• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1291-1300
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• 2020

Recently, a deep learning-based non-profiling side-channel analysis was proposed. The deep learning-based non-profiling analysis is a technique that trains a neural network model for all guessed keys and then finds the correct secret key through the difference in the training metrics. As the performance of non-profiling analysis varies greatly depending on the neural network training model design, a correct model design criterion is required. This paper describes the two types of loss functions and eight labeling methods used in the training model design. It predicts the analysis performance of each labeling method in terms of non-profiling analysis and power consumption model. Considering the characteristics of non-profiling analysis and the HW (Hamming Weight) power consumption model is assumed, we predict that the learning model applying the HW label without One-hot encoding and the Correlation Optimization (CO) loss will have the best analysis performance. And we performed actual analysis on three data sets that are Subbytes operation part of AES-128 1 round. We verified our prediction by non-profiling analyzing two data sets with a total 16 of MLP-based model, which we describe.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.351-361
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• 2021

The seriousness of fire is rising because fire causes enormous damage to property and human life. Therefore, this study aims to predict various risk factors affecting fire by fire type. The predictive analysis of fire factors was carried out targeting Gyeonggi-do, which has the highest number of fires in the country. For the analysis, using machine learning methods SVM (Support Vector Machine), RF (Random Forest), GBRT (Gradient Boosted Regression Tree) the accuracy of each model was presented with a high fit model through MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error), and based on this, predictive analysis of fire factors in Gyeonggi-do was conducted. In addition, using machine learning methods such as SVM (Support Vector Machine), RF (Random Forest), and GBRT (Gradient Boosted Regression Tree), the accuracy of each model was presented with a high-fit model through MAE and RMSE. Predictive analysis of occurrence factors was achieved. Based on this, as a result of comparative analysis of three machine learning methods, the RF method showed a MAE = 1.765 and RMSE = 1.876, as well as the MAE and RMSE verification and test data were very similar with a difference between MAE = 0.046 and RMSE = 0.04 showing the best predictive results. The results of this study are expected to be used as useful data for fire safety management allowing decision makers to identify the sequence of dangers related to the factors affecting the occurrence of fire.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.169-184
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• 2021

Recent advances in science and technology leveraged various artistic tools. Interactive art based on various media technologies became popular in a short period, and is widely appreciated as a new form of art. This new form of art has a different method of expression from traditional art such as painting or sculpture. It aims to strike a balance among the artist, audience, and piece of art through interaction between the work and viewers. Viewers can take part in the creation process, going beyond the conventional way of art appreciation. This paper analyzes interactive art production techniques based on human senses from the artist's perspective. "Simulation-experience model" will be suggested after looking at several example artworks. Charming, which was produced based on this model, will be introduced and its meaning will be analyzed. The objective of this paper is to predict the future of interactive art and changes in the art form by studying interactive art production techniques based on human senses. We believe that the prediction is helpful in understanding the artistic and technological value and the social influence of interactive art in the future.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1889-1900
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• 2022

Roads are an essential factor in the physical functioning of modern society. The spatial information of the road has much longer update cycle than the traffic situation information, and it is necessary to generate the information faster and more accurately than now. In this study, as a way to achieve that goal, the Pan-sharpening technique was applied to satellite images of Kompsat 3 and 3A to improve spatial resolution. Then, the data were used for road extraction using the semantic segmentation technique, which has been actively researched recently. The acquired Kompsat 3/3A pan-sharpened images were trained by putting it into a U-Net based segmentation model along with Massachusetts road data, and the applicability of the images were evaluated. As a result of training and verification, it was found that the model prediction performance was maintained as long as certain conditions were maintained for the input image. Therefore, it is expected that the possibility of utilizing satellite images such as Kompsat satellite will be even higher if rich training data are constructed by applying a method that minimizes the impact of surrounding environmental conditions affecting models such as shadows and surface conditions.

• Journal of Korea Port Economic Association
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• v.37 no.4
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• pp.33-40
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• 2021

Currently, the shipping and port industries are implementing strategies to improve port processing capabilities through the expansion and efficient operation of port logistics resources to survive fierce competition with rapidly changing trends. The calculation of the port's processing capacity is determined by the loading and unloading equipment installed at the dock, and the port's processing capacity can be improved through various methods, such as additional deployment of logistics resources or efficient operation of resources in use. However, it is difficult to expect an improvement effect in a short period of time because the additional deployment of logistics resources is clearly limited in time is clear. Therefore, it is a feasible way to find an efficient operation method for resources being used to improve processing capacity. Domestic ports are also actively promoting informatization and digitalization with the development of the 4th industrial revolution technology. However, the calculation of the number of Y/T (Yard Tractor) assignments in the current unloading process depends on expert experience, and related previous studies also focus on the allocations of Y/T or Calculation of the total number of Y/T required. Therefore, this study analyzed the factors affecting the number of Y/T allocations using the loading and unloading information of incoming ships, and based on this, cluster analysis, regression analysis, and deep neural network(DNN) model were used.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.71-80
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• 2022

In the past, fish diseases were bacterial in aqua farms, but in recent years, the frequency of fish diseases has increased as they have become viral and mixed. Viral diseases in an enclosed space called a aqua farm have a high spread rate, so it is very likely to lead to mass death. Fast identification of fish diseases is important to prevent group death. However, diagnosis of fish diseases requires a high level of expertise and it is difficult to visually check the condition of fish every time. In order to prevent the spread of the disease, an automatic identification system of diseases or fish is needed. In this paper, in order to improve the performance of the disease identification system of Paralichthys olivaceus based on deep learning, the existing pre-processing method is compared and tested. Target diseases were selected from three most frequent diseases such as Scutica, Vibrio, and Lymphocystis in Paralichthys olivaceus. The RGB, HLS, HSV, LAB, LUV, XYZ, and YCRCV were used as image pre-processing methods. As a result of the experiment, HLS was able to get the best results than using general RGB. It is expected that the fish disease identification system can be advanced by improving the recognition rate of diseases in a simple way.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.1
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• pp.57-78
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• 2022

The disc cutter and cutterbit, which are the most important factors to increase the excavation efficiency of TBM, are key factors in the design and construction of the cutter head. The arrangement, spacing, number, size, and material of disc cutters suitable for the ground conditions determine the success or failure of TBM construction. The disc cutter, which is a representative consumable part in TBM construction, can cause enormous disruption to the construction cost as well as the construction cost unless accurate prediction of wear and replacement cycle is accompanied. Therefore, in this study, the method of calculating the replacement cycle of the disc cutter calculated at the time of design for the slurry shield TBM field, and the depth of wear and replacement location of the disc cutter that occurred during actual construction were compared by analyzing the field data. For a quantitative comparison, weathered soil/weathered rock, soft rock, and hard rock were classified according to the ground in the section showing constant excavation data, and the trajectory of circle was different depending on the location of the disc cutter, so it was compared and analyzed.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.1
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• pp.79-94
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• 2022

The demand for tunnel construction is increasing as part of underground space development due to urban saturation. The shield TBM method minimizes vibration and noise and minimizes ground deformation that occurs simultaneously with excavation, and shield TBM is generally applied to tunnel construction in urban areas. The importance of urban ground settlement prediction is increasing day by day, and in the case of shield TBM construction, ground deformation is minimized, but ground settlement due to tunnel excavation inevitably occurs. Therefore, in this study, the correlation between shield TBM, which is highly applicable to urban areas, and ground settlement is analyzed to suggest the shield TBM construction factors that have a major effect on ground settlement. Correlation analysis was performed between the shield TBM construction data and ground settlement measurement data collected at the actual site, and the degree of correlation was expressed as a correlation coefficient "r". As a result, the main construction factors of shield TBM affecting ground settlement were thrust force, torque, chamber pressure, backfill pressure and muck discharge. Based on the results of this study, it is expected to contribute to the presentation of judgment criteria for major construction data so that the ground settlement can be predicted and controlled in advance when operating the shield TBM in the future.

• Journal of Korean Society of Rural Planning
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• v.27 no.4
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• pp.55-70
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• 2021

In this study, in the field of remote sensing, where the scope of application is rapidly expanding to fields such as land monitoring, disaster prediction, facility safety inspection, and maintenance of cultural properties, monitoring of rural space and surrounding environment using UAV is utilized. It was carried out to verify the possibility, and the following main results were derived. First, the aerial image taken with an unmanned aerial vehicle had a much higher image size and spatial resolution than the aerial image provided by the National Geographic Information Service. It was suitable for analysis due to its high accuracy. Second, the more the number of photographed photos and the more complex the terrain features, the more the point cloud included in the aerial image taken with the UAV was extracted. As the amount of point cloud increases, accurate 3D mapping is possible, For accurate 3D mapping, it is judged that a point cloud acquisition method for difficult-to-photograph parts in the air is required. Third, 3D mapping technology using point cloud is effective for monitoring rural space and rural resources because it enables observation and comparison of parts that cannot be read from general aerial images. Fourth, the digital elevation model(DEM) produced with aerial image taken with an UAV can visually express the altitude and shape of the topography of the study site, so it can be used as data to predict the effects of topographical changes due to changes in rural space. Therefore, it is possible to utilize various results using the data included in the aerial image taken by the UAV. In this study, the superiority of images acquired by UAV was verified by comparison with existing images, and the effect of 3D mapping on rural space monitoring was visually analyzed. If various types of spatial data such as GIS analysis and topographic map production are collected and utilized using data that can be acquired by unmanned aerial vehicles, it is expected to be used as basic data for rural planning to maintain and preserve the rural environment.

• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.2
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• pp.191-199
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• 2021

In this study, the correlation between the influencing factors on corrosion and Half Cell Potential(HCP) measurement was analyzed considering the three levels of W/C ratio, cover depth, and chloride concentration. The HCP increased with enlarged cover depth, so it was confirmed that the increment of cover depth was effective for control of corrosion. Based on the criteria, the case of 60mm cover depth showed excellent corrosion control with under -200mV, indicating increase of cover depth is an effective method for reducing intrusion of external deterioration factors. When fresh water was injected to the upper part of specimens, very low level of HCP was monitored, but in the case that concentrations of chloride were 3.5% and 7.0%, HCP dropped under -200mV. In addition, the case with high volume of unit binder showed lower HCP measurement like increasing cover depth. Multiple regression analysis was performed to evaluate the correlation between the corrosive influence factors and HCP results, showing high coefficient of determination of 0.97. However, there were limitations such as limited number of samples and measuring period. Through the additional corrosion monitoring and chloride content evaluation after dismantling the specimen, more reasonable prediction can be achieved for correlation analysis with relevant data.