• 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.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.10a
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• pp.329-332
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• 2003

Aquaculture have been important role to supply food resources for mankind. However, competitive power of domestic mariculture industry was declined due to increase of labor and feed expenditures, and quantity import of low-priced livefishes from the developing underdeveloped nations in North and South East Asia. Mass production and quality enhancement can be pointed out to overcome such an industrial environment in this decade. To meet these requirement, movable mariculture base remodeling feasible vessel of chemical tanker or crude oil carrier has been proposed for more advanced mariculture management system by using deep seawater from about 200m which is sustainablely clean, nutrient-rich and cold seawater. Deep seawater can be applied for control of seawater temperature for mariculture base and cultivation phytoplankton and seaweed as feed. Besides mariculture, strategic marketing can be implemented by raw water and ice of deep seawater. Feasibility of applying deep seawater was considered after evaluating general movable mariculture base and management system.

• Korean Journal of Microbiology
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• v.49 no.3
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• pp.282-285
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• 2013

Microbial diversities in the 300 m and 500 m deep seawaters near Goseong, Gangwon Province (South Korea), were investigated. Pyrosequencing of 16S rRNA genes of marine microbes resulted in 19,474 reads from the 300 m deep seawaters, which consisted of Alphaproteobacteria (57.41%) and Gammaproteobacteria (38.85%), and 82,806 reads from the 500 m deep seawaters, which consisted of Gammaproteobacteria (99.64%) mostly. Rhodobacterales (57.31%) were dominant in the 300 m deep seawaters, but Alteromonadales (45.65%) and Oceanospirillales (34.61%) were dominant in the 500 m deep seawaters. On the bases of operational taxonomic units and diversity indexes (Shannon and Simpson), biodiversity of marine bacteria in the 500 m deep seawaters was shown to be higher than that in the 300 m deep seawaters.

• Computers and Concrete
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• v.15 no.3
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• pp.357-372
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• 2015

This study reports the test results of twelve reinforced concrete deep beams. The deep beams were tested with loads applied through and supported by columns. The main variables studied were the shear span-to-depth ratios, and the horizontal and vertical stirrups. The shear strengths can be effectively enhanced for deep beams reinforced with both horizontal and vertical stirrups. The test results indicate the shear strengths of deep beams increase with the decrease of the shear span-to-depth ratios. The normalized shear strengths of the deep beams did not increase proportionally with an increase in effective depth. An analytical method for predicting the shear strengths of deep beams is proposed in this study. The shear strengths predicted by the proposed method and the strut-and-tie model of the ACI Code are compared with available test results. The comparison shows the proposed method can predict the shear strengths of reinforced concrete deep beams more accurately than the strut-and-tie model of the ACI Code.

• Structural Engineering and Mechanics
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• v.19 no.4
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• pp.361-379
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• 2005

Reinforced concrete deep beams have useful applications in tall buildings and foundations. Over the past two decades, numerous design models for deep beams were suggested. However even the latest design manuals still offer little insight into the design of deep beams in particular when complexities exist in the beams like web openings. A method commonly suggested for the design of deep beams with openings is the strut-and-tie model which is primarily used to represent the actual load transfer mechanism in a structural concrete member under ultimate load. In the present study, the development of the strut-and-tie model is transformed to the topology optimization problem of continuum structures. During the optimization process, both the stress and displacement constraints are satisfied and the performance of progressive topologies is evaluated. The influences on the strut-and-tie model in relation to different size, location and number of openings, as well as different loading and support conditions in deep beams are examined in some detail. In all, eleven deep beams with web openings are optimized and compared in nine groups. The optimal strut-and-tie models achieved are also compared with published experimental crack patterns. Numerical results have shown to confirm the experimental observations and to efficiently represent the load transfer mechanism in concrete deep beams with openings under ultimate load.

• PNF and Movement
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• v.10 no.2
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• pp.25-31
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• 2012

Purpose : The purpose of this study is to figure out the effects of Open Laser Microdiscectomy(OLM) on deep muscles by comparing multifidus and longissimus muscle size (cross section area; CSA) of pre and post operation. Methods : The subjects consisted of forty patients who had OLM. The data were analyzed with paired t-test comparing left and right deep muscle CSA of pre and post-operation, and both the deep muscle CSA of pre and post-operation, using SPSS ver. 15.0 program. Results : The results of this study showed a significant difference in deep muscle size (CSA) between pre and post operation (p<.05). Although there was not a meaningful difference between right and left deep muscle size (CSA) in pre operation (p>.05), there was a significant difference between both of them in post operation (p<.05). Conclusion : Therefore we made the conclusion that the operation causes decrease of muscle tone in deep muscles and muscle imbalance by causing muscle atrophy in the lumbar deep muscle after the operation.

• Journal of Distribution Science
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• v.20 no.11
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• pp.121-129
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• 2022

Purpose: Artificial intelligence is widely used, particularly in the popular neural network theory called Deep learning. The improvement of computing speed and capability expedited the progress of Deep learning applications. The application of Deep learning in education has various effects and possibilities in creating and managing educational content and services that can replace human cognitive activity. Among Deep learning, Deep fake technology is used to combine and synchronize human faces with voices. This paper will show how to develop e-Learning content videos using those technologies and open-source tools. Research design, data, and methodology: This paper proposes 4 step development process, which is presented step by step on the Google Collab environment with source codes. This technology can produce various video styles. The advantage of this technology is that the characters of the video can be extended to any historical figures, celebrities, or even movie heroes producing immersive videos. Results: Prototypes for each case are also designed, developed, presented, and shared on YouTube for each specific case development. Conclusions: The method and process of creating e-learning video contents from the image, video, and audio files using Deep fake open-source technology was successfully implemented.

• The Korean Journal of Food And Nutrition
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• v.21 no.4
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• pp.592-598
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• 2008

Deep sea water is found more than 200 m under the surface. As no sunlight reaches, no photosynthesis takes place, and it has very little organic matter or bacteria. In addition, deep sea water maintains a consistently low temperature throughout the year, and it does not mix with the water found closer to the surface, which means that its cleanliness is preserved. It is a long-term mature sea water resource that is rich in minerals. This paper examined the physical characteristics and the uses of deep sea water, a subject that has been attracting a great deal of public attention recently, together with the current status of domestic research into it and the direction of research in the USA and Japan, focusing on the existing literature. The aim of this paper was to provide are source to researchers in the field. Since the 1970s, scientists around the world have recognized the importance of deep sea water, and have been conducting research into it. In the USA, deep sea water has been researched with the view of its application to cooling, alternative energy, farming, and the development of new materials. In Japan, about 10 local self-governing bodies are currently promoting research and business relating to deep sea water, which has resulted in a number of products that have been released to the market. In Korea, the ministry of land transport and marine affairs has been studying deep sea water since 2000, and full-scale national R&D projects have been performed by 24 organizations, including KORDI, through industrial/academic cooperation. Large companies are participating in deep sea water research projects in several ways. A study of data foundusing NDSL relating to domestic studies of deep sea water found 50 theses, 177 domestic patents, 6 analyses, 2 reports, and 2 etc. in other areas.

• Structural Engineering and Mechanics
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• v.57 no.4
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• pp.657-680
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• 2016

Reinforced concrete (RC) deep beams are structural members that predominantly fail in shear. Therefore, determining the shear strength of these types of beams is very important. The strut-and-tie method is commonly used to design deep beams, and this method has been adopted in many building codes (ACI318-14, Eurocode 2-2004, CSA A23.3-2004). In this study, the efficiency of artificial neural networks (ANNs) in predicting the shear strength of RC deep beams is investigated as a different approach to the strut-and-tie method. An ANN model was developed using experimental data for 214 normal and high-strength concrete deep beams from an existing literature database. Seven different input parameters affecting the shear strength of the RC deep beams were selected to create the ANN structure. Each parameter was arranged as an input vector and a corresponding output vector that includes the shear strength of the RC deep beam. The ANN model was trained and tested using a multi-layered back-propagation method. The most convenient ANN algorithm was determined as trainGDX. Additionally, the results in the existing literature and the accuracy of the strut-and-tie model in ACI318-14 in predicting the shear strength of the RC deep beams were investigated using the same test data. The study shows that the ANN model provides acceptable predictions of the ultimate shear strength of RC deep beams (maximum $R^2{\approx}0.97$). Additionally, the ANN model is shown to provide more accurate predictions of the shear capacity than all the other computed methods in this study. The ACI318-14-STM method was very conservative, as expected. Moreover, the study shows that the proposed ANN model predicts the shear strengths of RC deep beams better than does the strut-and-tie model approaches.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.49 no.5
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• pp.363-367
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• 2004

The deep irrigation of rice plants brings about some beneficial effects such as reduced tiller production which results in the formation of bigger panicles, prevention of chilling injury, reduced weed growth, etc. The present study was carried out to examine the involvement of ethylene in the suppression of tiller production due to deep water irrigation in rice (cv. Dongjinbyeo). The ethylene production was induced in leaf sheath within 24 hours after the deep water irrigation and has increased even until 30 days after the treatment, recording 4.5-fold increase as compared to the shallow-irrigated rice plants. In the deep water irrigated rice plants, ethylene was accumulated to a high concentration in the air space of submerged leaf sheath as the irrigated water deterred the diffusion of ethylene out of the leaf sheath and ethylene biosynthesis was accelerated by the deep irrigation as well. The ethylene concentration recorded 35-fold increase in the deep-irrigated rice plants for 35 days. The tiller production was reduced significantly by the deep irrigation with water, the tiller bud, especially tertiary tiller bud differentiation being suppressed by the deepwater irrigation treatment, whereas the rice plants deep-irrigated with solutions containing $10^{-5}$ M or $10^{-6}$ M silver thiosulfate (STS), an action inhibitor of ethylene, showed the same or even higher production of tillers than those irrigated shallowly with water. This implies that the ethylene is closely linked with the suppression of tiller production due to deep water irrigation. In conclusion, ethylene, which was induced by hypoxic stress and accumulated in the leaf sheath due to submergence, played a key role in suppressing the tiller production of the deepwater irrigated rice.