• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.37-41
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• 2021

This paper presents a study to implement a touch screen capable of real-time response processing in a low-end embedded system. This was done by introducing an algorithm using an interpolation method to represent real-time response characteristics when a touch input is performed. In this experiment, we applied a linear interpolation algorithm that estimates random data by deriving a first-order polynomial from 2-point data. We also applied a Lagrange interpolation algorithm that estimates random data by deriving a quadratic polynomial from 3-point data. As a result of the experiment, it was found that the Lagrange interpolation method was more complicated than the linear interpolation method, and the processing speed was slow, so the text was not smooth. When using the linear interpolation method, it was confirmed that the speed displayed on a screen is 2.4 times faster than when using the Lagrange interpolation method. For real-time response characteristics, it was confirmed that smaller size of the executable file of the algorithm is more advantageous than the superiority of the algorithm itself. In conclusion, in order to secure real-time response characteristics in a low-end embedded system, it was confirmed that a relatively simple linear interpolation algorithm performs touch operations with better real-time response characteristics than the Lagrange interpolation method.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.446-457
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• 2020

Crescentic sand bar in the coastal zone of eastern Korea is a common morphological feature and the rhythmic patterns exist constantly except for high wave energy events. However, four consecutive typhoons that directly and indirectly affected the East Sea of Korea from September to October in 2019 impacted the formation of longshore uniform sand bar and overall shoreline retreats (approx. 2 m) although repetitive erosion and accretion patterns exist near the shoreline. Widely used XBeach to predict storm erosions in the beach is utilized to investigate the morphological response to a series of storms and each storm impact (NE-E wave incidence). Several calibration processes for improved XBeach modeling are conducted by recently reported calibration methods and the optimal calibration set obtained is applied to the numerical simulation. Using observed wave, tide, and pre & post-storm bathymetries data with optimal calibration set for XBeach input, XBeach successfully reproduces erosion and accretion patterns near MSL (BSS = 0.77 (Erosion profile), 0.87 (Accretion profile)) and observed the formation of the longshore uniform sandbar. As a result of analysis of simulated total sediment transport vectors and bed level changes at each storm peak Hs, the incident wave direction contributes considerable impact to the behavior of crescentic sandbar. Moreover, not only the wave height but also storm duration affects the magnitude of the sediment transport. However, model results suggest that additional calibration processes are needed to predict the exact crest position of bar and bed level changes across the inner surfzone.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.45-55
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• 2021

This paper has proposed an algorithm that detecting for dense small vehicle in large image efficiently. It is consisted of two Ensemble Deep-Learning Network algorithms based on Coarse to Fine method. The system can detect vehicle exactly on selected sub image. In the Coarse step, it can make Voting Space using the result of various Deep-Learning Network individually. To select sub-region, it makes Voting Map by to combine each Voting Space. In the Fine step, the sub-region selected in the Coarse step is transferred to final Deep-Learning Network. The sub-region can be defined by using dynamic windows. In this paper, pre-defined mapping table has used to define dynamic windows for perspective road image. Identity judgment of vehicle moving on each sub-region is determined by closest center point of bottom of the detected vehicle's box information. And it is tracked by vehicle's box information on the continuous images. The proposed algorithm has evaluated for performance of detection and cost in real time using day and night images captured by CCTV on the road.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.9 no.4
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• pp.11-22
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• 1999

Least-mean-square(LMS) adaptive filters have proven to be extremely useful in a number of signal processing tasks. However LMS adaptive filter suffer from a slow rate of convergence for a given steady-state mean square error as compared to the behavior of recursive least squares adaptive filter. In this paper an efficient signal interference control technique is introduced to improve the convergence speed of LMS algorithm with tap weighted vectors updating which were controled by reusing data which was abandoned data in the Adaptive transversal filter in the scheme with data recycling buffers. The computer simulation show that the character of convergence and the value of MSE of proposed algorithm are faster and lower than the existing LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computation. Adaptive transversal filter with proposed data recycling buffer algorithm could efficiently reject ISI of channel and increase speed of convergence in avoidance burden of computational complexity in reality when it was experimented having the same condition of LMS algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.141-148
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• 2021

This paper presents a method to detect zebra-crossing using deep learning which combines SegNet and ResNet. For the blind, a safe crossing system is important to know exactly where the zebra-crossings are. Zebra-crossing detection by deep learning can be a good solution to this problem and robotic vision-based assistive technologies sprung up over the past few years, which focused on specific scene objects using monocular detectors. These traditional methods have achieved significant results with relatively long processing times, and enhanced the zebra-crossing perception to a large extent. However, running all detectors jointly incurs a long latency and becomes computationally prohibitive on wearable embedded systems. In this paper, we propose a model for fast and stable segmentation of zebra-crossing from captured images. The model is improved based on a combination of SegNet and ResNet and consists of three steps. First, the input image is subsampled to extract image features and the convolutional neural network of ResNet is modified to make it the new encoder. Second, through the SegNet original up-sampling network, the abstract features are restored to the original image size. Finally, the method classifies all pixels and calculates the accuracy of each pixel. The experimental results prove the efficiency of the modified semantic segmentation algorithm with a relatively high computing speed.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.3
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• pp.68-76
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• 2021

Storage racks have suffered huge losses due to earthquakes, but related research and regulations are relatively insufficient non-structural elements compared to the structural elements. In this study, we tried to experimentally analyze the behavioral characteristics of storage racks due to external force according to the fixing conditions of the column-foundations connection of storage racks. In general, the column-foundations connection of storage racks is installed according to the user's convenience without installation standards and regulations. For this reason, this study conducted a behavior analysis test on four full-scale storage racks with the condition of column-foundations connection of four typical storage racks. The behavior characteristics analysis test was performed by two-direction of the shake table with El-Centro seismic wave. To confirm the behavior characteristics according to the magnitude of the seismic load, 50% ~ 150% of the seismic waves were increased by 50% for each test. In addition, a resonance search test was conducted to confirm the natural frequency of each storage racks foundations fixing condition. Among the data obtained through the test, the displacement of the top layer and the permanent displacement after the test were compared for each condition to analyze the behavior characteristics of the column-foundations fixed conditions of the storage racks. As a result, the change of natural frequency was small in storage racks due to the change of the conditions of the foundations, and the behavior characteristics were changed due to the difference of the restoring force due to the change of the storage racks foundations condition rather than the influence of the natural frequency of the input load.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.167-177
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• 2022

In order to obtain satellite image products using the image transmitted to the ground station after capturing the satellite images, many image pre/post-processing steps are involved. During the pre/post-processing, when converting from level 1R images to level 1G images, geometric correction is essential. An interpolation method necessary for geometric correction is inevitably used, and the quality of the level 1G images is determined according to the accuracy of the interpolation method. Also, it is crucial to speed up the interpolation algorithm by the level processor. In this paper, we proposed a lightweight CNN-based interpolation method required for geometric correction when converting from level 1R to level 1G. The proposed method doubles the resolution of satellite images and constructs a deep learning network with a lightweight deep convolutional neural network for fast processing speed. In addition, a feature map fusion method capable of improving the image quality of multispectral (MS) bands using panchromatic (PAN) band information was proposed. The images obtained through the proposed interpolation method improved by about 0.4 dB for the PAN image and about 4.9 dB for the MS image in the quantitative peak signal-to-noise ratio (PSNR) index compared to the existing deep learning-based interpolation methods. In addition, it was confirmed that the time required to acquire an image that is twice the resolution of the 36,500×36,500 input image based on the PAN image size is improved by about 1.6 times compared to the existing deep learning-based interpolation method.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.1-10
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• 2022

Deep neural networks are widely used to solve various problems. In a fully connected neural network, the nonlinear activation function is a function that nonlinearly transforms the input value and outputs it. The nonlinear activation function plays an important role in solving the nonlinear problem, and various nonlinear activation functions have been studied. In this study, we propose a combined parametric activation function that can improve the performance of a fully connected neural network. Combined parametric activation functions can be created by simply adding parametric activation functions. The parametric activation function is a function that can be optimized in the direction of minimizing the loss function by applying a parameter that converts the scale and location of the activation function according to the input data. By combining the parametric activation functions, more diverse nonlinear intervals can be created, and the parameters of the parametric activation functions can be optimized in the direction of minimizing the loss function. The performance of the combined parametric activation function was tested through the MNIST classification problem and the Fashion MNIST classification problem, and as a result, it was confirmed that it has better performance than the existing nonlinear activation function and parametric activation function.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.345-352
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• 2022

Recent LiDAR(Light Detection And Ranging) sensor is used for scanning object around in real-time. This sensor can detect movement of the object and how it has changed. As the production cost of the sensors has been decreased, LiDAR begins to be used for various industries such as facility guard, smart city and self-driving car. However, LiDAR has a large input data size due to its real-time scanning process. So another way for processing a large amount of data are needed in LiDAR system because it can cause a bottleneck. This paper proposes edge computing to compress massive point cloud for processing quickly. Since laser's reflection range of LiDAR sensor is limited, multiple LiDAR should be used to scan a large area. In this reason multiple LiDAR sensor's data should be processed at once to detect or recognize object in real-time. Edge computer compress point cloud efficiently to accelerate data processing and decompress every data in the main cloud in real-time. In this way user can control LiDAR sensor in the main system without any bottleneck. The system we suggest solves the bottleneck which was problem on the cloud based method by applying edge computing service.

• Journal of the Korean Geotechnical Society
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• v.38 no.11
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• pp.149-161
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• 2022

This study selected the indexes for evaluating the damage of the railway embankments due to liquefaction from the earthquake damage cases of railway embankments. The study correlated the selected indexes and the settlement of the embankment crest from the dynamic numerical analysis. Further, the correlation was used to develop a method for evaluating the liquefaction damage to the railway embankment. The damage cases and damage types were analyzed, and referring to the liquefaction damage assessment method for other structures, the embankment height (H), the non-liquefiable layer thickness (H₁), and the liquefaction potential index were selected as indexes for evaluating the damage. The study performed dynamic effective stress analyses on the railway embankment, and the PM4-Sand model was applied as the constitutive liquefaction model for the embankment foundation ground. The model's validity was first verified by comparing it with the existing dynamic centrifugal model test results performed on the railway embankment. Nine sites where the foundation ground can be liquefied were selected from the data of 549 embankments of the Honam High-speed Railway in Korea. Further, dynamic numerical analyses using four seismic waves as input earthquake load were performed for the selected site sections. The numerical analysis results confirmed the correlation between the evaluation indexes and the embankment crest settlement. A method for efficiently evaluating the damage to the embankment due to liquefaction was proposed using the chart obtained from this correlation.