Transformation Function Research Articles

Investigation on the transmission of Love waves due to an impulsive line source in a heterogeneous double porous rock structure.

The region around the reservoir sometimes encounters the seismic response of ground. The phenomena of Love waves propagation also caused by seismic activity which can further significantly influenced by an impulsive line source present in the rock structures of ground. The current study elucidates the propagation behavior of Love waves induced by an impulsive line source within the anisotropic layered heterogeneous double porous rock structures commonly found in proximity to the reservoir areas. The expression of the dispersion relation for the Love waves propagation due to an impulsive line source has been derived by using the Fourier transformation and Green's function technique. The above-mentioned rock composite structure is consisted of two different portions: upper transversely isotropic double porous (TIDP) layer medium and lower heterogeneous isotropic double porous half-space (IDP) medium. In the aforesaid half-space medium, both types of heterogeneities viz. linear and quadratic have been considered. The impacts of heterogeneity parameters associated with the lower heterogeneous IDP half-space, porosity parameter (corresponds to both upper TIDP layer and lower IDP half-space) and anisotropic parameter of TIDP layer on Love waves phase velocity have also been studied. Some special outlines have also been depicted graphically.

Resolving Contrast and Detail Trade-Offs in Image Processing with Multi-Objective Optimization

This article addresses the complex challenge of simultaneously enhancing contrast and detail in an image, where improving one property often compromises the other. This trade-off is tackled using a multi-objective optimization approach. Specifically, the proposal’s model integrates the sigmoid transformation function and unsharp masking highboost filtering with the NSGA-II algorithm. Additionally, a posterior preference articulation is introduced to select three key solutions from the Pareto front: the maximum contrast solution, the maximum detail solution, and the knee point solution. The proposed technique is evaluated on a range of image types, including medical and natural scenes. The final solutions demonstrated significant superiority in terms of contrast and detail compared to the original images. The three selected solutions, although all are optimal, captured distinct characteristics within the images, offering different solutions according to field preferences. This highlights the method’s effectiveness across different types and enhancement requirements and emphasizes the importance of the proposed preferences in different contexts.

On the study of solitary wave dynamics and interaction phenomena in the ultrasound imaging modelled by the fractional nonlinear system

This research work focuses on investigating the propagation of ultrasonic waves, which propagate mechanical vibrations of molecules or particles inside materials. Ultrasound imaging is extensively used and deeply rooted in the medical field. The key technologies that form the basis for many different uses in the area include transducers, contrast agents, pulse compression, beam shaping, tissue harmonic imaging, techniques for measuring blood flow and tissue motion, and three-dimensional imaging. The third-order non-linear β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta$$\\end{document}-fractional Westervelt model has been used as a governing model in the imaging process for securing the different wave structures. The exact solutions of different types, including mixed, dark, singular, bright-dark, bright, complex and combined solitons are extracted. These solutions are obtained by using two newly introduced techniques, namely modified generalized Riccati equation mapping method and modified generalized exponential rational function method. Moreover, breather, lump and other waves are extracted by the assistance of logarithmic transformation and different test functions. The used methodologies are extremely effective and possess substantial computing capacity to effectively address the different solutions with a high level of accuracy in these systems. The techniques used are well-known for being effective, simple, and flexible enough to integrate multiple soliton systems into a unified framework. In addition, we provide 2D and 3D graphs that explain the behavior of the solution at various parameter values, under the influence of β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta$$\\end{document}-fractional derivatives. The results offered in this study may improve the comprehension of the nonlinear dynamic behavior of the specific system and confirm the efficacy of the approaches used. We expect that our approaches will be beneficial for a wide range of nonlinear models and other problems in the related fields.

Image shadow removal based on pseudo-illuminated region and log domain transformation

ABSTRACT The presence of shadows in images can significantly degrade image quality. To address the challenges, this study utilizes pseudo-illumination regions and logarithmic domain transformations. Firstly, the input shadow image and its corresponding shadow region are segmented and clustered into multiple image blocks. Then, a comprehensive consideration of distance, color, and texture features is employed to construct pseudo illumination regions. Subsequently, the designed logarithmic domain transformation function is utilized for shadow removal. Finally, color correction and optimization of shadow edges complete the overall process. The feasibility of this approach has been preliminarily verified through experimental analysis.

Simulating overtaking interactions of dust-acoustic N-shock waves in oppositely charged dusty plasmas with Q-distributed electrons

Simulating overtaking interactions (OIs) of dust-acoustic N-shock waves (DANSWs) in dust plasmas with opposite polarities and q-distributed electrons and Boltzmann distributed ions are examined in this work. Reductive perturbation theory is used to obtain a Burgers’ equation (BE), and dust-acoustic N-shock wave solutions are derived using the Cole-Hopf transformation and exponential function. Numerical simulations show that the evolution and OIs of DANSWs and their electric fields are modified by the effects of negative/positive dust fluid kinematic viscosity, the ratio of negative dust to positive dust mass, and the nonextensive q-distribution of electrons. This work may help explain the properties of dust-acoustic shock waves in cometary tails and Jupiter's magnetosphere that support the propagation of nonlinear waves.

Virtual cleaning of sooty mural hyperspectral images using the LIME model and improved dark channel prior

Murals, as important carriers of cultural heritage and historical records, showcase artistic, aesthetic, social, and political significance. In ancient times, religious activities such as burning incense and candles in temples led to many murals being polluted by soot, causing them to darken, lose details, and, in severe cases, completely blacken. As a result, the development of efficient virtual cleaning methods has become a key strategy for addressing this issue. In this study, we use synthetic true colour and false colour images in different bands of the hyperspectral spectrum, and use a guided filter fusion technique to fuse these two images into a new image of the sooty mural. Through analyzing the histograms and colour distribution scatterplots of the synthetic sooty mural images, we observed significant similarities to low-luminance images. To enhance the synthesized murals, we applied the LIME model. In addition, comparisons of the histograms and colour distribution scatterplots of the enhanced sooty mural images with those of haze images revealed notable similarities. Therefore, we applied the dark channel prior algorithm to remove soot from the mural images. Considering that soot particles are larger than haze particles, we introduced guided filtering to refine the transmission map and created a nonlinear transformation function to enhance its details. In terms of both visual perception and quantitative analysis, the proposed method significantly outperforms previous methods in the virtual cleaning of sooty murals. This technology can not only restore the colours and details of murals but also provide new clues for subsequent mural studies, allowing people to once again appreciate the true beauty of the murals.

Active fault-tolerant control with prescribed performance and reachability judgement for the altitude ground test facility

This research proposes an active fault-tolerant control (AFTC) method based on reachability judgement with asymmetric appointed-time prescribed performance to ensure the safety of altitude ground test facility (AGTF) under potential valve actuator and sensor faults. Firstly, a long short-term memory (LSTM) neural network is employed for fault diagnosis. Given the unique characteristics of AGTF, the equivalent multiplicative fault is introduced to ensure the successful fitting of the fault diagnosis network. Then, a reachability judgement network based on the distance field on grids (DFOG) method is designed to assess the impact of faults on subsequent tasks of the system, thereby ensuring the safety of the AGTF. Subsequently, this study proposes an error transformation function of prescribed performance control (PPC) that ensures error convergence even in cases of asymmetric performance function. This enables the performance boundaries to be further reduced to improve the control performance of the system. Finally, experiments validate the effectiveness of the methods proposed in this manuscript.

Targeted recognition and enhanced biotransformation of phytochemicals by a dual-functional cellulose-based hydrogel bioreactor

The combined technology of molecular imprinting and immobilization is a promising strategy for improving biocatalytic performance. In this work, a dual-functional cellulose-based hydrogel bioreactor with targeted recognition and transformation functions was innovatively designed by cellulose-based molecularly imprinted polymers (CM-MIPs) hydrogel coupled with layered double hydroxide immobilized enzyme (LDH-E). Firstly, CM-MIPs hydrogel was prepared by using phlorizin, 4-pinylpyridine, and cellulose microspheres as template molecule, functional monomer, and support material, respectively. Meanwhile, layered double hydroxide (LDH) taken as a carrier to immobilize β-glucosidase. The prepared LDH was layered sheet-like structure with ultra-thin thickness of approximately only 1.5 nm, and β-glucosidase was immobilized on both sides of the LDH. Further, the dual-functional bioreactor was constructed by the anion exchange, which possessed maximum targeted adsorption capacity to phlorizin of 15.25 mg/g, exhibited 1.2 folds higher transformation efficiency than LDH-E, and the phloretin content increased 26 folds to that in Lithocarpus litseifolius (Hance) Chu leaves extracts. Moreover, the transformation efficiency remained above 70 % even after five consecutive transformations. Overall, the dual-functional bioreactor has broad prospects for the application in targeted recognition and transformation of phytochemicals, and provides a new insight on multifunctional bio-based reactors in natural production field.

DCPNet: Deformable Control Point Network for image enhancement

In this paper, we present a novel image enhancement network consisting of global and local color enhancement. The proposed network model is constructed using global transformation functions, which are formed by a set of piece-wise quadratic curves and a local color enhancement network based on the encoder–decoder network. To adaptively and dynamically control the ranges of each piece-wise curve, we introduce deformable control points (DCPs), which determine the overall structure of the global transformation functions. The parameters for piece-wise quadratic curve fitting and DCPs are estimated using the proposed DCP network (DCPNet). DCPNet processes a down-sampled image to derive the DCP parameters: The DCP offsets and the curve parameters. Then, we obtain a set of DCPs from the DCP offsets and connect each adjacent DCP pair by using the curve parameter to construct a global transformation function for each color channel. The original input images are then transformed based on the resulting transformation functions to obtain globally enhanced images. Finally, the intermediate image is fed into the local enhancement network, which has a U-Net architecture, to produce the spatially enhanced images. Extensive experimental results demonstrate the superiority of the proposed method over state-of-the-art methods in various image enhancement tasks, such as image retouching, tone-mapping, and underwater image enhancement.

Ferrihydrite regulated tire-wear microplastics biofilm for enhanced nitrogen transformation in surface water

Tire-wear microplastics (T-MPs) are increasingly present in surface water, which can intact with ferrihydrite and influence biofilm formation on T-MPs. However, the interaction of T-MPs and ferrihydrite on biofilm formation and its effect on nitrogen transformation in surface water is still unknown. In this paper, four sets of experiments were set up in simulated surface water inoculated with precultured bacteria, respectively, A: simulated surface water, B: addition of ferrihydrite, C: addition of T-MPs, and D: addition of both T-MPs and ferrihydrite. Characterization results showed that co-exposure of T-MPs and ferrihydrite increased roughness and decreased hydrophobicity in the surface of T-MPs, and resulted in higher biomass and EPS values in biofilm of T-MPs with ferrihydrite. Under co-exposure, the nitrate removal efficiency for 12 h was increased by 32.37 % and 25.74 % comparing with single ferrihydrite and T-MPs, and the accumulation and removal of nitrite as well as the production of higher concentrations of nitrous oxide indicated that the co-exposure contributed to the denitrification and stimulated nitrogen transformation. Furthermore, α-diversity index characterized a rich microbial community, especially the enrichment of functional microorganisms Firmicutes and Thauera, which were altered to enhance nitrogen transformation function (glutamine synthetase, nitronate monooxygenase). Ferrihydrite promoted complete denitrification process in the system and strengthened the role of nitrogen transformation microorganisms in the T-MPs biofilm. The abundance of key enzymes and functional genes (napA, nrtC, nirB, nosZ) increased with co-exposure T-MPs and ferrihydrite, and genes associated with nitrate and nitrite reduction (narG, narI) exhibited higher abundance in biofilm of T-MPs compared to the surrounding water. This research provided important insights into the effects of T-MPs co-exposure interactions with ferrihydrite on nitrogen transformation in surface water.

The effects of earthworm species on organic matter transformations and soil microbial communities are only partially related to their bioturbation activity

Earthworms are pivotal in shaping soil ecological processes through their bioturbation activity and organic matter consumption. Earthworm species are known to have different impacts on soil structure, but only a small number of species have been studied so far, and few studies have examined how earthworms simultaneously affect soil functions. Here, we measured the impact of different earthworm species on soil structure (bioturbation function), carbon (C) and nitrogen (N) dynamics and the microbial community (organic matter transformation function), while exploring the links between these functions, across distinct soil compartments (surface casts, below-ground drilosphere, and bulk soil). Six earthworm species (Lumbricus terrestris, Allolobophora chlorotica, Octolasion cyaneum, Octodrilus complanatus, Aporrectodea caliginosa meridionalis and Microscolex dubius) of different ecological categories and functional groups were incubated in soil cores with soil and alfalfa litter for 6 weeks. Our results on the bioturbation function illustrated a great diversity of behaviors and confirmed the relevance of a functional classification based on bioturbation metrics. The main microbial hotspots were surface casts, whose characteristics allowed to distinguish two groups of species. Octod. complanatus, L. terrestris and M. dubius induced high humidity (respectively, +57, +48, +74%), high C (respectively, 19.9, 24.8, 33.2 g kg⁻1 dry soil) and N (respectively, 2, 2.3, 3.2 g kg⁻1 dry soil) content and microbial community selection, promoting C and N mineralization. The three other species had a lower impact. The below-ground drilosphere only showed specific characteristics in the case of L. terrestris. The effects of the studied species on the organic matter transformation function did not align with their bioturbation activities nor with their ecological category. These findings show that the contribution of earthworms to C and N turnover is only partially dependent on their bioturbation effects and suggest the usefulness of developing distinct functional groups based on the specific soil functions under consideration.

Pseudo Oversampling Based on Feature Transformation and Fuzzy Membership Functions for Imbalanced and Overlapping Data

Class imbalance in data poses challenges for classifier learning, drawing increased attention in data mining and machine learning. The occurrence of class overlap in real-world data exacerbates the learning difficulty. In this paper, a novel pseudo oversampling method (POM) is proposed to learn imbalanced and overlapping data. It is motivated by the point that overlapping samples from different classes share the same distribution space, and therefore information underlying in majority (negative) overlapping samples can be extracted and used to generate additional positive samples. A fuzzy logic-based membership function is defined to assess negative overlaps using both local and global information. Subsequently, the identified negative overlapping samples are shifted into the positive sample region by a transformation matrix, centered around the positive samples. POM outperforms 15 methods across 14 datasets, displaying superior performance in terms of metrics of <i>G<sub>m</sub></i>, <I>F</I><sub>1</sub> and <I>AUC</I>.

Robust adaptive asymmetric state constraint control of brush DC motor systems driving a one-link robot manipulator: A feasibility-condition-free method

In this paper, we investigate the tracking control problem for a class of brush direct current (DC) motor systems driving a one-link robot manipulator subject to asymmetric full-state constraints. By constructing a state-dependent nonlinear transformation function (NTF), we present an adaptive robust dynamic surface control (DSC) strategy that can directly address both symmetric and asymmetric state constraints, so that there is no need to convert the problem of state constraint into the constraints on tracking errors as necessitated by the Barrier Lyapunov Function (BLF)-based existing works. Furthermore, by employing the first-order filter and constructing a new coordinate transformation, the demanding feasibility condition imposed on the BLF methods is removed, allowing the designer more freedom to select design parameters. Moreover, it is worth mentioning that under the proposed nonlinear transformation function the extra condition on the constraining function is not required. The effectiveness of the proposed control is verified via the Simulation results.

Semi-analytical solutions for steady-state bending-bending coupled forced vibrations of a rotating wind turbine blade by means of Green's functions

Wind power has received significant attention in recent years as a renewable energy source. Wind turbine blades, characterized by their long lengths, are prone to damage as a result of aeroelastic instability. This paper aims to derive semi-analytical solutions for steady-state forced vibrations of rotating wind turbine blades, considering the coupling of bending, bending, and unsteady aerodynamic loads. The novelty of this work lies in the use of the Green's function method to solve differential dynamic equations with variable coefficients, which are induced by aerodynamic forces. To model a wind turbine blade, the Euler-Bernoulli beam model is employed, and Greenberg's expressions are taken into consideration. Governing equations for coupled vibrations of the blade are then determined. In order to solve the governing equations, displacement solutions are decomposed into quasi-static displacements and dynamic displacements. The Laplace transformation and Green's function methods are utilized to obtain fundamental solutions of the governing equations. Subsequently, employing the principle of superposition, Fredholm integral equations for steady-state forced vibration of a rotating wind turbine blade are derived. To spatially discretize Fredholm integral equations, compound trapezoid formulae and central finite difference approximations are employed. This discretization process leads to the formation of a system of algebraic equations. Semi-analytical solutions for coupled forced vibrations of the rotating wind turbine blade are obtained by solving the algebraic equations. In the numerical solution part, validation of proposed solutions are verified by comparing them with numerical and finite element solutions in the literature. Influences of some important physical parameters, such as the rotating velocity, the setting angle, the cone angle, the inflow ratio, and damping, on vibration responses of blades are discussed.

A Generic Self-Supervised Framework of Learning Invariant Discriminative Features.

Self-supervised learning (SSL) has become a popular method for generating invariant representations without the need for human annotations. Nonetheless, the desired invariant representation is achieved by utilizing prior online transformation functions on the input data. As a result, each SSL framework is customized for a particular data type, for example, visual data, and further modifications are required if it is used for other dataset types. On the other hand, autoencoder (AE), which is a generic and widely applicable framework, mainly focuses on dimension reduction and is not suited for learning invariant representation. This article proposes a generic SSL framework based on a constrained self-labeling assignment process that prevents degenerate solutions. Specifically, the prior transformation functions are replaced with a self-transformation mechanism, derived through an unsupervised training process of adversarial training, for imposing invariant representations. Via the self-transformation mechanism, pairs of augmented instances can be generated from the same input data. Finally, a training objective based on contrastive learning is designed by leveraging both the self-labeling assignment and the self-transformation mechanism. Despite the fact that the self-transformation process is very generic, the proposed training strategy outperforms a majority of state-of-the-art representation learning methods based on AE structures. To validate the performance of our method, we conduct experiments on four types of data, namely visual, audio, text, and mass spectrometry data and compare them in terms of four quantitative metrics. Our comparison results demonstrate that the proposed method is effective and robust in identifying patterns within the tested datasets.

Interactive Table Synthesis With Natural Language.

Tables are a ubiquitous data format for insight communication. However, transforming data into consumable tabular views remains a challenging and time-consuming task. To lower the barrier of such a task, research efforts have been devoted to developing interactive approaches for data transformation, but many approaches still presume that their users have considerable knowledge of various data transformation concepts and functions. In this study, we leverage natural language (NL) as the primary interaction modality to improve the accessibility of average users to performing complex data transformation and facilitate intuitive table generation and editing. Designing an NL-driven data transformation approach introduces two challenges: 1) NL-driven synthesis of interpretable pipelines and 2) incremental refinement of synthesized tables. To address these challenges, we present NL2Rigel, an interactive tool that assists users in synthesizing and improving tables from semi-structured text with NL instructions. Based on a large language model and prompting techniques, NL2Rigel can interpret the given NL instructions into a table synthesis pipeline corresponding to Rigel specifications, a declarative language for tabular data transformation. An intuitive interface is designed to visualize the synthesis pipeline and the generated tables, helping users understand the transformation process and refine the results efficiently with targeted NL instructions. The comprehensiveness of NL2Rigel is demonstrated with an example gallery, and we further confirmed NL2Rigel's usability with a comparative user study by showing that the task completion time with NL2Rigel is significantly shorter than that with the original version of Rigel with comparable completion rates.

Adaptive Fuzzy Tracking Control With Global Prescribed-Time Prescribed Performance for Uncertain Strict-Feedback Nonlinear Systems.

For strict-feedback systems with mismatched uncertainties, adaptive fuzzy control techniques are developed to provide global prescribed performance with prescribed-time convergence. First, a class of prescribed-time prescribed performance functions are designed to quantify the performance constraints of the tracking error. Additionally, a novel error transformation function is provided to eliminate the initial value limitations and resolve the singularity issue in previous research. To ensure the convergence of the tracking error into a prescribed bounded region within a prescribed time and satisfactory transient performance, controllers with or without approximating structures are established. Notably, the settling time and initial condition of the prescribed performance function are completely independent of the initial tracking error and system parameters, thereby improving upon existing results. Furthermore, the disadvantage of the semi-global boundedness of tracking error induced by dynamic surface control can be eliminated through the use of a novel Lyapunov-like energy function. Finally, the effectiveness of the proposed strategies is validated through numerical simulations performed on practical examples.

Coal-rock interface real-time recognition based on the improved YOLO detection and bilateral segmentation network

To improve the accuracy and efficiency of coal-rock interface recognition, this study proposes a model built on the real-time detection algorithm, you only look once (YOLO), and the lightweight bilateral segmentation network. Simultaneously, the regional similarity transformation function and dragonfly algorithm are introduced to enhance the quality of coal-rock images. The comparison with three other models demonstrates the superior edge inference performance of the proposed model, achieving a mean Average Precision (mAP) of 90.2 at the Intersection over Union (IoU) threshold of 0.50 (mAP50) and 81.4 across a range of IoU thresholds from 0.50 to 0.95 (mAP[50,95]). Furthermore, to maintain high accuracy and real-time recognition capabilities, the proposed model is optimized using the open visual inference and neural network optimization toolkit, resulting in a 144.97% increase in the mean frames per second. Experimental results on four actual coal faces confirm the efficacy of the proposed model, showing a better balance between accuracy and efficiency in coal-rock image recognition, which supports further advancements in coal mining intelligence.

Prescribed performance dynamic surface control based on dual extended state observer for 2-dof hydraulic cutting arm

In tunnel section forming operations, the boom-type roadheader tracking target trajectory with high precision is greatly significant in avoiding over and under excavation and improving excavation efficiency. However, there exist complex cutting loads, measurement noise, and model uncertainties, seriously degrading the tracking performance of traditional nominal model-based controllers. Hence, this study first fully analyzes the kinematics of all members of the cutting mechanism and establishes its complete multi-body dynamic model using the Lagrange method. Furthermore, a dual extended state observer is designed to estimate the mechanical system’s angular velocity and unmodeled disturbances and actuators’ uncertain nonlinearities. In particular, introducing a nonlinear filter replaces the traditional first-order filter in dynamic surface technology, overcoming the “explosion of complexity” while attenuating the conservatism of gains tuning. Then, a dual extended state observer-based prescribed performance dynamic surface controller is developed for roadheaders for the first time. Simultaneously, integrating an improved error transformation function into controller design effectively avoids the online computational burden caused by traditional logarithmic operations. Utilizing Lyapunov theory, the cutting system’s prescribed transient response and steady-state performance are guaranteed. Finally, the proposed controller’s effectiveness is verified by comparative experiments on the roadheader.

General output constraints control of interconnected nonlinear systems with sensor faults

This paper delves into the problem of generalized output-constrained control for interconnected nonlinear systems, where sensor faults are modeled as time-varying unknown functions. First, a barrier function related to an output transformation function is proposed, which can meet the diverse constraint requirements by adjusting some parameters and functions. Second, the sensor fault is described as an unknown time-varying function, and its difficulties to the controller are successfully overcome with the help of adaptive control technology. Then, an output related function is introduced into the controller to handle the interconnected functions without approximators, while retaining the uncertainties in the interconnected functions. Based on the dynamic surface control technology, an adaptive general output constraint fault-tolerant controller is proposed. With the aid of the Lyapunov stability theory, it rigorously proves that the output satisfies the pre-given constraint requirements, and all signals of the closed-loop systems are bounded. Finally, the numerical simulation is presented to illustrate the validity of the proposed algorithm.