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      Simulation of Transmission System of Crawler Self-propelled Rotary Tiller Based on Deep Learning

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      1 , 2 , , 3
      Computational Intelligence and Neuroscience
      Hindawi

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          Abstract

          Because of its good performance, crawler-type running gear plays a very important role in the fields of modern agriculture. This article aims to study the construction of the drive system of the crawler self-propelled rotary tiller with the deep learning network and carry out the system simulation experiment. In this article, deep learning-related algorithms, auto-encoding networks, convolutional neural networks, and structural design of crawler self-propelled rotary tillers are proposed. It then used the self-developed crawler-type rotary tiller and straw paddle machine to compare the field operation performance with the combination of ordinary wheeled tractors and rotary tillers. The experimental results show that the tillage performance indicators such as the working depth, tillage depth stability, ground flatness, stubble pressing depth, and vegetation coverage qualification rate of the “crawler self-propelled tractor + straw stubble pulper” are better than those of “wheel tractor + ordinary rotary tiller” and “crawler tractor + ordinary rotary tiller,” increased by 9.92% and 4.88%, 4.31% and 4.13%, 42.59% and 19.12%, 40.15% and 34.57%, and 13.04% and 7.16%, respectively. The mechanical transplanting index was significantly better than other treatments. The yield increase effect of the field test is remarkable, with the average yield increase rate of 9.63% and 4.57%, which is suitable for popularization and application in the southern double-cropping rice area.

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          Most cited references28

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          A survey on deep learning in medical image analysis

          Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.
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            Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

            The implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. Here, we establish a diagnostic tool based on a deep-learning framework for the screening of patients with common treatable blinding retinal diseases. Our framework utilizes transfer learning, which trains a neural network with a fraction of the data of conventional approaches. Applying this approach to a dataset of optical coherence tomography images, we demonstrate performance comparable to that of human experts in classifying age-related macular degeneration and diabetic macular edema. We also provide a more transparent and interpretable diagnosis by highlighting the regions recognized by the neural network. We further demonstrate the general applicability of our AI system for diagnosis of pediatric pneumonia using chest X-ray images. This tool may ultimately aid in expediting the diagnosis and referral of these treatable conditions, thereby facilitating earlier treatment, resulting in improved clinical outcomes. VIDEO ABSTRACT.
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              Deep learning with convolutional neural networks for EEG decoding and visualization

              Abstract Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end‐to‐end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end‐to‐end EEG analysis, but a better understanding of how to design and train ConvNets for end‐to‐end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task‐related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG‐based brain mapping. Hum Brain Mapp 38:5391–5420, 2017. © 2017 Wiley Periodicals, Inc.
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                Author and article information

                Contributors
                Journal
                Comput Intell Neurosci
                Comput Intell Neurosci
                cin
                Computational Intelligence and Neuroscience
                Hindawi
                1687-5265
                1687-5273
                2022
                30 July 2022
                : 2022
                : 6078223
                Affiliations
                1Institute of Agricultural Engineering, Jiangxi Academy of Agricultural Sciences, Nanchang 330200, Jiangxi, China
                2Jiangxi Intelligent Agricultural Machinery Equipment Engineering Research Center, Nanchang 330200, Jiangxi, China
                3Department of Electronic Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
                Author notes

                Academic Editor: Rahim Khan

                Author information
                https://orcid.org/0000-0001-8464-9971
                Article
                10.1155/2022/6078223
                9356833
                35942446
                244d274a-b04a-4c88-9c86-7e45662d0138
                Copyright © 2022 Luofa Wu and Yanqi Wu.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 18 April 2022
                : 25 May 2022
                : 9 June 2022
                Funding
                Funded by: Ministry of Agriculture and Rural Areas
                Categories
                Research Article

                Neurosciences
                Neurosciences

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