• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.197-205
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• 2024

Development of AI and big data-based algorithms to advance and optimize the recognition and detection performance of various static/dynamic vehicles in front and around the vehicle at a time when securing driving safety is the most important point in the development and commercialization of autonomous vehicles. etc. are being studied. However, there are many research cases for recognizing the same vehicle by using the unique advantages of radar and camera, but deep learning image processing technology is not used, or only a short distance is detected as the same target due to radar performance problems. Therefore, there is a need for a convergence-based vehicle recognition method that configures a dataset that can be collected from radar equipment and camera equipment, calculates the error of the dataset, and recognizes it as the same target. In this paper, we aim to develop a technology that can link location information according to the installation location because data errors occur because it is judged as the same object depending on the installation location of the radar and CCTV (video).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.411-412
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• 2022

In recent years, the number of foot disease patients is increasing due to bad walking habits and wearing shoes. With the development of shoes and living environment, no one can be relieved from foot disease. A study on the effect of shoe design characteristics on satisfaction and purchase intention when a patient with foot disease purchases orthopedic shoes. The shoe design characteristics consisted of functionality, convenience, and aesthetics. This study is conducted from various perspectives, such as frequency of participation in social activities, purpose of wearing, and status and characteristics of each foot disease. It is expected that the research results will lead to the development of orthopedic shoe designs that can be usefully worn by patients with orthopedic mild foot disease in their daily life, and to lead to practical use and satisfaction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.11
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• pp.305-313
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• 2019

Recently, spatial information is being constructed actively based on the images obtained by drones. Because occlusion areas occur due to buildings as well as many obstacles, such as trees, pedestrians, and banners in the urban areas, an efficient way to resolve the problem is necessary. Instead of the traditional way, which replaces the occlusion area with other images obtained at different positions, various models based on deep learning were examined and compared. A comparison of a type of feature descriptor, HOG, to the machine learning-based SVM, deep learning-based DNN, CNN, and RNN showed that the CNN is used broadly to detect and classify objects. Until now, many studies have focused on the development and application of models so that it is impossible to select an optimal model. On the other hand, the upgrade of a deep learning-based detection and classification technique is expected because many researchers have attempted to upgrade the accuracy of the model as well as reduce the computation time. In that case, the procedures for generating spatial information will be changed to detect the occlusion area and replace it with simulated images automatically, and the efficiency of time, cost, and workforce will also be improved.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.240-257
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• 2022

Autonomous vehicles use various local sensors such as camera, radar, and lidar to perceive the surrounding environment. However, it is difficult to predict the movement of vulnerable road users using only local sensors that are subject to limits in cognitive range. This is true especially when these users are blocked from view by obstacles. Hence, this paper developed an algorithm for collision avoidance with VRU using V2X information. The main purpose of this collision avoidance system is to overcome the limitations of the local sensors. The algorithm first evaluates the risk of collision, based on the current driving condition and the V2X information of the VRU. Subsequently, the algorithm takes one of four evasive actions; steering, braking, steering after braking, and braking after steering. A simulation was performed under various conditions. The results of the simulation confirmed that the algorithm could significantly improve the performance of the collision avoidance system while securing vehicle stability during evasive maneuvers.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.2
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• pp.69-81
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• 2009

Using a computational fluid dynamics (CFD) model, the effects of building complexes constructed under an urban renewal plan on air flows in an urban area were investigated. For this, the geographic information system (GIS) data were used as the input data of the CFD model and four experiments were numerically simulated for different inflow directions (westerly, southerly, easterly, and northerly cases). Before constructing building complexes under the urban renewal plan, wind speed at the pedestrian level was very low around buildings because of decrease in wind speed by the drag effect of the densely distributed low-rise buildings. As the high-rise buildings were constructed and building density decreased by the urban renewal plan, wind speed at the pedestrian level increased compared to that before the urban renewal plan because the drag effect by the buildings decreases and the channeling effect satisfying the mass continuity partially appeared at the spaces among the high-rise buildings. At the upper levels, wind speed partially increased inside the high-rise buildings due to the channeling effect but it remarkably decreased across a vast extent of the downwind regions due to the generation of the recirculation zone and the drag effect of the high-rise buildings.

• Spatial Information Research
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• v.22 no.6
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• pp.33-43
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• 2014

As the number of people who live indoor space has been increased, the interest in 3D indoor spatial information has been grown. Object-Oriented 3D indoor space modeling including indoor spatial information has performed in level of detail 4, and modeling data is able to be constructed based on various raw data which are as-built drawing, laser scanning, BIM data, and camera. 3D indoor space modeling has been worked based on established indoor space modeling process, and the result can be used for various application fields such as indoor space pedestrian navigation, facility management, disaster management, and so on. However, the modeling process has limitations to perform indoor space modeling efficiently, because the process is complicated and wastes time at modeling work. In this paper, we propose evaluation on practical use of raw data for 3D indoor space modeling purpose on supporting efficient indoor space modeling through analyzing the established process. Therefore, we define the requirements to evaluate the practical use of raw data and propose the verification method. In addition, as-built drawing which has been used in Seoul 3D indoor space modeling project will be applied to proposed method as a raw data.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.91-100
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• 2010

Existing traffic safety studies have focused on identifying the relationship among roadway crashes, highway design and incremental weather condition such as rainy/ice weather. However, it is hard to find researches that studied the effect of sun glare on traffic safety although there are abundant evidences demonstrating that fatal traffic crashes are attributed to the sun glare. Affecting drivers'vision particularly during the morning or the evening time when the sun positions close to the horizon, sun glare directly deteriorate drivers'judgmental capability. In this paper, we numerically analyze the effect of sun glare on the drivers'vision in time and space domains. Applied to the roadways around St Louis area in the United States, the GIS based simulation analysis identifies the time of day in a year and the segments of highways that are potentially influenced by the sun glare. This study evidentially confirms the fact that roadway bounded for West and East directions have longer time influenced by sun glare particularly during Spring and Fall season than other roadways. The computational result provides risky time periods of day at intersections or pedestrian crossings where the sun glare potentially endangers traffic safety, which be utilized to reduce the crashes due to the sun glare.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.200-215
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• 2023

With the rapid advance of autonomous driving technology, the related vehicle market is experiencing explosive growth, and it is anticipated that the era of fully autonomous vehicles will arrive in the near future. However, along with the development of autonomous driving technology, questions regarding its safety and reliability continue to be raised. Concerns among technology adopters are increasing due to media reports of accidents involving autonomous vehicles. To promote the improvement of the safety of autonomous vehicles, it is essential to analyze previous accident cases and identify their causes. Therefore, in this study, we aimed to analyze the factors influencing the severity of autonomous vehicle accidents using previous accident cases and related data. The data used for this research primarily comprised autonomous vehicle accident reports collected and distributed by the California Department of Motor Vehicles (CA DMV). Spatial information on accident locations and additional traffic data were also collected and utilized. Given that the primary data used in this study were accident reports, a Poisson regression analysis was conducted to model the expected number of accidents. The research results indicated that the severity of autonomous vehicle accidents increases in areas with low lighting, the presence of bicycle or bus-exclusive lanes, and a history of pedestrian and bicycle accidents. These findings are expected to serve as foundational data for the development of algorithms to enhance the safety of autonomous vehicles and promote the installation of related transportation infrastructure.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.127-137
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• 2011

We have developed a wearable device that can convert sensor data into real-time step counts and activity levels. Sensor data on gait were acquired using a triaxial accelerometer. A test was performed according to a test protocol for different walking speeds, e.g., slow walking, walking, fast walking, slow running, running, and fast running. Each test was carried out for 36 min on a treadmill with the participant wearing a portable gas analyzer (K4B2), an Actical device, and the device developed in this study. The signal vector magnitude (SVM) was used to process the X, Y, and Z values output by the triaxial accelerometer into one representative value. In addition, for accurate step-count detection, we used three algorithms: an heuristic algorithm (HA), the adaptive threshold algorithm (ATA), and the adaptive locking period algorithm (ALPA). A regression equation estimating the energy expenditure (EE) was derived by using data from the accelerometer and information on the participants. The recognition rate of our algorithm was 97.34%, and the performance of the activity conversion algorithm was better than that of the Actical device by 1.61%.

• Journal of Convergence for Information Technology
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• v.9 no.8
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• pp.140-147
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• 2019

The purpose of this study is to find biomechanical parameters for optimal shoes production through an ergonomic usability assessment of five existing types of shoes preferred by B-BOY. Ten experts and ten non-experts participated in the experiment, and 12 infrared cameras (Qualis, Oqus), force plate (Kistler, 9286AA) and foot pressure plate (Zebris Gmbh, Zebris PDM-System) were used to obtain the data. The results of the study are as follows. First, P shoes with a friction coefficient of 0.38 and a free moment of 0.32 N/m/kg are desirable in terms of traction capability and safety. Second, on the cushion, it was found that the N shoes 2.51 N, sec/kg and non-expert, and 2.86 N and sec/kg were suitable. Third, it is deemed appropriate for C shoes with a forefoot average pressure of 10.11 KPa (right), 10.05 KPa (left), and V shoes with a rearfoot average pressure of 8.4 KPa (right) and 8.36 KPa (left). In conclusion, the combination of the structure and material of V shoes should be developed for traction and stability, N shoes for cushion, and walking balance for C and V shoes.