Minimum Entropy Research Articles

Non-Gaussian disturbance rejection control for multivariate stochastic systems using moment-generating function

In this paper, a non-Gaussian disturbance rejection control algorithm for a class of nonlinear multivariate stochastic systems is studied. Based on the moment-generating functions obtained from the deduced probability density functions of the output tracking errors, a new criterion representing the stochastic properties of the system is proposed, motivated by a minimum entropy design. A time-variant linear model can be established by the sampled moment-generating functions. Using this model, a control algorithm is developed that minimizes the newly developed criterion. Moreover, a stability analysis is performed for the closed-loop control system. Finally, simulation results of a numerical example demonstrate the effectiveness of the presented control algorithm. The contribution and novelty of this work can be summarized as follows: (1) a novel non-Gaussian disturbance rejection control scheme is proposed based on the minimum entropy principle, (2) the randomness of the multi-variable non-Gaussian stochastic nonlinear system is attenuated based on the new performance criterion, (3) a theoretical convergence analysis has been given for the proposed control system, and (4) a potential framework has been established for the design of a general stochastic system control.

Minimum entropy deconvolution enhanced by KLOF and phase editing for fault diagnosis of rotating machinery

Repetitive impulses are critical characteristics in vibration signals of fault rotating machinery and minimum entropy deconvolution (MED) is an effective tool to extract these characteristics for fault diagnosis. However, early fault-induced impulses are usually weak and submerged in heavy background noise when rotating machinery operate in severe environment. Under this circumstance, MED generally fails to isolate useful impulses from contaminated signals. To overcome the shortcoming, this paper proposed a kernel local outlier factor MED enhanced by phase editing method (KPMED) for fault diagnosis of rotating machinery. First, non-Gaussian noise components in fault signals are removed by kernel-based local outlier factor (KLOF) in data cleaning way. Second, the residual Gaussian noise components are eliminated by phase editing (PE) in complex domain with phase perspective. Finally, the fault-induced repetitive periodic impulses are extracted by selecting MED filtered signal with large value of kurtosis for diagnosis. The effectiveness of KPMED is validated by a simulated bearing fault signal and two vibration signals collected from a pinion of a wind turbine and a rolling element bearing, respectively. Compared with original MED and its corresponding variants, KPMED is more accurate and suitable for fault diagnosis of rotating machinery under severe working conditions.

Application of a coarse-to-fine minimum entropy deconvolution method for rotating machines fault detection

Due to the severe working condition and long-term service, the key rotating parts including the bearing and gearbox, are susceptible to damage. Blind deconvolution which can eliminate the influence of the transfer path and enhance the fault-related feature is widely used for machinery fault diagnosis. Not only the objective function but the initial filter can influence the convergence properties of the deconvolution algorithm. For example, in practical application, the classical MED trends to converge to fault-unrelated outliers if the initial filter coefficients are not appropriate. Motivated by this, the effect of initialization on the convergence of MED is firstly investigated. Subsequently, a coarse-to-fine MED (CFMED) is designed to highlight the fault-induced repetitive impulses. Specifically, the procedure of pre-iterating is used to search for the convergence direction and coarsely update the initial filter. Then, the dropout step is introduced to drop out the redundant candidates and reserve the expected solution by quantitatively measuring the sparsity of all possible solutions in the frequency domain. Meanwhile, CFMED is more robust to filter size than MED. Finally, the simulation and experimental data with bearing and planetary gearbox fault verify CFMED is more suitable for the fault diagnosis of rotating machines compared with original MED.

Effect of calcium hydroxide on pasting, thermal, and water‐adsorption behavior, and the flow properties of nixtamalized corn flour

AbstractNixtamalized corn flour (NCF) has gained favor worldwide due to the growing popularity of Mexican cuisine. This research evaluates the effect of the amount of calcium hydroxide (Ca(OH)2) employed during the nixtamalization on the water‐adsorption, thermodynamic, and flow properties of NCF. In addition, NCF pasting and thermal properties were evaluated to relate bulk‐powder properties to wet‐dispersion conditions. Ca(OH)2 amounts of 1.5 and 2.0% induced higher ash and calcium content as well as lower fat and protein content. Also, pasting properties and gelatinization enthalpy (ΔHgel) values, in addition to yellowish NCF, were related to Ca(OH)2 concentration. With the independence of the Ca(OH)2 amounts employed, no important differences in the equilibrium's moisture content, as well as a negligible effect of temperature on adsorption behavior, were observed. For prepared NCF, the moisture content corresponding to minimum integral entropy was not observed within the range of the aw analyzed; thus, GAB parameters are more reliable for suggesting the storage conditions. NCF with a low amount of Ca(OH)2 exhibited higher values of mean caking strength (≈1.81 N) and cohesiveness index (≈13.99 × 10−5 N m g−1), in addition to higher resistance to flow. Therefore, the amount of Ca(OH)2 positively influenced flow properties, whereas wet‐dispersion properties such as lower maximal peak viscosity and ΔHgel values were obtained.Practical applicationsThis study contributes useful information to the manufacturers of NCF concerning the amount of Ca(OH)2 needed to obtain NCF with desirable physical properties while attempting to affect thermal and pasting properties to a lesser extent. In addition, this study characterizes and provides the adequate amount of Ca(OH)2 employed the during the nixtamalization process to improve the flowability of NCF in industry during handling. Finally, the absence of a moisture content corresponding to minimum integral‐adsorption entropy provides clarification for NCF producers regarding the long‐term storage stability of NCF.

DeDoc2 Identifies and Characterizes the Hierarchy and Dynamics of Chromatin TAD-Like Domains in the Single Cells.

Topologically associating domains (TADs) are functional chromatin units with hierarchical structure. However, the existence, prevalence, and dynamics of such hierarchy in single cells remain unexplored. Here, a new generation TAD-like domain (TLD) detection algorithm, named deDoc2, to decode the hierarchy of TLDs in single cells, is reported. With dynamic programming, deDoc2 seeks genome partitions with global minimal structure entropy for both whole and local contact matrix. Notably, deDoc2 outperforms state-of-the-art tools and is one of only two tools able to identify the hierarchy of TLDs in single cells. By applying deDoc2, it is showed that the hierarchy of TLDs in single cells is highly dynamic during cell cycle, as well as among human brain cortex cells, and that it is associated with cellular identity and functions. Thus, the results demonstrate the abundance of information potentially encoded by TLD hierarchy for functional regulation. The deDoc2 can be freely accessed at https://github.com/zengguangjie/deDoc2.

The microbiome of an outpatient rehabilitation clinic and predictors of contamination: A pilot study.

Understanding sources of microbial contamination in outpatient rehabilitation (REHAB) clinics is important to patients and healthcare providers. The purpose of this study was to characterize the microbiome of an outpatient REHAB clinic and examine relationships between clinic factors and contamination. Forty commonly contacted surfaces in an outpatient REHAB clinic were observed for frequency of contact and swiped using environmental sample collection kits. Surfaces were categorized based on frequency of contact and cleaning and surface type. Total bacterial and fungal load was assessed using primer sets specific for the 16S rRNA and ITS genes, respectively. Bacterial samples were sequenced using the Illumina system and analyzed using Illumina-utils, Minimum Entropy Decomposition, QIIME2 (for alpha and beta diversity), LEfSe and ANCOM-BC for taxonomic differential abundance and ADONIS to test for differences in beta diversity (p<0.05). Porous surfaces had more bacterial DNA compared to non-porous surfaces (median non-porous = 0.0016ng/μL, 95%CI = 0.0077-0.00024ng/μL, N = 15; porous = 0.0084 ng/μL, 95%CI = 0.0046-0.019 ng/μL, N = 18. p = 0.0066,DNA. Samples clustered by type of surface with non-porous surfaces further differentiated by those contacted by hand versus foot. ADONIS two-way ANOVA showed that the interaction of porosity and contact frequency (but neither alone) had a significant effect on 16S communities (F = 1.7234, R2 = 0.0609, p = 0.032). Porosity of surfaces and the way they are contacted may play an underestimated, but important role in microbial contamination. Additional research involving a broader range of clinics is required to confirm results. Results suggest that surface and contact-specific cleaning and hygiene measures may be needed for optimal sanitization in outpatient REHAB clinics.

A novel coupled array of multi-stable stochastic resonance under asymmetric trichotomous noise and its application in rolling bearing compound fault diagnosis

Rolling bearings are often subject to various types of damage in actual industrial operations. However, the early fault information is weak and completely overwhelmed by interference from noise, which is difficult to detect. In this paper, a novel coupled array of multi-stable stochastic resonance (NCAMSR) driven by asymmetric trichotomous noise is proposed for compound fault diagnosis. The Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is used to separate the information of different fault features in the compound fault signal, and the outputs of the coupled array system are weighted to find the best effect. The spectral mean signal-to-noise ratio (SMSNR) is designed and the system parameters and weights are optimized using the particle swarm algorithm (PSO). To alleviate the problem of output saturation, the NCAMSR potential model based on the improved Gaussian potential is proposed, and the superior unsaturation of the NCAMSR potential function model is proven by theoretical analysis and experiments. Finally, the excellent performance of NCAMSR in compound fault detection is further verified by using engineering experiments with bearing data from Machinery Faults & Rotor Dynamics Simulator and real damage samples generated from accelerated life tests at Paderborn University, Germany.

Entropy minimization of the non-Newtonian bio-hybrid (Fe3O4-CuO/blood) nanofluid flow over a linear extending sheet by means of induced magnetic field

The physiological system loses heat energy through the bloodstream to nearby cells. Such energy loss can lead to a quick death, anemia, severe hypothermia and high or low blood pressure to heart surgery. As a result, biomedical engineers and physicians are increasingly attracted to the study of entropy production to calculate the energy loss of biological systems. Furthermore, the thermodynamic state of entropy production is used to access cancer cells during chemotherapy treatment and heat transfer in tissues. The current model intends to explore the significance of the non-Fourier heat flux model on Eyring–Powell/Maxwell hybrid nanofluid (Fe3O4–CuO/blood) flow in a linear extending sheet with induced magnetic field and entropy generation. Suitable self-similarity variables are performed to convert momentum and thermal equations determined using the homotopy perturbation method into ordinary differential equations. The significance of distinct physical parameters such as thermal relaxation parameter, volume fraction, fluid parameter, magnetic Prandtl number, Biot number, Brinkman number, heat source, Eckert number, radiation and heat source on velocity, temperature, skin friction coefficient, Nusselt number, entropy production, streamlines and isotherm are represented through figures. It is recognized that the fluid friction irreversibility is comparatively higher than thermal irreversibility and highly dominates the total entropy generation. The nanoparticle volume fraction diminishes the velocity and induced magnetic field of both Eyring–Powell and Maxwell hybrid nanofluid. Fluid friction irreversibility is more in Maxwell fluid compared to the Eyring–Powell fluid.

Diversity maximization-based transfer diagnosis approach of rotating machinery

The existing transfer diagnosis methods based on entropy minimization are easy to lead to trivial solution. To solve this problem, a deep diversity maximization-based adversarial transfer diagnosis approach for rotating machinery is presented in this paper. Firstly, the deep convolution neural network is utilized as the feature encoder to learn the characteristics of vibration signals in different working conditions. The diversity maximization strategy is taken to balance the entropy minimization, so as to avoid trivial local minimum. The categories predicted by nontrivial domain adaptation method are more diverse. Moreover, the entropy is conducted to evaluate the uncertainty of the predicted result of the classifier. Using this deterministic strategy based on entropy to adjust the domain discriminator. The experimental study demonstrates the effectiveness of the developed method.

Optimised Electro-Osmotic Flow in Rectangular Microchannels with Smoothed Corners

Electro-osmotic flows are a means of circulating polar fluids through microchannels without resorting to mechanical pumping. The lack of moving parts, of noise and the ease of integration in silicon chips make them an interesting option for microchip cooling and miniaturized total analysis systems. This paper describes an optimization in terms of first- and second-law analysis of the cross-section of a microchannel subject to electro-osmotic flow. Starting from rectangular cross-sections of different aspect ratios, its corners are progressively smoothed and the resulting Poiseuille and Nusselt numbers computed. Performance evaluation criteria are then used to assess the change in, among others, heat transfer rate, temperature difference between wall and bulk fluid, and equivalent pumping power. The entropy number is also computed and the results commented. It is found that the latter criterion highlights a configuration of minimum entropy generation, whereas the trends of the heat transfer and temperature difference are opposite to that of the equivalent pumping power.

Discrete-Velocity-Direction Models of BGK-type with Minimum Entropy: I. Basic Idea

Discrete-Velocity-Direction Models of BGK-type with Minimum Entropy: I. Basic Idea

Sea Surface Moving Target Detection Using a Modified Keystone Transform on Wideband Radar Data

The echoes collected by wideband radar systems provide abundant information on target scatterers, which is beneficial to target detection, classification, and recognition. However, as the radar range resolution increases, range cell migration (RCM) during the coherent integration (CI) period happens much easier, which may cause a degradation of target detection probability. In addition, due to the target’s orientation and structure relative to the radar, the distribution characteristics of the target scatterers in high-resolution range profiles (HRRPs) and the detection window length may vary from pulse to pulse, which may reduce the performance of traditional energy integration (EI) detectors. To solve those problems, moving range-spread target (RST) detection combining the modified keystone transform (MKT) and improved EI (IEI) is proposed in this paper. Firstly, based on waveform entropy minimization, MKT using hunter–prey optimization (HPO) is introduced to reduce the CI gain loss. The target Doppler ambiguity factor is estimated using such an effective optimization technique. Then, the IEI detector optimized by the adaptive threshold and detection window is utilized to achieve target detection, which minimizes the sensitivity of the traditional EI detector to the detection window length. The proposed method significantly improves the performance of moving RSTs in sea clutter without prior knowledge of the target Doppler ambiguity factor. Experiments are conducted by comparing the proposed method with other competing methods on both simulation data and real sea clutter data. The results demonstrate that the proposed method can obtain the CI more efficiently and has a higher detection probability.

Minimal entropy conditions for scalar conservation laws with general convex fluxes

We are concerned with the minimal entropy conditions for one-dimensional scalar conservation laws with general convex flux functions. For such scalar conservation laws, we prove that a single entropy-entropy flux pair ( η ( u ) , q ( u ) ) (\eta (u),q(u)) with η ( u ) \eta (u) of strict convexity is sufficient to single out an entropy solution from a broad class of weak solutions in L l o c ∞ L^\infty _{\mathrm { loc}} that satisfy the inequality: η ( u ) t + q ( u ) x ≤ μ \eta (u)_t+q(u)_x\leq \mu in the distributional sense for some non-negative Radon measure μ \mu . Furthermore, we extend this result to the class of weak solutions in L l o c p L^p_{\mathrm {loc}} , based on the asymptotic behavior of the flux function f ( u ) f(u) and the entropy function η ( u ) \eta (u) at infinity. The proofs are based on the equivalence between the entropy solutions of one-dimensional scalar conservation laws and the viscosity solutions of the corresponding Hamilton-Jacobi equations, as well as the bilinear form and commutator estimates as employed similarly in the theory of compensated compactness.

Optimization Analysis of Thermodynamic Characteristics of Serrated Plate-Fin Heat Exchanger.

This study explores the use of Multi-Objective Genetic Algorithm (MOGA) for thermodynamic characteristics of serrated plate-fin heat exchanger (PFHE) under numerical simulation method. Numerical investigations on the important structural parameters of the serrated fin and the j factor and the f factor of PFHE are conducted, and the experimental correlations about the j factor and the f factor are determined by comparing the simulation results with the experimental data. Meanwhile, based on the principle of minimum entropy generation, the thermodynamic analysis of the heat exchanger is investigated, and the optimization calculation is carried out by MOGA. The comparison results between optimized structure and original show that the j factor increases by 3.7%, the f factor decreases by 7.8%, and the entropy generation number decreases by 31%. From the data point of view, the optimized structure has the most obvious effect on the entropy generation number, which shows that the entropy generation number can be more sensitive to the irreversible changes caused by the structural parameters, and at the same time, the j factor is appropriately increased.

The effects of staggered triangular ribs induced vortex flow on hydrothermal behavior and entropy generation in microchannel heat sink

For dissipating the increasing heat in high flux microelectronic device, an original microchannel heat sink with staggered triangular ribs induced vortex flow is designed. In this work, the width (Wch) and height (Hch) of a single microchannel with staggered triangular ribs (MC-STR) are both 0.2 mm, as well as the width (Wr), height (Hr) and length (Lr) of the triangular rib are in ranges of 0.025–0.15 mm, 0.012–0.072 mm and 0.06–0.16 mm, respectively. The influences of the relative rib width (Wr/Wch), relative rib height (Hr/Wch) and relative rib length (Lr/Wch) on the hydrothermal behavior and entropy generation in MC-STR are numerically analyzed at the Reynolds number (Re) of 243–639. The results show that the increases in Wr/Wch and Hr/Wch can effectively improves the thermal performance, and reduce augmentation entropy generation number, but increase the flow resistance. In addition, the rise of Lr/Wch has weak effect on hydrothermal behavior and entropy generation. Overall, the MC-STR with Wr/Wch = 0.5, Hr/Wch = 0.3 and Lr/Wch = 0.5 at Re = 639 yields the highest performance evaluation criterion (1.502). Moreover, the minimum augmentation entropy generation number (0.236) is achieved at MC-STR with Wr/Wch = 0.5, Hr/Wch = 0.36 and Lr/Wch = 0.4 as Re = 639.

Entropy generation analysis of turbulent flow in conical tubes with dimples: a numerical study

Minimizing the entropy generation rate is one of the key performance indicators for enhancing the thermal design of heat exchangers. This paper introduces a comprehensive numerical entropy generation analysis of turbulent water flow inside—newly proposed—conical tubes with dimples subjected to a constant heat flux. The effect of different tube diameter ratios (DR = 1, 1.5, 2, 3, and 5) and flow modes (convergent and divergent tube configurations) on the thermal, viscous, and total entropy generation rates is investigated within Reynolds number (Re) range of 3 × 103–40 × 103 using ANSYS-Fluent package. Realizable k-ε (RKE) turbulence model is adopted in this study. A well-validated 3D model was adopted to estimate the dimensionless indices: Bejan number (Be), enhanced entropy generation ratio (Ns,en), and the irreversibility distribution ratio ({phi }_{mathrm{s}}) to characterize the entropy generation performance and to compare conical dimpled tubes to smooth ones. The results showed that total entropy production rate values for dimpled tube geometries are lower than those for the corresponding smooth ones, especially for convergent dimpled tubes. Convergent dimpled tubes with DR in the range of 1.5–3 achieved the lowest total entropy production values over the whole Re range, with an average value of 0.20 W K−1, as compared to an average value of 0.34 W K−1 for the smooth configurations. The average Ns,en values for dimpled convergent tubes with DR = 1.5–3 are 0.46 and 0.80 at Re = 3000 and 40,000, with reductions of 50.54% and 3.61% at both Re values, respectively. The study also showed that the entropy generation analysis could provide an effective tool to highlight the optimal design of tube heat exchangers based on the minimum entropy generation and the enhanced entropy generation ratio.

Risk Assessment of Water and Sand Inrush in Mining under Thick Loose Layer Based on Comprehensive Weight-Cloud Model

Many deep mining mines in southwestern Shandong Province of China are covered with thick loose layers. When mining near the loose layers, there is a risk of water and sand inrush, which threatens the personal safety of miners. The prediction of sudden water and sand inrush is difficult due to the comprehensive influence of many factors, and the influencing factors are fuzzy and random. To solve this problem, in this paper, a new risk assessment method of water and sand inrush based on comprehensive weight and cloud model was proposed. Seven factors are selected as indexes: the aquifer thickness, the thickness ratio of sand layer to clay layer, the thickness of bottom clay layer, the coal seam thickness, the percentage of core recovery, the geological structure, and the bedrock thickness. The assessment index system is established, and the index is divided into three grades. A comprehensive weighting method, which combines analytic hierarchy process (AHP), entropy weight method (EWM), and minimum entropy principle, is used to reasonably assign the weight of index. Based on the cloud generator equation, the membership function is obtained. The assessment result of the assessment object is obtained by combining the membership degree and the weight of index. The comprehensive weight-cloud model assessment method is applied to the risk assessment of water and sand inrush in the 6311-2 working face in the sixth mining area of Baodian Coal Mine. According to the assessment results, the following conclusions can be drawn: (1) the bedrock thickness and the coal seam thickness are the main factors of water and sand inrush under loose layer mining; (2) the assessment results obtained by the comprehensive weight-cloud model method are consistent with the actual situation. The assessment method can provide scientific reference for the safe mining under the thick loose layer in the deep mines of southwest Shandong.

A signal-filtering and feature-enhancement method based on ensemble local mean decomposition and adaptive morphological filtering

The bearing fault signals from the spindle motors of computer numerical control machines are complex and non-linear due to being coupled to multiple subsystems. The complexity of industrial signals, with increased industrial noise, and the difference in fault features in different life cycles and different individual signals bring great challenges for fault feature extraction. In this paper, a signal-filtering and feature-enhancement method based on an ensemble local mean decomposition and adaptive morphological filtering (ELMD-AMF) method is proposed. First, the original vibration signal of the bearing is reconstructed by ELMD to reducing interference from background noise. Next, an improved feature-enhancement process based on AMF is constructed, a particle swarm optimization with maximum-weighted spectral kurtosis as an optimization objective is used to adaptively construct the size of the structural element, and a morphology hat product operator one is adapted to extract the periodic impulse features. Finally, the effectiveness of the method is proved by using an actual three-phase induction motor matched with an NTN ceramic bearing and a FAG metal bearing, respectively. Further, compared with minimum entropy deconvolution and fast kurtogram methods, the result proves that the proposed method has better performance for both early-failure and late-failure scenarios under real-world engineering conditions.

Importance of bioconvection flow on tangent hyperbolic nanofluid with entropy minimization

The amalgamation of microorganisms in the nanofluid is significant in beautifying the thermal conductivity of several systems, such as microfluid devices, chip-shaped microdevices, and enzyme biosensors. The current investigation studies mixed convective flow of the entropy minimization of unsteady MHD tangent hyperbolic nanoliquid because a stretching surface has motile densityviaconvective and slip conditions. For the novelty of this work, the variable transport characteristics caused by dynamic viscosity, thermal conductivity, nanoparticle mass permeability, and microbial organism diffusivity are considered. It is considered that the vertical sheet studying the flow. By using the appropriate alteration, the governing equations for the most recent flow analysis were altered into a non-dimension relation. Through MATLAB Software bvp4c, the PDE model equations have been made for these transformed equations. Engineering-relevant quantities against various physical variables include force friction, Nusselt number, Sherwood number, and microorganism profiles. The results showed good consistency compared to the current literature. Moreover, these outcomes revealed that augmentation in the magnitude of the magnetic field and velocity slip parameter declines the velocity profile. The reverse impact is studied inWe. In addition, heat transfer is typically improved by the influence of thermal radiation parameters, Brownian movement, and thermophoretic force. The physical interpretation has existed through graphical and tabular explanations.

A medical unsupervised domain adaptation framework based on Fourier transform image translation and multi-model ensemble self-training strategy.

Well-established segmentation models will suffer performance degradation when deployed on data with heterogeneous features, especially in the field of medical image analysis. Although researchers have proposed many approaches to address this problem in recent years, most of them are feature-adaptation-based adversarial networks, the problems such as training instability often arise in adversarial training. To ameliorate this challenge and improve the robustness of processing data with different distributions, we propose a novel unsupervised domain adaptation framework for cross-domain medical image segmentation. In our proposed approach, Fourier transform guided images translation and multi-model ensemble self-training are integrated into a unified framework. First, after Fourier transform, the amplitude spectrum of source image is replaced with that of target image, and reconstructed by the inverse Fourier transform. Second, we augment target dataset with the synthetic cross-domain images, performing supervised learning using the original source set labels while implementing regularization by entropy minimization on predictions of unlabeled target data. We employ several segmentation networks with different hyperparameters simultaneously, pseudo-labels are generated by averaging their outputs and comparing to confidence threshold, and gradually optimize the quality of pseudo-labels through multiple rounds self-training. We employed our framework to two liver CT datasets for bidirectional adaptation experiments. In both experiments, compared to the segmentation network without domain alignment, dice similarity coefficient (DSC) increased by nearly 34% and average symmetric surface distance (ASSD) decreased by about 10. The DSC values were also improved by 10.8% and 6.7%, respectively, compared to the existing model. We propose a Fourier transform-based UDA framework, the experimental results and comparisons demonstrate that the proposed method can effectively diminish the performance degradation caused by domain shift and performs best on the cross-domain segmentation tasks. Our proposed multi-model ensemble training strategy can also improve the robustness of the segmentation system.