• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.545-550
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• 2021

In this paper, a university curriculum of Automation Robotics Process(RPA) among several goals for digital transformation of companies was developed. In the process of the development of ICT technology and the 4th industrial revolution, the existing analog information has changed through the stages of digitization, digitalization, and digital transformation. Recently, digital transformation has been cited as an essential survival strategy following a disruptive revolution that changes the paradigm of existing industrial systems and business. It is defined as a continuous process by which a company adapts to or promotes disruptive changes in customers and markets by using digital capabilities to create new business models, products and services. To this end, process automation in companies or organizations is an important factor. Accordingly, the need for a curriculum of robotics processes automation in universities has been raised according to these changes, and the related education contents, which have been centered on companies, have been redesigned to introduce the curriculum for universities. Education contents are composed to help students to attain certificates of essential or advanced of AA.

• Earthquakes and Structures
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• v.20 no.1
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• pp.109-122
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• 2021

Significant progress in the oil and gas industry advances the application of pipeline into an intelligent era, which poses rigorous requirements on pipeline safety, reliability, and maintainability, especially when crossing seismic zones. In general, strike-slip faults are prone to induce large deformation leading to local buckling and global rupture eventually. To evaluate the performance and safety of pipelines in this situation, numerical simulations are proved to be a relatively accurate and reliable technique based on the built-in physical models and advanced grid technology. However, the computational cost is prohibitive, so one has to wait for a long time to attain a calculation result for complex large-scale pipelines. In this manuscript, an efficient and accurate surrogate model based on machine learning is proposed for strain demand prediction of buried X80 pipelines subjected to strike-slip faults. Specifically, the support vector regression model serves as a surrogate model to learn the high-dimensional nonlinear relationship which maps multiple input variables, including pipe geometries, internal pressures, and strike-slip displacements, to output variables (namely tensile strains and compressive strains). The effectiveness and efficiency of the proposed method are validated by numerical studies considering different effects caused by structural sizes, internal pressure, and strike-slip movements.

• International Journal of Advanced Culture Technology
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• v.9 no.2
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• pp.100-105
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• 2021

Contactless service is rapidly emerging as a new growth strategy due to consumers who are reluctant to the face-to-face situation in the global pandemic of coronavirus disease 2019 (COVID-19), and various technologies are being developed to support the fast-growing contactless service market. In particular, the restaurant industry is one of the most desperate industrial fields requiring technologies for contactless service, and the representative technical case should be a kiosk, which has the advantage of reducing labor costs for the restaurant owners and provides psychological relaxation and satisfaction to the customer. In this paper, we propose a solution to the restaurant's store operation through the unmanned kiosk using a state-of-the-art artificial intelligence (AI) technology of image recognition. Especially, for the products that do not have barcodes in bakeries, fresh foods (fruits, vegetables, etc.), and autonomous restaurants on highways, which cause increased labor costs and many hassles, our proposed system should be very useful. The proposed system recognizes products without barcodes on the ground of image-based AI algorithm technology and makes automatic payments. To test the proposed system feasibility, we established an AI vision system using a commercial camera and conducted an image recognition test by training object detection AI models using donut images. The proposed system has a self-learning system with mismatched information in operation. The self-learning AI technology allows us to upgrade the recognition performance continuously. We proposed a fully automated payment system with AI vision technology and showed system feasibility by the performance test. The system realizes contactless service for self-checkout in the restaurant business area and improves the cost-saving in managing human resources.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.6
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• pp.195-208
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• 2010

As information communication technologies have highly advanced, a large amount of user sensitive information can be easily collected and unexpectedly distributed. For user-friendly services, a service provider requires and processes more user information. However known privacy protection models take on a passive attitude toward user information protection and often involve serious weaknesses. In reality, information exposure by unauthorised access and mistakenly disclosure occurs frequently. In this paper, we study on the applicability of anonymous authentication services for fine-grained user privacy protection. We analyze authentication schemes and classify them according to the level of privacy newly defined in this paper. In addition, we identify security requirements that a privacy protection scheme based on anonymous authentication can achieve within legal boundary.

• Knowledge Management Research
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• v.22 no.2
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• pp.289-300
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• 2021

Proptech, which has applied information technology to the real estate market, is leading real estate transaction innovation by presenting various value creation models. This study categorizes and understands values that are created and shared in proptech-based businesses, and develops evaluation data that reflects the relative importance of individual value areas. To this end, the dimension of value creation of proptech was hierarchically constructed, and the degree of relative value creation of the sub-industries of the proptech industry was evaluated. In order to grasp the relative importance of the proposed indicators, AHP analysis was conducted for industry and academic experts. In the first stage, intangible values, relational values, and advanced values were presented. It was derived as weights between indicators through two-way comparison. This study aims to improve and develop the value-creation capability of the entire Korean proptech ecosystem in the future by evaluating the value-created competence of each sector of the proptech industry.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.270-281
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• 2021

With the development of information technology, the size of insurance fraud is increasing rapidly every year, and the method is being organized and advanced in conspiracy. Although various forms of prediction models are being studied to predict and detect this, insurance-related information is highly sensitive, which poses a high risk of sharing and access and has many legal or technical constraints. In this paper, we propose a machine learning insurance fraud prediction model based on blockchain, one of the most popular technologies with the recent advent of the Fourth Industrial Revolution. We utilize blockchain technology to realize a safe and trusted insurance information sharing system, apply the theory of social relationship analysis for more efficient and accurate fraud prediction, and propose machine learning fraud prediction patterns in four stages. Claims with high probability of fraud have the effect of being detected at a higher prediction rate at an earlier stage, and claims with low probability are applied differentially for post-reference management. The core mechanism of the proposed model has been verified by constructing an Ethereum local network, requiring more sophisticated performance evaluations in the future.

• Journal of Korean Dental Science
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• v.13 no.2
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• pp.52-58
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• 2020

Purpose: Using two types of electronic apex locators, this study aimed to investigate the differences in accuracy according to the evaluator and equipment. Materials and Methods: Artificial teeth of the lower first premolars and two mandibular acrylic models (A and B) were used in this study. In the artificial teeth, the pulp chamber was opened and the access cavity was prepared. Using calibrated digital Vernier calipers, the distance from the top of the cavity and the root apex was measured to assess the actual distance between two artificial teeth. The evaluation was conducted by 20 dentists, and each evaluator repeated measurements for each electronic apex locator five times. The difference between the actual distance from the top of the cavity to the root apex and the distance measured using electronic measuring equipment was compared. For statistical analysis, the Friedman test the Mann-Whitney U-test were conducted and the differences between groups were analyzed (α=0.05). Result: As for the accuracy of measurement according to the two types of electronic apex locators, the value of the measurement error was 0.4753 mm in Dentaport ZX and 0.3321 mm in E-Cube Plus. Moreover, electronic apex locators Dentaport ZX and E-Cube Plus showed statistically significant differences (P<0.05). As for the difference in the accuracy of the two types of electronic apex locators according to the evaluator, the resulting values differed depending on the evaluator and showed a statistically significant difference (P<0.001). Conclusion: Electronic apex locator E-Cube Plus showed higher accuracy than did Dentaport ZX. Nevertheless, both types of electronic apex locators showed 100% accuracy in finding the region within root apex ±0.5 mm zone. Furthermore, according to the evaluator, the two electronic apex locators showed different resulting values.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1700-1709
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• 2019

Recently, many researches have been under way to develop systems (services) to support the safety navigation of ships, and in these studies, common difficulties have been encountered in assessing the usefulness and effectiveness of the developed system. To solve these problems, we propose the DEVS-based ship navigation modeling and simulation technique. Following the preceding study, we analyze the COLREG rules and reflected to officer and helmsman agent models for decision making. Also we propose estimation and interpolation techniques to adopt the motion characteristics of the actual vessel to simulation. In addition, we implement the navigation simulation system to reflect the designed proposed methods, and we present five-scenarios to verify the developed simulation system. And we conduct simulations according to each scenario and the results were reconstructed. The simulation results confirm that the components modelled in each scenario enable to operate according to the navigation relationships.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.1-11
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• 2021

Document page segmentation is an important step in building a quality optical character recognition module. The study examined already existing work on the topic of page segmentation and focused on the development of a segmentation model that has greater functional significance for application in an organization, as well as broad capabilities for managing the quality of the model. The main problems of document segmentation were highlighted, which include a complex background of intersecting objects. As classes for detection, not only classic text, table and figure were selected, but also additional types, such as signature, logo and table without borders (or with partially missing borders). This made it possible to pose a non-trivial task of detecting non-standard document elements. The authors compared existing neural network architectures for object detection based on published research data. The most suitable architecture was RetinaNet. To ensure the possibility of quality control of the model, a method based on neural network modeling using the RetinaNet architecture is proposed. During the study, several models were built, the quality of which was assessed on the test sample using the Mean average Precision metric. The best result among the constructed algorithms was shown by a model that includes four neural networks: the focus of the first neural network on detecting tables and tables without borders, the second - seals and signatures, the third - pictures and logos, and the fourth - text. As a result of the analysis, it was revealed that the approach based on four neural networks showed the best results in accordance with the objectives of the study on the test sample in the context of most classes of detection. The method proposed in the article can be used to recognize other objects. A promising direction in which the analysis can be continued is the segmentation of tables; the areas of the table that differ in function will act as classes: heading, cell with a name, cell with data, empty cell.

• Journal of The Korean Astronomical Society
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• v.54 no.1
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• pp.9-16
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• 2021

The Ozone Dynamics Investigation Nano-Satellite (ODIN) is a CubeSat design proposed by Chungnam National University as contribution to the CubeSat Competition 2019 sponsored by the Korean Aerospace Research Institute (KARI). The main objectives of ODIN are (1) to observe the polar ozone column density (latitude range of 60° to 80° in both hemispheres) and (2) to investigate the chemical dynamics between stratospheric ozone and ozone depleting substances (ODSs) through spectroscopy of the terrestrial atmosphere. For the operation of ODIN, a highly efficient power system designed for the specific orbit is required. We present the conceptual structural design of ODIN and an analysis of power generation in a sun synchronous orbit (SSO) using two different configurations of 3U solar panels (a deployed model and a non-deployed model). The deployed solar panel model generates 189.7 W through one day which consists of 14 orbit cycles, while the non-deployed solar panel model generates 152.6 W. Both models generate enough power for ODIN and the calculation suggests that the deployed solar panel model can generate slightly more power than the non-deployed solar panel model in a single orbit cycle. We eventually selected the non-deployed solar panel model for our design because of its robustness against vibration during the launch sequence and the capability of stable power generation through a whole day cycle.