• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.128-141
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• 2018

In this paper, a methodology to analyze the potential safe benefit of six cooperative driver assistance systems via V2V (vehicle-to-vehicle) communications is proposed. Although it is quite necessary to assess social impact with respect to new safety technologies for cooperative vehicles with V2V communications, there are few studies in Korea to predict the quantitative safety benefit analysis. In this study, traffic accident scenarios are classified based on traffic fatality between passenger cars. The sequential collision type is classified for a multiple pile-up with respect to collision direction such as forward, side, head-on collisions. Then movement of surrounding vehicle is considered for the scenario classification. Next, the cooperative driver assistance systems such as forward collision warning, blind spot detection, and intersection movement assistance are related with the corresponding accident scenarios. Finally, it is summarized how much traffic fatality may be reduced potentially due to the V2V communication based safety services.

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.113-124
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• 2009

This paper suggests a new approach for providing information for driver safety at signalized intersections. Particularly dangerous situations at signalized intersections such as red-light violations, accelerating through yellow intervals, red-light running, and stopping abruptly due to the dilemma zone problem are considered in this study. This paper presents the development of a dangerous vehicle determination algorithm by collecting real-time vehicle speeds and times from multiple point detectors when the vehicles are traveling during phase-change. For an evaluation of this algorithm, VISSIM is used to perform a real-time multiple detection situation by changing the input data such as various inflow-volume, design speed change, driver perception, and response time. As a result the correct-classification rate is approximately 98.5% and the prediction rate of the algorithm is approximately 88.5%. This paper shows the sensitivity results by changing the input data. This result showed that the new approach can be used to improve safety for signalized intersections.

• Journal of Animal Science and Technology
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• v.63 no.6
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• pp.1453-1463
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• 2021

Feeding is the most important behavior that represents the health and welfare of weanling pigs. The early detection of feed refusal is crucial for the control of disease in the initial stages and the detection of empty feeders for adding feed in a timely manner. This paper proposes a real-time technique for the detection and recognition of small pigs using a deep-leaning-based method. The proposed model focuses on detecting pigs on a feeder in a feeding position. Conventional methods detect pigs and then classify them into different behavior gestures. In contrast, in the proposed method, these two tasks are combined into a single process to detect only feeding behavior to increase the speed of detection. Considering the significant differences between pig behaviors at different sizes, adaptive adjustments are introduced into a you-only-look-once (YOLO) model, including an angle optimization strategy between the head and body for detecting a head in a feeder. According to experimental results, this method can detect the feeding behavior of pigs and screen non-feeding positions with 95.66%, 94.22%, and 96.56% average precision (AP) at an intersection over union (IoU) threshold of 0.5 for YOLOv3, YOLOv4, and an additional layer and with the proposed activation function, respectively. Drinking behavior was detected with 86.86%, 89.16%, and 86.41% AP at a 0.5 IoU threshold for YOLOv3, YOLOv4, and the proposed activation function, respectively. In terms of detection and classification, the results of our study demonstrate that the proposed method yields higher precision and recall compared to conventional methods.

• Archives of Craniofacial Surgery
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• v.24 no.1
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• pp.18-23
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• 2023

Background: When performing reduction of zygomatic arch fractures, locating the inward portion of the fracture can be difficult. Therefore, this study investigated the discrepancy between the locations of the depression on the soft tissue and bone and sought to identify how to determine the inward portion of the fracture on the patient's face. Methods: We conducted a retrospective review of chart with isolated zygomatic arch fractures of type V in the Nam and Jung classification from March 2013 to February 2022. For consistent measurements, a reference point (RP), at the intersection between a vertical line passing through the end point of the root of the ear helix in the patient's side-view photograph and a transverse line passing through the longest horizontal axis of the external meatus opening, was established. We then measured the distance between the RP and the soft tissue depression in a portrait and the bone depression on a computed tomography (CT) scan. The discrepancy between these distances was quantified. Results: Among the patients with isolated zygomatic arch fractures, only those with a fully visible ear on a side-view photograph were included. Twenty-four patients met the inclusion criteria. There were four types of discrepancies in the location of the soft tissue depression compared to the bone depression: type I, forward and upward discrepancy (7.45 and 3.28 mm), type II, backward and upward (4.29 and 4.21 mm), type III, forward and downward (10.06 and 5.15 mm), and type IV, backward and downward (2.61 and 3.27 mm). Conclusion: This study showed that discrepancy between the locations of the depressions on the soft tissue and bone exists in various directions. Therefore, applying the transverse and vertical distances measured from a bone image of the CT scan onto the patient's face at the indicated RP will be helpful for predicting the reduction location.

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.210-217
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• 2022

Passive source depth estimation has been studied for decades since the source depth can be used for target classification, target tracking, etc. The purpose of this paper is to solve the problem of ambiguity in the previous paper [S.-il. Cho et al. (in Korean), J. Acoust. Soc. Kr. 38, 120-127 (2019)] that source depth is estimated in two points. The patterns of phase shift of Channel Impulse Response(CIR) reflected in ocean surface and bottom is used for removing ambiguity of the source depth estimation, and after removing ambiguity, source depth is estimated at one point through the intersection of CIR. In order to extract CIR in case of unknown source signal and continuous signal or noise, Ray-based blind deconvolution is used. The proposed algorithm is demonstrated through numerical simulation in ocean waveguide.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.792-799
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• 2022

Building Information Modeling (BIM) technology is a key component of modern construction engineering and project management workflows. As-is BIM models that represent the spatial reality of a project site can offer crucial information to stakeholders for construction progress monitoring, error checking, and building maintenance purposes. Geometric methods for automatically converting raw scan data into BIM models (Scan-to-BIM) often fail to make use of higher-level semantic information in the data. Whereas, semantic segmentation methods only output labels at the point level without creating object level models that is necessary for BIM. To address these issues, this research proposes a hybrid semantic-geometric approach for clutter-resistant floorplan generation from laser-scanned building point clouds. The input point clouds are first pre-processed by normalizing the coordinate system and removing outliers. Then, a semantic segmentation network based on PointNet++ is used to label each point as ceiling, floor, wall, door, stair, and clutter. The clutter points are removed whereas the wall, door, and stair points are used for 2D floorplan generation. A region-growing segmentation algorithm paired with geometric reasoning rules is applied to group the points together into individual building elements. Finally, a 2-fold Random Sample Consensus (RANSAC) algorithm is applied to parameterize the building elements into 2D lines which are used to create the output floorplan. The proposed method is evaluated using the metrics of precision, recall, Intersection-over-Union (IOU), Betti error, and warping error.

• Journal of Internet Computing and Services
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• v.9 no.5
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• pp.117-130
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• 2008

This paper proposes a stable and robust method to distinct the color similarity for clothes using the LBG algorithm under various light sources, Since the conventional methods, such as the histogram intersection and the accumulated histogram, are profoundly sensitive to the changing of light environments, the distinction of color similarity for the same cloth can be different due to the complicated light sources. To reduce the effects of the light sources, the properties of hue and saturation which consistently sustain the characteristic of the color under the various changes of light sources are analyzed to define the characteristic of the color distribution. In a two-dimensional space determined by the properties of hue and saturation, the LBG algorithm, a non-parametric clustering approach, is applied to examine the color distribution of images for each clothes. The color similarity of images is defined by the average of Euclidean distance between the mapping clusters which are calculated from the result of clustering of both images. To prove the stability of the proposed method, the results of the color similarity between our method and the traditional histogram analysis based methods are compared using a dozen of cloth examples that obtained under different light environments. Our method successively provides the classification between the same cloth image pair and the different cloth image pair and this classification of color similarity for clothe images obtains the 91.6% of success rate.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.6
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• pp.175-181
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• 2022

The condition of infrastructure deteriorates as the service life increases. Since most infrastructure in South Korea were intensively built during the period of economic growth, the proportion of outdated infrastructure is rapidly increasing now. Aging of such infrastructure can lead to safety accidents and even human casualties. To prevent these issues in advance, periodic and accurate inspection is essential. For this reason, the need for research to detect various types of damage using computer vision and deep learning is increasingly required in the field of remotely controlled or autonomous inspection. To this end, this study proposed a neural network structure that can detect concrete damage by classifying it into three types. In particular, the proposed neural network can detect them more accurately through a hierarchical learning technique. This neural network was trained with 2,026 damage images and tested with 508 damage images. As a result, we completed an algorithm with average mean intersection over union of 67.04% and F1 score of 52.65%. It is expected that the proposed damage detection algorithm could apply to accurate facility condition diagnosis in the near future.