• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.10a
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• pp.177-179
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• 2009

In order to investigate the possible application of ZnO films as a transparent conducting oxide (TCO) electrode, ZnO:Al films were prepared by RF magnetron sputtering method. The effects of surface treatment and doping concentration on the structural and electrical properties of ZnO films were mainly studied experimentally. Five-inch PDP cells using either a ZnO:Al or indium tin oxide (ITO) electrode were also fabricated separately under the same manufacturing conditions. The luminous properties of both the transparent conducting oxide electrode were measured and compared with each other. By doping the ZnO target with 2 wt% of Al2O3, the film deposited at a chemical surface treatment resulted in the minimum resistivity of 8.5 _ 10_4 U-cm and a transmittance of 91.7%. And DBD surface treatment resulted in the minimum resistivity of 8.5 _ 10_4 U-cm and a transmittance of 91.7%. Although the luminance and luminous efficiency of the transparent conducting oxide electrode using ZnO:AI are lower than those of the cell with the ITO electrode by about 10%, these values are sufficient enough to be considered for the normal operation of TCO.

• Asia pacific journal of information systems
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• v.16 no.1
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• pp.71-84
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• 2006

Case-based reasoning (CBR) is a reasoning technique that reuses past cases to find a solution to the new problem. It often shows significant promise for improving effectiveness of complex and unstructured decision making. It has been applied to various problem-solving areas including manufacturing, finance and marketing for the reason. However, the design of appropriate case indexing and retrieval mechanisms to improve the performance of CBR is still a challenging issue. Most of the previous studies on CBR have focused on the similarity function or optimization of case features and their weights. According to some of the prior research, however, finding the optimal k parameter for the k-nearest neighbor (k-NN) is also crucial for improving the performance of the CBR system. In spite of the fact, there have been few attempts to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. In this study, we introduce a genetic algorithm (GA) to optimize the number of neighbors to combine. This study applies the novel approach to Korean stock market. Experimental results show that the GA-optimized k-NN approach outperforms other AI techniques for stock market prediction.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.178-184
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• 2003

Case-based reasoning (CBR) often shows significant promise for improving effectiveness of complex and unstructured decision making. Consequently, it has been applied to various problem-solving areas including manufacturing, finance and marketing. However, the design of appropriate case indexing and retrieval mechanisms to improve the performance of CBR is still challenging issue. Most of previous studies to improve the effectiveness for CBR have focused on the similarity function or optimization of case features and their weights. However, according to some of prior researches, finding the optimal k parameter for k-nearest neighbor (k-NN) is also crucial to improve the performance of CBR system. Nonetheless, there have been few attempts which have tried to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. In this study, we introduce a genetic algorithm (GA) to optimize the number of neighbors to combine. This study applies the new model to the real-world case provided by an online shopping mall in Korea. Experimental results show that a GA-optimized k-NN approach outperforms other AI techniques for purchasing behavior forecasting.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.121-138
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• 2024

Artificial intelligence is crucial to manufacturing productivity. Understanding the difficulties in producing disruptions, especially in linear feed robot systems, is essential for efficient operations. These mechanical tools, essential for linear movements within systems, are prone to damage and degradation, especially in the LM guide, due to repetitive motions. We examine how explainable artificial intelligence (XAI) may diagnose wafer linear robot linear rail clearance and ball screw clearance anomalies. XAI helps diagnose problems and explain anomalies, enriching management and operational strategies. By interpreting the reasons for anomaly detection through visualizations such as Class Activation Maps (CAMs) using technologies like Grad-CAM, FG-CAM, and FFT-CAM, and comparing 1D-CNN with 2D-CNN, we illustrates the potential of XAI in enhancing diagnostic accuracy. The use of datasets from accelerometer and torque sensors in our experiments validates the high accuracy of the proposed method in binary and ternary classifications. This study exemplifies how XAI can elucidate deep learning models trained on industrial signals, offering a practical approach to understanding and applying AI in maintaining the integrity of critical components such as LM guides in linear feed robots.

• Journal of the Korean Society of Industry Convergence
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• v.9 no.1
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• pp.61-66
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• 2006

In perspective of saving natural resource and energy, today's automobiles are in process of regenerating by smaller and lighter. In order to achieve the sufficiency on the consumption of the fuel, new mechanism and new assembly are required. Therefore the expectations on the new materials are very high. Especially, AI materials are widely used to reduce the weight. AI that is used in automobiles is mostly casting material, and according to the innovation of technique is in rapid development. AI Die casting is an important field as today's trend of lightweight on automobiles. One of the parts in steering system, Valve Housing plays a role of reduce the operating effort of drivers. Unfortunately, the Valve Housing which is widely reliable to the most automobiles are not developed at this moment in our automobile industry. Therefore, they are produced by casting method which cost three times or even more expensive in production. If Valve Housing, which is a part of steering system is produced by Gravity Casting, the space that manufacturing equipment will be increased, and more time and workers would be brought into service. For such reason, Die Casting would replace Gravity Casting in order to minimize cost of time, manpower, and working space.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 1999.10a
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• pp.56-56
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• 1999

For advanced TFT-LCD manufacturing processes, dry etching of thin-film layers(a-Si, $SiN_x$, SID & gate electrodes, ITO etc.) is increasingly preferred instead of conventional wet etching processes. To dry etch Al gate electrode which is advantageous for reducing propagation delay time of scan signals, high etch rate, slope angle control, and etch uniformity are required. For the Al gate electrode, some metals such as Ti and Nd are added in Al to prevent hillocks during post-annealing processes in addition to gaining low-resistivity($<10u{\Omega}{\cdot}cm$), high performance to heat tolerance and corrosion tolerance of Al thin films. In the case of AI-Nd alloy films, however, low etch rate and poor selectivity over photoresist are remained as a problem. In this study, to enhance the etch rates together with etch uniformity of AI-Nd alloys, magnetized inductively coupled plasma(MICP) have been used instead of conventional ICP and the effects of various magnets and processes conditions have been studied. MICP was consisted of fourteen pairs of permanent magnets arranged along the inside of chamber wall and also a Helmholtz type axial electromagnets was located outside the chamber. Gas combinations of $Cl_2,{\;}BCl_3$, and HBr were used with pressures between 5mTorr and 30mTorr, rf-bias voltages from -50Vto -200V, and inductive powers from 400W to 800W. In the case of $Cl_2/BCl_3$ plasma chemistry, the etch rate of AI-Nd films and etch selectivity over photoresist increased with $BCl_3$ rich etch chemistries for both with and without the magnets. The highest etch rate of $1,000{\AA}/min$, however, could be obtained with the magnets(both the multi-dipole magnets and the electromagnets). Under an optimized electromagnetic strength, etch uniformity of less than 5% also could be obtained under the above conditions.

• Journal of Industrial Convergence
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• v.20 no.10
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• pp.87-94
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• 2022

This paper seeks to raise inflection points of battery manufacturing bases in Korea in the V4 region through the reorganization of new industrial technologies in accordance with ESG. As a result, the global supply chain market is cut off. The Russian-Ukraine war and the U.S.-China hegemony are competing in the economic crisis caused by COVID-19. It is showing diversification of new suppliers in an environment where mineral, grain procurement, gas, and even wheat imports from China and Russia are not possible. As a protective glocal, this area is used as a buffer zone(Pro-Russia, Hungary). to an isolated zone(anti-Russia, Poland) by war. In this paper, economic growth is expected to slow further due to the EU tapering period and high inflation in world countries. Due to these changes, the conversion of new tech industry and the contraction of Germany's structure due to energy supply may lose the driving force for economic growth over the past 20 years. This is caused by market disconnection(chasm) in the nominal indicators in this area. On the other hand, Korea should actively develop into the V4 area as an energy generation export (nuclear and electric hydrogen generation) area as a bypass development supply area due to the imbalance in the supply chain of rare earth materials that combines AI. By linking this industry, the energy platform can be scaled up and reliable supply technology (next generation BT, recycling technology) in diversification can be formed in countries around the world. This paper proves that in order to overcome the market chasm caused by the industries connection, new energy development and platform size can be achieved and reliable supply technology (next-generation battery and recycling technology, Low-cost LFP) can be diversified in each country.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.1 no.1
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• pp.55-62
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• 2002

This investigation reports the grinding characteristics of titanium alloy(Ti-6AI-4V). Grinding experiments were performed at various grinding conditions. The grinding forces were measured to investigate the grindability of titanium alloy with the five different wheels including Green carbide, Alumina, Resin Diamond, Resin CBN and Vitrified CBN. To investigate the grinding characteristics of titanium alloy grinding force, force ratio, specific grinding energy and grinding-ratio were measured. Surface roughness was also measured with tracer and the ground surfaces were observed with SEM Residual stress measurement was conducted on the X-Ray Diffractometer. Force ratio of grinding of titanium alloy was very lower than that of grinding of SKD-11 Surface roughness with Resin Diamond wheel was a little larger and rougher surface than that with other wheels Grinding ratio of titanium alloy was a little lower than that of other materials. Grinding ratio of titanium alloy with Diamond wheel was almost six times larger than that With CBN wheel. As a result of five different wheels, the most excellent wheel in grinding of Titanium alloy was Resin Diamond wheel.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.30 no.8 s.251
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• pp.772-779
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• 2006

Window is a critical component in the design of energy-efficient buildings. To minimize the heat loss, insulation performance of the glazing has to be improved. Manufacturing of vacuum glazing has been motivated by the possibility of making windows of very good thermal insulation properties for such applications. It is made by maintaining vacuum in the gap between two glass panes. Pillars are placed between them to withstand the atmospheric pressure. Edge covers are applied to reduce conduction through the edge. Accurate measurements have been made of the radiative heat transfer, the pillar conduction and the gas conduction using a guarded hot plate apparatus. Vacuum glazing is found to have low thermal conductance roughly below $1W/m^2K$. Among the heat transfer modes of residual gas conduction, conduction through support pillar and the radiative heat transfer between the glass panes, the last one is the most dominant to the overall thermal conductance. Vacuum glazing using very low emittance AI-coated glass has an overall thermal conductance of about $0.7W/m^2K$.