• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.491-499
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• 2018

Runway visual range (RVR), one of the important indicators of aircraft takeoff and landing, is affected by meteorological conditions such as temperature, humidity, etc. It is important to estimate the RVR at the time of arrival in advance. This study estimated the RVR of the local airport after 1 hour by upgrading the RVR estimation model using the proposed deep learning network. To this end, the advancement of the estimation model was carried out by changing the time interval of the meteorological data (temperature, humidity, wind speed, RVR) as input value and the linear conversion of the results. The proposed method generates estimation model based on the past measured meteorological data and estimates the RVR after 1 hour and confirms its validity by comparing with measured RVR after 1 hour. The proposed estimation model could be used for the RVR after 1 hour as reference in small airports in regions which do not forecast the RVR.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.114-126
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• 2009

In this paper, we propose a PSNR(peak-to-peak signal to noise ratio) estimation method exploiting visual rhythm information for the reconstructed video frames at the customer's STB(Set-top Box). Key idea is that we can estimate the PSNR by using VR(visual rhythm) information even though a VR consists of the pixels in a vertical direction of a 2D(2-dimensional) video frame, because VR is the 1D projected version of a 2D video frame approximately. Simulation results show that the estimated PSNR from VR information is closely related to the PSNR from 2D video frames. The advantages of the proposed scheme includes that it can monitor the video quality efficiently while minimizing the computation load of STB, and show the location, duration and occurrence count of severe picture degradation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2529-2551
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• 2022

Visual servoing (VS) based on the Kalman filter (KF) algorithm, as in the case of KF-based image-based visual servoing (IBVS) systems, suffers from three problems in uncalibrated environments: the perturbation noises of the robot system, error of noise statistics, and slow convergence. To solve these three problems, we use an IBVS based on KF, African vultures optimization algorithm enhanced extreme learning machine (AVOA-ELM), and fuzzy logic (FL) in this paper. Firstly, KF online estimation of the Jacobian matrix. We propose an AVOA-ELM error compensation model to compensate for the sub-optimal estimation of the KF to solve the problems of disturbance noises and noise statistics error. Next, an FL controller is designed for gain adaptation. This approach addresses the problem of the slow convergence of the IBVS system with the KF. Then, we propose a visual servoing scheme combining FL and KF-AVOA-ELM (FL-KF-AVOA-ELM). Finally, we verify the algorithm on the 6-DOF robotic manipulator PUMA 560. Compared with the existing methods, our algorithm can solve the three problems mentioned above without camera parameters, robot kinematics model, and target depth information. We also compared the proposed method with other KF-based IBVS methods under different disturbance noise environments. And the proposed method achieves the best results under the three evaluation metrics.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.7
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• pp.83-91
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• 2004

The determination of camera position and orientation from known correspondences of 3D reference points and their images is known as pose estimation in computer vision or space resection in photogrammetry. This paper discusses estimation of transform parameters using the pattern matching method with 2D images only. In general, the 3D reference points or lines are needed to find out the 3D transform parameters, but this method is applied without the 3D reference points or lines. It uses only two images to find out the transform parameters between two image. The algorithm is simulated using Visual C＋＋ on Windows 98.

• Journal of Mechanical Science and Technology
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• v.18 no.6
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• pp.961-971
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• 2004

Methods for determination of the crack opening stress intensity factor ($K_{op}$) and for estimation of the effective stress intensity factor range ($\Delta{K}_{eff}$) are evaluated for crack growth test data of aluminum alloys. Three methods of determining $K_{op}$, visual measurement, ASTM offset compliance method, and the neural network method proposed by Kang and Song, and three methods of estimating $\Delta{K}_{eff}$, conventional, the 2/PIO and 2/PI methods proposed by Donald and Paris, are compared in a quantitative manner by using evaluation criteria. For all $K_{op}$ determination methods discussed, the 2/PI method of estimating $\Delta{K}_{eff}$ provides good results. The neural network method of determining $K_{op}$ provides good correlation of crack growth data. It is recommended to use 2/PI estimation with the neural $K_{op}$ determination method. The ASTM offset method used in conjunction with 2/PI estimation shows a possibility of successful application. It is desired to improve the ASTM method.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.08a
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• pp.117-120
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• 2003

Fractal mathematics is being highlighted as a research method for classification of image. But the application of Fractal dimension(FD) has been required the complicated calculation method because of its complex repetition progressing. In this paper, it has been developed the new approach method to express the Fractal Dimension(FD) for aging level calculation and estimation system of outside insulator using special image processing algorithm. As a result after FD testing, the recognized aging estimation of FD has a very characteristics compared to the conventional visual inspection.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.20 no.2
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• pp.183-187
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• 2007

Fractal mathematics is being highlighted as a research method for classification of image. But the application of Fractal dimension(FD) has been requited the complicated calculation method because of its complex repetition progressing. In this paper, it has been developed the new approach method to express the Fractal Dimension(FD) for aging level calculation and estimation system of outside insulator using special image processing algorithm. As a result after FD testing, the recognized aging estimation of FD has a very characteristics compared to the conventional visual inspection.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.05a
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• pp.55-58
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• 2000

Fractal mathematics is being highlighted as a research method for classification of image. But the application of Fractal dimension(FD) has been required the complicated calculation method because of its complex repetition progressing. In this paper, it has been developed the new approach method to express the Fractal Dimension(FD) for aging level calculation and estimation system of outside insulator using special image processing algorithm. As a result after FD testing, the recognized aging estimation of FD has a very characteristics compared to the conventional visual inspection.

• Journal of the Korea institute for structural maintenance and inspection
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• v.2 no.3
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• pp.235-244
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• 1998

In the design of bridges, the points of concem are the landscape design, the function, safety and economical efficiency. But most of studies have been performed on structural engineering. The study on the landscape design of bridges has not been done in korea. Therefore, in this research, the design method of bridges by the judgement of structural engineering and landscape engineering has been proposed, through the process to decide the shape of bridges. Also, the research studies a problem about the visual safety of the structural shape in the landscape design of bridges. The visual experiments applied to the seven models about the shape of hunch in bridge pier. The experiment was made in moving velocity of view point, steady looking time and track of eyeball movement.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.4
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• pp.349-355
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• 2015

Underwater robots generally show better performances for tasks than humans under certain underwater constraints such as. high pressure, limited light, etc. To properly diagnose in an underwater environment using remotely operated underwater vehicles, it is important to keep autonomously its own position and orientation in order to avoid additional control efforts. In this paper, we propose an efficient method to assist in the operation for the various disturbances of a remotely operated vehicle for the diagnosis of underwater structures. The conventional AHRS-based bearing estimation system did not work well due to incorrect measurements caused by the hard-iron effect when the robot is approaching a ferromagnetic structure. To overcome this drawback, we propose a sensor fusion algorithm with the camera and AHRS for estimating the pose of the ROV. However, the image information in the underwater environment is often unreliable and blurred by turbidity or suspended solids. Thus, we suggest an efficient method for fusing the vision sensor and the AHRS with a criterion which is the amount of blur in the image. To evaluate the amount of blur, we adopt two methods: one is the quantification of high frequency components using the power spectrum density analysis of 2D discrete Fourier transformed image, and the other is identifying the blur parameter based on cepstrum analysis. We evaluate the performance of the robustness of the visual odometry and blur estimation methods according to the change of light and distance. We verify that the blur estimation method based on cepstrum analysis shows a better performance through the experiments.