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Detecting and responding appropriately to temporal changes in the shoreline is an important task for protecting coasts. Video monitoring has been utilized as a powerful tool for detecting shoreline changes. Existing shoreline-tracking methods include the threshold methods, colour intensity gradient methods, and neural networks, which involve ad-hoc assignment of the threshold values, drawing shore-normal transects, and heavy preliminary training for each coast with many data, respectively. The study applies a new boundary tracking method using Minimum Convolution of Gaussian Weight and Squared Differences (MCGWSD). The new method is fast and effective in a sense that it does not need ad-hoc threshold, drawing of transects, or pre-training. This method tracks boundary lines between two zones with no thickness by inversely tracking every pixel of the late image. The MCGWSD method is first examined for various image distortions, i.e. translation, linear deformation, angular deformation, and rotation of images. Images of a part of orange peel are chosen for the test, where a boundary line is artificially drawn, not necessarily following clear object boundary, but crosses over small patterns. The new method satisfactorily tracks the movement of boundary line at the tests. Then field video images of Jangsa Beach between 1 September 2020 and 15 September 2020, when typhoons Maysak and Haishen hit the coast, are examined to track the shoreline movement. Ground truth shoreline information at the coast during the time is not available, and results of existing colour intensity gradient method PIMACS are assumed true. According to PIMACS results on the beach width along two transects during the period, the shoreline underwent a movement up to 6 m. The new MCGWSD method tracks the shoreline position, and its results show good agreement with PIMACS results along two transects. The merits of the present method are that it produces shoreline change over the whole domain, and shore-normal transects are not needed. The present method effectively tracks the shoreline retreat or advance of as small as 1 pixel of image. The new method could be used for tracking shoreline change at arbitrary geometry even with sharp corners.

Characterizing the influence factors of exhaust gas based on the suspended road dust on paved roads, according to the number of vehicles and their distance with regard to driving pattern, is important in order to provide a coefficient for driving patterns to find a model equation. This has been a limitation of previous studies, in which this was difficult to carry out in a large area reflecting various driving patterns because some sections were selected according to empirical measurement results, and only one vehicle measurement was used to find the level of road dust. This study measured the concentration of suspended road dust that could occur, depending on the vehicle’s driving patterns, on an experimental road in Yongin, South Korea, from May to July 2023. The study was conducted to determine the degree of the effect of exhaust gas, according to the concentration of suspended road dust generated, by determining the separation distance based on real-time measurements. This study attempted to determine the changes in suspended road dust based on driving patterns in urban areas and factor in the concentration of suspended road dust with regard to emission characteristics in terms of exhaust gas and particulate matter with a diameter of 10 microns or less (PM10). This was in accordance with conditions evaluated using mobile laboratories, based on suspended-PM10-concentration-measuring equipment. This study mainly focused on the following main topics: (1) increasing the level of suspended particulate matter at less than 10 m intervals produced by exhaust gas; (2) decreasing the level of suspended road dust with an increase in the number of vehicles, with the area measured at a distance of three cars in front showing the lowest level of suspended road dust in the air and a low level for the rear vehicle; (3) demonstrating that PM10 is effective in measuring the generation of suspended road dust; and (4) evaluating suspended road dust levels by road section. Based on the results, this research is necessary to more appropriately set the focus of analyses that aim to characterize suspended road dust according to exhaust gas and PM10 content in silt.

As the amount of aged pavement increases, functional damage, such as spalling, occurs frequently on Portland Cement Concrete pavement (PCC) in South Korea. However, the existing management method does not properly reflect the scope of deterioration of the pavement causing early damage. To overcome the problem of the existing repair method, this study evaluated the deterioration of functional damage on the surface of the slab as soundness through ultrasonic velocity measurement method among non-destructive testing (NDT) techniques and suggested a method to estimate the depth of deterioration. To develop a method for estimating the depth of the deterioration a slab, a preliminary investigation was conducted to check the range of ultrasonic velocity of concrete pavement in South Korea and to evaluate the variability of NDT equipment. Based on the ultrasonic velocity, the sound rating of concrete pavement was graded from 5 for “very good” to 0 for “very poor”, and the tendency of the ultrasonic velocity to increase according to the depth of the deteriorated areas was confirmed.

Characterizing the influencing factors of resuspended dust on paved roads according to the atmospheric environment and traffic conditions is important to provide a basis for road atmospheric pollution control measures suitable for various road environments in the future. This study attempts to identify factors in the concentration of resuspended dust according to the level of road dust loading and PM10 emission characteristics according to atmospheric weather environment and traffic conditions using real-time vehicle-based resuspended PM10 concentration measuring equipment. This study mainly focuses on the following main topics: (1) the increased level of resuspended dust according to vehicle speed and silt loading (sL) level; (2) difference between atmospheric pollution at adjacent monitoring station concentration and background concentration levels on roads due to atmospheric weather changes; (3) the correlation between traffic and weather factors with resuspended dust levels; (4) the evaluation of resuspended dust levels by road section. Based on the results, the necessity of research to more appropriately set the focus of analysis in order to characterize the resuspended dust according to changes in the traffic and weather environment in urban areas is presented.

A nonlinear kernel with a bias is proposed here in the convolutional neural network. Negative square of subtraction between input image pixel numbers and the kernel coefficients are convolved to conform new feature map through the convolution layer in convolutional neural network. The operation is nonlinear from the input pixel point of view, as well as from the kernel weight coefficient point of view. Maximum-pooling may follow the feature map, and the results are finally fully connected to the output nodes of the network. While using gradient descent method to train relevant coefficients and biases, the gradient of the square of subtraction term appears in the whole gradient over each kernel coefficient. The new subtraction kernel is applied to two sets of images, and shows better performance than the existing linear convolution kernel. Each coefficient of the nonlinear subtraction kernel has quite image-equivalent meaning on top of pure mathematical number. The subtraction kernel works equally for a given black and white image set and its reversed version or for a given gray image set and its reversed version. This attribute becomes important when patterns are mixed with light color and dark color, or mixed with background color, and still both sides are equally important. [ABSTRACT FROM AUTHOR]

We investigated the incidence of diagnosed diabetes in South Korean adults (aged ≥20 years) by analyzing data for the National Health Insurance Service–National Sample Cohort. From 2006 to 2015, the overall incidence rate of diagnosed diabetes decreased by approximately 0.1% per year until 2015. Although, this trend was observed in individuals aged 40 years or over, the rate increased slightly in the 20–29 and 30–39 years age groups, from 0.5 to 0.7 and 2.0 to 2.6 per 1,000 individuals, respectively. The proportion of obese young adults with diabetes increased remarkably, from 51.4% in 2006 to 72.4% in 2015. Thus, young adults need early identification and weight-control strategies to prevent diabetes.