• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.944-953
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• 2020

Object detection plays a crucial role in a self-driving system. With the advances of image recognition based on deep convolutional neural networks, researches on object detection have been actively explored. In this paper, we proposed a lightweight model of the mask R-CNN, which has been most widely used for object detection, to efficiently predict location and shape of various objects on the road environment. Furthermore, feature maps are adaptively re-calibrated to improve the detection performance by applying an attention module to the neural network layer that plays different roles within the mask R-CNN. Various experimental results for real driving scenes demonstrate that the proposed method is able to maintain the high detection performance with significantly reduced network parameters.

• Journal of the Korean Society of Radiology
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• v.17 no.4
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• pp.551-556
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• 2023

Angiography equipment is used to evaluate and treat coronary artery disease. As a common feature of equipment, radiation is used, and function development for dose reduction is being carried out by each company. Therefore, the difference depending on whether DCR installed in angiography equipment is used is analyzed from a radiological point of view to prove the effect. Among 431 patients who underwent coronary artery intervention from March 2021 to February 2023, 250 patients with retrospective data were selected. And than among the 250 subjects obtained, 91 patients used the cardiovascular roadmap function during single-vessel intervention, and 159 patients did not use the roadmap. When DCR was used, total dose area product (34.57 uGy/m² : 69.15 uGy/m²), total air kerma dose (688.47 mGy : 1640.4 mGy), fluoroscopy dose (23.87 uGy/m² : 49.91 uGy/m²) and fluoroscopy time (723.55 s : 366.03 s), total number of images (17 : 26) showed lower values and were statistically significant than those not used. The use of DCR function in single vessel coronary intervention is thought to be radiologically safer as single vessel coronary intervention using dynamic cardiovascular DCR showed lower perspective time and perspective dose than procedures performed without the DCR.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.178-185
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• 2023

Rapid urbanization and advancements in technology have led to a surge in the number of automobiles, resulting in frequent traffic accidents, and consequently, an increase in human casualties and economic losses. Therefore, there is a need for technology that can predict the risk of traffic accidents to prevent them and minimize the damage caused by them. Traffic accidents occur due to various factors including traffic congestion, the traffic environment, and road conditions. These factors give traffic accidents spatiotemporal characteristics. This paper analyzes traffic accident data to understand the main characteristics of traffic accidents and reconstructs the data in a time series format. Additionally, an LSTM-MLP based model that excellently captures spatiotemporal characteristics was developed and utilized for traffic accident prediction. Experiments have proven that the proposed model is more rational and accurate in predicting the risk of traffic accidents compared to existing models. The traffic accident risk prediction model suggested in this paper can be applied to systems capable of real-time monitoring of road conditions and environments, such as navigation systems. It is expected to enhance the safety of road users and minimize the social costs associated with traffic accidents.

• The Journal of Sustainable Design and Educational Environment Research
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• v.12 no.3
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• pp.58-67
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• 2013

This study is to analyze the satisfaction and the image evaluation of landscape elements in outdoor space by types of the university campus. The results are as follows. 1) Out of outdoor elements at university campus, planting area, resting area, access road, and water feature are recognized as major landscape elements. Among them, planting area and access roads are evaluated low in terms of satisfaction levels, therefore, improvement on these elements are required. 2) In outdoor space image evaluation, university campus has image such as 'simple', 'clear', and 'safe'. By scale of universities, both 'A' university, which is the biggest in terms of size of campus, and 'B' university, which has a medium sized campus, have a positive image. However, 'C' university, which is the smallest in terms of size of campus, has a passive and negative image. 3) 6 factors are extracted through Factor Analysis for image evaluation. All of the universities show positive image in the categories of 'clarity' and 'familiarity', however, 'B' university and 'C' university show negative image in the category of 'scale'. 4) In Correlation Analysis between landscape elements satisfaction level and image evaluation, it is showed that the group of landscape facility becomes a relation factor of overall image evaluation. As a result, the higher satisfaction level goes, the better image evaluation of overall outdoor space at university campus is.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.2
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• pp.110-116
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• 2016

Autonomous vehicles must obey the traffic laws in order to drive actual roads. Traffic signs erected at the side of roads explain the road traffic information or regulations. Therefore, traffic sign recognition is necessary for the autonomous vehicles. In this paper, color characteristics are first considered to detect traffic sign candidates. Subsequently, we establish HOG (Histogram of Oriented Gradients) features from the detected candidate and recognize the traffic sign through a SVM (Support Vector Machine). However, owing to various circumstances, such as changes in weather and lighting, it is difficult to recognize the traffic signs robustly using only SVM. In order to solve this problem, we propose a tracking algorithm with RANSAC-based motion estimation. Using two-point motion estimation, inlier feature points within the traffic sign are selected and then the optimal motion is calculated with the inliers through a bundle adjustment. This approach greatly enhances the traffic sign recognition performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3598-3614
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• 2020

With the increase of motor vehicles and tourism demand, some traffic problems gradually appear, such as traffic congestion, safety accidents and insufficient allocation of traffic resources. Facing these challenges, a model of Spatio-Temporal Dilated Convolutional Network (STDGCN) is proposed for assistance of extracting highly nonlinear and complex characteristics to accurately predict the future traffic flow. In particular, we model the traffic as undirected graphs, on which graph convolutions are built to extract spatial feature informations. Furthermore, a dilated convolution is deployed into graph convolution for capturing multi-scale contextual messages. The proposed STDGCN integrates the dilated convolution into the graph convolution, which realizes the extraction of the spatial and temporal characteristics of traffic flow data, as well as features of road occupancy. To observe the performance of the proposed model, we compare with it with four rivals. We also employ four indicators for evaluation. The experimental results show STDGCN's effectiveness. The prediction accuracy is improved by 17% in comparison with the traditional prediction methods on various real-world traffic datasets.

• Computers and Concrete
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• v.14 no.6
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• pp.727-743
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• 2014

The management of existing concrete bridges has become a major social concern in many developed countries due to the large number of bridges exhibiting signs of significant deterioration. This problem has increased the demand for effective maintenance and renewal planning. In order to implement an appropriate management procedure for a structure, a wide array of corrective strategies must be evaluated with respect to not only the condition state of each defect but also safety, economy and sustainability. This paper describes a new performance evaluation system for existing concrete bridges. The system evaluates performance based on load carrying capability and durability from the results of a visual inspection and specification data, and describes the necessity of maintenance. It categorizes all girders and slabs as either unsafe, severe deterioration, moderate deterioration, mild deterioration, or safe. The technique employs an expert system with an appropriate knowledge base in the evaluation. A characteristic feature of the system is the use of neural networks to evaluate the performance and facilitate refinement of the knowledge base. The neural network proposed in the present study has the capability to prevent an inference process and knowledge base from becoming a black box. It is very important that the system is capable of detailing how the performance is calculated since the road network represents a huge investment. The effectiveness of the neural network and machine learning method is verified by comparing diagnostic results by bridge experts.

• Journal of the Korean Geotechnical Society
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• v.21 no.1
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• pp.115-121
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• 2005

At present, continual road constructions to connect from city to city are needed due to the geographical feature of Korea that about $70\%$ of the territory is mountainous area. Thus, the generation of large cut-slope has been inevitably formed. As a means of reinforcement on the cut-slope, in case of destructive disasters such as a snowstorm, pile embedment method is widely adopted. The pile embedment method is to resist possible move of soil by embedding piles from the surface to the immovable ground and then delivering the load from the piles to the immovable ground. In this study this writer analyzes the limitation of empirically used pile construction depth and its spacing through the numerical analysis. As a result, he suggests the most effective pile construction depth and space.

• Journal of Korea Multimedia Society
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• v.19 no.6
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• pp.1003-1013
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• 2016

This paper proposes a framework of superpixel-based vehicle detection method using plane normal vector in disparity space. We utilize two common factors for detecting vehicles: Hypothesis Generation (HG) and Hypothesis Verification (HV). At the stage of HG, we set the regions of interest (ROI) by estimating the lane, and track them to reduce computational cost of the overall processes. The image is then divided into compact superpixels, each of which is viewed as a plane composed of the normal vector in disparity space. After that, the representative normal vector is computed at a superpixel-level, which alleviates the well-known problems of conventional color-based and depth-based approaches. Based on the assumption that the central-bottom of the input image is always on the navigable region, the road and obstacle candidates are simultaneously extracted by the plane normal vectors obtained from K-means algorithm. At the stage of HV, the separated obstacle candidates are verified by employing HOG and SVM as for a feature and classifying function, respectively. To achieve this, we trained SVM classifier by HOG features of KITTI training dataset. The experimental results demonstrate that the proposed vehicle detection system outperforms the conventional HOG-based methods qualitatively and quantitatively.

• KIEAE Journal
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• v.14 no.4
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• pp.91-96
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• 2014

The behavior of walking involves our action of seeing things. It is the intention of this research that the cognitive process of perceiving things along the path can affect the way we sense the length of the journey. The theory generally accepted in this line of thought is the 'feature accumulation theory'. It assumes that if the journey includes many objects or memorable features, then our memory recalls that journey much farther than it really was. This study set up a real-life experiment by asking university students about their mental memory of the two different routes in the campus. One is a longer path that has not much to look at except trees and the other a shorter path yet with many buildings, sign boards and street furnitures. The subjects processed their mental image in the brain based on their experience. They showed a strong tendency that the path with more features were remembered longer while that with less features shorter. More interestingly, it was found that as their experience increases, they become more accurate about the exact length of the questioned paths. The result corroborates the theory that human perception of space is based on the topological understanding of surroundings rather than geometric understanding.