• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.3
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• pp.117-122
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• 2023

Gaze estimation, which automatically recognizes where a user is currently staring, and object detection based on estimated gaze point, can be a more accurate and efficient way to understand human visual behavior. in this paper, we propose a method to detect the objects within the region of interest around the gaze point. Specifically, after estimating the 3D gaze point, a region of interest based on the estimated gaze point is created to ensure that object detection occurs only within the region of interest. In our experiments, we compared the performance of general object detection, and the proposed object detection based on region of interest, and found that the processing time per frame was 1.4ms and 1.1ms, respectively, indicating that the proposed method was faster in terms of processing speed.

• Journal of the Korea Society of Computer and Information
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• v.14 no.11
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• pp.179-186
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• 2009

In this paper, we propose a head detection method based on vertical and horizontal pixel histogram analysis in order to overcome drawbacks of the previous head detection approach using Haar-like feature-based face detection. In the proposed method, we create the vertical and horizontal foreground pixel histogram images from the background subtraction image, which represent the number of foreground pixels in the same vertical or horizontal position. Then we extract feature points of a head region by applying Harris corner detection method to the foreground pixel histogram images and by analyzing corner points. The proposal method shows robust head detection results even in the face image covering forelock by hairs or the back view image in which the previous approaches cannot detect the head regions.

• Biomolecules & Therapeutics
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• v.30 no.2
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• pp.170-178
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• 2022

The airway epithelium is equipped with the ability to resist respiratory disease development and airway damage, including the migration of airway epithelial cells and the activation of TLR3, which recognizes double-stranded (ds) RNA. Primary cilia on airway epithelial cells are involved in the cell cycle and cell differentiation and repair. In this study, we used Beas-2B human bronchial epithelial cells to investigate the effects of the TLR3 agonist polyinosinic:polycytidylic acid [Poly(I:C)] on airway cell migration and primary cilia (PC) formation. PC formation increased in cells incubated under serum deprivation. Migration was faster in Beas-2B cells pretreated with Poly(I:C) than in control cells, as judged by a wound healing assay, single-cell path tracking, and a Transwell migration assay. No changes in cell migration were observed when the cells were incubated in conditioned medium from Poly(I:C)-treated cells. PC formation was enhanced by Poly(I:C) treatment, but was reduced when the cells were exposed to the ciliogenesis inhibitor ciliobrevin A (CilioA). The inhibition of Beas-2B cell migration by CilioA was also assessed and a slight decrease in ciliogenesis was detected in SARS-CoV-2 spike protein (SP)-treated Beas-2B cells overexpressing ACE2 compared to control cells. Cell migration was decreased by SP but restored by Poly(I:C) treatment. Taken together, our results demonstrate that impaired migration by SP-treated cells can be attenuated by Poly(I:C) treatment, thus increasing airway cell migration through the regulation of ciliogenesis.

• Journal of Animal Science and Technology
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• v.65 no.3
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• pp.519-534
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• 2023

Rumination in cattle is closely related to their health, which makes the automatic monitoring of rumination an important part of smart pasture operations. However, manual monitoring of cattle rumination is laborious and wearable sensors are often harmful to animals. Thus, we propose a computer vision-based method to automatically identify multi-object cattle rumination, and to calculate the rumination time and number of chews for each cow. The heads of the cattle in the video were initially tracked with a multi-object tracking algorithm, which combined the You Only Look Once (YOLO) algorithm with the kernelized correlation filter (KCF). Images of the head of each cow were saved at a fixed size, and numbered. Then, a rumination recognition algorithm was constructed with parameters obtained using the frame difference method, and rumination time and number of chews were calculated. The rumination recognition algorithm was used to analyze the head image of each cow to automatically detect multi-object cattle rumination. To verify the feasibility of this method, the algorithm was tested on multi-object cattle rumination videos, and the results were compared with the results produced by human observation. The experimental results showed that the average error in rumination time was 5.902% and the average error in the number of chews was 8.126%. The rumination identification and calculation of rumination information only need to be performed by computers automatically with no manual intervention. It could provide a new contactless rumination identification method for multi-cattle, which provided technical support for smart pasture.

• The Journal of Bigdata
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• v.8 no.1
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• pp.73-82
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• 2023

Marine debris not only affects the survival of marine life, water pollution, and scenery but also has secondary effects on economic loss and human health. While research on underwater and surface debris is actively ongoing, solutions to marine debris in hard-to-reach blind spots are being developed slowly. To address this problem, we utilize drones to detect and track marine debris in blind spots such as tetrapods. The detected debris is then visualized by calculating its location coordinates using the drone's GPS, altitude, and heading values. The proposed method of using drones for detecting marine debris and matching it with longitude and latitude coordinates provides an effective solution to the problem of marine debris in blind spots.

• Journal of Sensor Science and Technology
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• v.31 no.3
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• pp.156-162
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• 2022

This study estimates the relative position between body segments using segment orientation and segment-to-joint center (S2J) vectors. In many wearable motion tracking technologies, the S2J vector is treated as a constant based on the assumption that rigid body segments are connected by a mechanical ball joint. However, human body segments are deformable non-rigid bodies, and they are connected via ligaments and tendons; therefore, the S2J vector should be determined as a time-varying vector, instead of a constant. In this regard, our previous study (2021) proposed a method for determining the time-varying S2J vector from the learning dataset using a regression method. Because that method uses a deformation-related variable to consider the deformation of S2J vectors, the optimal variable must be determined in terms of estimation accuracy by motion and segment. In this study, we investigated the effects of deformation-related variables on the estimation accuracy of the relative position. The experimental results showed that the estimation accuracy was the highest when the flexion and adduction angles of the shoulder and the flexion angles of the shoulder and elbow were selected as deformation-related variables for the sternum-to-upper arm and upper arm-to-forearm, respectively. Furthermore, the case with multiple deformation-related variables was superior by an average of 2.19 mm compared to the case with a single variable.

• Journal of Drive and Control
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• v.20 no.4
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• pp.1-8
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• 2023

In recent years, robots have been utilized in various industries to reduce workload and enhance work efficiency. The following mobility offers users convenience by autonomously tracking specific locations and targets without the need for additional equipment such as forklifts or carts. In this paper, deep learning techniques were employed to recognize individuals and assign each of them a unique identifier to enable the recognition of a specific person even among multiple individuals. To achieve this, the distance and angle between the robot and the targeted individual are transmitted to respective controllers. Furthermore, this study explored the control methodology for mobility that tracks a specific person, utilizing Simultaneous Localization and Mapping (SLAM) and Proportional-Integral-Derivative (PID) control techniques. In the PID control method, a genetic algorithm is employed to extract the optimal gain value, subsequently evaluating PID performance through simulation. The SLAM method involves generating a map by synchronizing data from a 2D LiDAR and a depth camera using Real-Time Appearance-Based Mapping (RTAB-MAP). Experiments are conducted to compare and analyze the performance of the two control methods, visualizing the paths of both the human and the following mobility.

• Structural Engineering and Mechanics
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• v.90 no.1
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• pp.19-26
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• 2024

In recent years, an increasing number of experimental studies have shown that the practical application of mature active control systems requires consideration of robustness criteria in the design process, including the reduction of tracking errors, operational resistance to external disturbances, and measurement noise, as well as robustness and stability. Good uncertainty prediction is thus proposed to solve problems caused by poor parameter selection and to remove the effects of dynamic coupling between degrees of freedom (DOF) in nonlinear systems. To overcome the stability problem, this study develops an advanced adaptive predictive fuzzy controller, which not only solves the programming problem of determining system stability but also uses the law of linear matrix inequality (LMI) to modify the fuzzy problem. The following parameters are used to manipulate the fuzzy controller of the robotic system to improve its control performance. The simulations for system uncertainty in the controller design emphasized the use of acceleration feedback for practical reasons. The simulation results also show that the proposed H∞ controller has excellent performance and reliability, and the effectiveness of the LMI-based method is also recognized. Therefore, this dynamic control method is suitable for seismic protection of civil buildings. The objectives of this document are access to adequate, safe, and affordable housing and basic services, promotion of inclusive and sustainable urbanization, implementation of sustainable disaster-resilient construction, sustainable planning, and sustainable management of human settlements. Simulation results of linear and non-linear structures demonstrate the ability of this method to identify structures and their changes due to damage. Therefore, with the continuous development of artificial intelligence and fuzzy theory, it seems that this goal will be achieved in the near future.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.2
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• pp.65-74
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• 2013

A research program for the roe deer (Capreolus pygargus) has been set up at the Jeju Experimental Forest of Warm-temperate and Subtropical Forest Research Center in Jeju Island. To explore the home range size and habitat environment, 3 males and 3 females have been captured and released with GPS-CDMA based telemetry since 24th April 2012. Among them 2 females were captured at Hannam Forest of Seoguipo, were pregnant and monitored by the tracking system. There are significantly different patterns in behavior around the parturition. After parturition they show recurrence behavior toward one point in the forest, while they have irregular patterns in moving before. To calculate the home range size, the MCP (minimum convex polygon) and Kernel Method are applied through the extension of ESRI ArcView GIS 3.2a. The pregnant female captured 9th May 2012 has the size of MCP=67ha and Kernel 95%=0.5ha and the pregnant female captured 12th July 2012 has the size of MCP=82ha and Kernel 95%=0.9ha. Although a fawn could move immediately just after the birth likely others to avoid any risks, they stay at very narrow space significantly, and the size become wider when more time goes by. Furthermore, they mainly have a home range away from human activity area such as forest tracking roads. The habitat environment for the parturition is summarized as 40years old cryptomeria forests with new sprouting shrubs for foods, which are the controlled forest through the thinning and removing shrubs 2 years ago. This means that forest works could cause positive results for the parturition and survival of young. The period of parturition is earlier than highland in Jeju Island, the size of home range is narrower than other countries, and the habitat environment of the shelter for a fawn is similar to previous research in other countries.

• Investigative Magnetic Resonance Imaging
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• v.17 no.3
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• pp.181-191
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• 2013

Purpose : To evaluate the usefulness of in vivo magnetic resonance (MR) imaging for tracking intravenously injected superparamagnetic iron oxide (SPIO)-labeled human umbilical vein endothelial cells (HUVECs) in an acute renal failure (ARF) rat model. Materials and Methods: HUVECs were labeled with SPIO and poly-L-lysine (PLL) complex. Relaxation rates at 1.5-T MR, cell viability, and labeling stability were assessed. HUVECs were injected into the tail vein of ARF rats (labeled cells in 10 rats, unlabeled cells in 2 rats). Follow-up serial $T2^*$-weighted gradient-echo MR imaging was performed at 1, 3, 5 and 7 days after injection, and the MR findings were compared with histologic findings. Results: There was an average of $98.4{\pm}2.4%$ Prussian blue stain-positive cells after labeling with SPIOPLL complex. Relaxation rates ($R2^*$) of all cultured HUVECs at day 3 and 5 were not markedly decreased compared with that at day 1. The stability of SPIO in HUVECs was maintained during the proliferation of HUVECs in culture media. In the presence of left unilateral renal artery ischemia, $T2^*$-weighted MR imaging performed 1 day after the intravenous injection of labeled HUVECs revealed a significant signal intensity (SI) loss exclusively in the left renal outer medulla regions, but not in the right kidney. The MR imaging findings at days 3, 5 and 7 after intravenous injection of HUVECs showed a SI loss in the outer medulla regions of the ischemically injured kidney, but the SI progressively recovered with time and the right kidney did not have a significant change in SI in the same period. Upon histologic analysis, the SI loss on MR images was correspondent to the presence of Prussian blue stained cells, primarily in the renal outer medulla. Conclusion: MR imaging appears to be useful for in vivo monitoring of intravenously injected SPIO-labeled HUVECs in an ischemically injured rat kidney.