• Journal of Legislation Research
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• no.53
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• pp.177-211
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• 2017

In the course of the emergence and development of new ICT technologies and services such as Big Data, Internet of Things and Artificial Intelligence, the future will change by these new innovations in the Fourth Industrial Revolution. The future of this fourth industrial revolution will change and our future will be data-based society or economy. Since there is personal information at the center of it, the development of the economy through the utilization of personal information will depend on how to make the personal information protection laws. In Korea, which is trying to lead the 4th industrial revolution, it is a legal interest that can not give up the use of personal information, and also it is an important legal benefit that can not give up the personal interests of individuals who want to protect from personal information. Therefore, it is necessary to change the law on personal information protection in a rational way to harmonize the two. In this regard, this article discusses the problems of duplication and incompatibility of the personal information protection law, the scope of application of the personal information protection law and the uncertainty of the judgment standard, the lack of flexibility responding to the demand for the use of reasonable personal information, And there is a problem of reverse discrimination against domestic area compared to the regulated blind spot in foreign countries. In order to solve these problems and to improve the legislation of personal information protection in the era of the fourth industrial revolution, we proposed to consider both personal information protection and safe use by improving the purpose and regulation direction of the personal information protection law. The balance and harmony between the systematical maintenance of the personal information protection legislation and laws and regulations were also set as important directions. It is pointed out that the establishment of rational judgment criteria and the legislative review to clarify it are necessary for the constantly controversial personal information definition regulation and the method of allowing anonymization information as the intermediate domain. In addition to the legislative review for the legitimate and non-invasive use of personal information, there is a need to improve the collective consent system for collecting personal information to differentiate the subject and to improve the legislation to ensure the effectiveness of the regulation on the movement of personal information between countries. In addition to the issues discussed in this article, there may be a number of challenges, but overall, the protection and use of personal information should be harmonized while maintaining the direction indicated above.

• Journal of KIBIM
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• v.14 no.2
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• pp.25-34
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• 2024

This study proposes a new methodology to increase the flexibility and efficiency of the design data evaluation system by combining Building Information Modeling (BIM) technology in the architectural industry, OpenAI's interactive artificial intelligence, and ChatGPT. BIM technology plays an important role in digitally modeling and managing architectural information. Since architectural information is included, research and development are underway to review and evaluate BIM data according to conditions through program development. However, in the process of reviewing BIM design data, if the review criteria or evaluation criteria according to design change occur frequently, it is necessary to update the program anew. In order for designers or reviewers to apply the changed criteria, requesting a program developer will delay time. This problem was studied by using ChatGPT to modify and update the design data evaluation program code in real time. In this study, it is aimed to improve the changing standards and accuracy by enabling programming non-professionals to change the design regulations and calculation standards of the BIM evaluation program system using ChatGPT. In this study, in the BIM-based design certification automation evaluation program, a program in which the automation evaluation method is being studied based on the design certification evaluation manual was first used. In the design certification automation evaluation program, the programming non-majors checked the automation evaluation code by linking ChatGPT, and the changed calculation criteria were created and modified interactively. As a result of the evaluation, the change in the calculation standard was explained to ChatGPT and the applied result was confirmed.

• Convergence Security Journal
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• v.23 no.4
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• pp.81-92
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• 2023

In recent years, the ROK military is promoting Defense Innovation 4.0 with the goal of fostering strong military based on science and technology equipped with artificial intelligence(AI) to prepare for the upcoming population cliff. In particular, at the present time of increased threats of North Korea, the South Korean military is seeking to deal with a decrease in military service resources through the introduction of a Scientific Guard System using advanced technology. TICN which is a core basic communication system to ensure the integrated combat capability of the ROK military is, however, limited to use as a based network for the emerging Scientific Guard System due to the narrow transmission bandwidth with widely spread poor reception area. To deal with this problem, this paper proposes TVWS-based Scientific Guard Systems with TVWS-based wireless network construction technology that has been available for free in Korea since 2017. The TVWS-based Scientific Guard System proposed in this paper, when compared to the existing wired network-based Scientific Guard Systems, has various advantages in terms of minimizing operational gaps, reducing construction costs, and flexibility in installation and operation.

• Journal of Sensor Science and Technology
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• v.32 no.2
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• pp.88-94
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• 2023

Recently, piezoelectric tactile sensors have garnered considerable attention in the field of texture recognition owing to their high sensitivity and high-frequency detection capability. Despite their remarkable potential, improving their mechanical flexibility to attach to complex surfaces remains challenging. In this study, we present a flexible piezoelectric sensor that can be bent to an extremely small radius of up to 2.5 mm and still maintain good electrical performance. The proposed sensor was fabricated by controlling the thickness that induces internal stress under external deformation. The fabricated piezoelectric sensor exhibited a high sensitivity of 9.3 nA/kPa ranging from 0 to 10 kPa and a wide frequency range of up to 1 kHz. To demonstrate real-time texture recognition by rubbing the surface of an object with our sensor, nine sets of fabric plates were prepared to reflect their material properties and surface roughness. To extract features of the objects from the detected sensing data, we converted the analog dataset to short-term Fourier transform images. Subsequently, texture recognition was performed using a convolutional neural network with a classification accuracy of 97%.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.19-43
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• 2016

This study examines the causes of the Asian exchange rate crisis and compares it to the European Monetary System crisis. In 1997, emerging countries in Asia experienced financial crises. Previously in 1992, currencies in the European Monetary System had undergone the same experience. This was followed by Mexico in 1994. The objective of this paper lies in the generation of useful insights from these crises. This research presents a comparison of South Korea, United Kingdom and Mexico, and then compares three different models for prediction. Previous studies of economic crisis focused largely on the manual construction of causal models using linear techniques. However, the weakness of such models stems from the prevalence of nonlinear factors in reality. This paper uses a structural equation model to analyze the causes, followed by a neural network model to circumvent the linear model's weaknesses. The models are examined in the context of predicting exchange rates In this paper, data were quarterly ones, and Consumer Price Index, Gross Domestic Product, Interest Rate, Stock Index, Current Account, Foreign Reserves were independent variables for the prediction. However, time periods of each country's data are different. Lisrel is an emerging method and as such requires a fresh approach to financial crisis prediction model design, along with the flexibility to accommodate unexpected change. This paper indicates the neural network model has the greater prediction performance in Korea, Mexico, and United Kingdom. However, in Korea, the multiple regression shows the better performance. In Mexico, the multiple regression is almost indifferent to the Lisrel. Although Lisrel doesn't show the significant performance, the refined model is expected to show the better result. The structural model in this paper should contain the psychological factor and other invisible areas in the future work. The reason of the low hit ratio is that the alternative model in this paper uses only the financial market data. Thus, we cannot consider the other important part. Korea's hit ratio is lower than that of United Kingdom. So, there must be the other construct that affects the financial market. So does Mexico. However, the United Kingdom's financial market is more influenced and explained by the financial factors than Korea and Mexico.

• The Journal of Industrial Distribution & Business
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• v.11 no.6
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• pp.29-39
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• 2020

Purpose: The purpose of this study is, in the situation where rapid response to the rapidly changing environment is required due to the development of the fourth industrial revolution such as artificial intelligence, virtual reality, and the internet of things, robotics, big data, additive manufacturing, bio-health, sharing economy and in the organizational culture aspiring toward the innovation of a major company, small business and a public institution, to analyze what influence a job-engagement and stress make, and what influence individual's self-efficacy as a moderator mediator makes, and to offer basic data for improving job-engagement and lowering job-stress. Research design, data, and methodology: For doing this, the literature and the empirical studies were combined. Deriving innovation-oriented organizational culture as factors affecting the job engagement and job stress through the literature, and have established hypotheses to verify them. We have collected data of 281 from ex,ecutives and staff-members working in areas including major company, small business and officials (the central government, a local public service, the prosecution, the police, and school). And these data were analyzed by SPSS 23 version. Results: Based on these data, the results of analysis were as follows; First, the innovation-oriented organizational culture which was recognized by organizational members had effect on job-stress. Second, the innovation-oriented organizational culture which was recognized by organizational members influenced job-stress. Third, in the relationship between the innovation-oriented organizational culture and job-engagement, self-efficacy did not influenced job-engagement. Finally, in the relationship between the innovation-oriented organizational culture and job-stress, self-efficacy influenced job-stress. Conclusions: Innovation-oriented organizational culture places importance on the organization's adaptability and flexibility in the external environment, so companies need to establish an innovation-oriented organizational culture favorable to achieving survival and successful innovation, and to develop and disseminate programs of positive and continuous organizations to improve task enthusiasm, reduce task stress, and enhance organizational performance. In the future, it will be necessary to verify the effectiveness of various organizational culture types through comparative analysis with companies that actively maintain an innovation-oriented organizational culture (Google, Kakao, etc.) and companies that prefer hierarchy-oriented organizational culture, relationship-oriented organizational culture, and market-oriented organizational culture.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.1-17
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• 2017

The deep learning framework is software designed to help develop deep learning models. Some of its important functions include "automatic differentiation" and "utilization of GPU". The list of popular deep learning framework includes Caffe (BVLC) and Theano (University of Montreal). And recently, Microsoft's deep learning framework, Microsoft Cognitive Toolkit, was released as open-source license, following Google's Tensorflow a year earlier. The early deep learning frameworks have been developed mainly for research at universities. Beginning with the inception of Tensorflow, however, it seems that companies such as Microsoft and Facebook have started to join the competition of framework development. Given the trend, Google and other companies are expected to continue investing in the deep learning framework to bring forward the initiative in the artificial intelligence business. From this point of view, we think it is a good time to compare some of deep learning frameworks. So we compare three deep learning frameworks which can be used as a Python library. Those are Google's Tensorflow, Microsoft's CNTK, and Theano which is sort of a predecessor of the preceding two. The most common and important function of deep learning frameworks is the ability to perform automatic differentiation. Basically all the mathematical expressions of deep learning models can be represented as computational graphs, which consist of nodes and edges. Partial derivatives on each edge of a computational graph can then be obtained. With the partial derivatives, we can let software compute differentiation of any node with respect to any variable by utilizing chain rule of Calculus. First of all, the convenience of coding is in the order of CNTK, Tensorflow, and Theano. The criterion is simply based on the lengths of the codes and the learning curve and the ease of coding are not the main concern. According to the criteria, Theano was the most difficult to implement with, and CNTK and Tensorflow were somewhat easier. With Tensorflow, we need to define weight variables and biases explicitly. The reason that CNTK and Tensorflow are easier to implement with is that those frameworks provide us with more abstraction than Theano. We, however, need to mention that low-level coding is not always bad. It gives us flexibility of coding. With the low-level coding such as in Theano, we can implement and test any new deep learning models or any new search methods that we can think of. The assessment of the execution speed of each framework is that there is not meaningful difference. According to the experiment, execution speeds of Theano and Tensorflow are very similar, although the experiment was limited to a CNN model. In the case of CNTK, the experimental environment was not maintained as the same. The code written in CNTK has to be run in PC environment without GPU where codes execute as much as 50 times slower than with GPU. But we concluded that the difference of execution speed was within the range of variation caused by the different hardware setup. In this study, we compared three types of deep learning framework: Theano, Tensorflow, and CNTK. According to Wikipedia, there are 12 available deep learning frameworks. And 15 different attributes differentiate each framework. Some of the important attributes would include interface language (Python, C ++, Java, etc.) and the availability of libraries on various deep learning models such as CNN, RNN, DBN, and etc. And if a user implements a large scale deep learning model, it will also be important to support multiple GPU or multiple servers. Also, if you are learning the deep learning model, it would also be important if there are enough examples and references.