• Steel and Composite Structures
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• v.51 no.4
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• pp.417-440
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• 2024

Artificial neural networks (ANN) have been the focus of several studies when it comes to evaluating the pile's bearing capacity. Nonetheless, the principal drawbacks of employing this method are the sluggish rate of convergence and the constraints of ANN in locating global minima. The current work aimed to build four ANN-based prediction models enhanced with methods from the black hole algorithm (BHA), league championship algorithm (LCA), shuffled complex evolution (SCE), and symbiotic organisms search (SOS) to estimate the carrying capacity of piles in cold climates. To provide the crucial dataset required to build the model, fifty-eight concrete pile experiments were conducted. The pile geometrical properties, internal friction angle 𝛗 shaft, internal friction angle 𝛗 tip, pile length, pile area, and vertical effective stress were established as the network inputs, and the BHA, LCA, SCE, and SOS-based ANN models were set up to provide the pile bearing capacity as the output. Following a sensitivity analysis to determine the optimal BHA, LCA, SCE, and SOS parameters and a train and test procedure to determine the optimal network architecture or the number of hidden nodes, the best prediction approach was selected. The outcomes show a good agreement between the measured bearing capabilities and the pile bearing capacities forecasted by SCE-MLP. The testing dataset's respective mean square error and coefficient of determination, which are 0.91846 and 391.1539, indicate that using the SCE-MLP approach as a practical, efficient, and highly reliable technique to forecast the pile's bearing capacity is advantageous.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.143-147
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• 2024

Image noise classification is a classical problem in the field of image processing, machine learning, deep learning and computer vision. In this paper, image noise classification is performed using deep learning. Keras deep learning library of TensorFlow is used for this purpose. 6900 images images are selected from the Kaggle database for the classification purpose. Dataset for labeled noisy images of multiple type was generated with the help of Matlab from a dataset of non-noisy images. Labeled dataset comprised of Salt & Pepper, Gaussian and Sinusoidal noise. Different training and tests sets were partitioned to train and test the model for image classification. In deep neural networks CNN (Convolutional Neural Network) is used due to its in-depth and hidden patterns and features learning in the images to be classified. This deep learning of features and patterns in images make CNN outperform the other classical methods in many classification problems.

• Computers and Concrete
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• v.34 no.4
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• pp.477-487
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• 2024

The in-situ stresses of concrete are an essential index for assessing the safety performance of concrete structures. Conventional methods for pore pressure release often face challenges in selecting drilling ring parameters, uncontrollable stress release, and unstable detection accuracy. In this paper, the parameters affecting the results of the concrete ring hole stress release method are cross-combined, and finite elements are used to simulate the combined parameters and extract the stress release values to establish a training set. The GridSearchCV function is utilized to determine the optimal hyperparameters. The mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) are used as evaluation indexes to train the gradient boosting decision tree (GBDT) algorithm, and the other three common algorithms are compared. The RMSE of the GBDT algorithm for the test set is 4.499, and the R² of the GBDT algorithm for the test set is 0.962, which is 9.66% higher than the R² of the best-performing comparison algorithm. The model generated by the GBDT algorithm can accurately calculate the concrete in-situ stresses based on the drilling ring parameters and the corresponding stress release values and has a high accuracy and generalization ability.

• Journal of the Korean Geotechnical Society
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• v.30 no.5
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• pp.47-54
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• 2014

Although shear wave velocity ($V_s$) is an important design factor in seismic design, the measurement is not usually made in typical field investigation due to time and economic limitations. In the present study, an investigation was made to predict sand $V_s$ based on the standard penetration test (SPT) results by using artificial neural network (ANN) model. A total of 650 dataset composed of SPT-N value ($N_{60}$), water content, fine content, specific gravity for input data and $V_s$ for output data was used to build and train the ANN model. The sensitivity analysis was then performed for the trained ANN to examine the effect of the input variables on the $V_s$. Also, the ANN model was compared with seven existing empirical models on the performance. The sensitivity analysis results revealed that the effect of the SPT-N value on $V_s$ is significantly greater compared to other input variables. Also, when compared with the empirical models using Nash-Sutcliffe Model Efficiency Coefficient (NSE) and Root Mean Square Error (RMSE), the ANN model was found to exhibit the highest prediction capability.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.8
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• pp.673-683
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• 2016

The purpose of the NCS-based curriculum is to prepare students for practical competencies to perform tasks demanded by industries by applying nationally and industrially developed and certified NCS to the currently held curriculum. In order to achieve this academic goal, education institutions must dedicate their utmost efforts to train trainees to attain flexibility in rapidly changing industry environments and competitiveness to perform the various tasks demanded by industries. The 47th article of the Higher Education Law explains that the purpose of the College corresponds with that of the NCS-based curriculum because the contribution of national and social development is related to the development of an industry. The college purpose and the NCS-based curriculum require close collaboration and cooperation between industries and colleges. This thesis analyzes cases of industry and college cooperation, researches adequate cases that suit the purpose of the NCS-based curriculum and proposes an appropriate industrial-education cooperation model that suits college and NCS-based curriculum by analyzing the cooperation model held by highly acknowledged universities oversea. In addition, this paper proposes a process and guideline to establish the industrial-education cooperation model. The industrial-education cooperation model proposed herein not only practically aligns the NCS-based curriculum to fit the industry environments, but also supports each party in pursuing and achieving its goals through an effective cooperative structure. The NCS-based industrial education model is anticipated to improve colleges' employment rates, allow industries' procurement of brilliant human resources, and contribute to the development of domestic industries through effective college education and training competent professionals for society.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.137-142
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• 2010

Although multi-model speech recognizer has been shown to be quite successful in noisy speech recognition, the results were based on general speech front-ends which do not take into account noise adaptation techniques. In this paper, for the accurate evaluation of the multi-model based speech recognizer, we adopted a quite noise-robust speech front-end, AFE, which was proposed by the ETSI for the noisy DSR environment. For the performance comparison, the MTR which is known to give good results in the DSR environment has been used. Also, we modified the structure of the multi-model based speech recognizer to improve the recognition performance. N reference HMMs which are most similar to the input noisy speech are used as the acoustic models for recognition to cope with the errors in the selection of the reference HMMs and the noise signal variability. In addition, multiple SNR levels are used to train each of the reference HMMs to improve the robustness of the acoustic models. From the experimental results on the Aurora 2 databases, we could see better recognition rates using the modified multi-model based speech recognizer compared with the previous method.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.419-432
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• 2019

Most of deep learning model training was proceeded by supervised learning, which is to train labeling data composed by inputs and corresponding outputs. Labeling data was directly generated manually, so labeling accuracy of data is relatively high. However, it requires heavy efforts in securing data because of cost and time. Additionally, the main goal of supervised learning is to improve detection performance for 'True Positive' data but not to reduce occurrence of 'False Positive' data. In this paper, the occurrence of unpredictable 'False Positive' appears by trained modes with labeling data and 'True Positive' data in monitoring of deep learning-based CCTV accident detection system, which is under operation at a tunnel monitoring center. Those types of 'False Positive' to 'fire' or 'person' objects were frequently taking place for lights of working vehicle, reflecting sunlight at tunnel entrance, long black feature which occurs to the part of lane or car, etc. To solve this problem, a deep learning model was developed by simultaneously training the 'False Positive' data generated in the field and the labeling data. As a result, in comparison with the model that was trained only by the existing labeling data, the re-inference performance with respect to the labeling data was improved. In addition, re-inference of the 'False Positive' data shows that the number of 'False Positive' for the persons were more reduced in case of training model including many 'False Positive' data. By training of the 'False Positive' data, the capability of field application of the deep learning model was improved automatically.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.127-134
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• 2022

Recently, policies and research to prevent increasing construction accidents have been actively conducted in the domestic construction industry. In previous studies, the prediction model developed to prevent construction accidents mainly used only structured data, so various characteristics of construction sites are not sufficiently considered. Therefore, in this study, we developed a machine learning-based construction accident prediction model that enables the characteristics of construction sites to be considered sufficiently by using both structured and text-type unstructured data. In this study, 6,826 cases of construction accident data were collected from the Construction Safety Management Integrated Information (CSI) for machine learning. The Decision forest algorithm and the BERT language model were used to train structured and unstructured data respectively. As a result of analysis using both types of data, it was confirmed that the prediction accuracy was 95.41 %, which is improved by about 20 % compared to the case of using only structured data. Conclusively, the performance of the predictive model was effectively improved by using the unstructured data together, and construction accidents can be expected to be reduced through more accurate prediction.

• Journal of IKEEE
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• v.28 no.2
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• pp.150-157
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• 2024

In this paper, we propose a novel algorithm for predicting the number of apples on an apple tree using a deep learning-based object detection model and a polynomial regression model. Measuring the number of apples on an apple tree can be used to predict apple yield and to assess losses for determining agricultural disaster insurance payouts. To measure apple fruit load, we photographed the front and back sides of apple trees. We manually labeled the apples in the captured images to construct a dataset, which was then used to train a one-stage object detection CNN model. However, when apples on an apple tree are obscured by leaves, branches, or other parts of the tree, they may not be captured in images. Consequently, it becomes difficult for image recognition-based deep learning models to detect or infer the presence of these apples. To address this issue, we propose a two-stage inference process. In the first stage, we utilize an image-based deep learning model to count the number of apples in photos taken from both sides of the apple tree. In the second stage, we conduct a polynomial regression analysis, using the total apple count from the deep learning model as the independent variable, and the actual number of apples manually counted during an on-site visit to the orchard as the dependent variable. The performance evaluation of the two-stage inference system proposed in this paper showed an average accuracy of 90.98% in counting the number of apples on each apple tree. Therefore, the proposed method can significantly reduce the time and cost associated with manually counting apples. Furthermore, this approach has the potential to be widely adopted as a new foundational technology for fruit load estimation in related fields using deep learning.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.517-524
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• 2024

This study aims to train and implement a deep learning model for the fusion of website creation and artificial intelligence, in the era known as the AI revolution following the launch of the ChatGPT service. The deep learning model was trained using 3,000 collected web page images, processed based on a system of component and layout classification. This process was divided into three stages. First, prior research on AI models was reviewed to select the most appropriate algorithm for the model we intended to implement. Second, suitable web page and paragraph images were collected, categorized, and processed. Third, the deep learning model was trained, and a serving interface was integrated to verify the actual outcomes of the model. This implemented model will be used to detect multiple paragraphs on a web page, analyzing the number of lines, elements, and features in each paragraph, and deriving meaningful data based on the classification system. This process is expected to evolve, enabling more precise analysis of web pages. Furthermore, it is anticipated that the development of precise analysis techniques will lay the groundwork for research into AI's capability to automatically generate perfect web pages.