• 생물환경조절학회지
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• 제30권4호
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• pp.377-382
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• 2021

참외는 줄기를 땅 위에서 포복재배로 유인하는 것이 일반적으로써 노동강도가 강해서 농업인 근골격계 질환의 원인이 되므로 작업강도를 낮추고 품질도 향상시키기 위한 새로운 재배 방법을 찾고자 본 실험을 수행하였다. 그 결과, 줄기를 상향으로 유인하는 처리구가 생육 및 광합성 특성이 좋았고, 근활력은 하향 줄기 유인 처리구에서 좋은 것으로 나타났다. 상품 수량에 있어서는 상향 처리구가 4.055kg/10a, 하향 처리구가 3,983kg/10a으로 통계적인 유의성은 없었다. 줄기유인 작업에 대한 작업자세 평가의 경우, 기존 포복재배가 상향, 하향 재배방식 보다 위험수준이 높은 것으로 평가되었다. 결론적으로, 참외 수경 수직재배는 작물 생육, 수확량 및 작업 노동강도 등을 고려해 볼 때 기존 포복재배 방식을 대체할 수 있는 새로운 재배방법이라고 판단되고, 참외 줄기 유인 방법별로 수량 등에 유의성이 없으므로 상향 줄기유인 방법이나 하향 줄기 유인방법 중에서 하우스의 구조나 재배자의 의향에 따라서 선택하여 수직재배를 하면 될 것으로 사료된다.

• Smart Structures and Systems
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• 제29권1호
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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.

• 한국지반공학회논문집
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• 제27권11호
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• pp.71-81
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• 2011

동착강도는 동토지반 말뚝기초 설계시 지지력을 결정하는 주요 설계정수이다. 동착강도는 동결온도, 구속응력, 말뚝표면 특성, 토사종류 등 다양한 인자들에 의해 동시다발적인 영향을 받는 것으로 보고되고 있다. 하지만 동착강도에 대한 연구는 소수의 인자들만 반영할 수 있는 실험연구를 중심으로 수행되어온 경향이 있으며, 설계정수로서 동착강도를 산정하기 위한 방법들은 동결온도, 말뚝재료 등의 주요 인자들만을 고려할 수 있는 한계가 있어 왔다. 본 연구는 인공신경망 이론을 동착강도 산정에 활용함으로서 다양한 영향인자 조건에서 동착강도를 예측할 수 있는 방안을 모색하기 위한 목적으로 수행되었다. 인공신경망 학습을 위하여 총 5종류의 연구사례로부터 137개의 자료를 수집하였으며, 그 중 100개를 학습자료로, 37개를 실증자료로 구분하였다. 연구결과 단계적 인공신경망 학습을 통하여 동착강도 산정 시 다양한 영향인지를 다차원적으로 고려하여 예측하는 방법이 병행되어야 할 필요성을 확인하였으며, 5개 영향인자를 동시에 고려하여 동착강도를 예측할 수 있는 신뢰성 높은 학습결과를 도출 및 검증하였다. 또한, 매개변수 연구결과 동착강도는 동결온도와 말뚝재료의 변화에 가장 민감하게 반응하는 것으로 나타났고 수직응력에 의한 영향은 일부 온도구간에서만 뚜렷하게 나타나며 토사종류와 재하속도의 변화에 따라 동착강도가 증가하는 경향이 변화하는 특성을 나타내었다.

• 지구물리와물리탐사
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• 제26권4호
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• pp.211-228
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• 2023

방조제의 모니터링에는 지구물리학적 비파괴 검사인 GPR (Ground Penetrating Radar) 탐사가 주로 이용된다. GPR 반응은 상황에 따라 복잡한 양상을 보이므로 자료의 처리와 해석은 전문가의 주관적 판단에 의존하며, 이는 오 탐지의 가능성을 불러옴과 동시에 시간이 오래 걸린다는 단점이 있다. 따라서 딥 러닝을 이용하여 GPR 탐사자료의 공동을 탐지하는 다양한 연구들이 수행되고 있다. 딥 러닝 기반 방법은 데이터 기반 방법으로써 풍부한 자료가 필요하나 GPR 탐사의 경우 비용 등의 이유로 학습에 이용할 현장 자료가 부족하다. 따라서 본 논문에서는 데이터 증강 전략을 이용하여 딥 러닝 기반 방조제 GPR 탐사자료 공동 탐지 모델을 개발하였다. 다년간 동일한 방조제에서 탐사 자료를 사용하여 데이터 세트를 구축하였으며, 컴퓨터 비전 분야의 객체 탐지 모델 중 YOLO (You Look Only Once) 모델을 이용하였다. 데이터 증강 전략을 비교 및 분석함으로써 최적의 데이터 증강 전략을 도출하였고, 초기 모델 개발 후 앵커 박스 클러스터링, 전이 학습, 자체 앙상블, 모델 앙상블 기법을 단계적으로 적용하여 최종 모델 도출 후 성능을 평가하였다.

• Geomechanics and Engineering
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• 제35권2호
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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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• 제24권4호
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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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• 제12권3호
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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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• 제15권4호
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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.

• 한국재난정보학회 논문집
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• 제14권1호
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• pp.101-106
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• 2018

지난해 9월 강원도 소재 모부대에서 진지공사를 마치고 부대로 복귀하던 병사가 인근 사격 훈련장에서 날아온 도비탄에 의해 사망하는 사고가 발생하여 국민을 경악하게 하였으며, 군조사결과 이 사고의 원인은 도비탄에 의한 총상 사고로 결론 지었다. 지금까지 도비탄에 의한 사고는 수차례 발생하였지만 이번 개인화기 사격장에서 도비탄에 의한 직접적인 총상사고는 매우 이례적인 사고였다. 이러한 문제점을 해소하기 위해 군 부대 개인화기 사격장을 안전적인 측면에서 분석하여 영구적이고 과학적인 미국의 사례를 적용하여 개선방안을 제시하고자 한다.

• 대한간호학회지
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• 제33권6호
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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.