• 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 Institute of Information Security & Cryptology
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• v.19 no.2
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• pp.11-21
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• 2009

The NTRU cryptosystem proposed by Hoffstein et al. in 1990s is a public key cryptosystem based on hard lattice problems. NTRU has many advantages compared to other public key cryptosystems such as RSA and elliptic curve cryptosystems. For example, it guarantees high speed encryption and decryption with the same level of security, and there is no known quantum computing algorithm for speeding up attacks against NTRD. In this paper, we analyze the security of NTRU against the simple power analysis (SPA) attack and the statistical power analysis (STPA) attack such as the correlation power analysis (CPA) attack First, we implement NTRU operations using NesC on a Telos mote, and we show how to apply CPA to recover a private key from collected power traces. We also suggest countermeasures against these attacks. In order to prevent SPA, we propose to use a nonzero value to initialize the array which will store the result of a convolution operation. On the other hand, in order to prevent STPA, we propose two techniques to randomize power traces related to the same input. The first one is random ordering of the computation sequences in a convolution operation and the other is data randomization in convolution operation.

• Journal of IKEEE
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• v.25 no.2
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• pp.388-398
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• 2021

This paper proposes a particle swarm optimization (PSO)-based peak shaving scheme using energy storage system (ESS) for electricity tariff reduction. The proposed scheme compares the actual load with the estimated load consumption, calculates the additional output power that the ESS needs to discharge additionally to reduce peak load, and adds the input. In addition, in order to compensate for the additional power, the process of allocating power to the determined point is performed, and an optimization that minimizes the average of the load expected at the active power allocations using PSO so that the allocated value does not affect the peak load. To investigated the performance of the proposed scheme, case study of small and large load prediction errors was conducted by reflecting actual load data and load prediction algorithm. As a result, when the proposed scheme is performed with the ESS charge and discharge control to reduce electricity tariff, even when the load prediction error is large, the peak load is successfully reduced, and the peak load reduction effect of 17.8% and electricity tariff reduction effect of 6.02% is shown.

• Journal of IKEEE
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• v.25 no.2
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• pp.273-279
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• 2021

In this paper, we propose an algorithm that shortens the learning time by performing individual learning using partitioning the deep learning structure. The proposed algorithm consists of four processes: network classification origin setting process, feature vector extraction process, feature noise removal process, and class classification process. First, in the process of setting the network classification starting point, the division starting point of the network structure for effective feature vector extraction is set. Second, in the feature vector extraction process, feature vectors are extracted without additional learning using the weights previously learned. Third, in the feature noise removal process, the extracted feature vector is received and the output value of each class is learned to remove noise from the data. Fourth, in the class classification process, the noise-removed feature vector is input to the multi-layer perceptron structure, and the result is output and learned. To evaluate the performance of the proposed algorithm, we experimented with the Extended Yale B face database. As a result of the experiment, in the case of the time required for one-time learning, the proposed algorithm reduced 40.7% based on the existing algorithm. In addition, the number of learning up to the target recognition rate was shortened compared with the existing algorithm. Through the experimental results, it was confirmed that the one-time learning time and the total learning time were reduced and improved over the existing algorithm.

• 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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.41-46
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• 2021

As data acquisition using unmanned aerial vehicles is widely used, as one of the ways to increase the accuracy of photogrammetry using unmanned aerial vehicles, a method of inputting both vertical and oblique images in bundle adjustment of aerial triangulation has been proposed. In this study, in order to find a suitable method for increasing the accuracy of photogrammetry, the accuracy of the case of adjusting the oblique images taken at different shooting angles and the case of adjusting the oblique images with different shooting angles at the same time with the vertical images were compared. As a result of the study, it was found that the error of the checkpoint decreases as the angle of the input oblique images increases. In particular, when the vertical images and oblique images are used together, the height error decreases significantly as the angle of the oblique images increases. The current 『Aerial Photogrammetry Work Regulation』 requires RMSE (Root Mean Square Error), which is the same as GSD (Ground Spatial Distance) of a vertical image. When using an oblique images with a shooting angle of 50°, a result close to this standard is obtained. If the vertical images and the 50° oblique images were adjusted at the same time, the work regulations could be satisfied. Using the results of this study, it is expected that photogrammetry using low-cost cameras mounted on unmanned aerial vehicles will become more active.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.169-178
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• 2022

Morphemes are most primitive units in a language that lose their original meaning when segmented into smaller parts. In Korean, a sentence is a sequence of eojeols (words) separated by spaces. Each eojeol comprises one or more morphemes. Korean morphological analysis (KMA) is to divide eojeols in a given Korean sentence into morpheme units. It also includes assigning appropriate part-of-speech(POS) tags to the resulting morphemes. KMA is one of the most important tasks in Korean natural language processing (NLP). Improving the performance of KMA is closely related to increasing performance of Korean NLP tasks. Recent research on KMA has begun to adopt the approach of machine translation (MT) models. MT is to convert a sequence (sentence) of units of one domain into a sequence (sentence) of units of another domain. Neural machine translation (NMT) stands for the approaches of MT that exploit neural network models. From a perspective of MT, KMA is to transform an input sequence of units belonging to the eojeol domain into a sequence of units in the morpheme domain. In this paper, we propose a deep learning model for KMA. The backbone of our model is based on the BERT-fused model which was shown to achieve high performance on NMT. The BERT-fused model utilizes Transformer, a representative model employed by NMT, and BERT which is a language representation model that has enabled a significant advance in NLP. The experimental results show that our model achieves 98.24 F1-Score.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.10-18
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• 2022

Purpose: For smooth performance of flood analysis due to heavy rain disasters at energy storage facilities in the Incheon area, field surveys, observational surveys, and pre-established reports and drawings were analyzed. Through the field survey, the characteristics of pipelines and rivers that have not been identified so far were investigated, and based on this, the input data of the SWMM model selected for inundation analysis was constructed. Method: In order to determine the critical duration through the probability flood analysis according to the calculation of the probability rainfall intensity by recurrence period and duration, it is necessary to calculate the probability rainfall intensity for an arbitrary duration by frequency, so the research results of the Ministry of Land, Transport and Maritime Affairs were utilized. Result: Based on this, the probability of rainfall by frequency and duration was extracted, the critical duration was determined through flood analysis, and the rainfall amount suggested in the disaster prevention performance target was applied to enable site safety review. Conclusion: The critical duration of the base was found to be a relatively short duration of 30 minutes due to the very gentle slope of the watershed. In general, if the critical duration is less than 30 minutes, even if flooding occurs, the scale of inundation is not large.

• Journal of Radiation Protection and Research
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• v.47 no.2
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• pp.99-106
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• 2022

Background: The radionuclide inventory calculation codes such as ORIGEN and FISPACT collapse neutron reaction libraries with energy spectra and generate an effective one-group cross-section. Since the nuclear cross-section data, energy group (g) structure, and other input details used by the two codes are different, there may be differences in each code's activation inventory calculation results. In this study, the calculation results of neutron-induced activation inventory using ORIGEN and FISPACT were compared and analyzed regarding radioactive waste classification and worker exposure during nuclear decommissioning. Materials and Methods: Two neutron spectra were used to obtain the comparison results: Watt fission spectrum and thermalized energy spectrum. The effective one-group cross-sections were generated for each type of energy group structure provided in ORIGEN and FISPACT. Then, the effective one-group cross-sections were analyzed by focusing on ⁵⁹Ni, ⁶³Ni, ⁹⁴Nb, ⁶⁰Co, ¹⁵²Eu, and ¹⁵⁴Eu, which are the main radionuclides of stainless steel, carbon steel, zircalloy, and concrete for decommissioning nuclear power plant (NPP). Results and Discussion: As a result of the analysis, ¹⁵⁴Eu and ⁵⁹Ni may be overestimated or underestimated depending on the code selection by up to 30%, because the cross-section library used for each code is different. When ORIGEN-44g, -49g, and -238g structures are selected, the differences of the calculation results of effective one-group cross-section according to group structure selection were less than 1% for the six nuclides applied in this study, and when FISPACT-69g, -172g, and -315g were applied, the difference was less than 1%, too. Conclusion: ORIGEN and FISPACT codes can be applied to activation calculations with their own built-in energy group structures for decommissioning NPP. Since the differences in calculation results may occur depending on the selection of codes and energy group structures, it is appropriate to properly select the energy group structure according to the accuracy required in the calculation and the characteristics of the problem.

• Korean Journal of Construction Engineering and Management
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• v.23 no.4
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• pp.57-64
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• 2022

A plant factory is a facility that creates an artificial environment in a controlled space and produces plants systematically through automated facilities. However, automation in the cultivation process is insufficient compared to the internal environment control technology in plant factories. This causes the problem of an increase in operating costs due to the input of a large number of workers. Therefore, this study aims to evaluate economic feasibility by comparing before and after introducing automation in the cultivation process of plant factories. The target plant factory to be analyzed was selected, and the break-even point analysis method was used by comparing the cost required compared to the operating period. As a result, the break-even point was analyzed to be 3.4 years when automation was introduced into six processes for plant cultivation. Therefore, it can be judged that the introduction of automation is excellent in terms of economic feasibility when the target plant factory has been operated for more than 3.4 years. This study is expected to be used as basic data to analyze the economic feasibility of introducing automation in domestic and foreign plant factories.