• Composites Research
• /
• v.36 no.3
• /
• pp.167-172
• /
• 2023

The manufacturing technology of composite material is applicable with filler characteristics maintaining low cost, flexibility, and easy process to develope the various functional composite materials. To realize functional composites, various researches on the high performance of composite materials using graphene as a filler is being actively conducted. In this study, physical and chemical properties were investigated using graphene to improve high functional properties. Graphene oxide (GO) was prepared using graphane nanoplatelet (GNP), and reduced graphene oxide (R-GO) was formed by reducing GO. The physical properties of GO and R-GO were analyzed, and the reliability of the manufactured method was reviewed by comparing that of GNP results. As a result of analysis by Raman spectroscopy, in the case of R-GO, it was confirmed that the intensity of D-peak and G-peak decreased compared to GO, and an increase of 0.08 was observed through the ratio of ID/IG. For the FTIR results, GO and RGO has a repeating C-C and C=C connection structure unlike GNP. GO and R-GO show clear peaks for C-O bond, C=C bond, C=O bond, and O-H bonding. As a result of X-ray diffraction analysis, GNP showed a wide diffraction peak at 25.86° of (002) plane characteristics, whereas GO and R-GO showed peaks corresponding to (001) and (100) planes. It was also found that the interlayer distance of GO increased by about 2.6 times compared to GNP.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.4D
• /
• pp.351-360
• /
• 2010

In 2010, the number of registered vehicles reached almost at 17.48 millions in Korea. This dramatic increase of vehicles influenced to increase the number of traffic accidents which is one of the serious social problems and also to soar the personal and economic losses in Korea. Through this research, an enhanced intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network will be developed in order to obtain the important data for developing the countermeasures of traffic accidents and eventually to reduce the traffic accidents in Korea. Firstly, this research has investigated the influencing factors of road geometric features on the traffic volume of each approaching for the intersections where traffic accidents and congestions frequently take place and, a linear regression model of traffic accidents and traffic conflicts were developed by examining the relationship between traffic accidents and traffic conflicts through the statistical significance tests. Secondly, this research also developed an intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network through applying the intersection traffic volume, the road geometric features and the specific variables of traffic conflicts. Lastly, this research found out that the developed model is better than the existed forecasting models in terms of the reliability and accuracy by comparing the actual number of traffic accidents and the predicted number of accidents from the developed model. In conclusion, it is expect that the cost/effectiveness of any traffic safety improvement projects can be maximized if this developed intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network use practically at field in the future.

• Journal of the Korean Crystal Growth and Crystal Technology
• /
• v.34 no.1
• /
• pp.1-7
• /
• 2024

β-Ga₂O₃ is a material with a wide band gap of ~4.8 eV and a high breakdown-voltage of 8 MV/cm, and is attracting much attention in the field of power device applications. In addition, compared to representative WBG semiconductor materials such as SiC, GaN and Diamond, it has the advantage of enabling single crystal growth with high growth rate and low manufacturing cost [1-4]. In this study, we succeeded in growing a 10 mm thick β-Ga₂O₃ single crystal doped with 0.3 mol% SnO₂ through the EFG (Edge-defined Film-fed Growth) method using multi-slit structure. The growth direction and growth plane were set to [010]/(010), respectively, and the growth speed was about 12 mm/h. The grown β-Ga₂O₃ single crystal was cut into various crystal planes (010, 001, 100, ${\bar{2}}01$) and surface processed. The processed samples were compared for characteristics according to crystal plane through analysis such as XRD, UV/VIS/NIR/Spec., Mercury Probe, AFM and Etching. This research is expected to contribute to the development of power semiconductor technology in high-voltage and high-temperature applications, and selecting a substrate with better characteristics will play an important role in improving device performance and reliability.

• KIPS Transactions on Software and Data Engineering
• /
• v.13 no.1
• /
• pp.1-16
• /
• 2024

Over recent years, 6 Sigma has become a key methodology in manufacturing for quality improvement and cost reduction. However, challenges have arisen due to the difficulty in analyzing large-scale data generated by smart factories and its traditional, formal application. To address these limitations, a big data-based 6 Sigma approach has been developed, integrating the strengths of 6 Sigma and big data analysis, including statistical verification, mathematical optimization, interpretability, and machine learning. Despite its potential, the practical impact of this big data-based 6 Sigma on manufacturing processes and management performance has not been adequately verified, leading to its limited reliability and underutilization in practice. This study investigates the efficiency impact of DX SS, a big data-based 6 Sigma, on manufacturing processes, and identifies key success policies for its effective introduction and implementation in enterprises. The study highlights the importance of involving all executives and employees and researching key success policies, as demonstrated by cases where methodology implementation failed due to incorrect policies. This research aims to assist manufacturing companies in achieving successful outcomes by actively adopting and utilizing the methodologies presented.

• The Journal of the Acoustical Society of Korea
• /
• v.26 no.7
• /
• pp.361-365
• /
• 2007

Aero-pulsation noise, generally caused by geometric asymmetry of a rotating device, is one of considerable sources of annoyance in passenger cars using the turbocharged diesel engine. Main source of this noise is the compressor wheel in the turbocharger system, and can be reduced by after-treatment devices such as silencers, but which may increase the manufacturing cost. More effective solution is to improve the geometric symmetry over all, or to control the quality of components by sorting out inferior ones. The latter is more simple and reasonable than the former in view of manufacturing. Thus, an appropriate discrimination method should be needed to evaluate aero-pulsation noise level at the production line. In this paper, we introduce the accurate method which can measure the noise level of aero-pulsation and also present its evaluation criteria. Besides verifying the reliability of a measurement system - a rig test system-, we analyze the correlation between the results from rig tests and those from vehicle tests. The gage R&R method is carried out to check the repeatability of measurements over 25 samples. From the result, we propose the standard specification which can discriminate inferior products from superior ones on the basis of aero-pulsation noise level.

• Journal of the Korea Society for Simulation
• /
• v.33 no.2
• /
• pp.1-11
• /
• 2024

A cyber-physical system is a technology that connects the physical systems of a manufacturing environment with a cyber space to enable simulation. One of the major challenges in this technology is the seamless communication between these two environments. In complex manufacturing processes, it is crucial to adapt to various protocols of manufacturing equipment and ensure the transmission and reception of a large volume of data without delays or errors. In this study, we propose a method for constructing agent models for real-time simulation-capable cyberphysical systems. To achieve this, we design data collection units as independent agent models and effectively integrate them with existing simulation tools to develop the overall system architecture. To validate the proposed structure and ensure reliability, we conducted empirical testing by integrating various equipment from a real-world smart microfactory system to assess the data collection capabilities. The experiments involved testing data delay and data gaps related to data collection cycles. As a result, the proposed approach demonstrates flexibility by enabling the application of various internal data collection methods and accommodating different data formats and communication protocols for various equipment with relatively low communication delays. Consequently, it is expected that this approach will promote innovation in the manufacturing industry, enhance production line efficiency, and contribute to cost savings in maintenance.

• Industry Promotion Research
• /
• v.9 no.1
• /
• pp.21-29
• /
• 2024

In the rapidly advancing information society, electronic devices, including smartphones and tablets, are increasingly digitized and equipped with high-performance features such as flexible displays. This study focused on optimizing the manufacturing process for Transparent Conductive Films (TCF) by using the cost-effective conductive polymer PEDOT and transparent substrate PET as alternatives to expensive materials in flexible display technology. The variables considered are production speed (m/min), coating maximum temperature (℃), and PEDOT supply speed (rpm), with surface resistivity (Ω/□) as the response parameter, using Response Surface Methodology (RSM). Optimization results indicate the ideal conditions for production: a speed of 22.16 m/min, coating temperature of 125.28℃, and PEDOT supply at 522.79 rpm. Statistical analysis validates the reliability of the results (F value: 18.37, P-value: < 0.0001, R²: 0.9430). Under optimal conditions, the predicted surface resistivity is 145.75 Ω/□, closely aligned with the experimental value of 142.97 Ω/□. Applying these findings to mass production processes is expected to enhance production yields and decrease defect rates compared to current practices. This research provides valuable insights for the advancement of flexible display manufacturing.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.28 no.5
• /
• pp.1-9
• /
• 2024

Recently, the TBM tunnel construction method has been in the spotlight as tunnel excavation under urban areas such as the Metropolitan Rapid Transit (GTX) has been actively carried out. Although the construction cost of the TBM tunnel is high, it is relatively free from noise and vibration compared to the NATM tunnel method, so it is well known to be a suitable construction method for application to the lower part of urban areas. In particular, when the stratum passes through the shallow section, it can have a great impact on existing upper structures and obstacles, so accurate numerical analysis considering various variables is required when designing the TBM tunnel. Unlike other tunnel construction methods, TBM tunnels build linings by assembling factory-made segments. Unlike NATM tunnels, segment lining has connections between segments, so how to the connection status between segments is reflected can have a significant impact on securing the reliability of analysis results. Therefore, in this paper, a segment joint model(Janssen Model) was applied to the lining for seismic analysis of the TBM tunnel, and the tunnel's behavioral characteristics were analyzed after numerical analysis using nonlinear models according to urban railway seismic design standards.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.139-161
• /
• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• The Journal of Bigdata
• /
• v.8 no.1
• /
• pp.111-129
• /
• 2023

With the diffusion of digital innovation, the adoption of innovative medical technologies based on artificial intelligence is increasing in the medical field. This is driving the launch and adoption of AI-based SaMD(Software as a Medical Device), but there is a lack of research on the factors that influence the adoption of SaMD by medical institutions. The purpose of this study is to identify key factors that influence medical institutions' decisions to adopt AI-based SaMDs, and to analyze the weights and priorities of these factors. For this purpose, we conducted Delphi surveys based on the results of literature studies on technology acceptance models in healthcare industry, medical AI and SaMD, and developed a research model by combining HOTE(Human, Organization, Technology and Environment) framework and HABIO(Holistic Approach {Business, Information, Organizational}) framework. Based on the research model with 5 main criteria and 22 sub-criteria, we conducted an AHP(Analytical Hierarchy Process) analysis among the experts from domestic medical institutions and SaMD providers to empirically analyze SaMD adoption factors. The results of this study showed that the priority of the main criteria for determining the adoption of AI-based SaMD was in the order of technical factors, economic factors, human factors, organizational factors, and environmental factors. The priority of sub-criteria was in the order of reliability, cost reduction, medical staff's acceptance, safety, top management's support, security, and licensing & regulatory levels. Specifically, technical factors such as reliability, safety, and security were found to be the most important factors for SaMD adoption. In addition, the comparisons and analyses of the weights and priorities of each group showed that the weights and priorities of SaMD adoption factors varied by type of institution, type of medical institution, and type of job in the medical institution.