• Journal of Bio-Environment Control
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• v.30 no.4
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• pp.377-382
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• 2021

Oriental melon (Cucumis melo L.) is generally cultivated on the ground by creeping culture. A farmer has a higher workload for training stems. This study was conducted to find out a new cultivation of oriental melon to reduce a workload and improve the quality of fruit. There were three treatments for training stem of oriental melon; upward stem growing, downward stem growing, control (creeping stem growing). The results of the plant growth and the net photosynthesis showed higher in upward stem growing. The root activity was higher in downward stem attract. The yield was not significant as 4,055kg/10a in upward stem attract and 3,983kg/10a in downward stem attract. According to the results of the ergonomic agricultural workload evaluation, in the case of the working posture, the working posture of creeping cultivation methods (squatting, bending) showed a higher risk level than the upward and downward cultivation methods. Therefore, it is recommended the upward stem attract of oriental melon is a new cultivation as well as an alternative method for creeping stem attract in terms of improving the plant growth and yield, and reducing the workload.

• Smart Structures and Systems
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• v.29 no.1
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• pp.1-16
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• 2022

Despite recent breakthroughs in deep learning and computer vision fields, the pixel-wise identification of tiny objects in high-resolution images with complex disturbances remains challenging. This study proposes a modified U-net for tiny crack segmentation in real-world steel-box-girder bridges. The modified U-net adopts the common U-net framework and a novel Self-Attention-Self-Adaption (SASA) neuron as the fundamental computing element. The Self-Attention module applies softmax and gate operations to obtain the attention vector. It enables the neuron to focus on the most significant receptive fields when processing large-scale feature maps. The Self-Adaption module consists of a multiplayer perceptron subnet and achieves deeper feature extraction inside a single neuron. For data augmentation, a grid-based crack random elastic deformation (CRED) algorithm is designed to enrich the diversities and irregular shapes of distributed cracks. Grid-based uniform control nodes are first set on both input images and binary labels, random offsets are then employed on these control nodes, and bilinear interpolation is performed for the rest pixels. The proposed SASA neuron and CRED algorithm are simultaneously deployed to train the modified U-net. 200 raw images with a high resolution of 4928 × 3264 are collected, 160 for training and the rest 40 for the test. 512 × 512 patches are generated from the original images by a sliding window with an overlap of 256 as inputs. Results show that the average IoU between the recognized and ground-truth cracks reaches 0.409, which is 29.8% higher than the regular U-net. A five-fold cross-validation study is performed to verify that the proposed method is robust to different training and test images. Ablation experiments further demonstrate the effectiveness of the proposed SASA neuron and CRED algorithm. Promotions of the average IoU individually utilizing the SASA and CRED module add up to the final promotion of the full model, indicating that the SASA and CRED modules contribute to the different stages of model and data in the training process.

• Journal of the Korean Geotechnical Society
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• v.27 no.11
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• pp.71-81
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• 2011

Adfreeze bond strength is a primary design parameter, which determines bearing capacity of pile foundation in frozen ground. It is reported that adfreeze bond strength is influenced by various affecting factors like freezing temperature, confining pressure, characteristics of pile surface, soil type, etc. However, several limited researches have been performed to obtain adfreeze bond strength, for past studies considered only few affecting factors such as freezing temperature and type of pile structures. Therefore, there exists a limitation of estimating the design parameter of pile foundation with various factors in frozen ground. In this study, artificial neural network algorithm was involved to predict adfreeze bond strength with various affecting factors. From past five studies, 137 data for various experimental conditions were collected. It was divided by 100 training data and 37 testing data in random manner. Based on the analysis result, it was found that it is necessary to consider various affecting factors for the prediction of adfreeze bond strength and the prediction with artificial neural network algorithm provides enough reliability. In addition, the result of parametric study showed that temperature and pile type are primary affecting factors for adfreeze bond strength. And it was also shown that vertical stress influences only certain temperature zone, and various soil types and loading speeds might cause the change of evolution trend for adfreeze bond strength.

• Geophysics and Geophysical Exploration
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• v.26 no.4
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• pp.211-228
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• 2023

Ground-penetrating radar (GPR) surveys are commonly used to monitor embankments, which is a nondestructive geophysical method. The results of GPR surveys can be complex, depending on the situation, and data processing and interpretation are subject to expert experiences, potentially resulting in false detection. Additionally, this process is time-intensive. Consequently, various studies have been undertaken to detect cavities in GPR survey data using deep learning methods. Deep-learning-based approaches require abundant data for training, but GPR field survey data are often scarce due to cost and other factors constaining field studies. Therefore, in this study, a deep- learning-based model was developed for embankment GPR survey cavity detection using data augmentation strategies. A dataset was constructed by collecting survey data over several years from the same embankment. A you look only once (YOLO) model, commonly used in computer vision for object detection, was employed for this purpose. By comparing and analyzing various strategies, the optimal data augmentation approach was determined. After initial model development, a stepwise process was employed, including box clustering, transfer learning, self-ensemble, and model ensemble techniques, to enhance the final model performance. The model performance was evaluated, with the results demonstrating its effectiveness in detecting cavities in embankment GPR survey data.

• Geomechanics and Engineering
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• v.35 no.2
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• pp.121-133
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• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

• Korean Journal of Radiology
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• v.24 no.4
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• pp.294-304
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• 2023

Objective: We aimed to investigate whether image standardization using deep learning-based computed tomography (CT) image conversion would improve the performance of deep learning-based automated hepatic segmentation across various reconstruction methods. Materials and Methods: We collected contrast-enhanced dual-energy CT of the abdomen that was obtained using various reconstruction methods, including filtered back projection, iterative reconstruction, optimum contrast, and monoenergetic images with 40, 60, and 80 keV. A deep learning based image conversion algorithm was developed to standardize the CT images using 142 CT examinations (128 for training and 14 for tuning). A separate set of 43 CT examinations from 42 patients (mean age, 10.1 years) was used as the test data. A commercial software program (MEDIP PRO v2.0.0.0, MEDICALIP Co. Ltd.) based on 2D U-NET was used to create liver segmentation masks with liver volume. The original 80 keV images were used as the ground truth. We used the paired t-test to compare the segmentation performance in the Dice similarity coefficient (DSC) and difference ratio of the liver volume relative to the ground truth volume before and after image standardization. The concordance correlation coefficient (CCC) was used to assess the agreement between the segmented liver volume and ground-truth volume. Results: The original CT images showed variable and poor segmentation performances. The standardized images achieved significantly higher DSCs for liver segmentation than the original images (DSC [original, 5.40%-91.27%] vs. [standardized, 93.16%-96.74%], all P < 0.001). The difference ratio of liver volume also decreased significantly after image conversion (original, 9.84%-91.37% vs. standardized, 1.99%-4.41%). In all protocols, CCCs improved after image conversion (original, -0.006-0.964 vs. standardized, 0.990-0.998). Conclusion: Deep learning-based CT image standardization can improve the performance of automated hepatic segmentation using CT images reconstructed using various methods. Deep learning-based CT image conversion may have the potential to improve the generalizability of the segmentation network.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.226-232
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• 2020

Repetition of landing with visual control in sports and training is common, yet it remains unknown how landing with visual control affects postural stability and lower limb kinetics. The purpose of this study was to test the hypothesis that landing with visual control will influence on lower limb control for intrinsic dynamic postural stability. Kinematics and kinetics variables were recorded automatically when all participants (n=10, mean age: 22.00±1.63 years, mean heights: 177.27±5.45 cm, mean mass: 73.36±2.80 kg) performed drop landings from 30 cm platform. Visual control showed higher medial-lateral force, peak vertical force, loading rate than visual information condition. This was resulted from more stiff leg and less time to peak vertical force in visual control condition. Leg stiffness may decrease due to increase of perturbation of vertical center of gravity, but landing strategy that decreases impulse force was shifted in visual control condition during drop landing. These mechanism explains why rate of injury increase.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.4
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• pp.217-231
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• 2015

A novel fuzzy learning framework that employs fuzzy inference to solve the problem of Multiple Instance Learning (MIL) is presented. The framework introduces a new class of fuzzy inference systems called Multiple Instance Mamdani Fuzzy Inference Systems (MI-Mamdani). In multiple instance problems, the training data is ambiguously labeled. Instances are grouped into bags, labels of bags are known but not those of individual instances. MIL deals with learning a classifier at the bag level. Over the years, many solutions to this problem have been proposed. However, no MIL formulation employing fuzzy inference exists in the literature. Fuzzy logic is powerful at modeling knowledge uncertainty and measurements imprecision. It is one of the best frameworks to model vagueness. However, in addition to uncertainty and imprecision, there is a third vagueness concept that fuzzy logic does not address quiet well, yet. This vagueness concept is due to the ambiguity that arises when the data have multiple forms of expression, this is the case for multiple instance problems. In this paper, we introduce multiple instance fuzzy logic that enables fuzzy reasoning with bags of instances. Accordingly, a MI-Mamdani that extends the standard Mamdani inference system to compute with multiple instances is introduced. The proposed framework is tested and validated using a synthetic dataset suitable for MIL problems. Additionally, we apply the proposed multiple instance inference to fuse the output of multiple discrimination algorithms for the purpose of landmine detection using Ground Penetrating Radar.

• Journal of the Society of Disaster Information
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• v.14 no.1
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• pp.101-106
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• 2018

The ROK military conluded that the dead of a young soldier, walking back to his unit was caused by a ricochet after carrying our some construction work near firing range in Gangwon Province in last Septemebr. But when it was revealed that he was hit by a stray bullet cause, this fact caused public fury. To solve these problems, we will analyze the individual fire shooting range of our military units from a safe perspective and propose ways to improve it by applying the cases of the United States, which are both permanent and scientific.

• Journal of Korean Academy of Nursing
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• v.33 no.6
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• pp.743-752
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• 2003

Purpose: This study was conducted to combine the effects of pulmonary rehabilitation program (PRP) on exercise capacity/tolerance and general health status of COPD patients based on the primary research results examined the effects of PRP, Method: Seventeen studies were selected by the sampling criteria established to include the studies that reported enough statistics necessary to conduct meta-analysis. Result: According to the study results, the most effective indicators for exercise capacity/tolerance were exercise time (such as cycling time or treadmill walking time) and ground walking distance within given time (6 minutes or 12 minutes), whereas effects on such indicators as VE and VO$_2$ were not statistically significant. PRP induced significant effect on patients' general health status, frequently measured by physical, psycho-emotional, and holistic indicators, the enhancement on psycho-emotional dimension resulted from PRP was more prominent than those of the other dimensions. From the results, it was noted that the place where PRP was given and the contents of PRP exercised their influence on the outcome variables. Which body part was trained was also one of the important factors that influence on the patients' perception of dyspnea during exercise as well as on exercise capacity/tolerance. Conclusion: PRP including exercise training significantly improved the exercise capacity and general health status of COPD patients.