Random Weighting-Based Nonlinear Gaussian Filtering

Gao, Zhaohui; Gu, Chengfan; Yang, Jiahui; Gao, Shesheng; Zhong, Yongmin

doi:10.1109/access.2020.2968363

Cited by 19 publications

(8 citation statements)

References 34 publications

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“…To mitigate noise, enhance image quality, and accentuate edge information, a Gaussian filter was applied to the leaf images to achieve image denoising ( Gao et al., 2020 ). The contrast between the denoised and original images is improved ( Figure 4 ).…”

Section: Methodsmentioning

confidence: 99%

Potassium deficiency diagnosis method of apple leaves based on MLR-LDA-SVM

Xu,

Sun,

Wang

et al. 2023

Front. Plant Sci.

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IntroductionAt present, machine learning and image processing technology are widely used in plant disease diagnosis. In order to address the challenges of subjectivity, cost, and timeliness associated with traditional methods of diagnosing potassium deficiency in apple tree leaves. MethodsThe study proposes a model that utilizes image processing technology and machine learning techniques to enhance the accuracy of detection during each growth period. Leaf images were collected at different growth stages and processed through denoising and segmentation. Color and shape features of the leaves were extracted and a multiple regression analysis model was used to screen for key features. Linear discriminant analysis was then employed to optimize the data and obtain the optimal shape and color feature factors of apple tree leaves during each growth period. Various machine-learning methods, including SVM, DT, and KNN, were used for the diagnosis of potassium deficiency. ResultsThe MLR-LDA-SVM model was found to be the optimal model based on comprehensive evaluation indicators. Field experiments were conducted to verify the accuracy of the diagnostic model, achieving high diagnostic accuracy during different growth periods. DiscussionThe model can accurately diagnose whether potassium deficiency exists in apple tree leaves during each growth period. This provides theoretical guidance for intelligent and precise water and fertilizer management in orchards.

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Section: Methodsmentioning

confidence: 99%

Potassium deficiency diagnosis method of apple leaves based on MLR-LDA-SVM

Xu,

Sun,

Wang

et al. 2023

Front. Plant Sci.

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“…Noise reduction in images can be effectively achieved using this filtering method, which is widely used to smooth images and suppress noise. The Gaussian filter selects a template (neighborhood) for each target pixel, and each target pixel is replaced with the weighted average gray value of pixels in the neighborhood [21]. The convolution matrix of the Gaussian filter in the two-dimensional space of 𝑥, 𝑦 is shown below.…”

Section: Gaussian Filteringmentioning

confidence: 99%

An overview of digital image analog noise removal based on traditional filtering

Zhang

2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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Due to interference in the transmission of external equipment, images can suffer from varying noise concentrations. As an immediate and practical step to reduce the impact of noise on an image and to improve its quality and visual presentation, filtering is the best method. The paper examines how traditional filtering methods restore a digital image under salt and pepper and Gaussian noise interference. Also included in this paper is an analysis of the advantages and challenges of traditional noise filtering, as well as suggestions for a filtering method for a variety of noise levels. Lastly, the effects of filtered noise pictures are evaluated through extensive experimental simulations, providing substantial assistance for related research.

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“…Commonly used filtering methods include random filters and Kalman filters (KFs). The random filtering method is based on a two-step Bayesian process, that includes time or measurement updates [67]. The KF assumes uncertainty in the dynamics of the Gaussian distribution system and uses the mean and covariance of the state vector for update adjustments [68].…”

Section: Offline Identificationmentioning

confidence: 99%

“…Ref. [67] used the nonlinear cascade system stability theory to decompose the 3D linear tracking system model into a cascade of two horizontal systems (horizontal tracking and vertical plane linear tracking), and then select the appropriate altitude angle. The instructions are further broken down into cascade position tracking and altitude tracking systems.…”

Section: Reinforcement Learningmentioning

confidence: 99%

AUV Trajectory Tracking Models and Control Strategies: A Review

2021

JMSE

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Autonomous underwater vehicles (AUVs) have been widely used to perform underwater tasks. Due to the environmental disturbances, underactuated problems, system constraints, and system coupling, AUV trajectory tracking control is challenging. Thus, further investigation of dynamic characteristics and trajectory tracking control methods of the AUV motion system will be of great importance to improve underwater task performance. An AUV controller must be able to cope with various challenges with the underwater vehicle, adaptively update the reference model, and overcome unexpected deviations. In order to identify modeling strategies and the best control practices, this paper presents an overview of the main factors of control-oriented models and control strategies for AUVs. In modeling, two fields are considered: (i) models that come from simplifications of Fossen’s equations; and (ii) system identification models. For each category, a brief description of the control-oriented modeling strategies is given. In the control field, three relevant aspects are considered: (i) significance of AUV trajectory tracking control, (ii) control strategies; and (iii) control performance. For each aspect, the most important features are explained. Furthermore, in the aspect of control strategies, mathematical modeling study and physical experiment study are introduced in detail. Finally, with the aim of establishing the acceptability of the reported modeling and control techniques, as well as challenges that remain open, a discussion and a case study are presented. The literature review shows the development of new control-oriented models, the research in the estimation of unknown inputs, and the development of more innovative control strategies for AUV trajectory tracking systems are still open problems that must be addressed in the short term.

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Random Weighting-Based Nonlinear Gaussian Filtering

Cited by 19 publications

References 34 publications

Potassium deficiency diagnosis method of apple leaves based on MLR-LDA-SVM

Potassium deficiency diagnosis method of apple leaves based on MLR-LDA-SVM

An overview of digital image analog noise removal based on traditional filtering

AUV Trajectory Tracking Models and Control Strategies: A Review

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