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A Novel Intensity-Corrected Blue Channel Compensation and Edge-Preserving Contrast Enhancement Using Laplace Filter and sigmoid function for Sand-Dust Image Enhancement

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IEEE ACCESS 2025年 13卷 43127-43144页

作者： Masood, Muhammad Khawaja Kashif Baro, Enrique Nava Roth, Pablo Otero Univ Malaga Inst Ocean Engn Res Malaga 29071 Spain Qassim Univ Elect Engn Dept Buraydah 52571 Saudi Arabia

Outdoor computer vision systems face significant challenges due to reduced visibility and severe color distortion in the images captured in sand-dust-affected environments. This study aims to improve the visibility of sand-dust-degraded images. To achieve this goal, a novel and effective method is proposed to remove the sand-dust color cast and enhance image visibility. The proposed method combines two essential color model methods to remove the sand-dust color cast and enhance image clarity. In the initial phase, sand-dust removal is achieved using a novel Intensity-corrected blue channel compensation along with white balancing for color adjustment based on the Red-Green-Blue (RGB) color model. In the next phase, a novel Edge-preserving contrast enhancement method is applied to improve the visibility under sand-dust conditions. This method consists of CLAHE, a Gaussian blur filter, a Laplace filter, and the sigmoid function. Using the Hue-Saturation-Value (HSV) color model, CLAHE is applied for contrast enhancement;the Gaussian blur filter removes high-frequency noise, and the Laplace filter enhances edge detection, all targeting the V (Value) channel to refine image details, while the sigmoid function adjusts saturation in the Saturation (S) channel, ensuring natural color balance and improved feature visibility. In-depth qualitative and quantitative evaluations are conducted on images with varying levels of sand-dust intensity (weak, moderate, strong, extreme). The proposed method shows superior performance in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and processing speed, while significantly reducing computational complexity. Compared to the state-of-the-art CNN and all previous methods, our proposed method is efficient for real-time applications with minimal hardware requirements, making it ideal for embedded vision systems. Furthermore, a novel Energy Efficiency Index (EEI) is used to assess computational cost-effectiveness. The ev

关键词： Image color analysis Image edge detection Image restoration Visualization Distortion Computational efficiency Green products Lighting Indexes Energy efficiency Sand-dust images intensity corrected blue channel compensation contrast enhancement Laplace filter sigmoid function CLAHE Gaussian blur filter

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A novel self-adaptive step size first-order method for structural reliability analysis based on modified sigmoid function and Armijo rule

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PROBABILISTIC ENGINEERING MECHANICS 2025年 79卷

作者： Xia, Yu Hu, Yiying Yu, Yingye Guangxi Univ Sci & Technol Sch Civil & Architectural Engn Liuzhou 545000 Peoples R China

Among the first-order reliability methods (FORM), the Hasofer-Lind and Rackwitz-Fiessler method (HL-RF), and the chaos control method (CC) rely on a fixed step size and cannot generate an adaptive one, making it difficult to achieve a satisfactory trade-off between efficiency and robustness when dealing with highly nonlinear problems. To address these drawbacks, this paper proposes two innovative first-order reliability methods: the sigmoidbased self-adaptive step size adjustment method (SSA) and the hybrid sigmoid-based self-adaptive step size adjustment method (HSSA). The iterative rotation angle is first determined for both proposed methods. In the SSA method, a modified sigmoid function is employed to enable nonlinear adaptive adjustments of the step size based on changes in the iterative turning angles, allowing for rapid convergence. Subsequently, the HSSA method incorporates the Armijo rule to further explore a more effective solution. Both proposed methods demonstrate strong computational merits, favorable performance, and user-friendly procedures, providing self-adaptive step sizes suitable for engineering problems, thus offering a broad range of applications. The paper introduces eight examples to showcase the remarkable performance of the two proposed methods. The results indicate that both methods exhibit significantly superior efficiency and robustness compared to other comparative analytical FORM methods when addressing highly nonlinear engineering challenges. Finally, a discussion is presented.

关键词： sigmoid function Self-adaptive step size Armijo rule Chaos control First-order reliability method Structural reliability

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Design of a sliding mode observer based on sigmoid function and its application in detecting small faults of induction motor

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2024年 1-33页

作者： Song, Lei Yu, Wenxin Zhao, Qizheng Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411201 Hunan Pro Peoples R China

In this paper, a fault detection method based on a new sigmoid function sliding mode observer (SMO) is proposed to detect early small faults of induction motors in a timely and accurate manner. Firstly, a dynamic model including stator and rotor winding faults and sensor minor faults is constructed. Secondly, by using the characteristics of the sigmoid function, a new power reaching law is designed which can be adjusted online according to the sliding mode surface. Then, the stability and reachability of the new reaching law are proved. In combination with the new reaching law, a new observer with fast reaching speed and good high-frequency chatter suppression ability is designed. Finally, in order to achieve induction motor fault detection, the residual between the actual value of the state variable and the observed value of the SMO is used as the fault detection variable. The simulation results have verified that the new sigmoid function-based SMO can not only detect motor faults, but also has higher tracking accuracy and better high-frequency chatter suppression ability compared to the traditional reaching law-based SMO.

关键词： Induction motor Minor fault detection sigmoid function New reaching law SMO

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Image stitching using sigmoid function to create perception mask

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第2期45卷 2047-2061页

作者： Ma, Mingxi Wang, Jinliang Nanchang Inst Technol Coll Sci Nanchang Jiangxi Peoples R China Nanchang Inst Technol Jiangxi Prov Key Lab Water Informat Cooperat Sen Nanchang Jiangxi Peoples R China

The point features of low-texture images are insufficient and unreliable, so it is difficult to achieve good alignment and easy to damage the image structure. To solve these problems, in this paper, we propose a new image stitching method by using the sigmoid function to create perception mask. Firstly, the point features and line features are used to improve the accuracy of image registration and the naturalness of distortion. Secondly, an energy function is used to optimize the alignment model. Finally, we propose to use sigmoid function to create perception mask image to reduce artifacts and retain image structure. The gradient domain fusion algorithm is combined to achieve image fusion. Experimental results are provided to demonstrate that the proposed method is superior to some previous methods in reducing artifacts and maintaining image structure.

关键词： Image stitching sigmoid function perception mask gradient domain fusion algorithm double features

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An Agent-Based Framework for Policy Simulation: Modeling Heterogeneous Behaviors With Modified sigmoid function and Evolutionary Training

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第4期10卷 1901-1913页

作者： Yu, Songmin Fraunhofer Inst Syst & Innovat Res D-76139 Karlsruhe Germany

This article proposes an agent-based policy simulation framework that can be applied to the cases satisfying: 1) the agents try to maximize some intertemporal preference and 2) the impacts of different factors on agents' behavioral tendency are monotonic. By combining the simulation and optimization methods, this framework balances the flexibility and validity of agent-based models (ABMs): the sigmoid function is modified and used to model agents' decision-making rules, and the evolutionary training method is used to calibrate agents' behavioral parameters. Based on an example for the emission trading scheme, the application of the framework is presented and evaluated in detail.

关键词： Behavioral sciences Biological system modeling Adaptation models Training Decision making Calibration Atmospheric modeling Agent-based model (ABM) evolutionary training flexibility and validity policy simulation framework sigmoid function

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Parameter Adaptive Control of the Virtual DC Machine Based on sigmoid function

Parameter Adaptive Control of the Virtual DC Machine Based o...

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2024 Vehicle Power and Propulsion Conference

作者： Gu, Yan Liu, Zhao Zhu, Lin Zhou, Yongkang Wang, Zechen Nanjing Univ Sci & Technol Sch Automat Nanjing Peoples R China

ISBN: (纸本)9798331541613;9798331541606

Due to the lack of inertia and damping in DC microgrids, virtual DC machine (VDCM) control is applied to DC converters to enhance the ability of the bus voltage to suppress fluctuations. However, the conventional VDCM adopts fixed parameters, which makes it more difficult for the system to achieve good dynamic performance while obtaining inertia and damping support. To address this problem, a parameter adaptive control based on the sigmoid function is proposed in this paper. By establishing the connection between the rate of voltage change and the control parameter, an adaptive sigmoid function is designed so that the parameter can be adjusted more smoothly and adaptively in the case of power fluctuations. Simulations and experimental results show that the proposed control achieves smaller voltage drop and shorter recovery time compared with the conventional VDCM control.

关键词： DC microgrid virtual DC machine adaptive control sigmoid function

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A novel feedrate scheduling method based on sigmoid function with chord error and kinematic constraints

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第3-4期119卷 1531-1552页

作者： Li, Hexiong Jiang, Xin Huo, Guanying Su, Cheng Wang, Bolun Hu, Yifei Zheng, Zhiming Beihang Univ Sch Math Sci Key Lab Math Informat & Behav Semant LMIB Beijing Peoples R China Peng Cheng Lab Shenzhen Guangdong Peoples R China Zhengzhou Aerotropolis Inst Artificial Intelligen Zhengzhou Henan Peoples R China

In high-speed CNC (compute numerical control) machining, the feedrate scheduling has played an important role to ensure machining quality and machining efficiency. In this paper, a novel feedrate scheduling method is proposed for generating smooth feedrate profile conveniently with the consideration of both chord error and kinematic error. First, a relationship between feedrate value and chord error is applied to determine the feedrate curve. Then, breaking points, which can split whole curve into several blocks, can be found out using proposed two step screening method. For every block, the feedrate profile based on sigmoid function is generated. With the consideration of kinematic limitation and machining efficiency, a time-optimal feedrate adjustment algorithm is proposed to further adjust feedrate value at breaking points. After achieving feedrate profile for each block, all blocks' feedrate profile will be connected smoothly. The resulting feedrate profile is more concise compared with the polynomial profile and more efficient compared with the trigonometric profile. Finally, simulations with two free-form NURBS curves are conducted and comparison with the sine-curve method is carried out to verify the feasibility and applicability of the proposed method. In order to further validate the feasibility of proposed method, machining simulation experiments are also conducted using Unigraphics NX.

关键词： Feedrate scheduling sigmoid function Curve splitting Time-optimal feedrate adjusting

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The generalized sigmoid function and its connection with logical operators

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2022年 143卷 121-138页

作者： Dombi, Jozsef Jonas, Tamas Univ Szeged Inst Informat Szeged Hungary Eotvos Lorand Univ Fac Econ Budapest Hungary

In this study, we present the operator-dependent sigmoid function, which is derived from a universal unary operator called the kappa function. Here, we describe how the generalized sigmoid function is related to representable uninorms (i.e., Dombi's aggregative operator). Namely, we show that the inverse of a generalized sigmoid function is an additive generator of the aggregative operator. We provide the necessary and sufficient conditions for the form of the function that transforms the aggregative operator into a conjunctive or disjunctive logical operator. This transformation is also based on the generalized sigmoid function. Here, we show how conjunctive and disjunctive operators, which form a De Morgan system with a negation, can be derived from the aggregative operator. We point out that, under certain conditions, a set of generalized sigmoid functions is closed under the negation and modifier operators. Lastly, we demonstrate an important connection between the weighted aggregative operator and the generalized sigmoid function. Based on this connection, we provide a new interpretation of the feed-forward neural networks. We show that a perceptron-based neural network can be modeled using the aggregative operator and the generalized sigmoid function. (c) 2022 The Author(s). Published by Elsevier Inc.

关键词： sigmoid function Uninorms Aggregative operators Pliant system Artificial neural networks Dombi operators

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DPSF: a Novel Dual-Parametric sigmoid function for Optical Coherence Tomography Image Enhancement

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2022年第4期60卷 1111-1121页

作者： Okuwobi, I. P. Ding, Z. Wan, J. Ding, S. Guilin Univ Elect Technol Sch Artificial Intelligence 1 Jinji Rd Guilin 541004 Peoples R China Guilin Med Univ Dept Ophthalmol Affiliated Hosp 1 Zhiyuan Rd Guilin 541199 Peoples R China

Speckle noise reduces the image contrast significantly making the highly scattering structures boundaries difficult to distinguish. This has limited the usage of optical coherence tomography (OCT) images in clinical routine and hindered its potential by depriving clinicians from assessing useful information that are needed in disease monitoring, treatment, progression, and decision making. To overcome this limitation, we propose a fast and robust OCT image enhancement framework using non-linear statistical parametric technique. In the proposed framework, we utilize prior statistical information to model the image to follow Gaussian distribution. After which, a newly designed dual-parametric sigmoid function (DPSF) is utilized to control the dynamic range and contrast level of the image. To balance the intensity range and contrast level, both linear and non-linear normalization operations are performed, then followed by a mapping operation to obtain the enhanced image. Experimentation results on the three OCT vendors show that the proposed method obtained high values in EME, PSNR, SSIM, rho, and low value in MSE of 36.72, 38.87, 0.87, 0.98, and 25.12 for Cirrus;40.77, 41.84, 0.89, 0.98, and 22.15 for Spectralis;and 30.81, 32.10, 0.81, 0.96, and 28.55 for Topcon OCT devices, respectively. The proposed DPSF framework performs better than the state-of-the-art methods and improves the interpretability and perception of the OCT images, which can provide clinicians and computer vision program with good quantitative and qualitative information.

关键词： Retinal diseases Optical coherence tomography sigmoid function Image enhancement Analysis Image processing

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A Modular Approximation Methodology for Efficient Fixed-Point Hardware Implementation of the sigmoid function

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2022年第10期69卷 10694-10703页

作者： Pan, Zhe Gu, Zonghua Jiang, Xiaohong Zhu, Guoquan Ma, De Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310058 Peoples R China Umea Univ Dept Appl Phys & Elect S-90187 Umea Sweden Res Ctr Intelligent Comp Syst Artificial Intelligence Res Inst Zhejiang Lab Hangzhou 310058 Peoples R China

The sigmoid function is a widely used nonlinear activation function in neural networks. In this article, we present a modular approximation methodology for efficient fixed-point hardware implementation of the sigmoid function. Our design consists of three modules: piecewise linear (PWL) approximation as the initial solution, Taylor series approximation of the exponential function, and Newton-Raphson method-based approximation as the final solution. Its modularity enables the designer to flexibly choose the most appropriate approximation method for each module separately. Performance evaluation results indicate that our work strikes an appropriate balance among the objectives of approximation accuracy, hardware resource utilization, and performance.

关键词： Hardware Taylor series Newton method Approximation error Artificial neural networks Quantization (signal) Input variables Artificial neural networks (NNs) FPGA hardware acceleration Newton-Raphson (NR) method sigmoid function

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