• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.882-897
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• 2020

Recently, the use of immersive media contents representing Panorama and 360° video is increasing. Since the viewing angle is limited to generate the content through a general camera, image stitching is mainly used to combine images taken with multiple cameras into one image having a wide field of view. However, if the parallax between the cameras is large, parallax distortion may occur in the stitched image, which disturbs the user's content immersion, thus an image stitching overcoming parallax distortion is required. The existing Seam Optimization based image stitching method to overcome parallax distortion uses energy function or object segment information to reflect the location information of objects, but the initial seam generation location, background information, performance of the object detector, and placement of objects may limit application. Therefore, in this paper, we propose an image stitching method that can overcome the limitations of the existing method by adding a weight value set differently according to the type of object to the energy value using object detection based on deep learning.

• Journal of Advanced Navigation Technology
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• v.25 no.3
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• pp.194-202
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• 2021

Ionosphere, one of the largest error sources, can pose potentially harmful threat to single-frequency GNSS (global navigation satellite system) user even after applying ionospheric corrections to their GNSS measurements. To quantitatively assess ionospheric impacts on the satellite navigation-based applications using simulation, the standard deviation of residual ionospheric errors is needed. Thus, in this paper, we determine conservative statistical quantity that covers typical residual ionospheric errors for nominal days. Extensive data-processing computes TEC (total electron content) estimates from GNSS measurements collected from the Korean reference station networks. We use Klobuchar model as a correction to calculate residual ionospheric errors from TEC (total electron content) estimate. Finally, an exponential delay model for residual ionospheric errors is presented as a function of local time and satellite elevation angle.

• Journal of Drive and Control
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• v.19 no.4
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• pp.19-28
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• 2022

This paper introduces an automatic steering system for straight traveling capable of being mounted on drivable agricultural machinery which user can handle it such as a tractor, a transplant, etc. The modular automatic steering device proposed in the paper is composed of RTK GNSS, IMU, HMI, hydraulic valve, and wheel sensor. The path generation method of the automatic steering system is obtained from two location information(latitude and longitude on each point) measured by GNSS in advance. From HMI, a straight path(AB line) can be created by connecting latitude and longitude on each point and the device makes the machine able to follow the path. During traveling along the reference path, it acquires the real time position data every sample time(0.1s), compares the reference with them and calculates the lateral deviation. The values of deviation are used to control the steering angle of the machine using hydraulic valve mounted on the axle of front wheel. In this paper, Pure Pursuit algorithm is applied used in autonomous vehicles frequently. For the analysis of traveling characteristics, field tests were executed about these conditions: velocity of 2, 3, 4km/h which is applied to general agricultural work and ground surface of solid(asphalt) and weak condition(soil) such as farmland. In the case of weak ground state, two experiments were executed about no-load(without work) and load(with work such as plowing). The maximum average deviations were presented 2.44cm, 7.32cm, and 11.34cm during traveling on three ground conditions : asphalt, soil without load and with load(plowing).

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.959-968
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• 2022

In this paper, we intend to develop a cart with the convenience of moving objects or items that are difficult to hold by the hand of the disabled and the elderly from outdoor activities to a moving location. Using GPS module, DC motor, Bluetooth module, magnetometer sensor, and Blynk application, the smart follow cart was programmed to accurately know the owner's location while following the cart. The values of latitude and longitude of the magnetometer sensor are set so that the correct leverage value can be obtained in the lead for fine adjustment of the start and stop angles so that the user's position can be freely detected. The above motor driver L298n was connected to the motor to drive the DC motor at a fine angle. A smart follow cart was implemented that receives location signals according to the direction of the owner by turning on/off the mobile phone app as a switching role by connecting the GPS through Bluetooth wireless communication using a smartphone.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.4
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• pp.723-730
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• 2023

Due to the recent development of battery technology, various types of means of transportation such as electric kickboards, Segways, and electric bicycles have emerged, which can be defined as Personal Mobility. In this paper, as the incidence of safety accidents increases due to the increase in the number of users of Personal Mobility, safety helmet devices that strengthen safety capabilities and peripheral recognition functions were studied. In order for the helmet to send a safety signal, Arduino was used as a base to set the value of the sensor according to changes in distance and angle using the ultrasonic sensor to minimize errors and ensure smooth recognition. In addition, a gyro sensor was used to turn on the direction indicator according to each slope. Using a CDS sensor, the LED is designed to turn on when it goes below 150 lux at night. Finally, it is possible to check whether a helmet is worn within 5cm, and when driving at an average speed, the direction indicator light is turned on at 10 degrees, and the LED is turned on at less than 150 lux.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.369-372
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• 2021

These days, people spend more time sitting at a desk for studies or work. Also, because people continue to use computers, smartphones, and tablet PCs often during break times, their posture is getting worse. Maintaining a position of bad posture for an extended period of time causes problems with the musculoskeletal system related to the neck, shoulders, and spine. Additionally, problems such as physical fatigue and posture deformation are predicted to expand to a wide range of age groups. Therefore, the core function of the system we are developing is to ensure correct sitting posture and to receive alert notifications via the created mobile application. To create the system, a flex sensor, pressure sensor, and tilt sensor are attached to a chair and utilized. The flex sensor detects and compares the amount of bending in the chair's posture and transmits this value to an Arduino Uno R3 board. Additionally, information such as body balance and incline angle are collected to determine whether or not the current sitting posture is correct. When the posture is incorrect, a notification is sent through the mobile application to indicate to the user and the monitoring app that their posture is not correct. The system proposed in this study is expected to be of great help in future posture-related research.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.15-20
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• 2023

In general, maintaining an incorrect sitting posture for a long time is widely known to adversely affect the spine. Recently, several researchers have been interested in the causal relationship between incorrect sitting posture and spinal diseases, and have been studying methods to precisely measure changes in sitting or standing posture to prevent spinal diseases. In previous studies, we have developed a sensor device capable of measuring real-time posture change, applied a momentum calculation algorithm to improve the accuracy of real-time posture change measurement, and verified the accuracy of the postural change measurement sensor. In this study, we developed a posture measurement and analysis device that considers changes in the center of body pressure through the developed sitting pressure measurement, and it confirmed the sensor as an auxiliary tool to increase the accuracy of posture correction training with improving the user's visual feedback.

• Geomechanics and Engineering
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• v.37 no.5
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• pp.475-498
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• 2024

The prediction of the susceptibility of soil to liquefaction using a limited set of parameters, particularly when dealing with highly unbalanced databases is a challenging problem. The current study focuses on different ensemble learning classification algorithms using highly unbalanced databases of results from in-situ tests; standard penetration test (SPT), shear wave velocity (V_s) test, and cone penetration test (CPT). The input parameters for these datasets consist of earthquake intensity parameters, strong ground motion parameters, and in-situ soil testing parameters. liquefaction index serving as the binary output parameter. After a rigorous comparison with existing literature, extreme gradient boosting (XGBoost), bagging, and random forest (RF) emerge as the most efficient models for liquefaction instance classification across different datasets. Notably, for SPT and V_s-based models, XGBoost exhibits superior performance, followed by Light gradient boosting machine (LightGBM) and Bagging, while for CPT-based models, Bagging ranks highest, followed by Gradient boosting and random forest, with CPT-based models demonstrating lower G_mean(error), rendering them preferable for soil liquefaction susceptibility prediction. Key parameters influencing model performance include internal friction angle of soil (ϕ) and percentage of fines less than 75 µ (F₇₅) for SPT and V_s data and normalized average cone tip resistance (q_c) and peak horizontal ground acceleration (a_max) for CPT data. It was also observed that the addition of V_s measurement to SPT data increased the efficiency of the prediction in comparison to only SPT data. Furthermore, to enhance usability, a graphical user interface (GUI) for seamless classification operations based on provided input parameters was proposed.

• Fashion & Textile Research Journal
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• v.26 no.1
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• pp.65-77
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• 2024

This study aimed to propose effective ways to integrate CLO into educational settings by conducting a comparative analysis of pattern functions in YUKA and CLO, specifically focusing on skirt prototypes and variations. CLO, being a 3D virtual sample CAD tool, is mainly used in education to facilitate the creation of 3D virtual clothing. In order to explore the applicability of CLO's pattern functions in pattern education, CAD education experts were asked to produce two types of skirt prototypes and two skirt variations. Subsequently, in-depth interviews were conducted. In addition, the skirt pattern creation process was recorded on video and used for comparative analysis of YUKA and CLO pattern functions. The comparison revealed that CLO provides the pattern tools necessary for drafting skirt prototypes. The learning curve for acquiring the skills necessary for drafting and transforming skirt prototypes was found to be relatively shorter for CLO compared to YUKA. In addition, due to CLO's surface-based pattern drawing method, it is difficult to move or copy only specific parts of the outline, and there are some limitations in drawing right angle lines. In the pattern transformation process, CLO's preview function proved to be advantageous, and it was highly rated on user convenience due to the intuitive UI. Thus, CLO shows promise for pattern drafting education and is deemed to have high scalability as it is directly linked to 3D virtual clothing.

• Progress in Medical Physics
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• v.17 no.4
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• pp.212-223
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• 2006

Positional accuracy of the on-board imager (OBI) isocenter with gantry rotation was presented in this paper. Three different type of automatic evaluation methods of discrepancies between therapeutic and OBI isocenter using digital image processing techniques as well as a procedure stated in the customer acceptance procedure (CAP) were applied to check OBI isocenter migration trends. Two kinds of kV x-ray image set obtained at OBI source angle of $0^{\circ},\;90^{\circ},\;180^{\circ},\;270^{\circ}$ and every $10^{\circ}$ and raw projection data for cone-beam CT reconstruction were used for each evaluation method. Efficiencies of the methods were also estimated. If a user needs to obtain an isocenter variation map with full gantry rotation, a method taking OBI image for every $10^{\circ}$ and fitting with 5th order polynomial was appropriate. However for a mere quality assurance (QA) purpose of OBI isocenter accuracy, it was adequate to use only four OBI Images taken at the OBI source angle of $0^{\circ},\;90^{\circ},\;180^{\circ}\;and\;270^{\circ}$. Maximal discrepancy was 0.44 mm which was observed between the OBI source angle of $90^{\circ}\;and\;180^{\circ}$ OBI isocenter accuracy was maintained below 0.5 mm for a year. Proposed QA program may be helpful to Implement a reasonable routine QA of the OBI isocenter accuracy without great efforts.