• Journal of the Society of Disaster Information
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• v.17 no.1
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• pp.112-119
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• 2021

Purpose: This study is based on the detailed inspection data for the last 6 years of fire-fighting high ladder vehicles, fire-fighting inflected ladder vehicles, fire-fighting chemical vehicles and fire-fighting pump vehicles used in front-line fire departments. The purpose is to contribute to the technological development of fire-fighting vehicles by grasping the implementation status of each city and province, the rate of defects, and the occurrence of defects by year. Method: The implementation status by city and province, defect incidence rate, and defect occurrences by year were analyzed. Result: From 2012 to 2017, when the average of 230 or more overhaul vehicles was requested, the results of each city/province show slight fluctuations, but the number of defects gradually decreased due to the old fire-fighting vehicle replacement project and the response of fire vehicle manufacturers. Conclusion: In the case of fire-fighting ladders, the incidence rate of defects was found to be in the order of elevator device, electric device, ladder device, and pneumatic supply device. And in the case of the fire fighting ladder, it was confirmed that the incidence of defects appeared in the order of the refractive ladder, hydraulic cylinder, hydraulic oil, and pneumatic supply device. In the case of fire-fighting chemical vehicles, it was confirmed that defects occurred in the powder fire extinguishing device, fire pump, vacuum pump, and pneumatic supply device.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.12
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• pp.387-402
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• 2020

Conventional automotive development has mainly focused on ensuring correctness and safety and security has been relatively neglected. However, as the number of automotive hacking cases has increased due to the increased Internet connectivity of automobiles, international organizations such as the United Nations Economic Commission for Europe(UNECE) are preparing cybersecurity regulations to ensure security for automotive development. As with other IT products, automotive cybersecurity regulation also emphasize the concept of "Security by Design", which considers security from the beginning of development. In particular, since automotive development has a long lifecycle and complex supply chain, it is very difficult to change the architecture after development, and thus Security by Design is much more important than existing IT products. The problem, however, is that no specific methodology for Security by Design has been proposed on automotive development process. This paper, therefore, proposes a specific methodology for Security by Design on Automotive development. Through this methodology, automotive manufacturers can simultaneously consider aspects of functional safety, and security in automotive development process, and will also be able to respond to the upcoming certification of UNECE automotive cybersecurity regulations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.167-178
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• 2021

Since 2014, the quality level survey of the defense industry has been carried out annually. The need for this was felt as the quality control policy was inadequately reflected in the survey results. In this study, to secure valid and reliable data, the quality level survey measurement indicators and questionnaires used previously were analyzed and developed as customized papers according to the size of the company. The questionnaire index was simplified through the elimination of unnecessary and redundant items to improve the response rate and reliability. In addition, a comprehensive score was computed based on the improvement index so that the result of the quantitative quality level survey could be calculated. Through this, it was possible to determine the quality level attained by the defense industry. Through the quality level survey, we intend to secure data that can analyze the quality management environment to maintain quality levels of the defense industry and identify ways to establish mid-to-long-term quality management strategies and improve systems to enhance the quality level of the defense industry in the future.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.29-37
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• 2021

In the event of a marine pollution accident, Korea, which is bordered by the sea on three sides, would suffer tremendous damage. According to an analysis of marine pollution accidents in Korea, most pollutants were small fishing boats and tugboats. In particular, urgent safety measures are required due to the high incidence of marine pollution accidents caused by stranded tugboats. This study aims to decrease secondary marine pollution accidents by preparing measures to prevent accidents by stranded tugboats, the main source of pollution accidents. To this end an experiment was conducted to mark 63 locations where stranded accidents have occurred on the GPS plotters of 61 tugboats operating in Boryeong sea area. Its effectiveness was then investigated. The result showed that there was no stranded accident by the subject ship during the 20-month experiment period. As a result of survey of the captains, 36 out of 40 respondents (90%) stated that the experiment was significantly helpful in ensuring safe navigation, thus confirming its effectiveness. Various measures were proposed to prevent stranded accidents, including providing information to manufacturers and users by marking the location of grounded accidents on GPS plotters and posting the location information of unknown reefs on the official website of institution Korea Hydrographic and Oceanographic Agency.

• Clean Technology
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• v.27 no.1
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• pp.33-38
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• 2021

Demand for lithium-based secondary batteries is greatly increasing with the explosive growth of related industries, such as mobile devices and electric vehicles. In Korea, there are several top-rated global lithium-ion battery manufacturers accounting for 40% of the global secondary battery business. Most discarded lithium secondary batteries are recycled as scrap to recover valuable metals, such as Nickel and Cobalt, but residual wastes are disposed of according to the residual lithium-ion concentration. Furthermore, there has not been an attempt on the possibility of water discharge system contamination due to the concentration of lithium ions, and the effluent water quality standards of public sewage treatment facilities are becoming stricter year after year. In this study, the as-received waste water generated from the cathode electrode coating process in the manufacturing of high-nickel-based NCM cathode material used for high-performance and long-term purposes was analyzed. We suggested a facile recycling process chart for waste water treatment. We revealed a correlation between lithium-ion concentration and pH effect according to the proposed waste water of each recycling process through analyzing standard water quality tests and daphnia ecological toxicity. We proposed a realistic waste water treatment plan for lithium electrode manufacturing plants via comparison with other industries' ecotoxicology.

• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.620-626
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• 2020

This research propose and simulate a solar power generation system monitoring system based on Modbus TCP communication using RaspberryPi, an IOT equipment, as a master and an inverter as a slave. In this model, various sensors are added to the RaspberryPi to add necessary information for monitoring solar power plants, and power generation prediction and monitoring information are transmitted to the smart phone through real-time power generation prediction. In addition, information that is continuously generated by the solar power plant is built on the server as big data, and a deep learning model for predicting power generation is trained and updated. As a result of the study, stable communication was possible based on Modbus TCP with the Raspberry Pi in the inverter, and real-time prediction was possible with the deep learning model learned in the Raspberry Pi. The server was able to train various deep learning models with big data, and it was confirmed that LSTM showed the best error with a learning error of 0.0069, a test error of 0.0075, and an RMSE of 0.0866. This model suggested that it is possible to implement a real-time monitoring system that is simpler, more convenient, and can predict the amount of power generation for inverters of various manufacturers.

• The Journal of Information Systems
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• v.30 no.1
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• pp.151-177
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• 2021

Purpose This paper aims to improve management performance by effectively responding to production needs and reducing inventory through synchronizing production planning and procurement in the aviation industry. In this study, the differences in production planning and execution were first analyzed in terms of demand, supply, inventory, and process using the big data collected from a domestic aircraft manufacturers. This paper analyzed the problems in procurement and inventory management using legacy big data from ERP system in the company. Based on the analysis, we performed a simulation to derive an efficient procurement and inventory management plan. Through analysis and simulation of operational data, we were able to discover procurement and inventory policies to effectively respond to production needs. Design/methodology/approach This is an empirical study to analyze the cause of decrease in inventory turnover and increase in inventory cost due to dis-synchronize between production requirements and procurement. The actual operation data, a total of 21,306,611 transaction data which are 18 months data from January 2019 to June 2020, were extracted from the ERP system. All them are such as basic information on materials, material consumption and movement history, inventory/receipt/shipment status, and production orders. To perform data analysis, it went through three steps. At first, we identified the current states and problems of production process to grasp the situation of what happened, and secondly, analyzed the data to identify expected problems through cross-link analysis between transactions, and finally, defined what to do. Many analysis techniques such as correlation analysis, moving average analysis, and linear regression analysis were applied to predict the status of inventory. A simulation was performed to analyze the appropriate inventory level according to the control of fluctuations in the production planing. In the simulation, we tested four alternatives how to coordinate the synchronization between the procurement plan and the production plan. All the alternatives give us more plausible results than actual operation in the past. Findings Based on the big data extracted from the ERP system, the relationship between the level of delivery and the distribution of fluctuations was analyzed in terms of demand, supply, inventory, and process. As a result of analyzing the inventory turnover rate, the root cause of the inventory increase were identified. In addition, based on the data on delivery and receipt performance, it was possible to accurately analyze how much gap occurs between supply and demand, and to figure out how much this affects the inventory level. Moreover, we were able to obtain the more predictable and insightful results through simulation that organizational performance such as inventory cost and lead time can be improved by synchronizing the production planning and purchase procurement with supply and demand information. The results of big data analysis and simulation gave us more insights in production planning, procurement, and inventory management for smart manufacturing and performance improvement.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.5
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• pp.411-429
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• 2022

Instrumentations are essential in NATM tunnels, however measuring instruments are installed and applied without performance verification procedures due to insufficient research on methods, procedures, regulations, etc. to verify the reliability of the measuring instruments. In this study, domestic and foreign regulations relating to the verification and calibration of instruments were investigated and necessities for accreditation standards were proposed. In order to identify the causes of the defects, an external inspection was performed on rock bolt stressmeter and rod extensometer, which are measuring instruments with relatively complex structures. For verifying the performance of these instruments, verification devices were developed that can load step-by-step and the causes of defects were identified in measuring instruments of nine domestic manufacturers. Through the performance test, a number of measuring instruments were found to be defective. It was important to test the performance of the instruments in the state of a finished product and accordingly performance inspection methods and procedures were proposed. The results of this study are expected to help preparing related regulations for verifying instrument performance and selecting instruments in the field.

• The Journal of the Acoustical Society of Korea
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• v.41 no.5
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• pp.589-600
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• 2022

In order to verify the reliability of Dry Test Bench (DTB) used for testing the output energy from ballistic extracorporeal shock wave therapeutic devices, the measurements with DTB were compared with the acoustic energy measured with a Laser Doppler Vibrometer (LDV) for a commercial ballistic ESWT device. It was shown that the mechanical energy detected with DTB had variability maintained within 5 % at the same output power setting and also had a linear correlation (adj. R² = 0.991) with the acoustic energy measured with the LDV for the entire output power settings. Using the correlation between the two methods and the correlation on the acoustic energy measured in between air and water with the LDV, the DTB measurement can be used to estimate the energy flux density in water with an average error of 7.85 % for the entire output power settings of the ballistic shock wave generator considered in the experiment. DTB provides information limited to the output mechanical energy and therefore it is not suitable for testing the various acoustic output parameters required in IEC61846 and IEC63045. However, DTB that is simple in measurement principles and easy to use is expected for manufacturers and clinical users to monitor the performance of ballistic Extracorporeal Shock Wave Therapy (ESWT) devices.

• The Journal of Information Systems
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• v.31 no.3
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• pp.177-195
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• 2022

Purpose This paper aims to prepare a full operational readiness by establishing an optimal flight plan considering the weather conditions in order to effectively perform the mission and operation of military aircraft. This paper suggests a flight prediction model and rules by analyzing the correlation between flight implementation and cancellation according to weather conditions by using big data collected from historical flight information of military aircraft supplied by Korean manufacturers and meteorological information from the Korea Meteorological Administration. In addition, by deriving flight rules according to weather information, it was possible to discover an efficient flight schedule establishment method in consideration of weather information. Design/methodology/approach This study is an analytic study using data mining techniques based on flight historical data of 44,558 flights of military aircraft accumulated by the Republic of Korea Air Force for a total of 36 months from January 2013 to December 2015 and meteorological information provided by the Korea Meteorological Administration. Four steps were taken to develop optimal flight prediction models and to derive rules for flight implementation and cancellation. First, a total of 10 independent variables and one dependent variable were used to develop the optimal model for flight implementation according to weather condition. Second, optimal flight prediction models were derived using algorithms such as logistics regression, Adaboost, KNN, Random forest and LightGBM, which are data mining techniques. Third, we collected the opinions of military aircraft pilots who have more than 25 years experience and evaluated importance level about independent variables using Python heatmap to develop flight implementation and cancellation rules according to weather conditions. Finally, the decision tree model was constructed, and the flight rules were derived to see how the weather conditions at each airport affect the implementation and cancellation of the flight. Findings Based on historical flight information of military aircraft and weather information of flight zone. We developed flight prediction model using data mining techniques. As a result of optimal flight prediction model development for each airbase, it was confirmed that the LightGBM algorithm had the best prediction rate in terms of recall rate. Each flight rules were checked according to the weather condition, and it was confirmed that precipitation, humidity, and the total cloud had a significant effect on flight cancellation. Whereas, the effect of visibility was found to be relatively insignificant. When a flight schedule was established, the rules will provide some insight to decide flight training more systematically and effectively.