be reduced to the solution of a large number of extremely simple calculations which may be performed in parallel.

Discrimination of Domestic Rice Cultivars by Capillary Electrophoresis (Capillary Electrophoresis를 이용한 국내산 쌀의 품종 판별)

  • Rhyu, Mee-Ra;Kim, Eun-Young;Ahn, Mee-Ok;Kim, Sang-Sook
    • Korean Journal of Food Science and Technology
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    • v.30 no.6
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    • pp.1252-1258
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    • 1998
  • Capillary electrophoresis (CE) with rice proteins was used to discriminate 10 domestic rice cultivars in less than 25 min. Most cultivars were differentiated quickly and easily using P-ACN buffer system. CE of rice prolamins allowed classifying ten varieties of Korean rice into three groups. Peak h was characteristic peak for Dongjinbyeo, Gaehwabyeo and Yongnambyeo which were classified into the group of Dongjinbyeo. Chuchungbyeo, Odaebyeo, Mangeumbyeo and Bonggwangbyeo easily differentiated from the group of Dongjinbyeo by the absence of peak h which were classified into the group of Chuchungbyeo. Peak g typical for Illpumbyeo, Hwaseungbyeo and Hwayoungbyeo accounted for 70% of total peak area. They belong to the group of Illpumbyeo. Some cultivars showed specific peak patterns among ten cultivars, Illpumbyeo was differentiated from others by several peaks between peak c and peak f, and the peak d was apparently detected in Odaebyeo not in others. Other minor differences were also found within each group. The result of the study showed that CE has potential for discrimination of rice cultivars. It also possesses the inherent advantages such as low mass requirements, fast seperations, and quantitative analysis through on-capillary UV detection.

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A Study on the Traffic Stream and Navigational Characteristics at the Adjacent Sea Area of Busan Central Wharf (부산 중앙부두 주변 해역의 교통 흐름 및 통항 특성에 관한 연구)

  • Kim Se-Won;Lee Yun-Sok;Park Young-Soo;Kim Jong-Sung;Yun Gwi-Ho;Kim Dae-Hee
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • 2005.10a
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    • pp.103-109
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    • 2005
  • At the adjacent sea area of Busan Central Wharf, a variety of vessels, such as middle-large passenger ships, small fast sailing ships, container ships, cargo ships and working ships as well as small miscellaneous vessels are freely sailing comparatively without special steering and sailing Rules and marine traffic control because exclusive wharfs in accord with their purpose and use have been arranged in each wharf. In this research, we analyzed traffic stream and navigational characteristics of main traffic route based on statistics and distribution of tracks by ship's type and tonnage of the passing vessels after conducting marine traffic survey twice using exclusive software by targeting the sea area during the period of time. We examined the traffic safety of the passing vessels by classifying the sea area by each function based on the analysis about this traffic situation, and analyzing the effect by designating 'Buknea passage'. We also studied the plan for the effective rearrangement of Central Wharf considering basically the traffic safety oif arrival and departure in a point if view of navigators.

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Cost-Benefit Analysis Method for Ageing Equipment of Chemical Plants Using Risk Assessment (위험성평가를 이용한 노후설비에 대한 비용 편익분석 방법)

  • Jung, Soomin;Jung, Changmo;Kang, Seok-Min;Chae, Seungbeen;Kang, Seung-Gyun;Ko, Jae Wook
    • Journal of the Korean Institute of Gas
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    • v.24 no.4
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    • pp.84-92
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    • 2020
  • Most facilities in chemical plants operate in environments that are outside the range of temperature and pressure that can be encountered on a daily basis, and are vulnerable to aging due to these stresses and environmental conditions. The facilities exposed to these conditions are not only likely to fail due to cumulative damage, but also lead to accidents if maintenance and replacement are not performed.Recommendation guidelines called risk-based inspection are widely used around the world-wide. However, limits exist for facilities that have already elapsed for a certain. As a result of the survey on the aging of Ulsan industrial complex in Korea, which carries out proper inspection, many of the facilities have been used for 20 years. Also, most of the facilities where the accident occurred have been in operation for more than 20 years. Therefore, this study suggested criteria for classifying devices that have exceeded a certain period of use as obsolete facilities. In addition, quantitative risk assessment was conducted. The safety investment method using the cost-benefit analysis method was proposed in order to calculate the loss cost and reduce the risk by expressing the risks of the corresponding aged facility as an Economic index. By utilizing the method of cost-benefit analysis of old facilities using the quantitative risk assessment presented in this study, it can be expected to improve the performance and life of old facilities, improve production efficiency and reliability of the system of facilities, change the recognition of safety management costs, increase employee stability, and reduce loss costs.

A Classification and Extraction Method of Object Structure Patterns for Framework Hotspot Testing (프레임워크 가변부위 시험을 위한 객체 구조 패턴의 분류 및 추출 방법)

  • Kim, Jang-Rae;Jeon, Tae-Woong
    • Journal of KIISE:Software and Applications
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    • v.29 no.7
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    • pp.465-475
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    • 2002
  • An object-oriented framework supports efficient component-based software development by providing a flexible architecture that can be decomposed into easily modifiable and composable classes. Object-oriented frameworks require thorough testing as they are intended to be reused repeatedly In developing numerous applications. Furthermore, additional testing is needed each time the framework is modified and extended for reuse. To test a framework, it must be instantiated into a complete, executable system. It is, however, practically impossible to test a framework exhaustively against all kinds of framework instantiations, as possible systems into which a framework can be configured are infinitely diverse. If we can classify possible configurations of a framework into a finite number of groups so that all configurations of a group have the same structural or behavioral characteristics, we can effectively cover all significant test cases for the framework testing by choosing a representative configuration from each group. This paper proposes a systematic method of classifying object structures of a framework hotspot and extracting structural test patterns from them. This paper also presents how we can select an instance of object structure from each extracted test pattern for use in the frameworks hotspot testing. This method is useful for selection of optimal test cases and systematic construction of executable test target.

Classifying a Strength of Dependency between classes by using Software Metrics and Machine Learning in Object-Oriented System (기계학습과 품질 메트릭을 활용한 객체간 링크결합강도 분류에 관한 연구)

  • Jung, Sungkyun;Ahn, Jaegyoon;Yeu, Yunku;Park, Sanghyun
    • KIPS Transactions on Software and Data Engineering
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    • v.2 no.10
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    • pp.651-660
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    • 2013
  • Object oriented design brought up improvement of productivity and software quality by adopting some concepts such as inheritance and encapsulation. However, both the number of software's classes and object couplings are increasing as the software volume is becoming larger. The object coupling between classes is closely related with software complexity, and high complexity causes decreasing software quality. In order to solve the object coupling issue, IT-field researchers adopt a component based development and software quality metrics. The component based development requires explicit representation of dependencies between classes and the software quality metrics evaluates quality of software. As part of the research, we intend to gain a basic data that will be used on decomposing software. We focused on properties of the linkage between classes rather than previous studies evaluated and accumulated the qualities of individual classes. Our method exploits machine learning technique to analyze the properties of linkage and predict the strength of dependency between classes, as a new perspective on analyzing software property.