• Restorative Dentistry and Endodontics
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• v.45 no.4
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• pp.51.1-51.10
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• 2020

Objectives: No studies have yet assessed vibration characteristics according to endodontic file length. Accordingly, the objective of the present study was to examine the vibration characteristics according to nickel-titanium file length and to compare these characteristics between different file systems. Materials and Methods: A total of 45 root canal models were divided into 3 experimental groups (n = 15 each) based on the file system used (ProTaper Gold [PTG], ProTaper Next, or WaveOne Gold [WOG]). Each experimental group was further divided into 3 subgroups according to file length (21, 25, or 31 mm). An electric motor (X-SMART PLUS) was used in the experiment. For each file system, vibrations generated when using a size 25 file were measured and used to calculate the average vibration acceleration. The differences in vibrations were analyzed using 1-way analysis of variance and the Scheffé post hoc test with a confidence interval of 95%. Results: In the PTG file system, significantly lower vibration acceleration was observed when using a 21-mm file than when using a 31-mm file. In the WOG file system, significantly stronger vibration acceleration was observed when using a 31-mm file than when using 21- or 25-mm files. Regardless of the file length, the WOG group exhibited significantly stronger vibration acceleration than the other 2 experimental groups. Conclusions: In clinical practice, choosing a file with the shortest length possible could help reduce vibrations. Additionally, consideration should be given to vibrations that could be generated when using WOG files with reciprocating motion.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.59-68
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• 2017

The flower and woman are beautiful but Euler's theorem and the symmetry are the best. Shannon applied his theorem to information and communication based on Euler's theorem. His theorem is the root of wireless communication and information theory and the principle of today smart phone. Their meeting point is $e^{-SNR}$ of MIMO(multiple input and multiple output) multiple antenna diversity. In this paper, Euler, who discovered the most beautiful formula($e^{{\pi}i}+1=0$) in the world, briefly guided Shannon's formula ($C=Blog_2(1+{\frac{S}{N}})$) to discover the origin of wireless communication and information communication, and these two masters prove a meeting at the Shannon limit, It reveals something what this secret. And we find that it is symmetry and element-wise inverse are the hidden secret in algebraic coding theory and triangular function.

• Journal of Korean Society of Steel Construction
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• v.23 no.5
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• pp.629-636
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• 2011

This paper describes the smart structural system that uses smart materials for real-time monitoring and active control of bolted joints in steel structures. The impedance-based structural health monitoring (SHM) techniques, which utilize the electro-mechanical coupling property of piezoelectric materials, was used to detect loose bolts in bolted joints. By monitoring the measured electrical impedance and comparing it with the measured baseline, a bolt loosening damage was detected. The damage was evaluated quantitatively using the damage metrics in conductance signature with respect to the healthy states. When loosening damage was detected in the bolted joint, the external heater actuated the shape memory alloy (SMA) washer. Then the heated SMA washer expanded axially and adjusted the bolt tension to restore the lost torque. An experiment was conducted by integrating the piezoelectric-material-based SHM function and the SMA-based active control function on a bolted joint, after which the performance of thesmart self-healing joint system was investigated.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.6
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• pp.517-524
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• 2017

The objective of this research is to improve the efficiency of data collection from many machine components on smart factory floors using IoT(Internet of things) techniques and cloud platform, and to make it easy to update outdated diagnostic schemes through online deployment methods from cloud resources. The short-term analysis is implemented by a micro-controller, and it includes machine-learning algorithms for inferring snapshot information of the machine components. For long-term analysis, time-series and high-dimension data are used for root cause analysis by combining a cloud platform and multivariate analysis techniques. The diagnostic results are visualized in a web-based display dashboard for an unconstrained user access. The implementation is demonstrated to identify its performance in data acquisition and analysis for rotating machinery.

• Smart Media Journal
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• v.11 no.3
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• pp.62-73
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• 2022

Researches on the prediction of domestic apartment sales price have been continuously conducted, but it is not easy to accurately predict apartment prices because various characteristics are compounded. Prior to predicting apartment sales price, the analysis of major factors, influencing on sale prices, is of paramount importance to improve the accuracy of sales price. Therefore, this study aims to analyze what are the factors that affect the apartment sales price in Gwangju, which is currently showing a steady increase rate. With 6 years of Gwangju apartment transaction price and various social factor data, several maching learning techniques such as multiple regression analysis, random forest, and deep artificial neural network algorithms are applied to identify major factors in each model. The performances of each model are compared with RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and R² (coefficient of determination). The experiment shows that several factors such as 'contract year', 'applicable area', 'certificate of deposit', 'mortgage rate', 'leading index', 'producer price index', 'coincident composite index' are analyzed as main factors, affecting the sales price.

• Smart Media Journal
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• v.12 no.11
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• pp.134-144
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• 2023

Nowadays, Deep Learning (DL) technology is being used in several government departments. South Korea imports a lot of seafood. If the demand for fishery products is not accurately predicted, then there will be a shortage of fishery products and the price of the fishery product may rise sharply. So, South Korea's Ministry of Ocean and Fisheries is attempting to accurately predict seafood imports using deep learning. This paper introduces the solution for the fish import prediction in South Korea using the Long Short-Term Memory (LSTM) method. It was found that there was a huge gap between the sum of consumption and export against the sum of production especially in the case of two species that are Hairtail and Pollock. An import prediction is suggested in this research to fill the gap with some advanced Deep Learning methods. This research focuses on import prediction using Machine Learning (ML) and Deep Learning methods to predict the import amount more precisely. For the prediction, two Deep Learning methods were chosen which are Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). Moreover, the Machine Learning method was also selected for the comparison between the DL and ML. Root Mean Square Error (RMSE) was selected for the error measurement which shows the difference between the predicted and actual values. The results obtained were compared with the average RMSE scores and in terms of percentage. It was found that the LSTM has the lowest RMSE score which showed the prediction with higher accuracy. Meanwhile, ML's RMSE score was higher which shows lower accuracy in prediction. Moreover, Google Trend Search data was used as a new feature to find its impact on prediction outcomes. It was found that it had a positive impact on results as the RMSE values were lowered, increasing the accuracy of the prediction.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.257-268
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• 2024

Drone-mounted hyperspectral sensors (DHSs) have revolutionized remote sensing in agriculture by offering a cost-effective and flexible platform for high-resolution spectral data acquisition. Their ability to capture data at low altitudes minimizes atmospheric interference, enhancing their utility in agricultural monitoring and management. This study focused on addressing the challenges of radiometric and geometric distortions in preprocessing drone-acquired hyperspectral data. Radiometric correction, using the empirical line method (ELM) and spectral reference panels, effectively removed sensor noise and variations in solar irradiance, resulting in accurate surface reflectance values. Notably, the ELM correction improved reflectance for measured reference panels by 5-55%, resulting in a more uniform spectral profile across wavelengths, further validated by high correlations (0.97-0.99), despite minor deviations observed at specific wavelengths for some reflectors. Geometric correction, utilizing a rubber sheet transformation with ground control points, successfully rectified distortions caused by sensor orientation and flight path variations, ensuring accurate spatial representation within the image. The effectiveness of geometric correction was assessed using root mean square error(RMSE) analysis, revealing minimal errors in both east-west(0.00 to 0.081 m) and north-south directions(0.00 to 0.076 m).The overall position RMSE of 0.031 meters across 100 points demonstrates high geometric accuracy, exceeding industry standards. Additionally, image mosaicking was performed to create a comprehensive representation of the study area. These results demonstrate the effectiveness of the applied preprocessing techniques and highlight the potential of DHSs for precise crop health monitoring and management in smart agriculture. However, further research is needed to address challenges related to data dimensionality, sensor calibration, and reference data availability, as well as exploring alternative correction methods and evaluating their performance in diverse environmental conditions to enhance the robustness and applicability of hyperspectral data processing in agriculture.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.51-55
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• 2020

In this paper, an improved FAM is proposed by adopting similarity learning in the existing FAM (Fuzzy Associative Memory) used in image restoration. Image restoration refers to the recovery of the latent clean image from its noise-corrupted version. In serious application like face recognition, this process should be noise-tolerant, robust, fast, and scalable. The existing FAM is a simple single layered neural network that can be applied to this domain with its robust fuzzy control but has low capacity problem in real world applications. That similarity measure is implied to the connection strength of the FAM structure to minimize the root mean square error between the recovered and the original image. The efficacy of the proposed algorithm is verified with significant low error magnitude from random noise in our experiment.

• Structural Engineering and Mechanics
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• v.60 no.3
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• pp.471-488
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• 2016

This study predicts the strength of rotary brace damper by analyzing a new set of probabilistic models using the usual method of multiple linear regressions (MLR) and advanced machine-learning methods of multivariate adaptive regression splines (MARS), Rotary brace damper can be easily assembled with high energy-dissipation capability. To investigate the behavior of this damper in structures, a steel frame is modeled with this device subjected to monotonic and cyclic loading. Several response parameters are considered, and the performance of damper in reducing each response is evaluated. MLR and MARS methods were used to predict the strength of this damper. Displacement was determined to be the most effective parameter of damper strength, whereas the thickness did not exhibit any effect. Adding thickness parameter as inputs to MARS and MLR models did not increase the accuracies of the models in predicting the strength of this damper. The MARS model with a root mean square error (RMSE) of 0.127 and mean absolute error (MAE) of 0.090 performed better than the MLR model with an RMSE of 0.221 and MAE of 0.181.

• Smart Structures and Systems
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• v.16 no.6
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• pp.1003-1021
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• 2015

MR dampers have been proposed for the control of cable vibration of cable-stayed bridge in recent years due to their high performance and low energy consumption. However, the highly nonlinear feature of MR dampers makes them difficult to be designed with efficient semi-active control algorithms. Simulation study has previously been carried out on the cable-MR damper system using a semi-active control algorithm derived based on the universal design curve of dampers and a bilinear mechanical model of the MR damper. This paper aims to verify the effectiveness of the MR damper for mitigating cable vibration through a full-scale experimental test, using the same semi-active control strategy as in the simulation study. A long stay cable fabricated for a real bridge was set-up with the MR damper installed. The cable was excited under both free and forced vibrations. Different test scenarios were considered where the MR damper was tuned as passive damper with minimum or maximum input current, or the input current of the damper was changed according to the proposed semi-active control algorithm. The effectiveness of the MR damper for controlling the cable vibration was assessed through computing the damping ratio of the cable for free vibration and the root mean square value of acceleration of the cable for forced vibration.