• Journal of Technology Innovation
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• v.22 no.3
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• pp.101-133
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• 2014

Climate change and low-carbon consumer movement is demanding proper response around the world while rising oil price increases consumers' needs for green car. As a preliminary study to establish an industrial platform for green car and bring out corporate strategies, this article aims to propose an academic research framework by using various methodologies including conceptual/mathematical modeling, system dynamics, and ABM from different angles. First, an analysis framework for the industrial platform was introduced to analyze green car cases, required elements were proposed, and econometrics was applied to build a basic model related to green platform (two-sided market). Also, to analyze from a dynamic perspective, a system dynamics model was applied to green car environment to build a system dynamics analysis model that is applicable to particular green car industry analysis. Lastly, an agent based model was used to study the way to activate the hybrid car market in Korea from individual consumers' perspective. Based on the result, vehicle policies that are either being enforced or planned to be enforced in the Korean HEV market can be analyzed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.08a
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• pp.14-21
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• 2004

It has already been two decades or more ever since the shipping and its related industries began to mature in advanced shipping countries. During that period, such countries have made various attempts for the survival of the industries. The advent of the so-called flags of convenience in a big way for the purpose of replacing crew members of their own expensive seamen by those of developing countries and the emergence of ship management companies, which are literally engaged in the management of ships, are the results of such movements. Some countries have been making efforts, as measures for the continued existence of the maritime industries, to create new marine-related businesses without regard to the traditional concepts of the industries. The movement toward the restructuring of a maritime society in Norway is well known as a typical example of such endeavors. The business of marine consultancy relating to maritime safety management field in our country is also a business that came into existence in such a stream toward the revitalization of the maritime society. In this paper, as well as placing in focus the current picture and problems of marine industries in our country, we would like to present approaches to tackle these problems employed by advanced industrial nations in the West, that is, moves toward the revival of maritime communities. Next, we propose, as one of the answers to solve such problems, the further development of a consulting business which takes advantage of simulators. Lastly, we show specific examples of application of a simulator to the consulting business, while commenting on the effects of its use.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.157-175
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• 2022

At this moment, a paradigm shift is taking place across all sectors of society for the transition movements to the digital economy. Various movements are taking place in the global agricultural industry to achieve innovative growth using big data which is a key resource of the 4th industrial revolution. Although the government is making various attempts to promote the use of big data, the movement of the agricultural industry as a key player in the use of big data, is still insufficient. Therefore, in this study, effects of performance expectations, effort expectations, social impact, facilitation conditions, based on the Unified Theory of Acceptance and Use of Technology(UTAUT), and innovation tendencies on the acceptance intention of big data were analyzed using the economic and practical benefits that can be obtained from the use of big data for agricultural-related companies as moderating variables. 333 questionnaires collected from agricultural-related companies were used for empirical analysis. The analysis results using SPSS v22.0 and Process macro v3.4 were found to have a significant positive (+) effect on the intention to accept big data by effort expectations, social impact, facilitation conditions, and innovation tendencies. However, it was found that the effect of performance expectations on acceptance intention was insignificant, with social impact having the greatest influence on acceptance intention and innovation tendency the least. Moderating effects of economic benefit and practical benefit between effort expectation and acceptance intention, moderating effect of practical benefit between social impact and acceptance intention, and moderating effect of economic benefit and practical benefit between facilitation condition and acceptance intention were found to be significant. On the other hand, it was found that economic benefits and practical benefits did not moderate the magnitude of the influence of performance expectations and innovation tendency on acceptance intention. These results suggest the following implications. First, in order to promote the use of big data by companies, the government needs to establish a policy to support the use of big data tailored to companies. Significant results can only be achieved when corporate members form a correct understanding and consensus on the use of big data. Second, it is necessary to establish and implement a platform specialized for agricultural data which can support standardized linkage between diverse agricultural big data, and support for a unified path for data access. Building such a platform will be able to advance the industry by forming an independent cooperative relationship between companies. Finally, the limitations of this study and follow-up tasks are presented.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Industry Promotion Research
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• v.5 no.3
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• pp.11-22
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• 2020

Recently, developed countries have been suffering from a weakening sense of community due to low birthrate, aging population, rapid population movement, rapid urbanization, and industrialization. As a result, participation in local autonomy of residents in advanced countries such as the U.S. and Japan is forming community organizations at the regional level. The purpose of this study is to study the legal system of American and Japanese village enterprises. We would also like to analyze the legal system of village enterprises in the United States and Japan and examine the examples of the legal system of village enterprises in the United States and Japan. Specifically, the first is to consider the concept, background, and type of village enterprise based on prior research. Second, review the institutional characteristics of American and Japanese village enterprises. Third, I would like to analyze the cases of legal systems for village businesses such as Seattle City in the U.S. and Setaga Baseball in Tokyo, Japan. Fourth, suggest implications according to the results of the study. The results of the study suggested the following. First, the village development project should be set up and subdivided into dedicated administrative organizations. This should establish a segmented administrative organization system to support village development by establishing branch offices to support administrative services tailored to each region. Second, the village-building project should secure independent financial resources. In other words, there is an excuse to seek ways to continuously secure independent funds without relying on the administration financially for the village development project. Third, village-building should be carried out in phases. The government should support the activities of residents and promote continuous projects through phased project implementation. Fourth, a foundation must be laid for the universities and specialized high schools in the community to operate programs for regional innovation, such as social innovation.

• Journal of Korea Technology Innovation Society
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• v.15 no.2
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• pp.242-261
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• 2012

Early stages of technology valuation have been often overlooked or under-represented. The early stage technologies are even riskier due to their inadequacy of commercial development and market applicability. More than 95% of patents fail to earn any revenues so that the majority of patents were valueless. Technology transfers from laboratories at universities and research institutes to industrial firms have increased to acquire value from invented technologies. Technology transfer, a process of transferring discoveries and innovations resulted from research to commercial sectors, typically comprises several steps: disclosing the discoveries and innovations, i.e., intellectual property (IP), evaluating the IP's economic prospects, securing a patent, copyright or trademark for the IP, commercializing the technology through licensing, forming a joint venture, or selling. At each of those stages in the research and development of technology, the value of technology would play a very important role of making decision on the movement toward the next step, however, the financial value of technology is not easy to determine due to a great amount of uncertainty in the course of research and development, and commercialization. This paper refers to technology embodied as devices, equipment, software or processes primarily developed at public research institutions such as universities. Sometimes it is also as the result of externally financed projects contracted with industry. Nearly always technology developed at public research entities results in laboratory prototypes. When it is required to define the technology transfer contract terms for the license of the university patrimonial rights to external funding companies or other interested parties, a question arises: what is the monetary value? In this paper, we present a method for technology valuation based on the identification of specific value points related to its development. The final technology value must be within previously defined value limits. This paper consists of the review of issues related to technology transfer and commercialization, the identification of characteristics of technologies in the early stage of technology development, the formulation of framework of methods to value the early stage technologies, and the conclusion and implication of the previous review.

• Journal of Service Research and Studies
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• v.8 no.3
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• pp.51-62
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• 2018

This study examines several evolutionary and alternative aspects of the system of existing education and suggests a more specific approach to the development of recent education that has evolved to the recent service economy era and a new approach to the human capacity of the World Economic Forum (WEF). We propose a stage of education system. We will change the meaning and choice of the job according to the rapid development of the 4th Industrial Revolution. Future occupational education also needs to be changed according to the expectation that job movement will happen frequently. The new education requires a model to prepare for the phenomenon of various convergence as technology collapses with the existing culture. And a higher-level educational philosophy is needed for human competence and the environment to actually connect industrial and social issues. The purpose of this study is to show the necessity of introduction of New Service Learning as a new system of education for super mobility. New Service Learning can be divided into five concepts: Innovation, Modernity, Sustainability, Humanity, and Technology. In future research, it is necessary to complement the research by empirically analyzing the concept of New Service Learning.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Journal of Korea Port Economic Association
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• v.25 no.3
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• pp.269-282
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• 2009

This study investigates the difference of behavioral patterns between the import container volume of all ports and that of Gwangyang port in Korea. All series span the period January 1999 to December 2008. I first test whether the series are stationary or not. I can reject the null hypothesis of a unit root in each of the level variables and of a unit root for the residuals from the cointegration at the 5 percent significance level. I hitherto make use of variance decompositions and impulse response functions, both of which have now been widely used to examine how much movement in one variable can be explained by innovations in different variables and how rapidly these fluctuations in one variable can be transmitted to another. The variance decompositions for the import container volume show that the proportions of the forecast error variance of import container volumes explained by themselves are 30 and 26 per cent after 12 months, respectively. As a result, innovations in exchange rate and business activity explain 70 and 74 per cent of the variance in the import container volume. All in all, innovation accounting indicates that import container volumes are not exogenous with respect to exchange rate and business activity. The impulse responses indicate that container volumes decrease sharply to the shocks in exchange rate and decay very slowly to its pre-shock level, while container volumes respond positively to the shocks in the business activity and disappear very slowly, showing that the shocks last very long. Furthermore Gwangyang port is more sensitive to the change of the exchange rate and the industrial production than all ports.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.2
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• pp.105-118
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• 2019

New manufactural innovation was generated with combination with Do It Yourself(DIY) culture and Information and Communication Technology(ICT). It led people to make their creative idea in real things and share them. This social movement has been called as 'Maker' culture. As maker culture was developed, the places named 'Makerspace' with high-tech equipment and sharing environment have been widely spread and gotten spotlight. Futhermore, makerspaces have been diffused rapidly in Korea; because of its importance for the fourth industrial revolution. However, the operation of makerspaces is not matured as much as its popularity, so problems occurred in operating aspects. The number of related studies is not enough to foster domestic maker culture in Korea. Of that, studies on operation of makerspaces were limited and the quantity of survey sample was insufficient. Therefore, firstly, in this study, operation elements of makerspaces were extracted by literature review. And, survey for examining the extracted elements was conducted to four policy makers and researchers, four makerspace operators and four makers. Final survey was carried out by Importance-Performance Analysis(IPA) method to fifty recipients composed of policy makers and researchers, operators, and makers. In result, importance located above performance in every elements and in-depth interview was followed to understand domestic surroundings and suggest way to invigorate makerspaces in Korea. The suggestion shows as follows. First, online and offline platform for makers should be expanded; second, makerspace should connect private sponsorship with makers or their projects; third, policy direction has to be improved from venturing business to diffusion of maker culture; fourth, basic maker education should be enlarged.