Onboard truck scales can accurately measure payload under static conditions. However, their performance is limited in accounting for dynamic environments encountered during driving, leading to inaccuracies in load estimation under real-world conditions. This study employs TruckCaliber, a dynamic state measurement system, to estimate real-time vehicle loads. Fusion sensor modules were installed on leaf spring suspensions and vehicle frames to collect tilt and IMU data. The system was implemented on a commercial truck, and driving tests were conducted with varying payloads. The analysis focused on curved sections under different dynamic conditions.

Safe operation of freight vehicles is an important issue for drivers, cargo, and other road users. In particular, the center of gravity of a freight vehicle is directly related to the stability of the vehicle, and this can fluctuate in real time depending on weight changes. Every time a freight vehicle loads or unloads cargo, its center of gravity changes, and these changes greatly affect the risk of vehicle rollover. We researched a continuous center of gravity measurement system for freight vehicles for safe driving.

With the mandatory implementation of ESC for trucks starting in 2023, domestic truck manufacturers in South Korea are advocating for a relaxation of the maximum safe slope angle to achieve cost savings. However, there is a lack of research on the dynamic safety of trucks based on ESC installation and the relaxation of the maximum safe slope angle. This study evaluates the relationship between static safety factor (SSF) and the maximum safe slope angle, analyzing the dynamic stability of trucks through simulation considering various experimental variables. The results quantitatively demonstrate the impact of relaxing the maximum safe slope angle on dynamic safety and provide recommendations for future safety regulations.

In zinc-air batteries, the gel polymer electrolyte (GPE) is an important factor for improving performance. The rigid physical properties of polyvinyl alcohol reduce ionic conductivity, which degrades the performance of the batteries. Zinc acetate is an effective additive that can increase ionic conductivity by weakening the bonding structure of polyvinyl alcohol. In this study, polymer electrolytes were prepared by mixing polyvinyl alcohol and zinc acetate dihydride. The material properties of the prepared polymer electrolytes were analyzed by Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), X-ray diffraction (XRD), and thermogravimetric analysis (TGA). Also, Electrochemical impedance spectroscopy was used to calculate ionic conductivity. The electrolyte resistances of GPE, 0.2 GPE, 0.4 GPE, and 0.6 GPE were 0.394, 0.338, 0.290, and 0.213 Ω, respectively. In addition, 0.6 GPE delivered 0.023 S/cm high ionic conductivity. Among all of the polymer electrolytes tested, 0.6 GPE showed enhanced cycle life performance and the highest specific discharge capacity of 11.73 mAh/cm2 at 10 mA. These results verified that 0.6 GPE improves the performance of zinc-air batteries.

PURPOSES : This study aims to perform a quantitative analysis of Forward Collision Warning and crash frequency using heavy vehicle driving data collected in expressway driving environments, and to classify the driving environments where Forward Collision Warnings of heavy vehicles occur for accident-prone areas and analyze their occurrence characteristics. METHODS : A bivariate Gaussian mixture model based on inter-vehicle distance gap and speed-acceleration parameters is used to classify the environment in which Forward Collision Warning occurs for heavy vehicles driving on expressways. For this analysis, Probe Vehicle Data of 80 large trucks collected by C-ITS devices of Korea Expressway Corporation from May to June 2022. Combined with accident information from the past five years, a detailed analysis of the classified driving environments is conducted. RESULTS : The results of the clustering analysis categorizes Forward Collision Warning environments into three groups: Group I (highdensity, high-speed), Group II (high-density, low-speed), and Group III (low-density, high-speed). It reveals a positive correlation between Forward Collision Warning frequency and accident rates at these points, with Group I prevailing. Road characteristics at sites with different accident incidences showed that on-ramps and toll gates had high occurrences of both accidents and warnings. Furthermore, acceleration deviation at high-accident sites was significant across all groups, with variable speed deviations noted for each warning group. CONCLUSIONS : The Forward Collision Warning of heavy vehicles on expressways is classified into three types depending on the driving environment, and the results of these environmental classifications can be used as a basis for building a road environment that reduces the risk of crashes for heavy vehicles.

Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational safety and reliability of the parcel loading system, a predictive maintenance platform was implemented based on the Naive Bayes-LSTM(Long Short Term Memory) model. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on a RabbitMQ, loading data in an InMemory method using a Redis, and managing snapshot DB in real time. Also, in this paper, as a verification of the Naive Bayes-LSTM predictive maintenance platform, the function of measuring the time for data collection/storage/processing and determining outliers/normal values was confirmed. The predictive maintenance platform can contribute to securing reliability and safety by identifying potential failures and defects that may occur in the operation of the parcel loading system in the future.

The demand for LNG Carrier and LNG fuel ships are increasing due to global carbon neutrality declaration and ship emissions regulation of IMO, domestic shipyards pay technology fees(about 5~10% of ship price per vessel) to GTT company in France for making LNG cargo hold. Localization of LNG cargo hold is needed to reduce technology fees and engage technological competitiveness, it is important to secure the critical technology like automation process development of insulation system process. Especially, the automation rate of membrane-type insulation system is very low due to interference caused by corrugation and difficulty in securing optimal variable welding condition. In this study, to solve this problem, automatic welding is performed using developed automatic welding equipment on STS304L steel which is used in flat and corner area of membrane-type LNG cargo hold's lap joint. After welding, Cross-sectional observations and Tensile strength tests were conducted to evaluate reliability of equipment and welding condition. As a result of the test, it was confirmed that the strength of the welded zone exceeded that of base material, and secured the optimal welding condition to apply automatic welding.

Since 2024, small business are also going to be ruled under the Serious Accident Punishment Act. As the scope of the law expands, the small logistics companies are required to pay more attention on preventing serious accidents on the field. Freight vehicle accidents can cause personnel accidents and cargo accidents which are the two serious accidents that the Serious Accident Punishment Act is trying to prevent. The purpose of this research are to study the factors that cause the serious accidents that happens in the small logistics companies and to suggest preventive. The results of the study shows that fall prevention is the top-priority and then driving experience, safety management, and cargo driving hours. However, the gaps between the evaluation values of each are not huge, which means all the preventives are significant.

The air transport industry is experiencing unprecedented fluctuations in aviation demand through the Covid-19 pandemic, and is more focused than ever on maintaining and generating business profitability. During the pandemic, demand for air cargo has soared, and the conversion business from passenger aircrafts to freighters(P2F) is drawing attention as a new business in the aviation maintenance industry. This study derives important factors to be considered in order to successfully carry out the P2F project through a wide range of cases and related literature, and analyzes the relative importance of each factor using the analytic hierarchy process. Through a survey of 33 aviation maintenance experts with more than 20 years of field experience, the importance of main factors and their sub factors was determined and implications were drawn. As a primary result, in order to succeed in the P2F project, the main factors were identified in the order of skill, finance, and location. The most important sub factors for each main factors were identified in order of securing airframe modification skill, securing infrastructure construction cost, and creating P2F business complex and district. The quantified success factors suggested the critical direction for the successful development of Korea's P2F business, and presented viable and specific business strategies and implementation plans for each factors.

In modern society, the delivery service market has grown explosively due to rapid changes in social structure and the recent COVID-19 pandemic. Therefore, various problems such as injury to workers and an increase in human accidents are occurring due to the loading and unloading of parcels. In order to solve this problem, domestic company n is developing a “robot-based cargo loading and unloading system”. In developing a new technology system, quantitative reliability targets should be set for efficient operation and development. In this paper, reliability analysis was conducted through field data for the pneumatic gripper of the “robot-based cargo loading system”. The reliability of the failure data was analyzed to estimate the distribution parameters and MTTF. Random data was derived for the probability of occurrence of a failure with the estimated value. By repeating the simulation to predict the number and year of failures according to the estimated parameters of the probability distribution, it was proposed as a method that reflects realistic probabilities rather than calculating with simple arithmetic using the average MTTF previously used in the field.