data expansion
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Author(s):  
QUAN HU ◽  
PING CAI

A method for estimating ground reaction force (GRF) with plantar pressure was proposed in this paper. The estimation model was constructed to approximate the nonlinear relationships between GRF and the plantar pressure according to the linear combinations of Gaussian kernel functions. Partial least squares regression (PLSR) was adopted to obtain model parameters and eliminate multicollinearity among the pressure components. The general model and subject-specific models were constructed for 12 male and 4 female subjects. Moreover, a data expansion method was introduced for the establishment of subject-specific model, which is implemented by searching and adopting the data with consistent statistical characteristics in a pre-established database. That approach is particularly meaningful for the group whose walking ability is limited or clinic where the force platform is not available. The NRMSEs (%) for general model were 5.27–7.85% (GRF_V), 7.35–8.53% (GRF_ML), and 8.82–10.54% (GRF_AP). The maximum NRMSEs (%) for subject-specific models were 5.02% (GRF_V), 9.91% (GRF_ML), and 10.23% (GRF_AP). Results showed that both general and subject-specific models achieved higher accuracy than existing methods such as linear regression and neural network methods.


2021 ◽  
Author(s):  
Shaira Tabassum ◽  
Md Mahmudur Rahman ◽  
Nuren Abedin ◽  
Md Moshiur Rahman ◽  
Mostafa Taufiq Ahmed ◽  
...  

Abstract Doctors in developing countries are too busy to write digital prescriptions. Ninety-seven percent of Bangladeshi doctors write handwritten prescriptions, the majority of which lack legibility. Prescriptions are harder to read as they contain multiple languages. This paper proposes a machine learning approach to recognize doctors' handwriting to create digital prescriptions. A ‘Handwritten Medical Term Corpus’ dataset is developed containing 17,431 samples of 480 medical terms. In order to improve the recognition efficiency, this paper introduces a data augmentation technique to widen the variety and increase the sample size. A sequence of line data is extracted from the augmented images of 1,591,100 samples and fed to a Bidirectional LSTM. Data augmentation includes pattern Rotating, Shifting and Stretching (RSS). Eight different combinations are applied to evaluate the strength of the proposed method. The result shows 93.0% average accuracy (max: 94.5%, min: 92.1%) using Bidirectional LSTM and RSS data augmentation. This accuracy is 19.6% higher than the recognition result with no data expansion. The proposed handwritten recognition technology can be installed in a smartpen for busy doctors which will recognize the writings and digitize them in real-time. It is expected that the smartpen will contribute to reduce medical errors, save medical costs and ensure healthy living in developing countries.


Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 981
Author(s):  
Yang Wu ◽  
Lihong Xu

The deep neural network-based method requires a lot of data for training. Aiming at the problem of a lack of training images in tomato leaf disease identification, an Adversarial-VAE network model for generating images of 10 tomato leaf diseases is proposed, which is used to expand the training set for training an identification model. First, an Adversarial-VAE model is designed to generate tomato leaf disease images. Then, a multi-scale residual learning module is used to replace single-size convolution kernels to enrich extracted features, and a dense connection strategy is integrated into the Adversarial-VAE networks to further enhance the image generation ability. The training set is expanded by the proposed model, which generates the same number of images by training 10,892 images of 10 leaves. The generated images are superior to those of InfoGAN, WAE, VAE, and VAE-GAN measured by the Frechet Inception Distance (FID). The experimental results show that using the extension dataset that is generated by the Adversarial-VAE model to train the Resnet identification model could improve the accuracy of identification effectively. The model proposed in this paper could generate enough images of tomato leaf diseases and provide a feasible solution for data expansion of tomato leaf disease images.


2021 ◽  
pp. 116138
Author(s):  
Samer Mohammed Jaber Mubarak ◽  
Andrew Crampton ◽  
Jennifer Carter ◽  
Simon Parkinson

2021 ◽  
Vol 11 (18) ◽  
pp. 8663
Author(s):  
Wen Chen ◽  
Chengwei Ju ◽  
Yanzhou Li ◽  
Shanshan Hu ◽  
Xi Qiao

The rapid and accurate identification of sugarcane stem nodes in the complex natural environment is essential for the development of intelligent sugarcane harvesters. However, traditional sugarcane stem node recognition has been mainly based on image processing and recognition technology, where the recognition accuracy is low in a complex natural environment. In this paper, an object detection algorithm based on deep learning was proposed for sugarcane stem node recognition in a complex natural environment, and the robustness and generalisation ability of the algorithm were improved by the dataset expansion method to simulate different illumination conditions. The impact of the data expansion and lighting condition in different time periods on the results of sugarcane stem nodes detection was discussed, and the superiority of YOLO v4, which performed best in the experiment, was verified by comparing it with four different deep learning algorithms, namely Faster R-CNN, SSD300, RetinaNet and YOLO v3. The comparison results showed that the AP (average precision) of the sugarcane stem nodes detected by YOLO v4 was 95.17%, which was higher than that of the other four algorithms (78.87%, 88.98%, 90.88% and 92.69%, respectively). Meanwhile, the detection speed of the YOLO v4 method was 69 f/s and exceeded the requirement of a real-time detection speed of 30 f/s. The research shows that it is a feasible method for real-time detection of sugarcane stem nodes in a complex natural environment. This research provides visual technical support for the development of intelligent sugarcane harvesters.


Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1111
Author(s):  
Feng Lin ◽  
Tian Hou ◽  
Qiannan Jin ◽  
Aiju You

Various floating debris in the waterway can be used as one kind of visual index to measure the water quality. The traditional image processing method is difficult to meet the requirements of real-time monitoring of floating debris in the waterway due to the complexity of the environment, such as reflection of sunlight, obstacles of water plants, a large difference between the near and far target scale, and so on. To address these issues, an improved YOLOv5s (FMA-YOLOv5s) algorithm by adding a feature map attention (FMA) layer at the end of the backbone is proposed. The mosaic data augmentation is applied to enhance the detection effect of small targets in training. A data expansion method is introduced to expand the training dataset from 1920 to 4800, which fuses the labeled target objects extracted from the original training dataset and the background images of the clean river surface in the actual scene. The comparisons of accuracy and rapidity of six models of this algorithm are completed. The experiment proves that it meets the standards of real-time object detection.


Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4867
Author(s):  
Lu Chen ◽  
Hongjun Wang ◽  
Xianghao Meng

With the development of science and technology, neural networks, as an effective tool in image processing, play an important role in gradual remote-sensing image-processing. However, the training of neural networks requires a large sample database. Therefore, expanding datasets with limited samples has gradually become a research hotspot. The emergence of the generative adversarial network (GAN) provides new ideas for data expansion. Traditional GANs either require a large number of input data, or lack detail in the pictures generated. In this paper, we modify a shuffle attention network and introduce it into GAN to generate higher quality pictures with limited inputs. In addition, we improved the existing resize method and proposed an equal stretch resize method to solve the problem of image distortion caused by different input sizes. In the experiment, we also embed the newly proposed coordinate attention (CA) module into the backbone network as a control test. Qualitative indexes and six quantitative evaluation indexes were used to evaluate the experimental results, which show that, compared with other GANs used for picture generation, the modified Shuffle Attention GAN proposed in this paper can generate more refined and high-quality diversified aircraft pictures with more detailed features of the object under limited datasets.


2021 ◽  
Vol 11 (7) ◽  
pp. 2016-2024
Author(s):  
Liming Liang ◽  
Zhimin Lan ◽  
Wen Xiong ◽  
Xiaoqi Sheng

Accurate extraction of retinal vessels is one important factor to computer-aided diagnosis for ophthalmologic diseases. Due to the low sensitivity and insufficient segmentation of tiny blood vessels within the existing segmentation algorithms, a novel retinal vessel segmentation algorithm is proposed, and its basis is on conditional generative adversarial nets, using W-net as generator. More specifically, firstly, the U-net is expanded to W-net through the skip connection, as the U-net is beneficial to the microvascular information transmission in the skip connection layer, then the network convergence is accelerated and the parameter utilization is improved. Secondly, the standard convolutions are replaced by the depth-wise separable convolutions, thus expanding the network and reducing the number of the parameters. Thirdly, the residual blocks are employed to mitigate the gradient disappearance and the explosion. Fourthly, during the proposed algorithm, each skip connection follows Squeeze-and-Excitation blocks so that the shallow features and deep features can be effectively fused through learning the interdependence of feature channel. Generally, the loss function of the conditional generative adversarial nets is modified to make the overall segmentation performance be optimal, while having strong global penalty ability in the whole game learning model. Finally, one experiment is carried out on the DRIVE dataset with image enhancement and data expansion. From the experiment results, the segmentation sensitivity reaches 87.18%, further the specificity, accuracy and AUC are 98.19%, 96.95% and 98.42% respectively, which show the overall performance and sensitivity are better than the existing algorithms.


2021 ◽  
Vol 24 ◽  
pp. 100153
Author(s):  
Djamila Romaissa Beddiar ◽  
Md Saroar Jahan ◽  
Mourad Oussalah

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1186
Author(s):  
Yixiao Zhang ◽  
Ying Lei

Structural monitoring provides valuable information on the state of structural health, which is helpful for structural damage detection and structural state assessment. However, when the sensors are exposed to harsh environmental conditions, various anomalies caused by sensor failure or damage lead to abnormalities of the monitoring data. It is inefficient to remove abnormal data by manual elimination because of the massive number of data obtained by monitoring systems. In this paper, a data anomaly detection method based on structural vibration signals and a convolutional neural network (CNN) is proposed, which can automatically identify and eliminate abnormal data. First, the anomaly detection problem is modeled as a time series classification problem. Data preprocessing and data augmentation, including data expansion and down-sampling to construct new samples, are employed to process the original time series. For a small number of samples in the data set, randomly increase outliers, symmetrical flipping, and noise addition methods are used for data expansion, and samples with the same label are added without increasing the original samples. The down-sampling method of symmetrically extracting the maximum value and the minimum value at the same time can effectively reduce the dimensionality of the input sample, while retaining the characteristics of the data to the greatest extent. Using hyperparameter tuning of the classification weights, CNN is more effective in dealing with unbalanced training sets. Finally, the effectiveness of the proposed method is proved by the anomaly detection of acceleration data on a long-span bridge. For the anomaly detection problem modeled as a time series classification problem, the proposed method can effectively identify various abnormal patterns.


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