• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.51-56
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• 2022

It is relevant and timely for the existence and prosperity of today's tourism to build up a stock of new abilities and a set of innovations. At present, the tourism industry is experiencing a new stage in its digital transformation. The newest technologies, which are now spreading en masse and one of which is rightfully considered to be blockchain technology, enable tourists to receive tourist services directly from the producers, which not only gives the consumer the opportunity to enjoy higher quality and inexpensive products but also increases the responsibility of the producer. The article analyzes research literature on the possibility of using blockchain technology in the tourism industry. Based on an expert survey, the main problems, prospects, and advantages of the implementation of blockchain technology in the tourism industry are identified. The paper proposes and analyzes an option for the use of blockchain technology on the basis of a blockchain project with a mobile app for users and a dedicated website and public API for travel service providers.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.459-466
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• 2013

Risk exists in all construction projects and resides among the collection of subcontractors and their array of individual activities. Wherever risk resides, the interrelation of participants to one another becomes paramount for the way in which risk is measured. Inherent risk becomes recognizable and quantifiable within network schedules in the form of consuming float - the flexibility to absorb delays. Allocating, owning, valuing, and expending such float in network schedules has been debated since the inception of the critical path method itself. This research investigates the foundational element of a three-part approach that examines how float can be traded as a commodity, a concept whose promise remains unfulfilled for lack of a holistic approach. The Capital Asset Pricing Model (CAPM) of financial portfolio theory, which describes the relationship between risk and expected return of individual stocks, is explored as an analogy to quantify the inherent risk of the participants in construction projects. The inherent relationship between them and their impact on overall schedule performance, defined as schedule risk -the likelihood of failing to meet schedule plans and the effect of such failure, is matched with the use of CAPM's beta component - the risk correlation measure of an individual stock to that of the entire market - to determine parallels with respect to the inner workings and risks represented by each entity or activity within a schedule. This correlation is the initial theoretical extension that is required to identify where risk resides within construction projects, allocate and commoditize it, and achieve actual tradability.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.4
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• pp.1-12
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• 2009

An RFID application needs to collect product's information scattered over the RFID network efficiently according to the globalization of RFID applied enterprises. To be informed of the stock status of products promptly in the supply chain network, especially, it is necessary to support queries that retrieve statistical information of tagged products. Since existing RFID network does not provide these kinds of queries, however, an application should request a query to several RFID middleware systems and analyze collected data directly. This process makes an application do a heavy computation for retrieving statistical information. To solve this problem, we define a new Distributed Continuous Query that finds information of tagged products from the global RFID network and provides statistical information to RFID applications. We also propose a Distributed Continuous Query System to process the distributed continuous query efficiently. To find out the movement of products via multiple RFID systems in real time, our proposed system uses Pedigree which represents trade information of items. Our system can also reduce the cost of query processing for removing duplicated data from multiple middleware systems by using Pedigree.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.335-351
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• 2011

In order to resolve data scarcity problem related to crisis, Oh and Kim (2007) proposed to use stability oriented approach which focuses a base period of financial market, fits asymptotic stationary autoregressive model to the base period and then compares the fitted model with the current market situation. Based on such approach, they developed financial market instability index. However, since neural network, their major tool, depends on the base period too heavily, their instability index tends to suffer from inaccuracy. In this study, we consider linear asymptotic stationary autoregressive model and neural network to fit the base period and produce two instability indexes independently. Then the two indexes are combined into one integrated instability index via newly proposed combining method. It turns out that the combined instability performs reliably well.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.23-30
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• 2022

Deep learning analyzes data to discover a series of rules and anticipates the future, helping us in various ways in our lives. For example, prediction of stock prices and agricultural prices. In this research, the results of solar photovoltaic power generation accompanied by weather are analyzed through deep learning in situations where the importance of solar energy use increases, and the amount of power generation is predicted. In this research, we propose a model using LSTM(Long Short Term Memory network) that stand out in time series data prediction. And we compare LSTM's performance with CNN(Convolutional Neural Network), which is used to analyze various dimensions of data, including images, and CNN-LSTM, which combines the two models. The performance of the three models was compared by calculating the MSE, RMSE, R-Squared with the actual value of the solar photovoltaic power generation performance and the predicted value. As a result, it was found that the performance of the LSTM model was the best. Therefor, this research proposes predicting solar photovoltaic power generation using LSTM.

• Journal of the Economic Geographical Society of Korea
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• v.26 no.3
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• pp.256-273
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• 2023

This study analyzed the structural changes in the global foreign direct investment (FDI) network using stock data from the International Monetary Fund's Coordinated Direct Investment Survey (CDIS) for 2009~2021. The results showed that the COVID-19 pandemic had a negative impact on the FDI links between countries and the activities of reciprocal relationships. The United States, the Netherlands, and the United Kingdom consistently play central roles in the global FDI network. The degree centrality of China has changed significantly over time in confronting the volatile situation of the world economy. Cross-tabulation analysis revealed a significant association between FDI clusters and geographic regions. Within each cluster, the linkage structure of FDI partners of closely connected individual countries has exhibited differential characteristics as the global economic environment changes.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.1
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• pp.109-119
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• 2016

Due to the fact that it is relatively easy to transfer technology between application developers or content providers, low entry barrier in the business causes fierce competition among the venture companies in mobile service industry. Our study examines a sustainable strategies to operate a business for venture companies that are in a highly competitive technology-intensive industry. In this paper, we examine how venture firms created a network and brought synergy effects, using network theory. Korean venture firm, YelloMobile, uses unique strategies of merger and acquisition through the method of swapping equity and thereby establishing network. We contribute to expand network theory by examining three elements of network: such as network structure, network governance mechanisms, and network contents.

• Journal of Animal Environmental Science
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• v.8 no.3
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• pp.199-208
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• 2002

This paper introduces the method to make management network about milking cow farm tasks. The object of this research was to design of biological measuring system and managing network system in a livestock farm. This auto-management system provides informations about individual cows' temperature, conductivity of milk and weight for efficient management of feeding, and milking works by a micro-processor and RS -485 type serial COM. ports. And measured bio-data which are basic informations for remote raising management are saved to user PC by serial communication between the PLC and user PC. Milking cow farm is divided into three working place to each measurement work and feed. The first working place is milking station which has two thermometers, a conduct meter and a scale set. The second working place is feeding station, and the third place is cattle cage. These are combined by network system and the PLC which is used to drive network and sub-modules. Sub-modules have a micro-process to control the sensor and to interface with network. The PLC which drive network and control sequence has two serial communication port to be linked with user PC for sending the measured data and for receiving data. Above all, in this study tells the sequence operating method by the driving scenario of breeding milk cow for livestock auto-management using the PLC and network system.

• Journal of Information Processing Systems
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• v.2 no.1
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• pp.23-27
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• 2006

This paper presents a feature selection technique based on distributional differences for efficient machine learning. Initial training data consists of data including many features and a target value. We classified them into positive and negative data based on the target value. We then divided the range of the feature values into 10 intervals and calculated the distribution of the intervals in each positive and negative data. Then, we selected the features and the intervals of the features for which the distributional differences are over a certain threshold. Using the selected intervals and features, we could obtain the reduced training data. In the experiments, we will show that the reduced training data can reduce the training time of the neural network by about 40%, and we can obtain more profit on simulated stock trading using the trained functions as well.

• International Commerce and Information Review
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• v.3 no.1
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• pp.177-196
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• 2001

The advancement of information technology provides a wide range of options for the corporate to cope with new business environments. As with most other businesses, the utilization of the logistics information technology can be an instrument to enhance the competitiveness of the company. Therefore it is essential to analyze how companies utilize and verify the relationship between company business performance and the level of information technology utilization, which is the objective of this paper. The questionnaire was sent to 300 companies listed on the stock market at random The author received 176 responses of which 142 were complete and valid. According to the analysis of questionnaires, the adoption level of information technologies was dependent on the perception of top managers on the importance of information technology in business competitiveness. Furthermore the level of relation between the information technology adoption and business performance was significant. Therefore businesses need to increase the utilization of information technologies, such as establishment of logistics information system, network with other business partners in order to business logistics performance.