• The Journal of Advanced Prosthodontics
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• v.11 no.3
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• pp.169-178
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• 2019

PURPOSE. While dental implants have displayed high success rates, poor mechanical fixation is a common complication, and their biomechanical response to occlusal loading remains poorly understood. This study aimed to develop and validate a computational model of a natural first premolar and a dental implant with matching crown morphology, and quantify their mechanical response to loading at the occlusal surface. MATERIALS AND METHODS. A finite-element model of the stomatognathic system comprising the mandible, first premolar and periodontal ligament (PDL) was developed based on a natural human tooth, and a model of a dental implant of identical occlusal geometry was also created. Occlusal loading was simulated using point forces applied at seven landmarks on each crown. Model predictions were validated using strain gauge measurements acquired during loading of matched physical models of the tooth and implant assemblies. RESULTS. For the natural tooth, the maximum vonMises stress (6.4 MPa) and maximal principal strains at the mandible ($1.8m{\varepsilon}$, $-1.7m{\varepsilon}$) were lower than those observed at the prosthetic tooth (12.5 MPa, $3.2m{\varepsilon}$, and $-4.4m{\varepsilon}$, respectively). As occlusal load was applied more bucally relative to the tooth central axis, stress and strain magnitudes increased. CONCLUSION. Occlusal loading of the natural tooth results in lower stress-strain magnitudes in the underlying alveolar bone than those associated with a dental implant of matched occlusal anatomy. The PDL may function to mitigate axial and bending stress intensities resulting from off-centered occlusal loads. The findings may be useful in dental implant design, restoration material selection, and surgical planning.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.151-159
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• 2019

As the era of big data, which is the foundation of the fourth industry, has come, most related industries are developing related solutions focusing on the technologies of data storage, statistical analysis and visualization. However, for the diffusion of bigdata technology, it is necessary to develop the prediction analysis technologies using artificial intelligence. But these advanced technologies are only possible by some experts now called data scientists. For big data-related industries to develop, a non-expert, called a citizen data scientist, should be able to easily access the big data analysis process at low cost because they have insight into their own data. In this paper, we propose a system for analyzing bigdata and building business models with the support of easy-to-use analysis system without knowledge of high-level data science. We also define the necessary components and environment for the prediction analysis system and present the overall service plan.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.697-710
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• 2021

This paper attempts to study the impact of Financial Literacy on Youth Entrepreneurial Intent in Pakistan. A closed-ended self-administered structured questionnaire covering financial literacy, computer knowledge, financial attitude, and financial knowledge with Entrepreneurial Intent was collected from young entrepreneurs. The research tried to investigate the education level with computer skill to inspect the effect of financial literateness on young generation Entrepreneurial Intent in the context of Pakistan. The research model was tested using PLS-SEM and authenticating a measurement model through the advanced methodology and their association with Entrepreneurial Intent. Results revealed that financial literacy and its two parts (financial attitude and financial knowledge) have a positive impact on Entrepreneurial Intent. The size of the joint impact of financial literacy and its components on Entrepreneurial Intent was assessed to be adequate. Entrepreneurial Intent is essential for creating new firms to maintain economic development. Furthermore, it is determined in this research that if youth has better financial knowledge and financial attitude, the probability of Entrepreneurial Intent increases. This suggests that if the youth in Pakistan desire to attain a higher limit of Entrepreneurial Intent, they must implement financial literacy models for enhancing and promoting their current Entrepreneurial Intent.

• Composites Research
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• v.34 no.1
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• pp.63-69
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• 2021

In order to develop an integrated heat dissipating material and gas barrier film for electronics, new polymer was designed and synthesized for preparing composites containing hexagonal boron nitride (hBN) filler. Depending on the size and content of the hBN filler, both thermal conductivity and oxygen transmission rate can be adjusted. The composite achieved a high thermal conductivity of 28.0 W·m-1·K-1 at most and the oxygen transmission rate was decreased by 62% compared to that of the filler free matrix. Effective filler aspect ratios could be estimated by comparing thermal conductivity and oxygen transmission rate with values predicted by theoretical models. Discrepancy on the aspect ratios extracted from thermal conductivity and oxygen transmission rate comparisons was also discussed.

• 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.