• 대한예방의학회:학술대회논문집
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• 1995.10a
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• pp.99-100
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• 1995

• Journal of the Society of Naval Architects of Korea
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• v.47 no.6
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• pp.836-842
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• 2010

Safety system including location monitoring system for blasting workers was studied. Positioning performance of the location monitoring system was highly dependent on communication protocol and the number of access points in the blasting cell. RTLS(Real Time Location System) is an important technology to develop the location information of workers and variously used to enhance workers safety. Location monitoring system with Cell-ID and RSSI wireless communication technology was verified to have a proper positioning performance for the steel block application.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.24 no.4
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• pp.547-555
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• 2014

Objective: This study was conducted to investigate the association between subjective distress symptoms and argon welding among workers in Gyeongnam Province shipyard. Method: 31 argon and 29 non-argon welding workers were selected as study subjects in order to measure concentrations of personal dust, welding fumes and other hazardous materials such as ZnO, Pb, Cr, FeO, MnO, Cu, Ni, $TiO_2$, MgO, NO, $NO_2$, $O_3$, $O_2$, $CO_2$, CO and Ar. An interviewer-administered questionnaire survey was also performed on the same subjects. The items queried were as follows: age, height, weight, working duration, welding time, welding rod amounts used, drinking, smoking, and rate of subjective distress symptoms including headache and other symptoms such as fever, vomiting and nausea, metal fume fever, dizziness, tingling sensations, difficulty in breathing, memory loss, sleep disorders, emotional disturbance, hearing loss, hand tremors, visual impairment, neural abnormality, allergic reaction, runny nose and stuffiness, rhinitis, and suffocation. Statistical analysis was performed using SPSS software, version 18. Data are expressed as the mean ${\pm}SD$. An ${\chi}^2$-test and a normality test using a Shapiro wilk test were performed for the above variables. Logistic regression analysis was also conducted to identify the factors that affect the total score for subjective distress symptoms. Result: An association was shown between welding type (argon or non-argon welding) and the total score for subjective distress symptoms. Among the rate of complaining of subjective distress symptoms, vomiting and nausea, difficulty breathing, and allergic reactions were all significantly higher in the argon welding group. Only the concentration of dust and welding fumes was shown to be distributed normally after natural log transformation. According to logistic regression analysis, the correlations of working duration and welding type (argon or non-argon) between the total score of subjective distress symptoms were found to be statistically significant (p=0.041, p=0.049, respectively). Conclusion: Our results suggest that argon welding could cause subjective distress symptoms in shipyard workers.

• Molecular & Cellular Toxicology
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• v.2 no.2
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• pp.134-140
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• 2006

Because shipyard workers are involved with various manufacturing process in shipyard industry, and they are exposed to many kinds of hazardous materials. Especially, painting workers were exposed polycyclic aromatic hydrocarbons (PAH). This study was conducted to assess the exposure status of PAH based on job-exposure matrix. We investigated the effect of genetic polymorphism of xenobiotic metabolism enzymes involved in PAH metabolism on levels of urinary metabolite. A total of 93 shipbuilding workers were recruited in this study. Questionnaire variables were age, sex, use of personal protective equipment, smoking, drinking, and work duration. The urinary metabolite was collected in the afternoon and corrected by urinary creatinine concentration. The genotypes of CYP1A1, CYP2E1, GSTM1, GSTT1 and UGT1A6 were investigated by using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) methods with DNA extracted from venous blood. Urinary 1-OHP levels were significantly higher in direct exposured group (spray and touch-up) than indirect exposed group. Urinary 1-OHP, concentration of the high exposure with wild type of UGT1A6 was significantlyhigher than that of the high exposure with other UGT1A6 genotype. In multiple regression analysis of urinary 1-OHP, the regression coefficient of job grade was statistically significant (p<0.05) and UGT1A6 was not significant but a trend (p<0.1). The grade of exposure affected urinary PAH concentration was statistically significant. But genetic polymorphism of xenobiotics metabolism enzymes was not statistically significant. Further investigation of genetic polymorphism with large sample size is needed.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.93-101
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• 2020

Shipyards are potential environments for using IoT services, sensor networks, and delay tolerant networks. Simulations of those services and networks strongly rely on human mobility models. Results obtained with an unrealistic model may not reflect the true performance of applications, protocols, and algorithms in a shipyard. A lot of synthetic models for human movements have been studied but most of them are generic and focus on the daily movements of humans on city scales. Nevertheless, workers in shipyards have unique movement characteristics such as movement speed, pause time, and attractions places. For instance, workers usually move to some places, where they work, and rarely move to other places in the factory. Movement characteristics of workers not only depend on workers but also on tasks, which they do. For instance, workers, who paint ships, have similar movement speed and pause time. Hence, in this paper, human movements in shipyards are studied. We propose a new human mobility model called the human mobility mode in shipyards (MIS). In MIS, workers are classified into multiple types. Movement characteristics of a worker are similar to other workers in the same type. Based on the visiting probability, workers have some places, where they frequently visits, and some places, where they rarely visit. We analyze real mobility traces and studie to achieve human movement characteristics from real traces. The results show that MIS provides a well-match to the movement characteristic from real traces.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.22 no.1
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• pp.42-51
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• 2012

Objectives: To compare the exposure level of extremely low frequency (ELF) magnetic fields among semiconductor workers, shipyard welders and office workers. Methods: To measure the ELF magnetic field concentration, EMDEX LITE (Enertech, USA) were used and monitored for eight hours continuously. Five companies handling the electric and magnetic field (EMF) source were investigated, which the exposure groups were classified into three groups: semiconductor workers, welders, and office workers. Welder group was chosen as a high exposed group and office group as a low exposed group. Results: The arithmetic mean (${\pm}SD$) and geometric mean (GSD) of personal exposure level of semiconductor workers were 0.73 (${\pm}1.33$) ${\mu}T$, 0.43 (2.88) ${\mu}T$, respectively. The ceiling value ranged between 0.18 and 123.2 ${\mu}T$. Welders were exposed high with the arithmetic mean value of 3.46 (${\pm}\;13.46$) ${\mu}T$ and geometric mean value of 0.45 (4.70) ${\mu}T$, respectively, and ceiling value range of 75.5~129.6 ${\mu}T$. The exposure levels of office workers were low compared to other exposed groups; the arithmetic mean 0.05 (${\pm}0.13$) ${\mu}T$, geometric mean 0.03 (2.38) ${\mu}T$ and ceiling value range 0.37~3.35 ${\mu}T$. This study revealed statistically significant differences of the mean ELF magnetic field exposure doses among three groups (p < 0.01). Conclusions: The average ELF magnetic field exposure doses of semiconductor workers were much higher than those of office workers in control group, but were lower than those of welders in high exposure group.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.6
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• pp.40-46
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• 2010

It is an important element increasing ship production to manage an accurate position of transporters(TP) and ship blocks in a shipyard. However, most works are presently being performed by judgment of a system manager and skilled workers. This paper introduced about the system for tracking an accurate position of the transporters and the blocks which are main mobile objects in the shipyard, and proposed a method to decide whether or not a loading/unloading state of the blocks, which is one of the most important functions of the tracking system. Three sensors were used in order to implement the method. One is a RFID reader to identify a target block, another is a RFID reader to estimate a position of the TP as it recognizes a underground tag. The other is a ultrasonic sensor to detect an object. Two experiments were carried out in the shipyard. After correcting errors found on the first experiment. we confirmed that the result could be applied to the shipbuilding yard from the final experiment.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.6
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• pp.601-605
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• 2020

In large ships with complex structures, it is difficult to locate workers. In particular, it is not easy to detect when a worker falls down, making it difficult to respond quickly. Thus, research is being conducted to detect fallen workers using a camera or by attaching a device to the body. Existing image-based fall detection systems have been designed to detect a person's body parts; hence, it is difficult to detect them in various ships and postures. In this study, the entire fall area was extracted and deep learning was used to detect the fallen shipworker based on the image. The data necessary for learning were obtained by recording falling states at the shipyard. The amount of learning data was augmented by flipping, resizing, and rotating the image. Performance evaluation was conducted with precision, reproducibility, accuracy, and a low error rate. The larger the amount of data, the better the precision. In the future, reinforcing various data is expected to improve the effectiveness of camera-based fall detection models, and thus improve safety.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.135-142
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• 2021

Estimating the locations of workers in a shipyard is beneficial for a variety of applications such as selecting potential forwarders for transferring data in IoT services and quickly rescuing workers in the event of industrial disasters or accidents. In this work, we propose a human movement stream processing system for estimating worker locations in shipyards based on Apache Spark and TensorFlow serving. First, we use Apache Spark to process location data streams. Then, we design a worker location prediction model to estimate the locations of workers. TensorFlow serving manages and executes the worker location prediction model. When there are requirements from clients, Apache Spark extracts input data from the processed data for the prediction model and then sends it to TensorFlow serving for estimating workers' locations. The worker movement data is needed to evaluate the proposed system but there are no available worker movement traces in shipyards. Therefore, we also develop a mobility model for generating the workers' movements in shipyards. Based on synthetic data, the proposed system is evaluated. It obtains a high performance and could be used for a variety of tasksin shipyards.

• Proceedings of the KWS Conference
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• 2002.10a
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• pp.412-417
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• 2002

In manufacturing of ships, problems to be solved are improvement of productivity and stabilization of quality due to the shortage of skilled workers. Working environment, in particular welding environment, is also to be improved. One solution among these problems is to rationalize and automate these working. This paper is focused on the welding automation technologies in shipbuilding industry. The features of shipbuilding in the aspect of automation are described, and the main welding robot systems to be developed by SRI are introduced in each working stages.