• International conference on construction engineering and project management
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• 2024.07a
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• pp.455-462
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• 2024

In the construction industry, known for its dynamic and hazardous environments, there exists a crucial demand for effective safety incident prevention. Traditional approaches to monitoring on-site safety, despite their importance, suffer from being laborious and heavily reliant on subjective, paper-based reports, which results in inefficiencies and fragmented data. Additionally, the incorporation of computer vision technologies for automated safety monitoring encounters a significant obstacle due to the lack of suitable training datasets. This challenge is due to the rare availability of safety accident images or videos and concerns over security and privacy violations. Consequently, this paper explores an innovative method to address the shortage of safety-related datasets in the construction sector by employing generative artificial intelligence (AI), specifically focusing on the Stable Diffusion model. Utilizing real-world construction accident scenarios, this method aims to generate photorealistic images to enrich training datasets for safety surveillance applications using computer vision. By systematically generating accident prompts, employing static prompts in empirical experiments, and compiling datasets with Stable Diffusion, this research bypasses the constraints of conventional data collection techniques in construction safety. The diversity and realism of the produced images hold considerable promise for tasks such as object detection and action recognition, thus improving safety measures. This study proposes future avenues for broadening scenario coverage, refining the prompt generation process, and merging artificial datasets with machine learning models for superior safety monitoring.

• Journal of Yeungnam Medical Science
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• v.17 no.1
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• pp.55-65
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• 2000

Purpose: To compare the efficacy, predictability, stability and safety of excimer laser photorefracive keratectomy(PRK) for myopia and photoastigmatic refractive keratectomy(PARK) for compound myopic astigmatism. Methods: Two-hundred and three eyes(l18 eyes < -7D spherical equivalent, 85 eyes ${\geq}$ -7D spherical equivalent) received excimer laser correction for compound myopic astigmatism and 152 eyes(116 eyes < -7D, 36 eyes ${\geq}$ -7D) for simple myopia. A VISX 20/20B $VisionKey^{TM}$ excimer laser was used to perform either PARK or PRK. Visual acuity with and without correction, refraction, IOP, corneal haze, and topography were evaluated at 1, 3, 6, and 12 months postoperatively. All patients were followed up for more than 12 months. Results: Postoperative refraction were generally stable after 3 months without significant early overcorrection. At 12 months, 110(94.8%) eyes that underwent PRK and 104(88.1%) eyes that underwent PARK achieved UCVA of 20/30 or better in the group who had lower than -7D correction. For eyes treated with -7D or more, these figures were 31(86.1%) eyes after PRK and 57(67.1%) eyes after PARK. The incidences of within 1D of plano refraction at 1 year follow-up were 97.4% after PRK and 93.2% after PARK in the group who had lower than -7D correction. For eyes treated with -7D or more, these figures were 80.6% after PRK and 70.6% after PARK. Conclusions Myopia with or without astigmatism was successfully treated in most of the eyes using PRK or PARK with VISX 20/20B $VisionKey^{TM}$ excimer laser. The predictability and stability of the postoperative refraction during the first 12 months seem to be quite reliable. Further improvement of excimer laser system and software should increase the clinical outcomes and safety of refractive procedures.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.205-218
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• 2022

Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.274-286
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• 2022

Multi-object tracking has been studied for a long time under computer vision and plays a critical role in applications such as autonomous driving and driving assistance. Multi-object tracking techniques generally consist of a detector that detects objects and a tracker that tracks the detected objects. Various publicly available datasets allow us to train a detector model without much effort. However, there are relatively few publicly available datasets for training a tracker model, and configuring own tracker datasets takes a long time compared to configuring detector datasets. Hence, the detector is often developed separately with a tracker module. However, the separated tracker should be adjusted whenever the former detector model is changed. This study proposes a system that can train a model that performs detection and tracking simultaneously using only the detector training datasets. In particular, a Siam network with augmentation is used to compose the detector and tracker. Experiments are conducted on public datasets to verify that the proposed algorithm can formulate a real-time multi-object tracker comparable to the state-of-the-art tracker models.

• Journal of Biosystems Engineering
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• v.28 no.6
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• pp.517-524
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• 2003

There have been worldwide research and development efforts to automate various processes of bio-production and those efforts will be expanded with priority given to tasks which require high intensive labor or produce high value-added product and tasks under hostile environment. In the field of bio-production capabilities of the versatility and robustness of automated system have been major bottlenecks along with economical efficiency. This paper introduces a new concept of automation based on tole-operation, which can provide solutions to overcome inherent difficulties in automating bio-production processes. Operator(farmer), computer, and automatic machinery share their roles utilizing their maximum merits to accomplish given tasks successfully. Among processes of greenhouse watermelon cultivation tasks such as pruning, watering, pesticide application, and harvest with loading were chosen based on the required labor intensiveness and functional similarities to realize the proposed concept. The developed system was composed of 5 major hardware modules such as wireless remote monitoring and task control module, wireless remote image acquisition and data transmission module, gantry system equipped with 4 d.o.f. Cartesian type robotic manipulator, exchangeable modular type end-effectors, and guided watermelon loading and storage module. The system was operated through the graphic user interface using touch screen monitor and wireless data communication among operator, computer, and machine. The proposed system showed practical and feasible way of automation in the field of volatile bio-production process.

• The Journal of the Acoustical Society of Korea
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• v.37 no.2
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• pp.99-109
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• 2018

The precision of the vibration-sensors, contact or non-contact types, is usually satisfactory for the practical measurement applications, but a sensor is confined to the measurement of a point or a direction. Although the precision and frequency span of the low-cost camera are inferior to these sensors, it has the merits in the cost and in the capability of simultaneous measurement of a large vibrating area. Furthermore, a camera can measure multi-degrees-of-freedom of a vibrating object simultaneously. In this study, the calibration method and the dynamic characteristics of the low-cost machine vision camera as a sensor are studied with a demonstrating example of the two-dimensional vibration of a cantilever beam. The planar image of the camera shot reveals two rectilinear and one rotational motion. The rectilinear vibration motion of a single point is first measured using a camera and the camera is experimentally calibrated by calculating error referencing the LDV (Laser Doppler Vibrometer) measurement. Then, by measuring the motion of multiple points at once, the rotational vibration motion and the whole vibration motion of the cantilever beam are measured. The whole vibration motion of the cantilever beam is analyzed both in time and frequency domain.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.3
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• pp.1-9
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• 2024

This study proposes a multi-scale template matching technique with image pyramids (TMI) to measure structural dynamic displacement using a vision sensor under atmospheric turbulence conditions and evaluates its displacement measurement performance. To evaluate displacement measurement performance according to distance, the three-story shear structure was designed, and an FHD camera was prepared to measure structural response. The initial measurement distance was set at 10m, and increased with an increment of 10m up to 40m. The atmospheric disturbance was generated using a heating plate under indoor illuminance condition, and the image was distorted by the optical turbulence. Through preliminary experiments, the feasibility of displacement measurement of the feature point-based displacement measurement method and the proposed method during atmospheric disturbances were compared and verified, and the verification results showed a low measurement error rate of the proposed method. As a result of evaluating displacement measurement performance in an atmospheric disturbance environment, there was no significant difference in displacement measurement performance for TMI using an artificial target depending on the presence or absence of atmospheric disturbance. However, when natural targets were used, RMSE increased significantly at shooting distances of 20 m or more, showing the operating limitations of the proposed technique. This indicates that the resolution of the natural target decreases as the shooting distance increases, and image distortion due to atmospheric disturbance causes errors in template image estimation, resulting in a high displacement measurement error.

• Journal of Biomedical Engineering Research
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• v.21 no.3 s.61
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• pp.321-331
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• 2000

Multiple 15-hue tests were designed and implemented on a PC in the study so as to quickly and quantitatively evaluate color vision acuity. Difficulty of the test was control)ed by the value of CDBACC (color difference between adjacent color chips) calculated using a CIELAB formula. The multiple 15-hue tests consist of eight of the hue tests (test 3-10) and three of the basic color (red, green, blue) tests (test 11-13). The 15 colors used for the hue tests were specified by the 15 color coordinates that were located at a constant distance (d = 2. 3. 5. 7, 10, 20, 30. 40) from white reference in the CIE chromaticity coordinate system and were separated by a constant color difference (CDBACC = 0.75, 1.1, 1.8. 2.5. 3.5. 7.5. 11, 14) from the adjacent chips. The color coordinates for the 15 chips for the basic color tests were the same as those of the 15 points spaced equally by a constant color difference (6.87 for the green color test. 7.27 for the red color test, 7.86 for the blue color test) from the white reference along the axis of red, green and blue. Thirty normal subjects who were not color blind were taken to undergo the multiple 15-hue tests. It was observed that most of the subjects correctly arranged color chips for the tests with CDBACC greater than 5, whereas no one correctly answered for those with CDBACC less than 2. Rapid changes in the number of the subjects correctly arranged took place when CDBACC of the tests was between 2 and 4.5. In the basic color tests, unlike the hue tests having similar values of CDBACC, it was seen that the subjects arranged color chips even less correctly. It was found that JNCD (just noticeable color difference) - a measure of color vision acuity was about 3 in average for the subjects. The JNCD was chosen as the value of the CDBACC of the test for which about $50\%$ of the subjects failed to successfully arrange color chips. ERCCA (error rate of color chips arrangement) for the test with CDBACC the same as the JNCD was shown to be about $20\%$. It is expected that the multi 15-hue tests implemented on a PC in the study will be an economical tool to quickly and quantitatively evaluate color vision acuity and, accordingly, the tests can be used for early diagnosis to massive potential patients suffering from diseases (ex. diabetes, glaucoma) which may induce changes in color vision acuity.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.837-839
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• 2012

When the for occurred, the driver does not get the vision is has difficult on driving. In this case, the probability of occurrence of accidents are very high level. To reduce accidents, this system provide drivers with the safety of ensure to measures that a service inform current situation. in this paper, the crash occur in fog to prevent accident using vehicle safety system to give a alarm and control. The proposed system is installed on the outside of the vehicle, humidity, and ambient light sensors inside the car from the information collected by the system controller for the detection of fog conditions using video equipment and then finally the fog occurs if you do not get the driver's field of events is causing the system.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.398-404
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• 2015

In this paper, we describe the design and performance of a prototype multi-rotor unmaned aerial vehicle( UAV) platform featuring an inertial measurement unit(IMU) based autonomous-flying for use in bluetooth communication environments. Although there has been a fair amount of study of free-flying UAV with multi-rotors, the more recent trend has been to outfit hexarotor helicopter with gimbal to support various services. This paper introduces the hardware and software systems toward very compact and autonomous hexarotors, where they can perform search, rescue, and surveillance missions without external assistance systems like ground station computers, high-performance remote control devices or vision system. The proposed system comprises the construction of the test hexarotor platform, the implementation of an IMU, mathematical modeling and simulation in the helicopter. Furthermore, the hexarotor helicopter with implemented IMU is connected with a micro controller unit(MCU)(ARM-cortex) board. The micro-controller is able to command the rotational speed of the rotors and to get the measurements of the IMU as input signals. The control simulation and experiment on the real system are implemented in the test platform, evaluated and compared against each other.