• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.53-62
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• 2023

This paper has suggested the necessity of introducing training contents using XR(Extended reality) technology as a way to lower the high rate of nursing accidents among unskilled technical personnel in domestic shipbuilding industry, focusing on cable pulling using winch. The occurrence rate of nursing accidents in the domestic shipbuilding industry was almost double(197.4%) (2017~2020) when compared with other manufacturing industries. In particular, it is worth noting that more than 31.8% of nursing accidents in the shipbuilding industry occurred among workers whose job experience is no more than 6 months. Most of new workers are seen to have hard time due to several factors such as lack of work information, inexperience, and unfamiliarity with the working environments. This indicates that it is essential to incorporate more effective training method that could help new workers become familiar with technical skills as well as working environments in a short period of time. Currently, education/training at the domestic shipyard is biased toward technical skills such as welding, painting, machine installation, and electrical installation. Contrary, even more important training required to get new workers used to the working environment has remained at a superficial level such as explaining ship building processes using 2D drawings. This may be the reason why it is inevitable to repeat similar training at OJT (On-the-Job Training) even at the leading domestic companies. Domestic shipbuilding industries have been attracting a lot of new workers thanks to recent economic recovery, which is very likely to increase the occurrence of disasters. In this paper, the introduction of training using XR technology was proposed, and as a specific example, the process of pulling cables using winches on ships was implemented as XR-based training content by using Unity. Using the developed content, it demonstrated that new workers can experience the actual work process in advance through simulation in a virtual space, thereby becoming more effective training content that can help new workers become familiar with the work environment.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.119-138
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• 2016

Since the Industrial Revolution, which made the mass production and mass distribution of standardized goods possible, machine-made (manufactured) products have accounted for the majority of the market. However, in recent years, the phenomenon of purchasing even more expensive handmade products has become a noticeable trend as consumers have started to acknowledge the value of handmade products, such as the craftsman's commitment, belief in their quality and scarcity, and the sense of self-esteem from having them,. Consumer interest in these handmade products has shown explosive growth and has been coupled with the recent development of three-dimensional (3D) printing technologies. Etsy.com is the world's largest online handmade platform. It is no different from any other online platform; it provides an online market where buyers and sellers virtually meet to share information and transact business. However, Etsy.com is different in that shops within this platform only deal with handmade products in a variety of categories, ranging from jewelry to toys. Since its establishment in 2005, despite being limited to handmade products, Etsy.com has enjoyed rapid growth in membership, transaction volume, and revenue. Most recently in April 2015, it raised funds through an initial public offering (IPO) of more than 1.8 billion USD, which demonstrates the huge potential of online handmade platforms. After the success of Etsy.com, various types of online handmade platforms such as Handmade at Amazon, ArtFire, DaWanda, and Craft is ART have emerged and are now competing with each other, at the same time, which has increased the size of the market. According to Deloitte's 2015 holiday survey on which types of gifts the respondents plan to buy during the holiday season, about 16% of U.S. consumers chose "homemade or craft items (e.g., Etsy purchase)," which was the same rate as those for the computer game and shoes categories. This indicates that consumer interests in online handmade platforms will continue to rise in the future. However, this high interest in the market for handmade products and their platforms has not yet led to academic research. Most extant studies have only focused on machine-made products and intelligent services for them. This indicates a lack of studies on handmade products and their intelligent services on virtual platforms. Therefore, this study used signaling theory and prior research on the effects of sellers' characteristics on their performance (e.g., total sales and price premiums) in the buyer-seller relationship to identify the key influencing e-Image factors (e.g., reputation, size, information sharing, and length of relationship). Then, their impacts on the performance of shops within the online handmade platform were empirically examined; the dataset was collected from Etsy.com through the application of web harvesting technology. The results from the structural equation modeling revealed that the reputation, size, and information sharing have significant effects on the total sales, while the reputation and length of relationship influence price premiums. This study extended the online platform research into online handmade platform research by identifying key influencing e-Image factors on within-platform shop's total sales and price premiums based on signaling theory and then performed a statistical investigation. These findings are expected to be a stepping stone for future studies on intelligent online handmade services as well as handmade products themselves. Furthermore, the findings of the study provide online handmade platform operators with practical guidelines on how to implement intelligent online handmade services. They should also help shop managers build their marketing strategies in a more specific and effective manner by suggesting key influencing e-Image factors. The results of this study should contribute to the vitalization of intelligent online handmade services by providing clues on how to maximize within-platform shops' total sales and price premiums.

• Radiation Oncology Journal
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• v.20 no.1
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• pp.41-52
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• 2002

Purpose : 3D conformal radiotherapy, the optimum dose delivered to the tumor and provided the risk of normal tissue unless marginal miss, was restricted by organ motion. For tumors in the thorax and abdomen, the planning target volume (PTV) is decided including the margin for movement of tumor volumes during treatment due to patients breathing. We designed the respiratory gating radiotherapy device (RGRD) for using during CT simulation, dose planning and beam delivery at identical breathing period conditions. Using RGRD, reducing the treatment margin for organ (thorax or abdomen) motion due to breathing and improve dose distribution for 3D conformal radiotherapy. Materials and Methods : The internal organ motion data for lung cancer patients were obtained by examining the diaphragm in the supine position to find the position dependency. We made a respiratory gating radiotherapy device (RGRD) that is composed of a strip band, drug sensor, micro switch, and a connected on-off switch in a LINAC control box. During same breathing period by RGRD, spiral CT scan, virtual simulation, and 3D dose planing for lung cancer patients were peformed, without an extended PTV margin for free breathing, and then the dose was delivered at the same positions. We calculated effective volumes and normal tissue complication probabilities (NTCP) using dose volume histograms for normal lung, and analyzed changes in doses associated with selected NTCP levels and tumor control probabilities (TCP) at these new dose levels. The effects of 3D conformal radiotherapy by RGRD were evaluated with DVH (Dose Volume Histogram), TCP, NTCP and dose statistics. Results : The average movement of a diaphragm was 1.5 cm in the supine position when patients breathed freely. Depending on the location of the tumor, the magnitude of the PTV margin needs to be extended from 1 cm to 3 cm, which can greatly increase normal tissue irradiation, and hence, results in increase of the normal tissue complications probabiliy. Simple and precise RGRD is very easy to setup on patients and is sensitive to length variation (+2 mm), it also delivers on-off information to patients and the LINAC machine. We evaluated the treatment plans of patients who had received conformal partial organ lung irradiation for the treatment of thorax malignancies. Using RGRD, the PTV margin by free breathing can be reduced about 2 cm for moving organs by breathing. TCP values are almost the same values $(4\~5\%\;increased)$ for lung cancer regardless of increasing the PTV margin to 2.0 cm but NTCP values are rapidly increased $(50\~70\%\;increased)$ for upon extending PTV margins by 2.0 cm. Conclusion : Internal organ motion due to breathing can be reduced effectively using our simple RGRD. This method can be used in clinical treatments to reduce organ motion induced margin, thereby reducing normal tissue irradiation. Using treatment planning software, the dose to normal tissues was analyzed by comparing dose statistics with and without RGRD. Potential benefits of radiotherapy derived from reduction or elimination of planning target volume (PTV) margins associated with patient breathing through the evaluation of the lung cancer patients treated with 3D conformal radiotherapy.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.583-603
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• 2023

Recently, the advancement of mechanical tunnel boring machine (TBM) technology and the characteristics of subsea railway tunnels subjected to hydrostatic pressure have led to the widespread application of shield TBM methods in the design and construction of subsea railway tunnels. Subsea railway tunnels are exposed in a constant pore water pressure and are influenced by the amplification of seismic waves during earthquake. In particular, seismic loads acting on subsea railway tunnels under various ground conditions such as soft ground, soft soil-rock composite ground, and fractured zones can cause significant changes in tunnel displacement and stress, thereby affecting tunnel safety. Additionally, the dynamic response of the ground and tunnel varies based on seismic load parameters such as frequency characteristics, seismic waveform, and peak acceleration, adding complexity to the behavior of the ground-tunnel structure system. In this study, a finite difference method is employed to model the entire ground-tunnel structure system, considering hydrostatic pressure, for the investigation of dynamic behavior of subsea railway tunnel during earthquake. Since the key factors influencing the dynamic behavior during seismic events are ground conditions and seismic waves, six analysis cases are established based on virtual ground conditions: Case-1 with weathered soil, Case-2 with hard rock, Case-3 with a composite ground of soil and hard rock in the tunnel longitudinal direction, Case-4 with the tunnel passing through a narrow fault zone, Case-5 with a composite ground of soft soil and hard rock in the tunnel longitudinal direction, and Case-6 with the tunnel passing through a wide fractured zone. As a result, horizontal displacements due to earthquakes tend to increase with an increase in ground stiffness, however, the displacements tend to be restrained due to the confining effects of the ground and the rigid shield segments. On the contrary, peak compressive stress of segment significantly increases with weaker ground stiffness and the effects of displacement restrain contribute the increase of peak compressive stress of segment.