• Physical Therapy Korea
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• v.26 no.1
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• pp.1-7
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• 2019

Background: The Microsoft Kinect which is a low-cost gaming device has been studied as a promise clinical gait analysis tool having satisfactory reliability and validity. However, its accuracy is only guaranteed when it is properly positioned in front of a subject. Objects: The purpose of this study was to identify the error when the Kinect was positioned at a $45^{\circ}$ angle to the longitudinal walking plane compare with those when the Kinect was positioned in front of a subject. Methods: Sixteen healthy adults performed two testing sessions consisting of walking toward and $45^{\circ}$ obliquely the Kinect. Spatiotemporal outcome measures related to stride length, stride time, step length, step time and walking speed were examined. To assess the error between Kinect and 3D motion analysis systems, mean absolute errors (MAE) were determined and compared. Results: MAE of stride length, stride time, step time and walking speed when the Kinect set in front of subjects were investigated as .36, .04, .20 and .32 respectively. MAE of those when the Kinect placed obliquely were investigated as .67, .09, .37, and .58 respectively. There were significant differences in spatiotemporal outcomes between the two conditions. Conclusion: Based on our study experience, positioning the Kinect directly in front of the person walking towards it provides the optimal spatiotemporal data. Therefore, we concluded that the Kinect should be placed carefully and adequately in clinical settings.

• Computers and Concrete
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• v.27 no.4
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• pp.333-341
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• 2021

Steel slag, an industrial reject from the steel rolling process, has been identified as one of the suitable, environmentally friendly materials for concrete production. Given that the coarse aggregate portion represents about 70% of concrete constituents, other economic approaches have been found in the use of alternative materials such as steel slag in concrete. Unfortunately, a standard framework for its application is still lacking. Therefore, this study proposed functional model equations for the determination of strength properties (compression and splitting tensile) of steel slag aggregate concrete (SSAC), using gene expression programming (GEP). The study, in the experimental phase, utilized steel slag as a partial replacement of crushed rock, in steps 20%, 40%, 60%, 80%, and 100%, respectively. The predictor variables included in the analysis were cement, sand, granite, steel slag, water/cement ratio, and curing regime (age). For the model development, 60-75% of the dataset was used as the training set, while the remaining data was used for testing the model. Empirical results illustrate that steel aggregate could be used up to 100% replacement of conventional aggregate, while also yielding comparable results as the latter. The GEP-based functional relations were tested statistically. The minimum absolute percentage error (MAPE), and root mean square error (RMSE) for compressive strength are 6.9 and 1.4, and 12.52 and 0.91 for the train and test datasets, respectively. With the consistency of both the training and testing datasets, the model has shown a strong capacity to predict the strength properties of SSAC. The results showed that the proposed model equations are reliably suitable for estimating SSAC strength properties. The GEP-based formula is relatively simple and useful for pre-design applications.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.57-67
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• 2018

The ensemble is a unified approach used for getting better performance by using multiple algorithms in machine learning. In this paper, we introduce boosting and bagging, which have been widely used in ensemble techniques, and design a method using support vector regression, radial basis function network, Gaussian process, and multilayer perceptron. In addition, our experiment was performed by adding a recurrent neural network and MOHID numerical model. The drifter data used for our experimental verification consist of 683 observations in seven regions. The performance of our ensemble technique is verified by comparison with four algorithms each. As verification, mean absolute error was adapted. The presented methods are based on ensemble models using bagging, boosting, and machine learning. The error rate was calculated by assigning the equal weight value and different weight value to each unit model in ensemble. The ensemble model using machine learning showed 61.7% improvement compared to the average of four machine learning technique.

• Steel and Composite Structures
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• v.45 no.2
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• pp.205-218
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• 2022

Proper calculation of splitting tensile strength (STS) of concrete has been a crucial task, due to the wide use of concrete in the construction sector. Following many recent studies that have proposed various predictive models for this aim, this study suggests and tests the functionality of three hybrid models in predicting the STS from the characteristics of the mixture components including cement compressive strength, cement tensile strength, curing age, the maximum size of the crushed stone, stone powder content, sand fine modulus, water to binder ratio, and the ratio of sand. A multi-layer perceptron (MLP) neural network incorporates invasive weed optimization (IWO), cuttlefish optimization algorithm (CFOA), and electrostatic discharge algorithm (ESDA) which are among the newest optimization techniques. A dataset from the earlier literature is used for exploring and extrapolating the STS behavior. The results acquired from several accuracy criteria demonstrated a nice learning capability for all three hybrid models viz. IWO-MLP, CFOA-MLP, and ESDA-MLP. Also in the prediction phase, the prediction products were in a promising agreement (above 88%) with experimental results. However, a comparative look revealed the ESDA-MLP as the most accurate predictor. Considering mean absolute percentage error (MAPE) index, the error of ESDA-MLP was 9.05%, while the corresponding value for IWO-MLP and CFOA-MLP was 9.17 and 13.97%, respectively. Since the combination of MLP and ESDA can be an effective tool for optimizing the concrete mixture toward a desirable STS, the last part of this study is dedicated to extracting a predictive formula from this model.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.6
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• pp.415-421
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• 2023

In this study, the piezoresistive properties of cementitious composites enhanced with carbon nanotubes for improved electrical conductivity were analyzed using a deep learning-based transformer algorithm. Experimental execution was performed in parallel for acquisition of training data. Previous studies on mixture design, specimen fabrication, chemical composition analysis, and piezoresistive performance testing are also reviewed in this paper. Notably, specimens in which fly ash substituted 50% of the binder material were fabricated and evaluated in this study, in addition to carbon nanotube-infused specimens, thereby exploring the potential enhancement of piezoresistive characteristics in conductive cementitious materials. The experimental results showed more stable piezoresistive responses in specimens with fly-ash substituted binder. The transformer model was trained using 80% of the gathered data, with the remaining 20% employed for validation. The analytical outcomes were generally consistent with empirical measurements, yielding an average absolute error and root mean square error between 0.069 to 0.074 and 0.124 to 0.132, respectively.

• Structural Engineering and Mechanics
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• v.91 no.6
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• pp.643-656
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• 2024

The prediction of the moment rotation behavior of semi-rigid connections has been the subject of extensive research. However, to improve the accuracy of these predictions, there is a growing interest in employing machine learning algorithms. This paper investigates the effectiveness of using Multi-gene genetic programming (MGGP) to predict the moment-rotation behavior of flush-end plate connections compared to that of artificial neural networks (ANN) and previous studies. It aims to automate the process of determining the most suitable equations to accurately describe the behavior of these types of connections. Experimental data was used to train ANN and MGGP. The performance of the models was assessed by comparing the values of coefficient of determination (R²), maximum absolute error (MAE), and root-mean-square error (RMSE). The results showed that MGGP produced more accurate, reliable, and general predictions compared to ANN and previous studies with an R² exceeding 0.99, an RMSE of 6.97, and an MAE of 38.68, highlighting its advantages over other models. The use of MGGP can lead to better modeling and more precise predictions in structural design. Additionally, an experimentally-based regression analysis was conducted to obtain the rotational capacity of FECs. A new equation was proposed and compared to previous ones, showing significant improvement in accuracy with an R² score of 0.738, an RMSE of 0.014, and an MAE of 0.024.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.28 no.2
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• pp.435-459
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• 1998

In order to achieve a successful endodontic treatment, root canals must be obturated three-dimensionally without causing any damage to apical tissues. Accurate length determination of the root canal is critical in this case. For this reason, I've used the conventional periapical radiography, Digora/sup (R)/(digital imaging system) and Root ZX/sup (R)/(the frequency dependent type apex locator) to measure the length of the canal and compare it with the true length obtained by cutting the tooth in half and measuring the length between the occlusal surface and the apical foramen. From the information obtained by these measurements, I was able to evaluate the accuracy and clinical usefulness of each systems. whether the thickness of files used in endodontic therapy has any effect on the measuring systems was also evaluated in an effort to simplify the treatment planning phase of endodontic treatment. 29 canals of 29 sound premolars were measured with #15, #20, #25 files by 3 different dentists each using the periapical radiography. Digora/sup (R)/ and Root ZX/sup (R)/. The measurements were then compared with the true length. The results were as follows: 1. In comparing mean discrepancies between measurements obtained by using periapical radiography(mean error: -0.449±0.444 mm), Digora/sup (R)/(mean error: -0.417±0.415 mm) and Root ZX/sup (R)/(mean error: 0.123±0.458 mm) with true length. periapical radiography and Digora/sup (R)/ system had statistically significant differences(p<0.05) in most cases while Root ZX/sup (R)/ showed none(p>0.05). 2. By subtracting values obtained by using periapical radiography, Digora/sup (R)/ and Root ZX/sup (R)/ from the true length and making a distribution table of their absolute values. the following analysis was possible. In the case of periapical film. 140 out of 261<53.6％) were clinically acceptable satisfying the margin of error of less than 0.5 mm. 151 out of 261 (53,6％) were acceptable in the Digora/sup (R)/ system while Root ZX/sup (R)/ had 197 out of 261(75.5％) within the limits of 0.5mm margin of error. 3. In determining whether the thickness of files has any effect on measuring methoths, no statistically significant differences were found(p>0.05). 4. In comparing data obtained from these methods in order to evaluate the difference among measuring methods, there was no statistically significant difference between periapical radiography and Digora/sup (R)/ system(p>0.05), but there was statistically significant difference between Root ZX/sup (R)/ and periapical radiography(p<0.05). Also there was statistically significant difference between Root ZX/sup (R)/ and Digora/sup (R)/ system(p<0.05). In conclusion, Root ZX/sup (R)/ was more accurate when compared with the Digora/sup (R)/ system and periapical radiography and seems to be more effective clinically in determining root canal length. But Root ZX/sup (R)/ has its limits in determining root morphology and number of roots and its accuracy becomes questionable when apical foramen is open due to unknown reasons. Therefore the combined use of Root ZX/sup (R)/ and the periapical radiography are mandatory. Digora/sup (R)/ system seems to be more effective when periapical radiographs are needed in a short period of time because of its short processing time and less exposure.

• Journal of Korea Water Resources Association
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• v.44 no.3
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• pp.221-230
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• 2011

The climate change has been observed in Korea as well as in the entire world recently. The rainstorm has been gradually increased and then the damage has been grown. It is getting important to predict short-term rainfall. The Korea Meteorological Administration (KMA) generates numerical model outputs which are computed by Global Data Assimilation and Prediction System (GDAPS) and Regional Data Assimilation and Prediction System (RDAPS). The KMA predicts rainfall using RDAPS results. RDAPS model generates 48 hours data which is organized 3 hours data accumulated at 00UTC and 12UTC. RDAPS results which are organized 3 hours time scale are converted into daily rainfall to compare observed daily rainfall. In this study, 9 cases are applied to convert RDAPS results to daily rainfall data. The MAP (mean areal precipitation) in Geum river basin are computed by using KMA which are 2005 are used. Finally, the best case which gives the close value to the observed rainfall data is obtained using the average absolute relative error (AARE) especially for the Geum River basin.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.57-72
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• 2022

In this paper, we aimed to examine the flowering dates of cherry blossom and the peak dates of maple leaves in South Korea, by the combination of temperature observation data from ASOS (Automated Surface Observing System) and NDVI (Normalized Difference Vegetation Index) from MODIS (Moderate Resolution Imaging Spectroradiometer). The more recent years, the faster the flowering dates and the slower the peak dates. This is because of the impacts of climate change with the increase of air temperature in South Korea. By reflecting the climate change, our statistical models could reasonably predict the plant phenology with the CC (Correlation Coefficient) of 0.870 and the MAE (Mean Absolute Error) of 3.3 days for the flowering dates of cherry blossom, and the CC of 0.805 and the MAE of 3.8 for the peak dates of maple leaves. We could suppose a linear relationship between the plant phenology DOY (day of year) and the environmental factors like temperature and NDVI, which should be inspected in more detail. We found that the flowering date of cherry blossom was closely related to the monthly mean temperature of February and March, and the peak date of maple leaves was much associated with the accumulated temperature. Amore sophisticated future work will be required to examine the plant phenology using higher-resolution satellite images and additional meteorological variables like the diurnal temperature range sensitive to plant phenology. Using meteorological grid can help produce the spatially continuous raster maps for plant phenology.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2068-2082
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• 2023

With the rapid development of deep learning, Depth Map Super-Resolution (DMSR) method has achieved more advanced performances. However, when the upsampling rate is very large, it is difficult to capture the structural consistency between color features and depth features by these DMSR methods. Therefore, we propose a color-image guided DMSR method based on iterative depth feature enhancement. Considering the feature difference between high-quality color features and low-quality depth features, we propose to decompose the depth features into High-Frequency (HF) and Low-Frequency (LF) components. Due to structural homogeneity of depth HF components and HF color features, only HF color features are used to enhance the depth HF features without using the LF color features. Before the HF and LF depth feature decomposition, the LF component of the previous depth decomposition and the updated HF component are combined together. After decomposing and reorganizing recursively-updated features, we combine all the depth LF features with the final updated depth HF features to obtain the enhanced-depth features. Next, the enhanced-depth features are input into the multistage depth map fusion reconstruction block, in which the cross enhancement module is introduced into the reconstruction block to fully mine the spatial correlation of depth map by interleaving various features between different convolution groups. Experimental results can show that the two objective assessments of root mean square error and mean absolute deviation of the proposed method are superior to those of many latest DMSR methods.