一种基于BP神经网络的四轴飞行器飞控优化方法
作者：
龚赛君;徐倩;周奶升;刘宝媛;崔治
期刊：
科技资讯 ,2019年17(13):11-12 ISSN：1672-3791
作者机构：
湖南城市学院信息与电子工程学院 湖南益阳 413000;湖南新型智慧城市研究院 湖南益阳 413000;[龚赛君; 周奶升; 徐倩; 崔治; 刘宝媛] 湖南城市学院
关键词：
串级PID;BP神经网络
摘要：
针对四轴飞行器串级PID控制器参数整定繁琐的问题,提出在串级PID控制器中加入BP神经网络的优化方法。利用Simulimk对加入BP神经网络前后的飞控系统进行仿真。结果表明,加入神经网络后飞控系统在超调量、响应时间和稳定性等方面都有了明显的提高。
语种：
中文
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基于STM32的四轴飞行器设计与实现
作者：
刘宝媛;龚赛君;崔治
期刊：
电子测试 ,2019年(19):22-23+37 ISSN：1000-8519
作者机构：
湖南城市学院信息与电子工程学院,湖南益阳,413000;[龚赛君; 崔治; 刘宝媛] 湖南城市学院
关键词：
四轴飞行器;避障;循迹
摘要：
本文设计了一种基于STM32F103RC单片机的四轴飞行器。该系统由核心控制模块、姿态传感器模块、超声波模块、摄像头模块等组成。姿态传感器模块、超声波模块和摄像头模块将检测到的姿态数据经过滤波和转化成四元数等操作后,通过串口传给核心控制模块里的PID控制器,最后输出PWM波给驱动模块。此外,采用超声波模块实现一键定高和避障,采用OpenMV摄像头模块用于循迹。测试结果表明,该系统实现了预期的设计功能。
语种：
中文
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地方高校电子类专业校企合作长效机制研究初探
作者：
崔治;肖卫初;王奕翔
期刊：
文教资料(高中版) ,2017年(15):135-136 ISSN：1004-8359
作者机构：
湖南城市学院 信息与电子工程学院,湖南 益阳,413000;[肖卫初; 崔治; 王奕翔] 湖南城市学院
关键词：
地方高校;校企合作;长效机制
摘要：
以政校行企深度融合为出发点，以构建科学系统的地方高校电子类专业校企合作长效机制为最终目标．综合考虑政、校、行、企各方诉求设计合理的政策保障机制、资源共享机制、利益分配机制，最终建立科学合理的地方高校电子类专业校企合作长效机制模型，从而拓展地方高校电子类专业人才培养途径，为培养适应地方经济发展需求的高素质人才服务。
语种：
中文
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Selection of optimal decomposition layer for thresholding denoising using singular spectrum analysis and wavelet entropy
作者：
Cui, Zhi;Cui, Xian-Pu
期刊：
International Journal of Multimedia and Ubiquitous Engineering ,2016年11(4):373-380 ISSN：1975-0080
作者机构：
[Cui, Zhi; Cui, Xian-Pu] School of Communication and Electronic Engineering, Hunan City University, Yiyang, China
关键词：
Denoising;Optimal decomposition layer;Singular spectrum analysis;Wavelet entropy
摘要：
To optimize the number of decomposition layers in wavelet threshold denoising for ultrasonic signals, we propose a self-adaptive algorithm of determining the number of decomposition layers based on singular spectrum analysis and wavelet entropy. First the noise-containing signals are decomposed by discrete wavelet transform. The slope of the singular value spectrum for each layer is estimated. Then the wavelet entropy over the signal subinterval is calculated for each layer. Finally the optimal number of decomposition layer is determined by combining the entropy ratio of detail coefficients to original signal and the slope of the singular value spectrum. The performance of the algorithm is evaluated using signal-to-noise ratio (SNR) and the relative error of the peak value (REPV). Experiment shows that the algorithm can self-adaptively determine the optimal number of decomposition layers and filter out the noise contained in the ultrasonic signals. It not only increases the SNR, but also preserves valuable components of the original signal. ©2016 SERSC.
语种：
英文
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Ultrasonic signal de-noising based on wavelet entropy and inter-scale correlation
作者：
Cui, Zhi;Cui, Xianpu
期刊：
International Journal of Multimedia and Ubiquitous Engineering ,2016年11(1):135-146 ISSN：1975-0080
作者机构：
[Cui, Xianpu; Cui, Zhi] School of Communication and Electronic Engineering, Hunan City University, China
关键词：
Correlation;De-noising;Ultrasonic signal;Wavelet entropy
摘要：
In this paper, we proposed an adaptive threshold de-noising method by combining wavelet entropy and inter-scale correlation. Different from the traditional wavelet threshold based de-noising methods, our method can be divided into three steps. First, we decompose the noisy signal by using discrete wavelet transform (DWT), calculate the value of inter-scale correlation of the decomposed wavelet coefficients, and delete the high frequency coefficients which are smaller than the value of inter-scale correlation. Secondly, we equally divide the processed high frequency coefficients into several subintervals, calculate the wavelet entropy of each subinterval, and decide the threshold of high frequency coefficients by combining wavelet entropy and adaptive threshold rules. Finally, we de-noise the high frequency coefficients by using logarithmic smoothing threshold function, and reconstruct the ultrasonic signal. Experiment results have shown that the proposed method is better than some other de-noising methods in terms of SNR (signal noise ratio) and SDR (signal distortion ration). ©2016 SERSC.
语种：
英文
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Sparsity based denoising of PET-CT images
作者：
Cui, Zhi;Cui, Xian-Pu
期刊：
International Journal of Multimedia and Ubiquitous Engineering ,2016年11(2):371-380 ISSN：1975-0080
通讯作者：
Cui, Zhi(zhicui@yeah.net)
作者机构：
[Cui, Zhi; Cui, Xian-Pu] Schoolof Communication and Electronic Engineering, Hunan City University, China
通讯机构：
Schoolof Communication and Electronic Engineering, Hunan City University, China
关键词：
Atom substitution;Detail compensation;Image denoising;Sparse representation
摘要：
In this paper, we propose an improved method for the removal of additive Gussian white noise from PET-CT images. Different from the traditional sparse representation based denoising methods, our method is composed of two distinctively steps such as the preliminary denoise and the detail compensation. By constructing a sparse representation model, denoising is formulated as an optimization problem that can be solved on an over-complete dictionary. The proposed method effectively trains this dictionary by using K-SVD algorithm with atom replace model. Then the preliminary denoised image is reconstructed through improved OMP algorithm with the fidelity factor of SSIM (Structural Similarity). The detail compensation image is obtained by using the difference between the noisy image and the preliminary de-noised image, and the improved OMP algorithm is employed again to get the denoised detail compensation image. Finally, the final denoised image is reconstructed by adding the denoised detail compensation image to the preliminary denoised image. Experiment results have shown that the proposed method is better than some other denoising methods in terms of PSNR and SSIM. ©2016 SERSC.
语种：
英文
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Wireless multimedia sensor network image de-noising via a detail-preserving sparse model
作者：
Cui, Zhi;Cui, Xian-Pu
期刊：
Cybernetics and Information Technologies ,2015年15(6):57-69 ISSN：1311-9702
作者机构：
[Cui, Zhi; Cui, Xian-Pu] School of Communication and Electronic Engineering, Hunan City University, Yiyang, China
关键词：
De-noising;Residual ratio;Sparse representation;Structural similarity;Wireless multimedia sensor network
摘要：
In this paper, we propose a Detail-Preserving Sparse Model (DPSM) for de-noising of images that are usually interfered by noise on the Wireless Multimedia Sensor Network (WMSN). Specifically, based on the Structural SIMilarity (SSIM), the DPSM first incorporates a structural-preserving constraint, which enables the structure in the reconstructed image to be close to the ideal nonoise image. In addition, the DPSM adopts a residual ratio as the stopping condition of the sparse solution algorithm (e.g., Orthogonal Matching Pursuit), which enables the structures to be reconstructed under high noise conditions. The experimental results on several WMSN images have demonstrated the superiority of the proposed DPSM method over several well-known de-noising approaches in terms of PSNR and SSIM.
语种：
英文
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An improved sparse representation model for robust image denoising
作者：
Cui, Zhi* ;Cui, Xianpu
期刊：
Chemical Engineering Transactions ,2015年46:175-180 ISSN：2283-9216
通讯作者：
Cui, Zhi
作者机构：
[Cui, Zhi; Cui, Xianpu] Hunan City Univ, Sch Commun & Elect Engn, Yiyang 413000, Peoples R China.
通讯机构：
[Cui, Zhi] H;Hunan City Univ, Sch Commun & Elect Engn, Yiyang 413000, Peoples R China.
会议名称：
International Conference on Applied Engineering and Management
会议时间：
SEP 11-14, 2015
会议地点：
Beijing, PEOPLES R CHINA
会议主办单位：
[Cui, Zhi;Cui, Xianpu] Hunan City Univ, Sch Commun & Elect Engn, Yiyang 413000, Peoples R China.
会议论文集名称：
Chemical Engineering Transactions
摘要：
Though the sparse representation has demonstrated to be a very effective tool to de-noise the images with low levels of noise, it usually losses the power to well preserve structural features in images with high levels of noise. In this paper, we propose an improved de-noising method for images with low signal- to noise ratio. Specifically, the proposed method takes the histogram structural similarity (HSSIM) as similarity factor to replace the reconstruction error as the new fidelity term, and finds the most appropriate sparse coefficients by using the modified orthogonal matching pursuit (OMP) algorithm which enables structures in the reconstructed image run as close as possible to the ideal image. In addition, the proposed method adaptively trains the initialized dictionary by using the K-singular value decomposition (K-SVD) algorithm based on HSSIM to assure the image structures can be well reconstructed under the high noise circumstance. Experiment results have shown that the proposed method is better than some well-known de-noising methods in terms of PSNR and edge-preserved index (EPI) in high noise condition. Copyright © 2015, AIDIC Servizi S.r.l.
语种：
英文
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Remote sensing image denoising via sparse representation with inter-scale correlation model
作者：
Cui, Zhi;Cui, Xian-Pu
期刊：
International Journal of Earth Sciences and Engineering ,2015年8(6):2809-2816 ISSN：0974-5904
作者机构：
[Cui, Zhi; Cui, Xian-Pu] School of Communication and Electronic Engineering, Hunan City University, Yiyang, 413000, China
关键词：
Denoising;Inter-scale correlation model;Remote sensing image;Sparse representation
摘要：
In this paper, we propose a denoising algorithm for remote sensing images based on sparse representation and inter-scale correlation model. First discrete wavelet transform is used to decompose the remote sensing images. The low-frequency coefficients are analyzed by training the over-complete dictionary with K-SVD algorithm, and the high-frequency coefficients by correlation analysis. The correlation quantity of high-frequency coefficients at each scale is calculated and compared with the high-frequency coefficients at the corresponding position. Thus the optimal high-frequency coefficient is chosen to ensure the accuracy in the thresholding of wavelet coefficients. Finally, the denoised image is reconstructed by using inverse wavelet transform. Experiments show that compared with some other well-known methods, the proposed algorithm is more applicable and can achieve higher PSNR and better visual effect and denoising effect. ©2015 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.
语种：
英文
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An improved sparse representation de-noising for keeping structural features
作者：
Cui, Zhi*
期刊：
Communications in Computer and Information Science ,2014年483:253-262 ISSN：1865-0929
通讯作者：
Cui, Zhi
作者机构：
[Cui, Zhi] Hunan City Univ, Sch Commun & Elect Engn, Yiyang 413000, Hunan, Peoples R China.
通讯机构：
[Cui, Zhi] H;Hunan City Univ, Sch Commun & Elect Engn, Yiyang 413000, Hunan, Peoples R China.
会议名称：
Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)
会议时间：
2014-11-01
会议地点：
长沙
会议主办单位：
[Cui, Zhi] Hunan City Univ, Sch Commun & Elect Engn, Yiyang 413000, Hunan, Peoples R China.
会议论文集名称：
Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)论文集
关键词：
Structural feature;Similarity factor;Sparse representation;Image de-noising
摘要：
Considering the current image de-noising methods may lose some structural features, this paper proposes an improved sparse representation based method by adopting the histogram structural similarity. When the initial over-complete dictionary was applied in the sparse decomposition, similarity factor could replace the reconstruction error as the factor of fidelity. The orthogonal matching pursuit algorithm(OMP) is used to reconstruct the denoised image. The experimental results show that the proposed method could provide better PSNR and HSSIM results compared with the wavelet transformation, the K-SVD algorithm and the method presented in [10], meanwhile, and the structural features can be reserved effectively by the proposed method.
语种：
英文
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自动控制原理教学改革探索与实践
作者：
崔治;肖卫初
期刊：
中国电力教育 ,2011年(30):200-201 ISSN：1007-0079
作者机构：
湖南城市学院通信与电子工程学院;湖南大学电气与信息工程学院 湖南 长沙 410000;湖南城市学院通信与电子工程学院信息工程教研室 湖南 益阳 413000
关键词：
自动控制原理;教学改革;教学方法
摘要：
针对自动控制原理课程教学改革的问题,以培养学生现代工程素质为目的,从师资队伍、教材建设、教学内容、教学方法、实验教学、考核方式六个方面进行了研究与探索,叙述了教学改革的成效并做出展望。
语种：
中文
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