• Journal of Industrial Technology
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• v.25 no.A
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• pp.151-156
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• 2005

Auto exhausts and air pollution can become trapped in the cabin of vehicle, reducing the amount of oxygen available for breathing. Driver may feel sleepy, headaches, nausea, confusion, dizziness and lower levels of oxygen can damage the driver's general health. Consequently, oxygen generator purified oxygen into the car to help driver get the oxygen driver's body needs. In this study, in order to evaluate the oxygen generator performance, the samples that the various conditions(humidity(50%, 100%), flow and oxygen concentration) were examined. There were three types of oxygen generator; sample 1($2.5{\ell}/min$, $36%O_2$), sample 2($4.5{\ell}/min$, $41%O_2$) and sample 3($5{\ell}/min$, $39%O_2$). As the humidity increased from 50% to 100%, the oxygen concentration of the sample 1(36%), 2(41%) and 3(39%) was reduced $31%O_2$, $38%O_2$ and $38%O_2$, respectively. Also, the each sample measured that effect of human in car on oxygen concentration, if the oxygen concentration is one person in car, each sample of oxygen concentration was $20.8%O_2$, $23.7%O_2$ and $21.2%O_2$. From the above results, it was shown that oxygen generator for car, if the oxygen concentration is increased, effect of humidity is reduced, and that in the sample of supplying a high-rate of oxygen, the oxygen concentration is increased. It was suggested that effect of humidity on oxygen generator for car can be reduced according to the supply of oxygen.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.7
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• pp.1556-1567
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• 2009

The death rate of pedestrian in car accidents in Korea is 2.5 times higher than the average of OECD countries'. If a system that can detect pedestrians and send alarm to drivers is built and reduces the rate, it is worth developing such a pedestrian detection system (PDS). Since the accident rate in which pedestrians are involved is higher at nighttime than in daytime, the adoption of nighttime PDS is being standardized by big auto companies. However, they are usually using night visions or multiple sensors, which are usually expensive. In this paper we suggest a method for nighttime PDS using single wide dynamic range (WDR) monochrome camera in visible spectrum band. In our experiments, pedestrians were accurately detected if only most edges of pedestrians could be obtained.

• Journal of Welding and Joining
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• v.30 no.3
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• pp.66-70
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• 2012

Welding is an essential process in the automotive industry. Most welding processes that are used for auto body is spot welding. And $CO_2$ arc welding is used in a small part. In production field, $CO_2$ arc welding process is decreased and spot welding process is increased due to welding quality is poor and defects are occurred in $CO_2$ arc welding process frequently. But $CO_2$ arc welding process should be used at robot interference parts and closed parts where spot welding couldn't. $CO_2$ welding is divided into lap welding and plug arc spot welding. In case of plug arc spot welding, burn through and under fill were caused in various welding environment such as different thickness combinations of base metal, teaching point, over the two steps welding and inconsistent voltage/current. It makes some problem like poor quality of welding area and decrease the productivity. In this study, we will evaluate the effect of teaching point through the weld pool behavior and bead geometry in the arc spot welding at the plut hole. Welding position is horizontal position. And galvanized steel sheet of 2.0mm thickness that has plug hole of 6mm diameter was used. Teaching point was changed by center, top, bottom, left and right of the plug hole. At each condition, the phenomenon of weld pool behavior was confirmed using a high-speed camera. As the result, we find the center of plug hole is the most optimal teaching point. In the other teaching point, under fill was occurred at the plug hole. This phenomenon is caused by gravity and surface tension. For performance of arc spot welding at the plug hole, the teaching condition should be controlled at a center of plug hole.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.9
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• pp.1131-1138
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• 2019

The importance of coatings has been increasing for different purposes such as prevention of static electricity of auto parts or products, improvement of abrasion and corrosion resistance, and enhancement of esthetics. As a method for measuring the thickness of a coating film, a contact method with probe is commonly used. However, it is problematic that accuracy of the sensor is degraded due to sensor output distortion or load phenomenon, which is caused by a change in magnetic permeability of the core. In this study, we propose a method to reduce the measurement error of the coating film by applying the optimized circuit design and the thickness measurement algorithm to the problems caused by the nonlinear characteristics. The tests result which have been taken with different thickness coating samples show that the measurement accuracy is within ${\pm}2%$.

• Fourth Industrial Review
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• v.1 no.1
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• pp.1-10
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• 2021

Purpose - This case study analyzes the internationalization strategy and innovation strategy as key factors contributing to the business success of CAP, a small and medium-sized manufacturing company in Korea producing auto parts such as wipers. This study describes the diversification strategies conducted by CAP Corporation and highlights the company's core competencies that have largely contributed to their global competitive success. Research design, data, and methodology - This paper provides in-depth case study on how CAP was able to grow into a hidden champion company, focusing on their strategies since its establishment. In particular, by analyzing the success factors centering on CAP's aggressive innovation strategy and internationalization strategy, it presents guidelines for small and medium-sized enterprises in Asian countries to become a Hidden Champion company. Result - CAP's product technology has successfully established innovative system on their product called 'vertebra spring' to distribute uniform pressure to the rubber to ensure performance as well as durability of their products. In order to continue benefiting from utilizing core competence and to continue pursuing technological advancement in the wiper industry, CAP has launched a wide range of products (flat blade, conventional blade, hybrid blade) applicable to 95% of the vehicle in the market. Conclusion - Taken together, CAP has many aspects of a hidden champion company by investing in R&D up to 8% of its annual sales to R&D investment even during the crises situation. This number is about 3.36 times higher than the average ratio of listed companies in Korea. Furthermore, the leadership of the management team as well as their vision toward the global market and strong commitment to innovation enabled CAP to become the world's fifth-largest wiper and Asia's No. 1 wiper manufacturer.

• International Journal of Advanced Culture Technology
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• v.10 no.3
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• pp.146-155
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• 2022

The purpose of this study is to investigate the recyclability of Shenlong in China using SWOT. The main analysis results are as follows. First, provided that the company's current capacity utilization rate is seriously insufficient, reducing staff is one among the effective ways. Second, Shenlong should open a web store to cater to young people's online shopping behavior, and further expand the brand visibility using national mainstream media and online shopping platforms like Taobao and JingDong to market Dongfeng Peugeot and Dongfeng Citroen on the whole network. Third, under the premise of maintaining the present best-selling models, Shenlong should appropriately reduce the amount of models, adjust the assembly capacity ratio of every model and every displacement in real time per the newest market trends, increase the agility of auto companies' production, and timely meet the wants of domestic consumers. Fourth, dual-brand coordination and channel integration are very necessary, and also the profitability and profitability of dealers are going to be further improved, thereby increasing sales. Fifth, target building new energy leading products of Shenlong, strive to attain the forefront of the industry within the sales of recent energy vehicles within 5 years, and gradually expand new energy vehicle products from passenger vehicles to passenger vehicles and commercial vehicles. Finally, the marketing field of Shenlong Automobile should achieve "three major changes", that is, change from a goal-driven type to a demand-driven type, cancel the bundling of outlet invoicing goals and delivery incentive tiers; start from basic capabilities, and set pragmatic and challenging goals; focus Channels, to realize following the activation of outlets, and single store sales increase.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.42
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• pp.22.1-22.6
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• 2020

Introduction: Facial fractures (FFs) occur after high- and low-energy trauma; differences in associated injuries and outcomes have not been well articulated. Objective: To compare the epidemiology, management, and outcomes of patients suffering FFs from high-energy and low-energy mechanisms. Methods: We conducted a 6-year retrospective local trauma registry analysis of adults aged 18-55 years old that suffered a FF treated at the Santa Barbara Cottage Hospital. Fracture patterns, concomitant injuries, procedures, and outcomes were compared between patients that suffered a high-energy mechanism (HEM: motor vehicle crash, bicycle crash, auto versus pedestrian, falls from height > 20 feet) and those that suffered a low-energy mechanism (LEM: assault, ground-level falls) of injury. Results: FFs occurred in 123 patients, 25 from an HEM and 98 from an LEM. Rates of Le Fort (HEM 12% vs. LEM 3%, P = 0.10), mandible (HEM 20% vs. LEM 38%, P = 0.11), midface (HEM 84% vs. LEM 67%, P = 0.14), and upper face (HEM 24% vs. LEM 13%, P = 0.217) fractures did not significantly differ between the HEM and LEM groups, nor did facial operative rates (HEM 28% vs. LEM 40%, P = 0.36). FFs after an HEM event were associated with increased Injury Severity Scores (HEM 16.8 vs. LEM 7.5, P <0.001), ICU admittance (HEM 60% vs. LEM 13.3%, P <0.001), intracranial hemorrhage (ICH) (HEM 52% vs. LEM 15%, P <0.001), cervical spine fractures (HEM 12% vs. LEM 0%, P = 0.008), truncal/lower extremity injuries (HEM 60% vs. LEM 6%, P <0.001), neurosurgical procedures for the management of ICH (HEM 54% vs. LEM 36%, P = 0.003), and decreased Glasgow Coma Score on arrival (HEM 11.7 vs. LEM 14.2, P <0.001). Conclusion: FFs after HEM events were associated with severe and multifocal injuries. FFs after LEM events were associated with ICH, concussions, and cervical spine fractures. Mechanism-based screening strategies will allow for the appropriate detection and management of injuries that occur concomitant to FFs. Type of study: Retrospective cohort study. Level of evidence: Level III.

• The Journal of Information Systems
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• v.31 no.3
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• pp.197-220
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• 2022

Purpose The purpose of this study is to identifies the demand pattern categorization of repair parts of Automotive After-sales Service(A/S) and proposes a demand prediction model for Auto repair parts using Long Short-Term Memory (LSTM) of artificial neural networks (ANN). The optimal parts inventory quantity prediction model is implemented by applying daily, weekly, and monthly the parts demand data to the LSTM model for the Lumpy demand which is irregularly in a specific period among repair parts of the Automotive A/S service. Design/methodology/approach This study classified the four demand pattern categorization with 2 years demand time-series data of repair parts according to the Average demand interval(ADI) and coefficient of variation (CV²) of demand size. Of the 16,295 parts in the A/S service shop studied, 96.5% had a Lumpy demand pattern that large quantities occurred at a specific period. lumpy demand pattern's repair parts in the last three years is predicted by applying them to the LSTM for daily, weekly, and monthly time-series data. as the model prediction performance evaluation index, MAPE, RMSE, and RMSLE that can measure the error between the predicted value and the actual value were used. Findings As a result of this study, Daily time-series data were excellently predicted as indicators with the lowest MAPE, RMSE, and RMSLE values, followed by Weekly and Monthly time-series data. This is due to the decrease in training data for Weekly and Monthly. even if the demand period is extended to get the training data, the prediction performance is still low due to the discontinuation of current vehicle models and the use of alternative parts that they are contributed to no more demand. Therefore, sufficient training data is important, but the selection of the prediction demand period is also a critical factor.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2A
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• pp.171-178
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• 2008

The object of this study is to propose a rate of vibration increase in the analysis of temporary rail non-fixed in the vertical direction and characterize the nonlinear dynamic behavior of temporary rail while considering longitudinal and latitudinal load, vibration and lifting. The rate of vibration increase is proposed through measurement of an actual structure that is largely affected by loading and vibration of the superstructure. Dynamic behavior was additionally characterized by the dynamic response resulting from nonlinear dynamic finite element analysis with vehicle loading, including the rate of vibration increase. As a result, the rate of vibration increase by the vibration of an Auto Bar Machine is determined as 7% and the maximum stress in the analysis of the nonlinear rail is increased 14.5% over that of linear rail, and temporary rail is shown to be very sensitive to the velocity of the superstructure.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.11-20
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• 2023

The cost of medical treatment for motor vehicle accidents is increasing every year. In this study, we created a model to predict long-term hospitalization(more than 18 days) among minor patients, which is the main item of increasing traffic accident medical expenses, using five algorithms such as decision tree, and analyzed the factors affecting long-term hospitalization. As a result, the accuracy of the prediction models ranged from 91.377 to 91.451, and there was no significant difference between each model, but the random forest and XGBoost models had the highest accuracy of 91.451. There were significant differences between models in the importance of explanatory variables, such as hospital location, name of disease, and type of hospital, between the long-stay and non-long-stay groups. Model validation was tested by comparing the average accuracy of each model cross-validated(10 times) on the training data with the accuracy of the validation data. To test of the explanatory variables, the chi-square test was used for categorical variables.