• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.1
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• pp.31-38
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• 2005

Importance of road that is country base equipment is occupying fair part. Therefore, establishment of road and maintenance expense for road management are increasing continuously. These problem can manage efficiently through data model construction that take advantage of framework data. But, because of difference of method of study in research institution, framework data research was constructed being overlapped until current. This is because framework data research was no access of application side. Therefore, National Geographic Information Institute presented subject framework data model guide through framework data model standardization business. This research constructed road management data model that take advantage of traffic framework data. Therefore, we can check equal data construction and reduce expense accordingly. Also, because there are not data model development instances by framework data model, it is difficult that judge whether is suitable to apply framework data model guide. Hence, in this study, the extended road management data medel and the suitability of framework data is presented.

• Safety and Health at Work
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• v.8 no.1
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• pp.29-35
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• 2017

Background: Despite our knowledge of the causes of cancer, millions of workers are involuntarily exposed to a wide range of known and suspected carcinogens in the workplace. To address this issue from a policy perspective, we developed a policy framework based on a prospective health policy analysis. Use of the framework was demonstrated for developing policies to prevent cancers associated with diesel engine exhaust (DEE), asbestos, and shift work, three occupational carcinogens with global reach and large cancer impact. Methods: An environmental scan of existing prospective health policy analyses was conducted to select and describe our framework parameters. These parameters were augmented by considerations unique to occupational cancer. Policy-related resources, predominantly from Canada, were used to demonstrate how the framework can be applied to cancers associated with DEE, asbestos, and shift work. Results: The parameters of the framework were: problem statement, context, jurisdictional evidence, primary prevention policy options, and key policy players and their attributes. Applying the framework to the three selected carcinogens illustrated multiple avenues for primary prevention, including establishing an occupational exposure limit for DEE, banning asbestos, and improving shift schedules. The framework emphasized the need for leadership by employers and government. Conclusion: To our knowledge, this is the first proposal for a comprehensive policy framework dedicated to the primary prevention of occupational cancer. The framework can be adapted and applied by key policy players in Canada and other countries as a guide of what parameters to consider when developing policies to protect workers' health.

• The Journal of Korea Robotics Society
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• v.3 no.3
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• pp.255-261
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• 2008

This paper describes a network framework that support network based humanoid. The framework utilizes middleware such as CORBA (ACE/TAO) that provides PnP capability for network based humanoid. The network framework transfers data gathered from a network based humanoid to a processing group that is distributed on a network. The data types are video stream, audio stream and control data. Also, the network framework transfers service data produced by the processing group to the network based humanoid. By using this network framework, the network based humanoid can provide high quality of intelligent services to user.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.480-483
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• 2003

We propose a new framework that aims at multi-purpose image recognition, a difficult task for the conventional rule-based systems. This framework is farmed based on the idea of computer-based learning algorithm. In this research, we introduce the new functions of an additional learning and a knowledge reconstruction on the Fuzzy Inference Neural Network (FINN) (1) to enable the system to accommodate new objects and enhance the accuracy as necessary. We examine the capability of the proposed framework using two examples. The first one is the capital letter recognition task from UCI machine learning repository to estimate the effectiveness of the framework itself, Even though the whole training data was not given in advance, the proposed framework operated with a small loss of accuracy by introducing functions of the additional learning and the knowledge reconstruction. The other is the scenery image recognition. We confirmed that the proposed framework could recognize images with high accuracy and accommodate new object recursively.

• Hwankyungkyoyuk
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• v.14 no.2
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• pp.51-67
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• 2001

In this study we developed a framework to evaluate environmental education programs through lived experience in nature and the framework was applied to a neat case,'2001 Green Camp'. The framework consists of 4 items; goals and objectives, instructional planning, teaching and learning, methods and learning operation and environment. Learning outcomes such as changes to the levels of knowledge, attitude, participation and environmental sensitivity are not included in the evaluation framework but evaluated through direct questions to students. Two researchers observed and evaluated programs with the framework. This study reflected various perspectives of researchers, teachers, students and staff members.

• The Journal of Society for e-Business Studies
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• v.10 no.1
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• pp.81-101
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• 2005

Dynamic changes of the recent business environment draw more attention to Business Process Management (BPM). In this research, we derive a comprehensive BPM framework, define the functional and technical requirements, and classify the requirements into the framework. Based on the framework we suggest a standard procedure for BPM planning and implementation along with critical considerations. In addition, we propose an evaluation framework, which includes a comprehensive checklist and the evaluation method. The proposed evaluation framework can be used as an evaluator's useful reference.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5
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• pp.1377-1387
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• 2000

Many companies are investing in the development of the PDM(Prodct Data Management) system to improve the productivities of manufacturing since people believe that PDM technology can give a new solution from planning to development. As the requirements of PDM grows, so many industries sped their own budget on developing common requirements and functionalities. In this paper, we describe the framework for a PDM application to promote reuse in a PDM system development area. However, developing the framework is not easy. In this paper, the current state and results of our development are described: 1) the phases of developing our framework, 2) abstruction strategies, 3) programing model based on repository. Although frameworks improve software reuse, frameworks are so large and complex that application developers require to understand framework in order to customize the framework. To support the customization of framework, development environment is developed, also.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.22 no.1
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• pp.18-22
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• 2011

Laryngeal framework surgery comprises medialization laryngoplasty and arytenoid adduction. Since their introduction in the 1970s, these procedures have become standard treatments for vocal fold paralysis and glottal incompetence. However, frequency of laryngeal framework surgery is conjectured to relatively decrease along with the introduction of injection laryngoplasty. In this manuscript, indications for laryngeal framework surgery were highlighted in contrast to those of injection laryngoplasty. The authors introduced the basic concepts and principles as well as surgical techniques of laryngeal framework surgery. Even though the incidence of major and/or minor complications after laryngeal framework surgery is not high, surgeons should be well aware of its possible complications and they should be familiar with tips and know-how to avoid or cope with complications.

• Journal of Information Technology Applications and Management
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• v.14 no.3
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• pp.151-168
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• 2007

RFID is an emerging technology and rapidly applied to various industries due to its high-tech characteristic and convenience. Although RFID provides valuable benefits. it might also generate serious privacy problems. Previous studies show that privacy issues should be incorporated in developing RFID systems and more detailed privacy protection methods. However. they just provide basic concept, rough guideline. and simple architecture about RFID privacy protection. Industry needs more structured framework and detailed systematic process to incorporate privacy issues into the RFID system. The purpose of this paper is to develop a framework and detailed process design of RFID privacy protection issues in retail industries. A framework is developed based on individual sensitivity concept, RFID contents, and interface with EPC global standard. Case study is applied to validate the framework and it turns out to be useful. It is expected that the proposed framework and process design would provide more systematic guide lines to solving RFID privacy problems.