• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.123-133
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• 2024

Existing autonomous driving technology has been developed based on sensors attached to the vehicles to detect the environment and formulate driving plans. On the other hand, it has limitations, such as performance degradation in specific situations like adverse weather conditions, backlighting, and obstruction-induced occlusion. To address these issues, cooperative autonomous driving technology, which extends the perception range of autonomous vehicles through the support of road infrastructure, has attracted attention. Nevertheless, the real-time analysis of the 3D centroids of objects, as required by international standards, is challenging using single-lens cameras. This paper proposes an approach to detect objects and estimate the centroid of vehicles using the fixed field of view of road infrastructure and pre-measured geometric information in real-time. The proposed method has been confirmed to effectively estimate the center point of objects using GPS positioning equipment, and it is expected to contribute to the proliferation and adoption of cooperative autonomous driving infrastructure technology, applicable to both vehicles and road infrastructure.

• Journal of the Korea Safety Management & Science
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• v.25 no.4
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• pp.141-151
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• 2023

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.

• Journal of the Korean Recycled Construction Resources Institute
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• v.12 no.1
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• pp.25-32
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• 2024

In this study, the mechanical properties of PHC pile concrete using alpha-type hemihydrate gypsum were evaluated. As the replacement ratio of alpha-type hemihydrate gypsum increased, the setting time rapidly accelerated. In particular, when replacement ratio exceeded 20 %, the setting time was shortened due to rapid hydration reaction, making it impossible to secure working time. As the replacement ratio of alpha-type hemihydrate gypsum increased, the ettringite and gypsum peaks tended to increase, and it is believed that the shrinkage of concrete decreased due to the increase in the ettringite peak. At a As the replacement ratio of 5 to 15 % for alpha-type hemihydrate gypsum, the compressive strength increased or was found to be equivalent to that of OPC. But at 20 % substitution, workability deteriorated due to rapid setting, so use of the 5 to 15 % range is considered appropriate.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.60 no.1
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• pp.57-70
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• 2024

In order to present a predictive drift model, Jeju National University's training ship was tested for about 11 hours and 40 minutes, and 81 samples that selected one of the entire samples at ten-minute intervals were subjected to regression analysis after verifying outliers and influence points. In the outlier and influence point analysis, although there is a part where the wind direction exceeds 1 in the DFBETAS (difference in Betas) value, the CV (cumulative variable) value is 6%, close to 1. Therefore, it was judged that there would be no problem in conducting multiple regression analyses on samples. The standard regression coefficient showed how much current and wind affect the dependent variable. It showed that current speed and direction were the most important variables for drift speed and direction, with values of 47.1% and 58.1%, respectively. The analysis showed that the statistical values indicated the fit of the model at the significance level of 0.05 for multiple regression analysis. The multiple correlation coefficients indicating the degree of influence on the dependent variable were 83.2% and 89.0%, respectively. The determination of coefficients were 69.3% and 79.3%, and the adjusted determination of coefficients were 67.6% and 78.3%, respectively. In this study, a more quantitative prediction model will be presented because it is performed after identifying outliers and influence points of sample data before multiple regression analysis. Therefore, many studies will be active in the future by combining them.

• Journal of Advanced Technology Convergence
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• v.3 no.1
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• pp.13-20
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• 2024

This paper discusses a study on a mobile application aimed at making public transportation more convenient for people with mobility challenges on both Android and iOS platforms. The research analyzes the limitations and weaknesses of existing mobile applications for public transportation from the perspective of individuals with mobility challenges. The goal is to overcome these limitations and provide an optimized user experience. The motivation behind this research stems from the recognition that people with mobility challenges face difficulties in their daily commute, and current public transportation applications do not adequately cater to their needs. Consequently, the study aims to develop a specialized mobile application for individuals with mobility challenges to support them in achieving greater independence in their daily travels.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.715-734
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• 2023

Purpose: Authors suggest a new alternative of preparedness capabilities for health care institutes to react to the new types of communicable diseases and the pandemic situations through the quality improvement and advancement of Negative Pressure Isolation Stretcher (NPIS). Methods: We employed FOCUS-PDCA, a representative quality improvement model. First, we identified and materialized necessary components of existing NPIS to be improved. Second, we fabricated an advanced NPIS prototype. Third, we carried out a performance test by the accreditation institute and an empirical evaluation at the communicable diseases trauma center, to locate the improvement pain points of the prototype. Finally, we draw a conclusion on quality improvement points to be considered during the production stage. Results: The suggested, NPIS outperforms the existing one in terms of communicable disease controls, patient treatments and the safety during the patient transfer, and the ease of use. Additionally, the suggested NPIS turns out to be a proper alternative to satisfy the requirements of long range transfer of patients with communicable disease. Conclusion: Authors developed a quality improved, NPIS prototype. There are several points that still remain to be improved. Additionally, we expect that the concurrent use of the suggested NPIS with the existing one will fortify the preparedness capabilities on reacting to the communicable diseases.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.37 no.5
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• pp.486-493
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• 2024

Lighting has been used for a long time as a medium to convey brightness from darkness, and through incandescent lamps and fluorescent lamps, LED light sources have now become the standard in the lighting industry. Recently, the lighting equipment industry has been undergoing rapid digital transformation, starting with smart lighting, and is evolving into smart lighting customized for individuals and spaces through the development of IoT technology, cloud-based services, and data analysis. However, the blue light emitted from digital devices (computers, smartphones, tablets, etc.) or LED lights stimulates the melanopsin in the optic ganglion cells in the retina of the eye, which in turn stimulates the secretion of melatonin through the pineal gland, which regulates the secretion of melatonin. This can reduce sleep quality or disrupt biological rhythms. This interaction between blue light and melatonin has such a significant impact on human sleep patterns and overall health that it is essential to reduce exposure to blue light, especially in the evening. Human-centered lighting refers to lighting that takes into account the effects of light on the physical and mental areas, such as human activity and awakening, improvement of sleep quality, and health management. Many research institutes study the effects in the visible area and the non-visible area. By studying the impact, it is expected to improve the quality of human life. In this study, we plan to study ways to implement human-centered lighting by collecting sunrise and sunset data and linking commercialized LED packages and control devices with open-source hardware.

• Journal of the Korean Society of Safety
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• v.39 no.3
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• pp.50-59
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• 2024

In recent years, building architecture has become increasingly complex and larger in scale to accommodate many people. In densely populated facilities, the interiors are becoming more intricate and high-rise, with narrow corridors, hallways, and stairs. This poses challenges for evacuating occupants in case of emergencies such as fires, making it crucial to assess the evacuation safety in advance. In evacuation safety research, there are significant limitations to theoretical studies owing to their association with crowd behavior and human evacuation characteristics, as well as the risks associated with experiments involving human participants. Consequently, evacuation experiments conducted using simulation-based methodologies are gaining recognition worldwide. However, crowd simulations face validation difficulties because of variations in crowd movement and evacuation characteristics across different cases and scenarios, as well as the challenge of accurately reflecting human characteristics during evacuations. In this study, we investigated validation methods for evacuation simulations using the SAFEGUARD validation data set (SGVDS) provided by the University of Greenwich, UK. The SGVDS collects data on crowd evacuations through actual evacuation tests conducted on ColorLine's large RO-PAX ferry and Royal Caribbean International's cruise ships. The accuracy of the crowd simulations can be validated by comparing SGVDS and crowd simulation results. This study will contribute to the development of highly accurate crowd simulations by verifying various crowd simulations.

• Journal of the Korea institute for structural maintenance and inspection
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• v.13 no.2 s.54
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• pp.122-128
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• 2009

This study is to improve constructability of LB-DECK construction in site such as inconvenience of main and distribution bars in arrangements LB-DECK Panel which is work is applied to many bridges these days as a permanent formwork. So, the constructability is improved by changing the method of allocation of main reinforcing bar and distribution bar which is reviewed for improving efficiency of design and construction process among the suggested methods. The crack shapes, deflections, and strains under static load of the improvement of LB-DECK Panel are compared and analyzed to former LB-DECK Panel. As a result, 13% of strength compared to before the improvement of LB-DECK Panel, and 10% of strength is increased in the case of slab.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.297-309
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• 2023

The degradation of coastal ecosystems and fishery environments is accelerating due to the recent phenomenon of invertebrate grazers. To effectively monitor and implement preventive measures for this phenomenon, the adoption of remote sensing-based monitoring technology for extensive maritime areas is imperative. In this study, we compared and analyzed the robustness of deep learning-based object detection modelsfor detecting and monitoring invertebrate grazersfrom underwater videos. We constructed an image dataset targeting seven representative species of invertebrate grazers in the coastal waters of South Korea and trained deep learning-based object detection models, You Only Look Once (YOLO)v7 and YOLOv8, using this dataset. We evaluated the detection performance and speed of a total of six YOLO models (YOLOv7, YOLOv7x, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x) and conducted robustness evaluations considering various image distortions that may occur during underwater filming. The evaluation results showed that the YOLOv8 models demonstrated higher detection speed (approximately 71 to 141 FPS [frame per second]) compared to the number of parameters. In terms of detection performance, the YOLOv8 models (mean average precision [mAP] 0.848 to 0.882) exhibited better performance than the YOLOv7 models (mAP 0.847 to 0.850). Regarding model robustness, it was observed that the YOLOv7 models were more robust to shape distortions, while the YOLOv8 models were relatively more robust to color distortions. Therefore, considering that shape distortions occur less frequently in underwater video recordings while color distortions are more frequent in coastal areas, it can be concluded that utilizing YOLOv8 models is a valid choice for invertebrate grazer detection and monitoring in coastal waters.