scholarly journals Ultrasonic Touch Sensing System Based on Lamb Waves and Convolutional Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2619
Author(s):  
Cheng-Shen Chang ◽  
Yung-Chun Lee
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Acoustic Waves ◽  
Steel Plate ◽  
Lamb Waves ◽  
Waveform Data ◽  
Position Sensing ◽  
Sensing System ◽  
Human Finger ◽  
And Control

A tactile position sensing system based on the sensing of acoustic waves and analyzing with artificial intelligence is proposed. The system comprises a thin steel plate with multiple piezoelectric transducers attached to the underside, to excite and detect Lamb waves (or plate waves). A data acquisition and control system synchronizes the wave excitation and detection and records the transducer signals. When the steel plate is touched by a finger, the waveform signals are perturbed by wave absorption and diffraction effects, and the corresponding changes in the output signal waveforms are sent to a convolutional neural network (CNN) model to predict the x- and y-coordinates of the finger contact position on the sensing surface. The CNN model is trained by using the experimental waveform data collected using an artificial finger carried by a three-axis motorized stage. The trained model is then used in a series of tactile sensing experiments performed using a human finger. The experimental results show that the proposed touch sensing system has an accuracy of more than 95%, a spatial resolution of 1 × 1 cm2, and a response time of 60 ms.

scholarly journals Algorithm of Strawberry Disease Recognition Based on Deep Convolutional Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Li Ma ◽  
Xueliang Guo ◽  
Shuke Zhao ◽  
Doudou Yin ◽  
Yiyi Fu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Abiotic Factors ◽  
Convolution Neural Network ◽  
Image Feature ◽  
Identification Methods ◽  
Yield And Quality ◽  
Disease Identification ◽  
Deep Convolution Neural Network ◽  
And Control

The growth of strawberry will be stressed by biological or abiotic factors, which will cause a great threat to the yield and quality of strawberry, in which various strawberry diseased. However, the traditional identification methods have high misjudgment rate and poor real-time performance. In today's era of increasing demand for strawberry yield and quality, it is obvious that the traditional strawberry disease identification methods mainly rely on personal experience and naked eye observation and cannot meet the needs of people for strawberry disease identification and control. Therefore, it is necessary to find a more effective method to identify strawberry diseases efficiently and provide corresponding disease description and control methods. In this paper, based on the deep convolution neural network technology, the recognition of strawberry common diseases was studied, as well as a new method based on deep convolution neural network (DCNN) strawberry disease recognition algorithm, through the normal training of strawberry image feature representation in different scenes, and then through the application of transfer learning method, the strawberry disease image features are added to the training set, and finally the features are classified and recognized to achieve the goal of disease recognition. Moreover, attention mechanism and central damage function are introduced into the classical convolutional neural network to solve the problem that the information loss of key feature areas in the existing classification methods of convolutional neural network affects the classification effect, and further improves the accuracy of convolutional neural network in image classification.

scholarly journals Convolutional Neural Network based Estimation of Gel-like Food Texture by a Robotic Sensing System

Robotics ◽  
2017 ◽  
Vol 6 (4) ◽  
pp. 37 ◽  
Author(s):  
Akihide Shibata ◽  
Akira Ikegami ◽  
Makoto Nakauma ◽  
Mitsuru Higashimori
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sensing System ◽  
Food Texture ◽  
Robotic Sensing

scholarly journals Multilabel Image Classification Based Fresh Concrete Mix Proportion Monitoring Using Improved Convolutional Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4638
Author(s):  
Han Yang ◽  
Shuang-Jian Jiao ◽  
Feng-De Yin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Real Time ◽  
Fresh Concrete ◽  
Full Scale ◽  
Classification Problems ◽  
Sensing System ◽  
Mix Proportion ◽  
Concrete Mix

Proper and accurate mix proportion is deemed to be crucial for the concrete in service to implement its structural functions in a specific environment and structure. Neither existing testing methods nor previous studies have, to date, addressed the problem of real-time and full-scale monitoring of fresh concrete mix proportion during manufacturing. Green manufacturing and safety construction are hindered by such defects. In this study, a state-of-the-art method based on improved convolutional neural network multilabel image classification is presented for mix proportion monitoring. Elaborately planned, uniformly distributed, widely covered and high-quality images of concrete mixtures were collected as dataset during experiments. Four convolutional neural networks were improved or fine-tuned based on two solutions for multilabel image classification problems, since original networks are tailored for single-label multiclassification tasks, but mix proportions are determined by multiple parameters. Various metrices for effectiveness evaluation of training and testing all indicated that four improved network models showed outstanding learning and generalization ability during training and testing. The best-performing one was embedded into executable application and equipped with hardware facilities to establish fresh concrete mix proportion monitoring system. Such system was deployed to terminals and united with mechanical and weighing sensors to establish integrated intelligent sensing system. Fresh concrete mix proportion real-time and full-scale monitoring and inaccurate mix proportion sensing and warning could be achieved simply by taking pictures and feeding pictures into such sensing system instead of conducting experiments in laboratory after specimen retention.

scholarly journals Image processing of transport objects using neural networks

2020 ◽  
Vol 164 ◽  
pp. 03036
Author(s):  
Daniil Loktev ◽  
Olga Lokteva
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Back Propagation ◽  
Transport Infrastructure ◽  
Automated System ◽  
Railway Track ◽  
Rolling Stock ◽  
Monitoring And Control ◽  
And Control

The paper is devoted to the development of an automated system model for monitoring and control of transport objects, based on the processing of images obtained using photo or video detectors, which can be installed on a fixed base near the transport highway for monitoring traffic flows and individual vehicles, and on rolling stock for monitoring transport infrastructure facilities. Image processing occurs by determining the function of blurring the image of an object, algorithms for extracting an image of an object using cascading classifiers and characteristic points, depending on the behavior of the object itself, as well as using a convolutional neural network. Machine learning of the convolutional neural network occurs when using the back propagation method of error. A neural network allows detecting objects of certain classes in the image, determining the parameters of their state and behavior. The proposed model with a movable hardware, which is responsible for obtaining the primary image, was tested on a section of the railway track to identify deviations of the state of the superstructure from the content standards, and a system with stationary photodetectors was tested to determine the parameters of moving vehicles. The obtained results of processing experimental data allowed drawing qualitative conclusions about the possibility of using the proposed algorithms and schemes for monitoring and control of various transport objects.

scholarly journals Text classification using convolutional neural network

2019 ◽  
pp. 219-226
Author(s):  
L E Sapozhnikova ◽  
O A Gordeeva
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Text Classification ◽  
Classification Accuracy ◽  
Convolutional Network ◽  
The Neural Network ◽  
Internet Information ◽  
And Control ◽  
Classification Quality ◽  
Control Samples

In this article, the method of text classification using a convolutional neural network is presented. The problem of text classification is formulated, the architecture and the parameters of a convolutional neural network for solving the problem are described, the steps of the solution and the results of classification are given. The convolutional network which was used was trained to classify the texts of the news messages of Internet information portals. The semantic preprocessing of the text and the translation of words into attribute vectors are generated using the open word2vec model. The analysis of the dependence of the classification quality on the parameters of the neural network is presented. The using of the network allowed obtaining a classification accuracy of about 84%. In the estimation of the accuracy of the classification, the texts were checked to belong to the group of semantically similar classes. This approach allowed analyzing news messages in cases where the text themes and the number of classification classes in the training and control samples do not equal.

scholarly journals Rapid prediction of earthquake ground shaking intensity using raw waveform data and a convolutional neural network

2020 ◽  
Vol 222 (2) ◽  
pp. 1379-1389
Author(s):  
Dario Jozinović ◽  
Anthony Lomax ◽  
Ivan Štajduhar ◽  
Alberto Michelini
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Central Italy ◽  
Error Variance ◽  
Data Streaming ◽  
Time Data ◽  
Origin Time ◽  
Waveform Data ◽  
Ground Shaking ◽  
Magnitude Comparison

SUMMARY This study describes a deep convolutional neural network (CNN) based technique to predict intensity measurements (IMs) of earthquake ground shaking. The input data to the CNN model consists of multistation, 3C acceleration waveforms recorded during the 2016 Central Italy earthquake sequence for M ≥ 3.0 events. Using a 10 s window starting at the earthquake origin time, we find that the CNN is capable of accurately predicting IMs at stations far from the epicentre which have not yet recorded the maximum ground shaking. The CNN IM predictions do not require previous knowledge of the earthquake source (location and magnitude). Comparison between the CNN model predictions and those obtained with the Bindi et al. GMPE (which requires location and magnitude) shows that the CNN model features similar error variance but smaller bias. Although the technique is not strictly designed for earthquake early warning, we find that it can provide useful estimates of ground motions within 15–20 s after earthquake origin time depending on various setup elements (e.g. times for data transmission, computation, latencies). The technique has been tested on raw data without any initial data pre-selection in order to closely replicate real-time data streaming. When noise examples were included with the earthquake data the CNN was found to be stable, accurately predicting the ground shaking intensity corresponding to the noise amplitude.

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