scholarly journals Quantitative Evaluation of Hand Washing Skills Based on Convolutional Neural Network for Nursing Education

Forma ◽  
2020 ◽  
Vol 35 (1) ◽  
pp. 15-19
Author(s):  
Kazushi Yamamoto ◽  
Miho Yoshii ◽  
Fumiya Kinoshita ◽  
Hideaki Touyama
Keyword(s):  
Neural Network ◽  
Nursing Education ◽  
Convolutional Neural Network ◽  
Quantitative Evaluation ◽  
Hand Washing

scholarly journals Quantitative Evaluation of Plant and Modern Urban Landscape Spatial Scale Based on Multiscale Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yang Wang ◽  
Moyang Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Information Extraction ◽  
Spatial Scale ◽  
Quantitative Evaluation ◽  
Urban Landscape ◽  
Kappa Coefficient ◽  
Deep Convolutional Neural Network ◽  
Landscape Information ◽  
Extraction Model

Modern urban landscape is a simple ecosystem, which is of great significance to the sustainable development of the city. This study proposes a landscape information extraction model based on deep convolutional neural network, studies the multiscale landscape convolutional neural network classification method, constructs a landscape information extraction model based on multiscale CNN, and finally analyzes the quantitative effect of deep convolutional neural network. The results show that the overall kappa coefficient is 0.91 and the classification accuracy is 93% by calculating the confusion matrix, production accuracy, and user accuracy. The method proposed in this study can identify more than 90% of water targets, the user accuracy and production accuracy are 99.78% and 91.94%, respectively, and the overall accuracy is 93.33%. The method proposed in this study is obviously better than other methods, and the kappa coefficient and overall accuracy are the best. This study provides a certain reference value for the quantitative evaluation of modern urban landscape spatial scale.

scholarly journals Multi-attribute self-attention guided vehicle local region detection based on convolutional neural network architecture

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142094434
Author(s):  
Jingbo Chen ◽  
Shengyong Chen ◽  
Linjie Bian
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Quantitative Evaluation ◽  
Network Architecture ◽  
Local Region ◽  
Training Data ◽  
Detection Methods ◽  
Data Set ◽  
Region Detection ◽  
Bounding Boxes

Many pieces of information are included in the front region of a vehicle, especially in windshield and bumper regions. Thus, windshield or bumper region detection is making sense to extract useful information. But the existing windshield and bumper detection methods based on traditional artificial features are not robust enough. Those features may become invalid in many real situations (e.g. occlude, illumination change, viewpoint change.). In this article, we propose a multi-attribute-guided vehicle discriminately region detection method based on convolutional neural network and not rely on bounding box regression. We separate the net into two branches, respectively, for identification (ID) and Model attributes training. Therefore, the feature spaces of different attributes become more independent. Additionally, we embed a self-attention block into our framework to improve the performance of local region detection. We train our model on PKU_VD data set which has a huge number of images inside. Furthermore, we labeled the handcrafted bounding boxes on 5000 randomly picked testing images, and 1020 of them are used for evaluation and 3980 as the training data for YOLOv3. We use Intersection over Union for quantitative evaluation. Experiments were conducted in three different latest convolutional neural network trunks to illustrate the detection performance of the proposed method. Simultaneously, in terms of quantitative evaluation, the performance of our method is close to YOLOv3 even without handcrafted bounding boxes.

scholarly journals Quantitative evaluation of sensitivity in confidential car exterior design

2018 ◽  
Vol 7 (1) ◽  
pp. 34 ◽  
Author(s):  
Takumi Kato ◽  
Kazuhiko Tsuda
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Product Design ◽  
Quantitative Evaluation ◽  
Manufacturing Industry ◽  
Input Image ◽  
Exterior Design

In recent years, the manufacturing industry has seen a shift in competition from performance, which can easily be evaluated numerically, to design which much more challenging to express numerically. The rise of companies that focus on design, such as Apple, Samsung and IKEA, is remarkable. However, design presents two challenges for the manufacturing industry. First, the sensory aspect of design is challenging to evaluate quantitatively, and unified evaluation indicators are not yet defined. Second, confidentiality of product design. In many cases, the design is kept in confidence within the companies, so it is often hesitated to investigate large customers. The above two problems increase the influence of the evaluator's experience and cause a situation that it is challenging to create a design desired by the customer. Therefore, the present study aims to enable inexpensive quantitative evaluation of automobile exterior design while maintaining confidentiality. We propose a technique that uses a convolutional neural network to link features extracted from accumulated design images to the sensitivity extracted from the customer's voice. This is then used to quantitatively evaluate an input image. 

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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