• (The) Research of the performance art and culture
• /
• no.32
• /
• pp.371-397
• /
• 2016

The function of the Duhak nongak(農樂) in Jecheon consist of Ceremony, Labor, and Entertainment. Before the commemorative rites for village god village people beat a small gong. This is a evidence of the drive away evil spirits in New Year's Eve of the lunar year. Before harden house site village peoples beat musical instrument noisily. In order to press hard the god of the earth they play musical instrument. Musical instrument not a simple musical instrument, but a purify and wish tool. When weeding a rice paddy Durae organized in Sangpung village. Durae are less effective than communal sharing of labor in weeding effect. Nonetheless, Nongak carried out core function in Durae. In order to participate Nonngak concours, peoples made a Pangut. From independence to 1970s people combine traditional nongak and Pangut. Since then village people played Pangut. From 1990s Pangut was played by Duhak Nonngak Preservation Association. Through the participation Nonngak concours, Duhak nongak was regrouped. Finally this nongak prepared Taeguk-jin, Snail-jin, Cross-jin, Sabang-jin, Sanggyeonrae Bans anggyeonrae-jin, Palbang-jin, Rope making and individual play. Duhak Nonngak promptly met the needs of the times. So, they could play Pangut. The feature of the Duhak Nonngak is that active cognition, highly skilled musician a patron and scouting a competent leader.

• Korean Journal of Heritage: History & Science
• /
• v.44 no.4
• /
• pp.56-69
• /
• 2011

A Gamsil is an ritual instrument which enshrine the ancestral tablets of four lines from late great-great-grandfather and grandmother to late father and mother. This has their soul. Juja's "Garye" informs its structure pattern. It is placed in Sadang and takes the form of a perfectly square box. It partitions four rooms and enshrine an ancestral tablet room by room. They get the ancestral tablets line up from west to east. And they also enshrine the ancestral tablets without his descendant together in Gamsil. A Gamsil is a space to enshrine the ancestral tablets and to place an order within a family. A social status and A family economic power become a standard in making and managing Sadang. Kingdom in Joseon period limited the ancestral tablets considering of his degree of official rank which descendant enshrined. A official servant can be stable economically in getting a stipend and build sadang in the house. While household a little in economic enshrine the ancestral tablets at a Gamsil placed at the a space of private home. His personal circumstances make size and pattern, place change in relation to Gamsil. A Gamsil looks like house in structure and pattern. It has the immortality of the soul. And it changed from a table size to a ancestral tablet size. This Gamsil is comfortable to move and is made considering of the width and height of household. The transition of Gamsil means institutions is in close to a family economic power in social change. Kingdom in Joseon early period makes a policy of a Gamyo's build and an ancestral enshrine in basement of Juja's "Garye". The transition of Gamsil gave a common people limitted socially and institutionally the service of late four lines. Most of people enshrine the ancestral tablets of their four lines in approaching of in the late of Joseon Dinasty. They compromise on their reality and cause. The transition of Gamsil implied many different things in social ; the authenticity search and a dignity expression of his family, the foundation for the diffusion of an ancestral service, a space sharing with ancestor and descendent, the increment of a family economic power etc.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
• /
• v.19
• /
• pp.224-242
• /
• 2003

The author believes that the main task of study in international trade usage and practice is the management of transactional risks involved in international sale of goods. They are foreign exchange risks, transportation risks, credit risk, risk of miscommunication, etc. In most cases, these risks are more serious and enormous than those involved in domestic sales. Historically, the merchant adventurers organized the voyage abroad, secured trade finance, and went around the ocean with their own or consigned cargo until around the $mid-19^{th}$ century. They did business faceto-face at the trade fair or the open port where they maintained the local offices, so-called "Trading House"(商館). Thererfore, the transactional risks might have been one-sided either with the seller or the buyer. The bottomry seemed a typical arrangement for risk sharing among the interested parties to the adventure. In this way, such organizational arrangements coped with or bore the transactional risks. With the advent of ocean liner services and wireless communication across the national border in the $19^{th}$ century, the business of merchant adventurers developed toward the clear division of labor; sales by mercantile agents, and ocean transportation by the steam ship companies. The international banking helped the process to be accelerated. Then, bills of lading backed up by the statute made it possible to conduct documentary sales with a foreign partner in different country. Thus, FOB terms including ocean freight and CIF terms emerged gradually as standard trade terms in which transactional risks were allocated through negotiation between the seller and the buyer located in different countries. Both of them did not have to go abroad with their cargo. Instead, documentation in compliance with the terms of the contract(plus an L/C in some cases) must by 'strictly' fulfilled. In other words, the set of contractual documents must be tendered in advance of the arrival of the goods at port of discharge. Trust or reliance is placed on such contractual paper documents. However, the container transport services introduced as international intermodal transport since the late 1960s frequently caused the earlier arrival of the goods at the destination before the presentation of the set of paper documents, which may take 5 to 10% of the amount of transaction. In addition, the size of the container vessel required the speedy transport documentation before sailing from the port of loading. In these circumstances, computerized processing of transport related documents became essential for inexpensive transaction cost and uninterrupted distribution of the goods. Such computerization does not stop at the phase of transportation but extends to cover the whole process of international trade, transforming the documentary sales into less-paper trade and further into paperless trade, i.e., EDI or E-Commerce. Now we face the other side of the coin, which is data security and paperless transfer of legal rights and obligations. Unfortunately, these issues are not effectively covered by a set of contracts only. Obviously, EDI or E-Commerce is based on the common business process and harmonized system of various data codes as well as the standard message formats. This essential feature of E-Commerce needs effective coordination of different divisions of business and tight control over credit arrangements in addition to the standard contract of sales. In a few word, information does not alway invite "trust". Credit flows from people, or close organizational tie-ups. It is our common understanding that, without well-orchestrated organizational arrangements made by leading companies, E-Commerce does not work well for paperless trade. With such arrangements well in place, participating E-business members do not need to seriously care for credit risk. Finally, it is also clear that E-International Commerce must be linked up with a set of government EDIs such as NACCS, Port EDI, JETRAS, etc, in Japan. Therefore, there is still a long way before us to go for E-Commerce in practice, not on the top of information manager's desk.

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