• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.10
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• pp.202-208
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• 2012

Fetal heart rate monitoring is important information to assess fetal well-being. Non-invasive fetal ECG (electrocardiography) can be derived from maternal abdominal signal. And various promising signal processing methods have been introduced to extract fetal ECG from mother's composite abdominal signal. However, non-invasive fetal ECG monitoring still has not been widely used in clinical practice due to insufficient reliable measurement and difficulty of signal processing. In application of signal processing method to extract fetal ECG, it might be lower signal to noise ratio due to time varying white Gaussian noise. In this paper, time varying Kalman smoother is proposed to remove white noise in fetal ECG and its feasibility is confirmed. Wiener process was set as Kalman system model and covariance matrix was modified according to white Gaussian noise level. Modified error covariance matrix changed Kalman gain and degree of smoothness. Optimal covariance matrix according to various amplitude in Gaussian white noise was extracted by 5 channel fetal ECG model, and feasibility of proposed method could be confirmed.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.3
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• pp.27-36
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• 2004

Detection and recognition of human faces in images can be considered as an important aspect for applications that involve interaction between human and computer. In this paper, we propose a face recognition method using eigenfaces and fuzzy neural networks. The Principal Components Analysis (PCA) is one of the most successful technique that have been used to recognize faces in images. In this technique the eigenvectors (eigenfaces) and eigenvalues of an image is extracted from a covariance matrix which is constructed form image database. Face recognition is Performed by projecting an unknown image into the subspace spanned by the eigenfaces and by comparing its position in the face space with the positions of known indivisuals. Based on this technique, we propose a new algorithm for face recognition consisting of 5 steps including preprocessing, eigenfaces generation, design of fuzzy membership function, training of neural network, and recognition. First, each face image in the face database is preprocessed and eigenfaces are created. Fuzzy membership degrees are assigned to 135 eigenface weights, and these membership degrees are then inputted to a neural network to be trained. After training, the output value of the neural network is intupreted as the degree of face closeness to each face in the training database.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.2
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• pp.30-39
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• 2009

Extraction of glomeruli region plays a very important role for diagnosing nephritis automatically. However, it is not easy to extract glomeruli region correctly because the difference between glomeruli region and other region is not obvious, simultaneously unevennesses that is brought in the sampling process and in the imaging process. In this study, a new method for extracting renal glomeruli region using genetic algorithm is proposed. The first, low and high resolution images are obtained by using Laplacian-Gaussian filter with ${\sigma}=2.1$ and ${\sigma}=1.8$, then, binary images by setting the threshold value to zero are obtained. And then border edge is detected from low resolution images, the border of glomeruli is expressed by a closed B-splines' curve line. The parameters that decide the closed curve line with this low resolution image prevent the noises and the border lines from breaking off in the middle by searching using genetic algorithm. Next, in order to obtain more precise border edges of glomeruli, the number of node points is increased and corrected in order from eight to sixteen and thirty two from high resolution images. Finally, the validity of this proposed method is shown to be effective by applying to the real images.

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.97-111
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• 2009

Image-based traffic information collection systems have entered widespread adoption and use in many countries since these systems are not only capable of replacing existing loop-based detectors which have limitations in management and administration, but are also capable of providing and managing a wide variety of traffic related information. In addition, these systems are expanding rapidly in terms of purpose and scope of use. Currently, the utilization of image processing technology in the field of traffic accident management is limited to installing surveillance cameras on locations where traffic accidents are expected to occur and digitalizing of recorded data. Accurately recording the sequence of situations around a traffic accident in a signal intersection and then objectively and clearly analyzing how such accident occurred is more urgent and important than anything else in resolving a traffic accident. Therefore, in this research, we intend to present a technology capable of overcoming problems in which advanced existing technologies exhibited limitations in handling real-time due to large data capacity such as object separation of vehicles and tracking, which pose difficulties due to environmental diversities and changes at a signal intersection with complex traffic situations, as pointed out by many past researches while presenting and implementing an active and environmentally adaptive methodology capable of effectively reducing false detection situations which frequently occur even with the Gaussian complex model analytical method which has been considered the best among well-known environmental obstacle reduction methods. To prove that the technology developed by this research has performance advantage over existing automatic traffic accident recording systems, a test was performed by entering image data from an actually operating crossroad online in real-time. The test results were compared with the performance of other existing technologies.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.2
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• pp.109-114
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• 2012

Honeycombs are dense structures that small cysts, which generally have about 2~10 mm in diameter, are surrounded by the wall of fibrosis. When honeycomb is found in the patients, the incidence of acute exacerbation is generally very high. Thus, the observation and quantitative measurement of honeycomb are considered as a significant marker for clinical diagnosis. In this point of view, we propose an automatic segmentation method using morphological image processing and assessment of the degree of clustering techniques. Firstly, image noises were removed by the Gaussian filtering and then a morphological dilation method was applied to segment lung regions. Secondly, honeycomb cyst candidates were detected through the 8-neighborhood pixel exploration, and then non-cyst regions were removed using the region growing method and wall pattern testing. Lastly, final honeycomb regions were segmented through the extraction of dense regions which are consisted of two or more cysts using cluster analysis. The proposed method applied to 80 High resolution computed tomography (HRCT) images and achieved a sensitivity of 89.4% and PPV (Positive Predictive Value) of 72.2%.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.254-265
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• 2021

Purpose: Inspection and diagnosis on the performance and safety through domestic port facilities have been conducted for over 20 years. However, the long-term development strategies and directions for facility renewal and performance improvement using the diagnosis history and results are not working in realistically. In particular, in the case of port structures with a long service life, there are many problems in terms of safety and functionality due to increasing of the large-sized ships, of port use frequency, and the effects of natural disasters due to climate change. Method: In this study, the maintenance history data of the gravity type quay in element level were collected, defined as big data, and a predictive approximation model was derived to estimate the pattern of deterioration and aging of the facility of project level based on the data. In particular, we compared and proposed models suitable for the use of big data by examining the validity of the state-based deterioration pattern and deterioration approximation model generated through machine learning algorithms of GP and SGP techniques. Result: As a result of reviewing the suitability of the proposed technique, it was considered that the RMSE and R² in GP technique were 0.9854 and 0.0721, and the SGP technique was 0.7246 and 0.2518. Conclusion: This research through machine learning techniques is expected to play an important role in decision-making on investment in port facilities in the future if port facility data collection is continuously performed in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.6
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• pp.547-553
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• 2020

As aging infrastructures increase along with time, the efficient maintenance becomes more significant and accurate responses from the sensors are pre-requisite. Among various responses, strain is commonly used to detect damage such as crack and fatigue. Optical fiber sensor is one of the promising sensing techniques to measure strains with high-durability, immunity for electrical noise, long transmission distance. Fiber Bragg Grating (FBG) is a point sensor to measure the strain based on reflected signals from the grating, while Brillouin Optic Correlation Domain Analysis (BOCDA) is a distributed sensor to measure the strain along with the optical fiber based on scattering signals. Although the FBG provides the signal with high accuracy and reproducibility, the number of sensing points is limited. On the other hand, the BOCDA can measure a quasi-continuous strain along with the optical fiber. However, the measured signals from BOCDA have low accuracy and reproducibility. This paper proposed a multi-fidelity data-fusion method based on Gaussian Process Regression to improve the fidelity of the strain distribution by fusing the advantages of both systems. The proposed method was evaluated by laboratory test. The result shows that the proposed method is promising to improve the fidelity of the strain.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.175-182
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• 2020

The non-contact eye tracking is a nonintrusive human-computer interface providing hands-free communications for people with severe disabilities. Recently. it is expected to do an important role in non-contact systems due to the recent coronavirus COVID-19, etc. This paper proposes a novel approach for an eye mouse using an eye tracking method based on a context-aware based AdaBoost multi-region classifier and ASSL algorithm. The conventional AdaBoost algorithm, however, cannot provide sufficiently reliable performance in face tracking for eye cursor pointing estimation, because it cannot take advantage of the spatial context relations among facial features. Therefore, we propose the eye-region context based AdaBoost multiple classifier for the efficient non-contact gaze tracking and mouse implementation. The proposed method detects, tracks, and aggregates various eye features to evaluate the gaze and adjusts active and semi-supervised learning based on the on-screen cursor. The proposed system has been successfully employed in eye location, and it can also be used to detect and track eye features. This system controls the computer cursor along the user's gaze and it was postprocessing by applying Gaussian modeling to prevent shaking during the real-time tracking using Kalman filter. In this system, target objects were randomly generated and the eye tracking performance was analyzed according to the Fits law in real time. It is expected that the utilization of non-contact interfaces.

• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.25-33
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• 2023

Noise generated during the acquisition and transmission of CT images acts as a factor that degrades image quality. Therefore, noise removal to solve this problem is an important preprocessing process in image processing. In this paper, we remove noise by using a deformable convolutional autoencoder (DeCAE) model in which deformable convolution operation is applied instead of the existing convolution operation in the convolutional autoencoder (CAE) model of deep learning. Here, the deformable convolution operation can extract features of an image in a more flexible area than the conventional convolution operation. The proposed DeCAE model has the same encoder-decoder structure as the existing CAE model, but the encoder is composed of deformable convolutional layers and the decoder is composed of conventional convolutional layers for efficient noise removal. To evaluate the performance of the DeCAE model proposed in this paper, experiments were conducted on CT images corrupted by various noises, that is, Gaussian noise, impulse noise, and Poisson noise. As a result of the performance experiment, the DeCAE model has more qualitative and quantitative measures than the traditional filters, that is, the Mean filter, Median filter, Bilateral filter and NL-means method, as well as the existing CAE models, that is, MAE (Mean Absolute Error), PSNR (Peak Signal-to-Noise Ratio) and SSIM. (Structural Similarity Index Measure) showed excellent results.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.410-418
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• 2023

Recently, advancements and commercialization in the field of maritime autonomous surface ships (MASS) has rapidly progressed. Concurrently, studies are also underway to develop methods for automatically surveying the condition of various on-board equipment remotely to ensure the navigational safety of MASS. One key issue that has gained prominence is the method to obtain values from analog gauges installed in various equipment through image processing. This approach has the advantage of enabling the non-contact detection of gauge values without modifying or changing already installed or planned equipment, eliminating the need for type approval changes from shipping classifications. The objective of this study was to identify a dynamically changing indicator needle within noisy images of analog gauges. The needle object must be identified because its position significantly affects the accurate reading of gauge values. An analog pressure gauge attached to an emergency fire pump model was used for image capture to identify the needle object. The acquired images were pre-processed through Gaussian filtering, thresholding, and morphological operations. The needle object was then identified through Hough Transform. The experimental results confirmed that the center and object of the indicator needle could be identified in images of noisy analog gauges. The findings suggest that the image processing method applied in this study can be utilized for shape identification in analog gauges installed on ships. This study is expected to be applicable as an image processing method for the automatic remote survey of MASS.