• Earthquakes and Structures
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• v.13 no.6
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• pp.551-559
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• 2017

A large number of isolation systems are designed without considering the non-uniform friction distribution in space. In order to analyze the effects of non-uniform friction distribution on the structural response of isolation system, this paper presented a simplified rolling-damper-spring isolation system and analyzed the structural responses under earthquakes. The numerical results indicate that the calculation errors related to the peak values of structural acceleration, relative displacement and residual displacement are sequentially growing because of the ignorance of non-uniform friction distribution. However, the influence rule may be weakened by the spring and damper actions, and the unreasonable spring constant may lead to the sympathetic vibration of isolation system. In the case when the friction variability is large and the damper action is little, the non-uniform friction distribution should be taken into consideration during the calculation process of the peak values of structural acceleration and relative displacement. The non-uniform friction distribution should be taken into full consideration regardless of friction variability degree in calculating the residual displacement of isolation system.

• Nuclear Engineering and Technology
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• v.40 no.1
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• pp.93-98
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• 2008

Several adhesive tapes have been studied in terms of their suitability for securing gold wires into positions for neutron flux measurements in the reactor core and irradiation facilities surrounding the core of the Open Pool Australian Light water (OPAL) reactor. Gamma ray spectrometry has been performed on each irradiated tape in order to identify and quantify activated components. Numerous metallic impurities have been identified in all tapes. Calculations relating to both the effective neutron shielding properties of the tapes and the error in measurement of the $^{198}Au$ activity caused by superfluous activity due to residual tape have been made. The most important identified effects were the prolonged cooling times required before safe enough levels of radioactivity to allow handling were reached, and extra activity caused by residual tape when measured with an ionisation chamber. Knowledge of the most suitable tape can allow a minimal contribution due to these effects, and the use of gamma spectrometry in preference to ionisation chamber measurements of the flux wires is shown to make all systematic errors due to the tape completely negligible.

• Journal of Electrochemical Science and Technology
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• v.10 no.3
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• pp.276-283
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• 2019

Nitinol is an alloy of nickel and titanium. Nitinol is one of the shape memory alloys(SMA) that are restored to a remembered form, changing the crystal structure at a given temperature. Because of these unique features, it is used in medical devices, high precision sensors, and aerospace industries. However, the conventional method of mechanical machining for nitinol has problems of thermal and residual stress after processing. Therefore, the electrochemical machining(ECM), which does not produce residual stress and thermal deformation, has emerged as an alternative processing technique. In addition, to replace the existing experimental planning methods, this study used deep neural network(DNN), which is the basis for AI. This method was shown to be more useful than conventional method of design of experiments(RSM, Taguchi, Regression) by applying deep neural network(DNN) to electrochemical machining(ECM) and comparing root mean square errors(RMSE). Comparison with actual experimental values has shown that DNN is a more useful method than conventional method. (DOE - RSM, Taguchi, Regression). The result of the machining was accurately and efficiently predicted by applying electrochemical machining(ECM) and deep neural network(DNN) to the shape memory alloy(SMA), which is a hard-mechinability material.

• Structural Engineering and Mechanics
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• v.83 no.1
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• pp.93-108
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• 2022

Real-time hybrid simulation (RTHS) was applied to investigate the train-bridge interaction of a high-speed railway system, where the railway bridge was selected as the numerical substructure, and the train was physically tested. The interaction between the two substructures was reproduced by a servo-hydraulic shaking table. To accurately reproduce the high-frequency interaction responses ranging from 10-25Hz using the hydraulic shaking table with an inherent delay of 6-50ms, an adaptive time series (ATS) compensation algorithm combined with the linear quadratic Gaussian (LQG) was proposed and implemented in the RTHS. Testing cases considering different train speeds, track irregularities, bridge girder cross-sections, and track settlements featuring a wide range of frequency contents were conducted. The performance of the proposed ATS+LQG delay compensation method was compared to the ATS method and RTHS without any compensation in terms of residual time delays and root mean square errors between commands and responses. The effectiveness of the ATS+LQG method to compensate time delay in RTHS with high-frequency responses was demonstrated and the proposed ATS+LQG method outperformed the ATS method in yielding more accurate responses with less residual time delays.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2627-2647
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• 2022

The easy scalability and low cost of range-free localization algorithms have led to their wide attention and application in node localization of wireless sensor networks. However, the existing range-free localization algorithms still have problems, such as large cumulative errors and poor localization performance. To solve these problems, an incremental strategy-based residual regression network is proposed for node localization in wireless sensor networks. The algorithm predicts the coordinates of the nodes to be solved by building a deep learning model and fine-tunes the prediction results by regression based on the intersection of the communication range between the predicted and real coordinates and the loss function, which improves the localization performance of the algorithm. Moreover, a correction scheme is proposed to correct the augmented data in the incremental strategy, which reduces the cumulative error generated during the algorithm localization. The analysis through simulation experiments demonstrates that our proposed algorithm has strong robustness and has obvious advantages in localization performance compared with other algorithms.

• Journal of Soil and Groundwater Environment
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• v.29 no.5
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• pp.1-13
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• 2024

Repeated tracer tests are often conducted to improve the accuracy of parameter estimation or are sometimes inevitably performed due to mechanical issues or human errors occurred during initial tracer tests. However, residual concentrations from preceding tracer tests can interfere with the injection concentrations of subsequent tests, potentially compromising accuracy of parameter estimation in those later tests. Additionally, repeated injections and interruptions can create transient flow conditions, which have not been adequately considered to date. In this study, a new analytical solution was developed to generate a type curve for repeated tracer tests under transient flow conditions. The solution was validated through numerical simulations. By using the proposed analytical solution, the residual concentration from preceding tracer tests can be effectively accounted for, enabling more accurate parameter estimation for subsequent tracer tests under transient flow conditions.

• Journal of The Korean Astronomical Society
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• v.50 no.2
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• pp.21-27
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• 2017

The autoregressive method provides a univariate procedure to predict the future sunspot number (SSN) based on past record. The strength of this method lies in the possibility that from past data it yields the SSN in the future as a function of time. On the other hand, its major limitation comes from the intrinsic complexity of solar magnetic activity that may deviate from the linear stationary process assumption that is the basis of the autoregressive model. By analyzing the residual errors produced by the method, we have obtained the following conclusions: (1) the optimal duration of the past time for the forecast is found to be 8.5 years; (2) the standard error increases with prediction horizon and the errors are mostly systematic ones resulting from the incompleteness of the autoregressive model; (3) there is a tendency that the predicted value is underestimated in the activity rising phase, while it is overestimated in the declining phase; (5) the model prediction of a new Solar Cycle is fairly good when it is similar to the previous one, but is bad when the new cycle is much different from the previous one; (6) a reasonably good prediction of a new cycle can be made using the AR model 1.5 years after the start of the cycle. In addition, we predict the next cycle (Solar Cycle 25) will reach the peak in 2024 at the activity level similar to the current cycle.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.301-307
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• 2022

Energy consumption has grown alongside dramatic population increases. Statistics show that buildings in particular utilize a significant amount of energy, worldwide. Because of this, building energy prediction is crucial to best optimize utilities' energy plans and also create a predictive model for consumers. To improve energy prediction performance, this paper proposes a ResNet-LSTM model that combines residual networks (ResNets) and long short-term memory (LSTM) for energy consumption prediction. ResNets are utilized to extract complex and rich features, while LSTM has the ability to learn temporal correlation; the dense layer is used as a regression to forecast energy consumption. To make our model more robust, we employed Huber loss during the optimization process. Huber loss obtains high efficiency by handling minor errors quadratically. It also takes the absolute error for large errors to increase robustness. This makes our model less sensitive to outlier data. Our proposed system was trained on historical data to forecast energy consumption for different time series. To evaluate our proposed model, we compared our model's performance with several popular machine learning and deep learning methods such as linear regression, neural networks, decision tree, and convolutional neural networks, etc. The results show that our proposed model predicted energy consumption most accurately.

• Journal of Korean Society of Environmental Engineers
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• v.29 no.3
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• pp.277-282
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• 2007

For more accurate and practical design of the residual treatment train at water treatment plants(WTPs), a computational program based on the commercial spreadsheet, Microsoft Excel, was developed. The computational program for the design of a residual treatment train(DRTT) works in three steps which estimate the residual production to be treated, analyze the mass balance, and determine the size of each unit process. Of particular interest in the DRTT program, is provision for a filter backwash recycle system consisting of surge tank and sedimentation basin for more efficient recycling of backwash water. When the DRTT program was applied to the Chungju WTP, the program was very beneficial in avoiding errors which might have occurred during arithmetic calculations and in reducing the time needed to get the output. It is anticipated that the DRTT program could be used for design of new WTPs as well as the rehabilitation of existing ones.

• Radiation Oncology Journal
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• v.36 no.2
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• pp.122-128
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• 2018

Purpose: To investigate positional uncertainty and its correlation with clinical parameters in spine stereotactic body radiotherapy (SBRT) using thermoplastic mask (TM) immobilization. Materials and Methods: A total of 21 patients who underwent spine SBRT for cervical or upper thoracic spinal lesions were retrospectively analyzed. All patients were treated with image guidance using cone beam computed tomography (CBCT) and 4 degrees-of-freedom (DoF) positional correction. Initial, pre-treatment, and post-treatment CBCTs were analyzed. Setup error (SE), pre-treatment residual error (preRE), post-treatment residual error (postRE), intrafraction motion before treatment (IM1), and intrafraction motion during treatment (IM2) were determined from 6 DoF manual rigid registration. Results: The three-dimensional (3D) magnitudes of translational uncertainties (mean ${\pm}$ 2 standard deviation) were $3.7{\pm}3.5mm$ (SE), $0.9{\pm}0.9mm$ (preRE), $1.2{\pm}1.5mm$ (postRE), $1.4{\pm}2.4mm$ (IM1), and $0.9{\pm}1.0mm$ (IM2), and average angular differences were $1.1^{\circ}{\pm}1.2^{\circ}$ (SE), $0.9^{\circ}{\pm}1.1^{\circ}$ (preRE), $0.9^{\circ}{\pm}1.1^{\circ}$ (postRE), $0.6^{\circ}{\pm}0.9^{\circ}$ (IM1), and $0.5^{\circ}{\pm}0.5^{\circ}$ (IM2). The 3D magnitude of SE, preRE, postRE, IM1, and IM2 exceeded 2 mm in 18, 0, 3, 3, and 1 patients, respectively. No association were found between all positional uncertainties and body mass index, pain score, and treatment location (p > 0.05, Mann-Whitney test). There was a tendency of intrafraction motion to increase with overall treatment time; however, the correlation was not statistically significant (p > 0.05, Spearman rank correlation test). Conclusion: In spine SBRT using TM immobilization, CBCT and 4 DoF alignment correction, a minimum residual translational uncertainty was 2 mm. Shortening overall treatment time and 6 DoF positional correction may further reduce positional uncertainties.