• Wind and Structures
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• v.38 no.1
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• pp.75-91
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• 2024

Reliable wind signal reconstruction can be beneficial to the operational safety of long-span bridges. Non-Gaussian characteristics of wind signals make the reconstruction process challenging. In this paper, non-Gaussian wind signals are converted into a combined prediction of two kinds of features, actual wind speeds and wind angles of attack. First, two decomposition techniques, empirical mode decomposition (EMD) and variational mode decomposition (VMD), are introduced to decompose wind signals into intrinsic mode functions (IMFs) to reduce the randomness of wind signals. Their principles and applicability are also discussed. Then, four artificial intelligence (AI) algorithms are utilized for wind signal reconstruction by combining the particle swarm optimization (PSO) algorithm with back propagation neural network (BPNN), support vector regression (SVR), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), respectively. Measured wind signals from a bridge site in a deep-cutting gorge are taken as experimental subjects. The results showed that the reconstruction error of high-frequency components of EMD is too large. On the contrary, VMD fully extracts the multiscale rules of the signal, reduces the component complexity. The combination of VMD-PSO-Bi-LSTM is demonstrated to be the most effective among all hybrid models.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.498-505
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• 2024

In this paper, a cable-driven endoscopic manipulator (CEM) is designed for the Chinese latest compact fusion reactor. The whole CEM arm is more than 3000 mm long and includes end vision tools, an endoscopic manipulator/control system, a feeding system, a drag chain system, support systems, a neutron shield door, etc. It can cover a range of ±45° of the vacuum chamber by working in a wrap-around mode, etc., to meet the need for observation at any position and angle. By placing all drive motors in the end drive box via a cable drive, cooling, and radiation protection of the entire robot can be facilitated. To address the CEM motion control problem, a discrete trajectory tracking method is proposed. By restricting each joint of the CEM to the target curve through segmental fitting, the trajectory tracking control is completed. To avoid the joint rotation angle overrun, a joint limit rotation angle optimization method is proposed based on the equivalent rod length principle. Finally, the CEM simulation system is established. The rationality of the structure design and the effectiveness of the motion control algorithm are verified by the simulation.

• ETRI Journal
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• v.46 no.4
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• pp.648-659
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• 2024

Author verification is challenging because of the diversity in writing styles. We propose an enhanced handwriting verification method that combines handcrafted and automatically extracted features. The method uses a genetic algorithm to reduce the dimensionality of the feature set. We consider offline Bangla handwriting content and evaluate the proposed method using handcrafted features with a simple logistic regression, radial basis function network, and sequential minimal optimization as well as automatically extracted features using a convolutional neural network. The handcrafted features outperform the automatically extracted ones, achieving an average verification accuracy of 94.54% for 100 writers. The handcrafted features include Radon transform, histogram of oriented gradients, local phase quantization, and local binary patterns from interwriter and intrawriter content. The genetic algorithm reduces the feature dimensionality and selects salient features using a support vector machine. The top five experimental results are obtained from the optimal feature set selected using a consensus strategy. Comparisons with other methods and features confirm the satisfactory results.

• Journal of Radiation Industry
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• v.18 no.3
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• pp.217-221
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• 2024

This study aims to evaluate the radiation dose received by workers within the auxiliary building of the Saeul Units 1 and 2 during a severe accident. To achieve this, representative accident scenarios were selected, and operator actions required by the severe accident management guidelines were derived to present a methodology for dose assessment. The study utilized MAAP5.06 to analyze severe accidents and employed MAAP DOSE to evaluate worker radiation exposure. Among the three operator actions considered, the direct spray action on the reactor building outer wall-side penetration resulted in the highest estimated radiation dose. This is likely because the workers are deployed near the reactor building penetration, exposing them to higher radiation levels. Future plans include the optimization of dose performance by comparing these findings with evaluations conducted using MCNP, and the development of a data-driven ALARA decision support system for predicting and diagnosing radiation exposure on nuclear sites to ensure worker safety during severe accidents.

• Nuclear Engineering and Technology
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• v.56 no.8
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• pp.3236-3241
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• 2024

Ion cyclotron resonance heating (ICRH) is an important auxiliary heating method applied to the China Fusion Engineering Test Reactor, which can effectively heat the ions and electrons in plasma. Owing to the harsh nuclear environment, neutronic analyses are required to verify tritium self-sufficiency and neutron-shielding requirements. In this study, a neutronics analysis of the ICRH antenna was conducted using the COre and System integrated engine for Reactor Monte Carlo (cosRMC) code to estimate the neutron flux, radiation damage, nuclear heating, gas generation rate of key components, and tritium breeding ratio (TBR), providing data support for the subsequent optimization of the shielding design. In addition, the neutron flux of the coils around the antenna was calculated to prevent the entry of neutrons that damage the magnetic field coils through the gaps between the port plugs and antenna, and the shielding effects of the port-plug antenna on the surrounding components were analyzed. Finally, the results obtained using the cosRMC and MCNP codes were compared, which and presented good agreement, thus verifying the reliability of the neutronic analysis using the cosRMC code.

• Archives of Metallurgy and Materials
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• v.67 no.4
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• pp.1459-1463
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• 2022

The soft magnetic properties of Fe-based amorphous alloys can be controlled by their compositions through alloy design. Experimental data on these alloys show some discrepancy, however, with predicted values. For further improvement of the soft magnetic properties, machine learning processes such as random forest regression, k-nearest neighbors regression and support vector regression can be helpful to optimize the composition. In this study, the random forest regression method was used to find the optimum compositions of Fe-Si-B-C alloys. As a result, the lowest coercivity was observed in Fe_80.5Si_3.63B_13.54C_2.33 at.% and the highest saturation magnetization was obtained Fe_81.83Si_3.63B_12.63C_1.91 at.% with R² values of 0.74 and 0.878, respectively.

• Geomechanics and Engineering
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• v.39 no.2
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• pp.171-179
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• 2024

Gravity wall combined with anchoring frame beam is widely adopted to support a high slope under complex geomorphic condition, in which the rigid gravity wall is adopted as a lower structure and the flexible anchoring frame beam serves as an upper structure. The seismic anchor force and the seismic active earth pressure are two essential issues for the seismic design of combined retaining structure in high seismic intensity area. In this study, an analytical model of combined retaining structure is established based on the upper bound theorem of limit analysis, and the formulas for seismic anchor force and seismic active earth pressure of combined retaining structure are derived. The results are optimized by using the global optimization algorithm. The proposed method is verified by a comparison with previous method. Moreover, the influence of main parameters on seismic anchor force and seismic active earth pressure is analyzed to facilitate the seismic design of such combined retaining structure.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.594-600
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• 2018

Landscape law was enacted in 2007 after the development of the Korea Planning Support System (KOPSS) in 2006. In addition, KOPSS was utilized by many local governments to improve reliability and optimization in 2010. In 2014, landscape law was fully revised, and it is likely to have a considerable impact on municipal ordinances and deliberations, which may have a considerable effect on the results of landscape reviews. This paper presents an analysis and verification of changes in the subject of deliberation by the amendment of the law and system, the method of deliberation, the composition of the scenery committee, and the introduction of KOPSS. We also propose a direction for improving the landscape deliberation system. As a result, the change of the number of deliberation items repeatedly increased and decreased due to the change of the deliberation subject and deliberation management according to the total revision of the resultant laws and institutions. In sum, it affected the deliberation decisions.

• Korean Chemical Engineering Research
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• v.59 no.2
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• pp.191-199
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• 2021

A distillation column, which is a main facility of the chemical process, separates the desired product from a mixture by using the difference of boiling points. The distillation process requires the optimization and the prediction of operation because it consumes much energy. The target process of this study is difficult to operate efficiently because the composition of feed flow is not steady according to the supplier. To deal with this problem, we could develop a data-driven model to predict operating conditions. However, data preprocessing is essential to improve the predictive performance of the model because the raw data contains outlier and noise. In this study, after optimizing the predictive model based long-short term memory (LSTM) and Random forest (RF), we used a low-pass filter and one-class support vector machine for data preprocessing and compared predictive performance according to the method and range of the preprocessing. The performance of the predictive model and the effect of the preprocessing is compared by using R2 and RMSE. In the case of LSTM, R2 increased from 0.791 to 0.977 by 23.5%, and RMSE decreased from 0.132 to 0.029 by 78.0%. In the case of RF, R2 increased from 0.767 to 0.938 by 22.3%, and RMSE decreased from 0.140 to 0.050 by 64.3%.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.25-33
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• 2021

With the development of machine learning techniques, various types of data such as vibration, temperature, and flow rate can be used to detect and diagnose abnormalities in machine conditions. In particular, in the field of the state monitoring of rotating machines, the fault diagnosis of machines using vibration data has long been carried out, and the methods are also very diverse. In this study, an experiment was conducted to collect vibration data from normal and abnormal compressors by installing accelerometers directly on rotary compressors used in household air conditioners. Data segmentation was performed to solve the data shortage problem, and the main features for the fault classification model were extracted through the chi-square test after statistical and physical features were extracted from the vibration data in the time domain. The support vector machine (SVM) model was developed to classify the normal or abnormal conditions of compressors and improve the classification accuracy through the hyperparameter optimization of the SVM.