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NOISE IDENTIFICATION FOR HYDRAULIC AXIAL PISTON PUMP BASED ON ARTIFICIAL NEURAL NETWORKS 被引量:1
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作者 YANG Jian XU Bing YANG Huayong 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2006年第1期120-123,共4页
The noise identification model of the neural networks is established for the 63SCY14 IB hydraulic axial piston pump. Taking four kinds of different port plates as instances, the noise identification is successfully ca... The noise identification model of the neural networks is established for the 63SCY14 IB hydraulic axial piston pump. Taking four kinds of different port plates as instances, the noise identification is successfully carried out for hydraulic axial piston pump based on experiments with the MATLAB and the toolbox of neural networks, The operating pressure, the flow rate of hydraulic axial piston pump, the temperature of hydraulic oil, and bulk modulus of hydraulic oil are the main parameters having influences on the noise of hydraulic axial piston pump. These four parameters are used as inputs of neural networks, and experimental data of the noise are used as outputs of neural networks, Error of noise identification is less than 1% after the neural networks have been trained. The results show that the noise identification of hydraulic axial piston pump is feasible and reliable by using artificial neural networks. The method of noise identification with neural networks is also creative one of noise theoretical research for hydraulic axial piston pump. 展开更多
关键词 Hydraulic axial piston pump Neural networks noise Identification MATLAB
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The dynamic relaxation form finding method aided with advanced recurrent neural network 被引量:1
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作者 Liming Zhao Zhongbo Sun +1 位作者 Keping Liu Jiliang Zhang 《CAAI Transactions on Intelligence Technology》 SCIE EI 2023年第3期635-644,共10页
How to establish a self‐equilibrium configuration is vital for further kinematics and dynamics analyses of tensegrity mechanism.In this study,for investigating tensegrity form‐finding problems,a concise and efficien... How to establish a self‐equilibrium configuration is vital for further kinematics and dynamics analyses of tensegrity mechanism.In this study,for investigating tensegrity form‐finding problems,a concise and efficient dynamic relaxation‐noise tolerant zeroing neural network(DR‐NTZNN)form‐finding algorithm is established through analysing the physical properties of tensegrity structures.In addition,the non‐linear constrained opti-misation problem which transformed from the form‐finding problem is solved by a sequential quadratic programming algorithm.Moreover,the noise may produce in the form‐finding process that includes the round‐off errors which are brought by the approximate matrix and restart point calculating course,disturbance caused by external force and manufacturing error when constructing a tensegrity structure.Hence,for the purpose of suppressing the noise,a noise tolerant zeroing neural network is presented to solve the search direction,which can endow the anti‐noise capability to the form‐finding model and enhance the calculation capability.Besides,the dynamic relaxation method is contributed to seek the nodal coordinates rapidly when the search direction is acquired.The numerical results show the form‐finding model has a huge capability for high‐dimensional free form cable‐strut mechanisms with complicated topology.Eventually,comparing with other existing form‐finding methods,the contrast simulations reveal the excellent anti‐noise performance and calculation capacity of DR‐NTZNN form‐finding algorithm. 展开更多
关键词 dynamic relaxation form‐finding noise‐tolerant zeroing neural network sequential quadratic programming TENSEGRITY
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Research on Restoration of Murals Based on Diffusion Model and Transformer
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作者 Yaoyao Wang Mansheng Xiao +2 位作者 Yuqing Hu Jin Yan Zeyu Zhu 《Computers, Materials & Continua》 SCIE EI 2024年第9期4433-4449,共17页
Due to the limitations of a priori knowledge and convolution operation,the existing image restoration techniques cannot be directly applied to the cultural relics mural restoration,in order to more accurately restore ... Due to the limitations of a priori knowledge and convolution operation,the existing image restoration techniques cannot be directly applied to the cultural relics mural restoration,in order to more accurately restore the original appearance of the cultural relics mural images,an image restoration based on the denoising diffusion probability model(Denoising Diffusion Probability Model(DDPM))and the Transformer method.The process involves two steps:in the first step,the damaged mural image is firstly utilized as the condition to generate the noise image,using the time,condition and noise image patch as the inputs to the noise prediction network,capturing the global dependencies in the input sequence through the multi-attentionmechanismof the input sequence and feedforward neural network processing,and designing a long skip connection between the shallow and deep layers in the Transformer blocks between the shallow and deep layers using long skip connections to fuse the feature information of global and local outputs to maintain the overall consistency of the restoration results;In the second step,taking the noisy image as a condition to direct the diffusion model to back sample to generate the restored image.Experiment results show that the PSNR and SSIM of the proposedmethod are improved by 2%to 9%and 1%to 3.3%,respectively,which are compared to the comparison methods.This study proposed synthesizes the advantages of the diffusionmodel and deep learningmodel to make themural restoration results more accurate. 展开更多
关键词 TRANSFORMER deep learning noise estimation network diffusion model mural restoration
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Multiple Data Augmentation Strategy for Enhancing the Performance of YOLOv7 Object Detection Algorithm 被引量:2
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作者 Abdulghani M.Abdulghani Mokhles M.Abdulghani +1 位作者 Wilbur L.Walters Khalid H.Abed 《Journal on Artificial Intelligence》 2023年第1期15-30,共16页
The object detection technique depends on various methods for duplicating the dataset without adding more images.Data augmentation is a popularmethod that assists deep neural networks in achieving better generalizatio... The object detection technique depends on various methods for duplicating the dataset without adding more images.Data augmentation is a popularmethod that assists deep neural networks in achieving better generalization performance and can be seen as a type of implicit regularization.Thismethod is recommended in the casewhere the amount of high-quality data is limited,and gaining new examples is costly and time-consuming.In this paper,we trained YOLOv7 with a dataset that is part of the Open Images dataset that has 8,600 images with four classes(Car,Bus,Motorcycle,and Person).We used five different data augmentations techniques for duplicates and improvement of our dataset.The performance of the object detection algorithm was compared when using the proposed augmented dataset with a combination of two and three types of data augmentation with the result of the original data.The evaluation result for the augmented data gives a promising result for every object,and every kind of data augmentation gives a different improvement.The mAP@.5 of all classes was 76%,and F1-score was 74%.The proposed method increased the mAP@.5 value by+13%and F1-score by+10%for all objects. 展开更多
关键词 Artificial intelligence object detection YOLOv7 data augmentation data brightness data darkness data blur data noise convolutional neural network
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Image Denoising Using Dual Convolutional Neural Network with Skip Connection
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作者 Mengnan Lü Xianchun Zhou +2 位作者 Zhiting Du Yuze Chen Binxin Tang 《Instrumentation》 2024年第3期74-85,共12页
In recent years, deep convolutional neural networks have shown superior performance in image denoising. However, deep network structures often come with a large number of model parameters, leading to high training cos... In recent years, deep convolutional neural networks have shown superior performance in image denoising. However, deep network structures often come with a large number of model parameters, leading to high training costs and long inference times, limiting their practical application in denoising tasks. This paper proposes a new dual convolutional denoising network with skip connections(DECDNet), which achieves an ideal balance between denoising effect and network complexity. The proposed DECDNet consists of a noise estimation network, a multi-scale feature extraction network, a dual convolutional neural network, and dual attention mechanisms. The noise estimation network is used to estimate the noise level map, and the multi-scale feature extraction network is combined to improve the model's flexibility in obtaining image features. The dual convolutional neural network branch design includes convolution and dilated convolution interactive connections, with the lower branch consisting of dilated convolution layers, and both branches using skip connections. Experiments show that compared with other models, the proposed DECDNet achieves superior PSNR and SSIM values at all compared noise levels, especially at higher noise levels, showing robustness to images with higher noise levels. It also demonstrates better visual effects, maintaining a balance between denoising and detail preservation. 展开更多
关键词 image denoising convolutional neural network skip connections multi-scale feature extraction network noise estimation network
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A PHEMT Based Wideband LNA for Wireless Applications 被引量:2
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作者 Muhammad Saad Khan ZHANG Hongxin +2 位作者 HE Pengfei Sulman Shahzad Rahat Ullah 《China Communications》 SCIE CSCD 2015年第10期108-116,共9页
This work is about the development of a super low noise amplifier with minimum power consumption and high gain for several wireless applications.The amplifier operates at frequency bands of 0.9-2.4 GHz and can be used... This work is about the development of a super low noise amplifier with minimum power consumption and high gain for several wireless applications.The amplifier operates at frequency bands of 0.9-2.4 GHz and can be used in many applications like Wireless local area network(WLAN),WiFi,Bluetooth,ZigBee and Global System for mobile communications(GSM).This new design can be employed for the IEEE 802.15.4 standard in industrial,scientific and medical(ISM) Band.The enhancement mode pseudomorphic high electron mobility transistor PHEMT is used here due to its high linearity,better performance and less noisy operation.The common source inductive degeneration method is employed here to enhance the gain of amplifier.The amplifier produces a gain of more than 17 dB and noise figure of about 0.5 dB.The lower values of S11 and S22 reflect the accuracy of impedance matching network placed at the input and output sides of amplifier.Agilent Advance Design System(ADS) is used for the design and simulation purpose.Further the layout of design is developed on the FR4 substrate. 展开更多
关键词 low noise amplifier phemt advanced design system wireless local area network global positioning system
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Beat Noise Limitation in Coherent Time-Spreading OCDMA Network
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作者 Ken-ichi Kitayama Koji Mutsushima 《光学学报》 EI CAS CSCD 北大核心 2003年第S1期727-728,共2页
The BER performance of the coherent time-spreading OCDMA network is analyzed by considering the MAI and beat noises as well as the other additive noises. The influence and solution for the beat noise issue are discussed.
关键词 OCDMA on it Beat noise Limitation in Coherent Time-Spreading OCDMA network in
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The design of a low-noise preamplifier for MRI
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作者 CAO XueMing ZU DongLin +2 位作者 ZHAO XuNa FAN Yang GAO JiaHong 《Science China(Technological Sciences)》 SCIE EI CAS 2011年第7期1766-1770,共5页
In this paper, a low-noise preamplifier for MRI is designed and studied. A noise matching network consisting of three elements is presented. To the single-stage AsGa-FET preamplifier working at 128 MHz, the measured g... In this paper, a low-noise preamplifier for MRI is designed and studied. A noise matching network consisting of three elements is presented. To the single-stage AsGa-FET preamplifier working at 128 MHz, the measured gain through network analyzer (HP8712C) and noise figure through noise figure analyzer (8970B) are 25 and 0.43 dB, respectively. 展开更多
关键词 MRI noise figure PREAMPLIFIER noise matching network GAIN source impedance
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