Two-dimensional correlation spectroscopy combined with deep learning method and HPLC method to identify the storage duration of porcini

2021 ◽  
pp. 106670
Author(s):  
Li Wang ◽  
Jie-qing Li ◽  
Tao Li ◽  
Hong-gao Liu ◽  
Yuan-zhong Wang
Keyword(s):  
Deep Learning ◽  
Hplc Method ◽  
Correlation Spectroscopy ◽  
Learning Method ◽  
Two Dimensional ◽  
Storage Duration

scholarly journals 3D Mesh Model Classification with a Capsule Network

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 99
Author(s):  
Yang Zheng ◽  
Jieyu Zhao ◽  
Yu Chen ◽  
Chen Tang ◽  
Shushi Yu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Traditional Method ◽  
Three Dimensional ◽  
Learning Method ◽  
Two Dimensional ◽  
3D Mesh ◽  
Mesh Model ◽  
3D Mesh Model ◽  
Dimensional Object

With the widespread success of deep learning in the two-dimensional field, how to apply deep learning methods from two-dimensional to three-dimensional field has become a current research hotspot. Among them, the polygon mesh structure in the three-dimensional representation as a complex data structure provides an effective shape approximate representation for the three-dimensional object. Although the traditional method can extract the characteristics of the three-dimensional object through the graphical method, it cannot be applied to more complex objects. However, due to the complexity and irregularity of the mesh data, it is difficult to directly apply convolutional neural networks to 3D mesh data processing. Considering this problem, we propose a deep learning method based on a capsule network to effectively classify mesh data. We first design a polynomial convolution template. Through a sliding operation similar to a two-dimensional image convolution window, we directly sample on the grid surface, and use the window sampling surface as the minimum unit of calculation. Because a high-order polynomial can effectively represent a surface, we fit the approximate shape of the surface through the polynomial, use the polynomial parameter as the shape feature of the surface, and add the center point coordinates and normal vector of the surface as the pose feature of the surface. The feature is used as the feature vector of the surface. At the same time, to solve the problem of the introduction of a large number of pooling layers in traditional convolutional neural networks, the capsule network is introduced. For the problem of nonuniform size of the input grid model, the capsule network attitude parameter learning method is improved by sharing the weight of the attitude matrix. The amount of model parameters is reduced, and the training efficiency of the 3D mesh model is further improved. The experiment is compared with the traditional method and the latest two methods on the SHREC15 data set. Compared with the MeshNet and MeshCNN, the average recognition accuracy in the original test set is improved by 3.4% and 2.1%, and the average after fusion of features the accuracy reaches 93.8%. At the same time, under the premise of short training time, this method can also achieve considerable recognition results through experimental verification. The three-dimensional mesh classification method proposed in this paper combines the advantages of graphics and deep learning methods, and effectively improves the classification effect of 3D mesh model.

scholarly journals The Storage Period Discrimination of Bolete Mushrooms Based on Deep Learning Methods Combined With Two-Dimensional Correlation Spectroscopy and Integrative Two-Dimensional Correlation Spectroscopy

2021 ◽  
Vol 12 ◽  
Author(s):  
Jian-E Dong ◽  
Ji Zhang ◽  
Tao Li ◽  
Yuan-Zhong Wang
Keyword(s):  
Deep Learning ◽  
External Validation ◽  
Storage Period ◽  
Scientific Basis ◽  
Correlation Spectroscopy ◽  
Data Sets ◽  
Two Dimensional ◽  
Mir Spectroscopy ◽  
Market Management ◽  
Spectral Data Analysis

Boletes are favored by consumers because of their delicious taste and high nutritional value. However, as the storage period increases, their fruiting bodies will grow microorganisms and produce substances harmful to the human body. Therefore, we need to identify the storage period of boletes to ensure their quality. In this article, two-dimensional correlation spectroscopy (2DCOS) images are directly used for deep learning modeling, and the complex spectral data analysis process is transformed into a simple digital image processing problem. We collected 2,018 samples of boletes. After laboratory cleaning, drying, grinding, and tablet compression, their Fourier transform mid-infrared (FT-MIR) spectroscopy data were obtained. Then, we acquired 18,162 spectral images belonging to nine datasets which are synchronous 2DCOS, asynchronous 2DCOS, and integrative 2DCOS (i2DCOS) spectra of 1,750–400, 1,450–1,000, and 1,150–1,000 cm–1 bands. For these data sets, we established nine deep residual convolutional neural network (ResNet) models to identify the storage period of boletes. The result shows that the accuracy with the train set, test set, and external validation set of the synchronous 2DCOS model on the 1,750–400-cm–1 band is 100%, and the loss value is close to zero, so this model is the best. The synchronous 2DCOS model on the 1,150–1,000-cm–1 band comes next, and these two models have high accuracy and generalization ability which can be used to identify the storage period of boletes. The results have certain practical application value and provide a scientific basis for the quality control and market management of bolete mushrooms. In conclusion, our method is novel and extends the application of deep learning in the food field. At the same time, it can be applied to other fields such as agriculture and herbal medicine.

scholarly journals Siamese Reconstruction Network: Accurate Image Reconstruction from Human Brain Activity by Learning to Compare

2019 ◽  
Vol 9 (22) ◽  
pp. 4749
Author(s):  
Lingyun Jiang ◽  
Kai Qiao ◽  
Linyuan Wang ◽  
Chi Zhang ◽  
Jian Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Brain ◽  
Brain Activity ◽  
Feature Space ◽  
Training Data ◽  
Reconstruction Method ◽  
Learning Method ◽  
Training Samples ◽  
Visual Reconstruction ◽  
Relationship Of

Decoding human brain activities, especially reconstructing human visual stimuli via functional magnetic resonance imaging (fMRI), has gained increasing attention in recent years. However, the high dimensionality and small quantity of fMRI data impose restrictions on satisfactory reconstruction, especially for the reconstruction method with deep learning requiring huge amounts of labelled samples. When compared with the deep learning method, humans can recognize a new image because our human visual system is naturally capable of extracting features from any object and comparing them. Inspired by this visual mechanism, we introduced the mechanism of comparison into deep learning method to realize better visual reconstruction by making full use of each sample and the relationship of the sample pair by learning to compare. In this way, we proposed a Siamese reconstruction network (SRN) method. By using the SRN, we improved upon the satisfying results on two fMRI recording datasets, providing 72.5% accuracy on the digit dataset and 44.6% accuracy on the character dataset. Essentially, this manner can increase the training data about from n samples to 2n sample pairs, which takes full advantage of the limited quantity of training samples. The SRN learns to converge sample pairs of the same class or disperse sample pairs of different class in feature space.

A Deep Learning Method for Frame Selection in Videos for Structure from Motion Pipelines

2021 ◽  
Author(s):  
Francesco Banterle ◽  
Rui Gong ◽  
Massimiliano Corsini ◽  
Fabio Ganovelli ◽  
Luc Van Gool ◽  
...  
Keyword(s):  
Deep Learning ◽  
Structure From Motion ◽  
Learning Method ◽  
Frame Selection

scholarly journals Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4595
Author(s):  
Parisa Asadi ◽  
Lauren E. Beckingham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ct Images ◽  
Ct Imaging ◽  
Learning Method ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

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