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This paper proposes a method to simultaneously detect and classify objects by using a deep learning model, specifically you only look once (YOLO), with pre-processed automotive radar signals. In conventional methods, the detection and classification in automotive radar systems are conducted in two successive stages; however, in the proposed method, the two stages are combined into one. To verify the effectiveness of the proposed method, we applied it to the actual radar data measured using our automotive radar sensor. According to the results, our proposed method can simultaneously detect targets and classify them with over 90% accuracy. In addition, it shows better performance in terms of detection and classification, compared with conventional methods such as density-based spatial clustering of applications with noise or the support vector machine. Moreover, the proposed method especially exhibits better performance when detecting and classifying a vehicle with a long body.

In sports motion analysis, observation is a prerequisite for understanding the quality of motions. This paper introduces a novel approach to detect and segment sports motions using a wearable sensor for supporting systematic observation. The main goal is, for convenient analysis, to automatically provide motion data, which are temporally classified according to the phase definition. For explicit segmentation, a motion model is defined as a sequence of sub-motions with boundary states. A sequence classifier based on deep neural networks is designed to detect sports motions from continuous sensor inputs. The evaluation on two types of motions (soccer kicking and two-handed ball throwing) verifies that the proposed method is successful for the accurate detection and segmentation of sports motions. By developing a sports motion analysis system using the motion model and the sequence classifier, we show that the proposed method is useful for observation of sports motions by automatically providing relevant motion data for analysis.

This study aims to investigate the effectiveness of para-aramid fiber sheet in enhancing the flexural performance of reinforced concrete (RC) beams made with Environmental-Friendly Recycled Coarse Aggregates. The experimental program examines the effect of substitution ratio of recycled aggregates (0%, 30%, and 50%), type of para-aramid fiber sheet (KN 206 RFL and KN AA070-RFL), and the method of fiber sheet attachment (bottom and bottom-side). The test results show that the ultimate load-carrying capacity of RC beams reinforced with para-aramid fiber sheet attached to the bottom and side parts increased by 23.9% compared to the unreinforced specimens. The main findings of the study include the identification of the BU-type attachment method as the most effective method for enhancing the flexural performance of reinforced concrete beams. The comparison of the experimental results with analytical predictions showed that the nominal flexural strength obtained from the experimental study was lower than the analytical predictions, but the ductile capacity of the specimens indicated the effectiveness of para-aramid fiber sheet reinforcement in EFRCA RC beams for flexural strength. The study highlights the potential of using para-aramid fiber sheet in improving the flexural behavior of RC beams made with recycled aggregates, offering a sustainable solution for the construction industry.

This study was conducted to evaluate the seismic performance of an exterior precast concrete (PC) beam–column joint with ultra-high-performance fiber-reinforced concrete (UHPFRC). Currently, 45 MPa non-shrinkage mortar is used as grouting for the connection between PC beams and columns. In this study, PC joint specimens were designed using 45 MPa non-shrinkage mortar and 120 MPa UHPFRC as a grouting agent for connecting PC members. The shear reinforcement effect of UHPFRC was confirmed to reduce shear cracks in the joint core; this trend was similar in the specimens with reduced shear rebars. The maximum moment of the test specimen with the corbel was slightly increased, but there was no significant difference, and the failure pattern also showed similar results to the specimen without the corbel. In the test specimen to which the U-shaped beam was applied, the attachment surface of ultra-high-performance concrete and normal concrete were separated, and a large decrease in strength was observed. Considering workability, U-shaped beam do not seem to have any major merits in general, such as increased strength and difficulty in manufacturing, and it was judged that it was effective to separate the PC beams from the column face through corbels. Shear reinforcement through UHPFRC is effective in relieving congestion by reducing shear reinforcement bars at the joint, and it is judged that it can be used as PC joint grouting due to its excellent fluidity.

A sequence of 3D models generated using volumetric capture has the advantage of retaining the characteristics of dynamic objects and scenes. However, in volumetric data, since 3D mesh and texture are synthesized for every frame, the mesh of every frame has a different shape, and the brightness and color quality of the texture is various. This paper proposes an algorithm to consistently create a mesh of 4D volumetric data using dynamic reconstruction. The proposed algorithm comprises remeshing, correspondence searching, and target frame reconstruction by key frame deformation. We make non-rigid deformation possible by applying the surface deformation method of the key frame. Finally, we propose a method of compressing the target frame using the target frame reconstructed using the key frame with error rates of up to 98.88% and at least 20.39% compared to previous studies. The experimental results show the proposed method’s effectiveness by measuring the geometric error between the deformed key frame and the target frame. Further, by calculating the residual between two frames, the ratio of data transmitted is measured to show a compression performance of 18.48%.

This paper proposes a new technique for performing 3D static-point cloud registration after calibrating a multi-view RGB-D camera using a 3D (dimensional) joint set. Consistent feature points are required to calibrate a multi-view camera, and accurate feature points are necessary to obtain high-accuracy calibration results. In general, a special tool, such as a chessboard, is used to calibrate a multi-view camera. However, this paper uses joints on a human skeleton as feature points for calibrating a multi-view camera to perform calibration efficiently without special tools. We propose an RGB-D-based calibration algorithm that uses the joint coordinates of the 3D joint set obtained through pose estimation as feature points. Since human body information captured by the multi-view camera may be incomplete, a joint set predicted based on image information obtained through this may be incomplete. After efficiently integrating a plurality of incomplete joint sets into one joint set, multi-view cameras can be calibrated by using the combined joint set to obtain extrinsic matrices. To increase the accuracy of calibration, multiple joint sets are used for optimization through temporal iteration. We prove through experiments that it is possible to calibrate a multi-view camera using a large number of incomplete joint sets.

As part of the research on cement composites using cellulose nanocrystal (CNC) aqueous solution instead of general water, this study produced high-toughness cement composites reinforced with flax and steel fibers to improve the tensile deformation capacity, assessing the isothermal conduction calorimetry analysis, drying shrinkage, and strength characteristics. The mixing amount of CNCs was 0.4, 0.8, and 1.2 vol.% by volume of cement, and an aqueous solution was prepared using the ultrasonication dispersion method. When comparing the results of the experiment according to the CNC mixing ratio, CNCs at 0.8 vol.% led to an improvement in the shrinkage rate and mechanical performance compared with the plain specimen.

The external jugular vein (EJV) is a superficial vein of the neck in dogs; its significance is evident in veterinary clinical practice, encompassing surgeries and interventional procedures. However, there have been no reports on EJV variations in canines, despite extensive studies on variations in the jugular veins in humans. This study aimed to use CT imaging to evaluate the prevalence of anatomic vascular variations of the EJVs in Shih Tzu dogs and to describe the clinical and CT characteristics of these vascular variants. This is a retrospective, multi-center study. The medical imaging records of Shih Tzu dogs that underwent pre- and post-contrast CT examinations of the head, neck, and thorax at the Veterinary Medical Teaching Hospital, Konkuk University, and 10 referral hospitals between 2015 and 2023 were reviewed. We defined five types of EJV vascular variants: normal (type I), unilateral hypoplasia (type II), unilateral aplasia (type III), bilateral hypoplasia (type IV), and bilateral aplasia (type V), based on the morphological and diameter differences observed in the transverse images of Shih Tzu dogs. CT images from 547 Shih Tzu dogs revealed 119 cases (21.7%) of EJV variants. Type I was observed in 428 dogs (78.2%), type II in 46 dogs (8.4%), type III in 41 dogs (7.5%), type IV in 14 dogs (2.6%), and type V in 18 dogs (3.3%). In types II-V, compensatory drainage through the internal jugular vein (IJV) was observed, often involving the medial passage of the maxillary or linguofacial veins. A moderate negative correlation (  = -0.5) was recorded between the hypoplastic EJV and the affected-side IJV (  < 0.01). Some cases exhibited other supplementary drainage routes, such as the hyoid venous arch or median thyroid vein. Additionally, 63 persistent left cranial vena cava (PLCVC) cases (11.9%) were identified among 529 Shih Tzu dogs, showing a significant association with EJV abnormalities (  < 0.05). Overall, this study identified anatomical variants of the EJV in Shih Tzu dogs and introduced a new classification system. These findings revealed that EJV variants and compensatory tributary enlargement were more prevalent than previously recognized, emphasizing the need to consider these nuances in veterinary procedures and imaging.

This study investigates the integration and prioritization of connected vehicle infrastructure services (CVISs) in mixed autonomy traffic systems using a fuzzy–analytic hierarchy process (Fuzzy–AHP). The study aims to enhance operational efficiency in environments where autonomous vehicles (AVs) and human‐driven vehicles (HVs) coexist. By evaluating 92 existing services, the research selects and prioritizes 17 critical services that address safety and efficiency challenges. The methodology involves a Fuzzy–AHP analysis to assess service importance and a modified–importance–performance analysis (M–IPA) to categorize services as either specialized or common based on their utility for AVs and HVs. The findings highlight the pivotal roles of emergency management, traffic operation, and pedestrian detection services in improving traffic safety and flow. This study contributes to the theoretical and practical understanding of CVIS implementation, offering a framework for policymakers and engineers to optimize infrastructure in mixed autonomy traffic scenarios.

Crack patterns provide critical information about the structural integrity and safety of concrete structures. However, until now, there has been a lack of sufficient studies on using the Finite Element (FE) method to investigate the characteristics of the crack patterns of reinforced concrete (RC) beams. Therefore, this study aims to develop an FE model to analyze the load–displacement and crack characteristics of a beam under a four-point bending test using the concrete damaged plasticity (CDP) model that accounts for the influence of mesh size. The simulation results were validated against experimental results, including mesh convergence analysis, energy balance, load characteristics, and crack patterns. A parametric study was then conducted using this model to investigate the influence of the rebar’s diameter, number, and spacing on the RC beam’s load–displacement characteristics and crack behavior. The findings demonstrate that the FE model accurately simulates the working behavior of the RC beam, with a maximum deviation at a cracking load of 8.7% and crack patterns with a maximum deviation in the mean crack height of 12.1%. In addition, the results of the parametric study suggest that the rebar configuration significantly affects the RC beam’s loading carrying capacity. This study provides deeper insights into the use of FE modeling for analyzing the behavior of RC beams, which can be useful for designing and optimizing structures in civil engineering.