• Journal of the Korean Society of Marine Environment & Safety
• /
• v.22 no.6
• /
• pp.593-599
• /
• 2016

Incheon Bridge is 13.38 km long with an 800 m span, connecting Incheon International Airport and Songdo International City, Per hour 73.8 vessels navigate this space. The purpose of this study was to suggest a safe passing speed based on the displacement of a vessel based on the safety criteria of Incheon Bridge's anti-collision fence, which was designed during its initial construction. As AASHTO LRFD suggested, vessel collision energy, vessel collision velocity, and the hydrodynamic mass coefficient were considered to derive a safe vessel traffic speed. Incheon Bridge's anti-collision fence was designed so that 100,000 DWT vessels can navigate at a speed of 10 knot. This research suggests a safe speed for vessel traffic through a comparative analysis of an experimental ship's (300,000 DWT) speed and cargo conditions, regulation speed has been calculated according to the collision energy under each set of conditions. Additionally, safe traffic vessel's safe speed was analyzed with reference to tidal levels. Results from the experimental ship showed that a vessel of maximum 150,000 DWT is able to pass Incheon Bridge at a maximum of 7 knots with an above average water level, and is able to pass the bridge with a maximum of 8 knots under ballast conditions.

• Journal of the Korean Society of Radiology
• /
• v.3 no.1
• /
• pp.29-39
• /
• 2009

In this paper has proposed to the recognition of the disease on medical images using neural network. The neural network is constructed as three-layers of the input-layer, the hidden-layer and the output-layer. The training method applied for the recognition of disease region is adaptive error back-propagation. The low-frequency region analyzed by DWT are expressed by matrix. The coefficient-values of the characteristic polynomial applied are n+1. The normalized maximum value +1 and minimum value -1 in the range of tangent-sigmoid transfer function are applied to be use as the input vector of the neural network. To prove the validity of the proposed methods used in the experiment with a simulation experiment, the input medical image recognition rate the evaluation of areas of disease. As a result of the experiment, the characteristic polynomial coefficient of low-frequency area matrix, conversed to 4 level DWT, was proved to be optimum to be applied to the feature parameter. As for the number of training, it was marked fewest in 0.01 of learning coefficient and 0.95 of momentum, when the adaptive error back-propagation was learned by inputting standardized feature parameter into organized neural network. As to the training result when the learning coefficient was 0.01, and momentum was 0.95, it was 100% recognized in fifty-five times of the stomach image, fifty-five times of the chest image, forty-six times of the CT image, fifty-five times of ultrasonogram, and one hundred fifty-seven times of angiogram.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.24 no.2
• /
• pp.157-162
• /
• 2018

The increase of risk in port due to the increase in ship size and sea level rises, the standard crown height will increase. In this study, cruise and container ships will need to raise their crown height due to the projected wind pressure areas becoming larger due to the ships' size increase. The mooring assessment was evaluated with the rise of the crown height. The cruise ship of GT 100,000 tons exceeded the permissible breaking force of the mooring line under the crown height conditions of wind speed of 30 kts when the wind direction was $45^{\circ}$ to the direction of the bow. Also, the elevation angle of the pier and mooring line was analyzed and exceeded the crown height, and it was determined that it is necessary to adjust the crown height. Container ships of DWT 100,000 tons were analyzed to exceed the limit of sway motion at the crown height and it was determined that they need to be adjusted to the minimum crown height standard.

• The Journal of the Acoustical Society of Korea
• /
• v.21 no.1
• /
• pp.82-88
• /
• 2002

In this paper, we proposed a audio indexing method that is used wavelet transform for audio data retrieval. It is difficult for audio data to make a efficient audio data index because of its own particular properties, such as requirement of large storage, real time to transfer and wide bandwidth. An audio data in del using wavelet transform make it possible to index and retrieval by using the particular wavelet transform properties. Our proposed indexing method doesn't separate data to several blocks. Therefore we use both high-pass and low-pass parts of last level coefficient of wavelet transform. Audio data indexing is made by applying the string matching algorithm to high-pass part and zero-crossing histogram to low-pass part. These are transformed to the continued strings, Through this method, we described a retrieval efficiency. The retrieval method is done by comparing the database index string to the query string and then data of minimum values is chosen to the result. Our simulation decided proper comparative coefficient and made known changing of retrieval efficiency versus audio data length. The results show that the proposed method improves retrieval efficiency compared to conventional method.

• Transactions on Control, Automation and Systems Engineering
• /
• v.3 no.4
• /
• pp.283-288
• /
• 2001

In this paper, we proposed a method for extracting facial characteristics of human being in an image. Given a pair of gray level sample images taken with and without human being, the face of human being is segmented from the image. Noise in the input images is removed with the help of Gaussian filters. Edge maps are found of the two input images. The binary edge differential image is obtained from the difference of the two input edge maps. A mask for face detection is made from the process of erosion followed by dilation on the resulting binary edge differential image. This mask is used to extract the human being from the two input image sequences. Features of face are extracted from the segmented image. An effective recognition system using the discrete wave let transform (DWT) is used for recognition. For extracting the facial features, such as eyebrows, eyes, nose and mouth, edge detector is applied on the segmented face image. The area of eye and the center of face are found from horizontal and vertical components of the edge map of the segmented image. other facial features are obtained from edge information of the image. The characteristic vectors are extrated from DWT of the segmented face image. These characteristic vectors are normalized between +1 and -1, and are used as input vectors for the neural network. Simulation results show recognition rate of 100% on the learned system, and about 92% on the test images.

• Journal of Internet Computing and Services
• /
• v.9 no.1
• /
• pp.33-41
• /
• 2008

Due to the advantage of wavelet transform such as the compatibility with JPEG2000, multi-resolution decomposition, and application of HVS(Human Visual System), watermarking algorithm based on wavelet transform (DWT) is recently mast interesting research subject. However, mast of researches are focused on theoretical aspects for the robustness rather than practical usage, and. may be not suitable too much complicated to use in practice. In this paper, robust logo watermarking algorithm based on DWT is proposed for large and huge data processing. The proposed method embeds the logo watermark by mapping of $8{\times}8$ blocks in order of the number of '1' of the original image and the randomized watermark image with LFSR. The original image is transformed by 2 level wavelet. The experimental results shows that the watermark is embedded successfully, and the proposed algorithm has the valuable robustness from the image processing like JPEG compression, low pass filter, high pass filter and changes in brightness and contrast.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.18 no.3
• /
• pp.155-162
• /
• 2008

In this paper we proposed a video watermarking algorithm based on a three dimension discrete wavelet transform (3D DWT) and direct spread spectrum (DSS). In the proposed method, the information watermark is embedded into followed frames, after sync watermark is embedded into the first frame. Input frames are divided into sub frames which are located odd row and even row. The sub frames are arranged as 3D frames, and transformed into 3D wavelet domain. In this domain the watermark is embedded using DSS. Existing video watermarking using 3D DWT is non-blind method but, proposed algorithm uses blind method. The experimental results show that the proposed algorithm is robust against frame cropping, noise addition, compression, etc. acquiring BER of 10% or below and sustains level of 40dB or above on the average.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.10
• /
• pp.63-71
• /
• 2009

In this study, we propose a revised dynamic ROI (Region-of-Interest) coding method in which the focused ROI is automatically extracted without help from users during the recovery process of low DOF (Depth-of-Field) JPEG2000 image. The proposed method creates edge mask information using high frequency sub-band data on a specific level in DWT (Discrete Wavelet Transform), and then identifies the edge code block for a high-speed ROI extraction. The algorithm scans the edge mask data in four directions by the unit of code block and identifies the edge code block simply and fastly using a edge threshold. As the results of experimentation applying for Implicit method, the proposed method showed the superiority in the side of speed and quality comparing to the existing methods.

• Proceedings of the IEEK Conference
• /
• 2000.06d
• /
• pp.230-233
• /
• 2000

In this paper, we proposed a wavelet-based digital watermarking algorithm using human visual system and subband-adaptive threshold. After the original image is transformed using discrete wavelet transform(DWT), the perceptually significant coefficients of the each subband excluding the lowest level subbands are utilized to embed the watermark. To select perceptually significant coefficients, we use subband-adaptive threshold. For the selected coefficients, the watermark is embedded by rising HVS. We tested the performance of the proposed algorithm compared with conventional watermarking algorithm by computer simulation. The experimental results show that the proposed algorithm is superior to the conventional algorithm.

• Journal of Korea Multimedia Society
• /
• v.17 no.2
• /
• pp.113-123
• /
• 2014

Electrophysiological recordings are considered a reliable method of assessing a person's alertness. Sleep medicine is asked to offer objective methods to measure daytime alertness, tiredness and sleepiness. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in recognition of alertness level. In this paper, EEG signals have been analyzed using wavelet transform as well as discrete wavelet transform and classification using statistical classifiers such as euclidean and mahalanobis distance classifiers and a promising method SVM (Support Vector Machine). As a result of simulation, the average values of accuracies for the Linear Discriminant Analysis (LDA)-Quadratic, k-Nearest Neighbors (k-NN)-Euclidean, and Linear SVM were 48%, 34.2%, and 86%, respectively. The experimental results show that SVM classification method offer the better performance for reliable classification of the EEG signal in comparison with the other classification methods.