Minimum Entropy Research Articles

A novel fault diagnosis approach of rolling bearing using intrinsic feature extraction and CBAM-enhanced InceptionNet

Rolling bearings play a crucial role as components in mechanical equipment. Malfunctioning rolling bearings can disrupt the normal operation of the equipment and pose safety hazards. Traditional deep learning-based methods for diagnosing faults in rolling bearings present several issues, such as insufficient feature information of fault samples, high model complexity and low accuracy. To overcome these challenges, this paper introduces an intelligent approach for rolling bearing fault diagnosis using intrinsic feature extraction and convolutional block attention module (CBAM)-enhanced InceptionNet. In our researches, variational mode decomposition (VMD) is adopted to decompose the original signal into multiple band-limited intrinsic mode functions (BLIMFs). In the decomposition process of VMD, the number of decomposition layers k is determined by center frequency method and the optimal BLIMF is chosen based on minimum envelope entropy. Subsequently, the continuous wavelet transform is employed to transform the optimal BLIMFs into time-frequency images. Finally, the obtained time-frequency images are fed into the proposed CBAM-enhanced InceptionNet for fault state diagnosis. Experiments on two different datasets prove that the method has stable and reliable accuracy. Comparative experiments have demonstrated that this method can reduce network model parameters and improve diagnosis efficiency while achieving high accuracy.

Homological growth of Artin kernels in positive characteristic

We prove an analogue of the Lück Approximation Theorem in positive characteristic for certain residually finite rationally soluble (RFRS) groups including right-angled Artin groups and Bestvina–Brady groups. Specifically, we prove that the mod p homology growth equals the dimension of the group homology with coefficients in a certain universal division ring and this is independent of the choice of residual chain. For general RFRS groups we obtain an inequality between the invariants. We also consider a number of applications to fibring, amenable category, and minimal volume entropy.

High-Resolution Azimuth Missing Data SAR Imaging Based on Sparse Representation Autofocusing

Due to significant electromagnetic interference, radar interruptions, and other factors, Azimuth Missing Data (AMD) may occur in Synthetic Aperture Radar (SAR) echo, resulting in severe defocusing and even false targets. An important approach to solving this problem is to utilize Compressed Sensing (CS) methods on AMD echo to reconstruct complete echo, which can be abbreviated as the AMD Imaging Algorithm (AMDIA). However, the State-of-the-Art AMDIA (SOA-AMDIA) do not consider the influence of motion phase errors, resulting in an unacceptable estimation error of the complete echo reconstruction. Therefore, in order to enhance the practical applicability of AMDIA, this article proposes an improved AMDIA using Sparse Representation Autofocusing (SRA-AMDIA). The proposed SRA-AMDIA aims to accurately focus the imaging result, even in the Phase Error AMD (PE-AMD) echo case. Firstly, a Phase-Compensation Function (PCF) based on the phase history of the scene centroid is designed. When the PCF is multiplied with the PE-AMD echo in the range-frequency domain, a coarse-focused sparse representation signal can be obtained in the range-Doppler domain. However, due to the influence of unknown PE, the sparsity of this sparse representation signal is unsatisfying, breaking the sparse constraints requirement of the CS method. Therefore, we introduced a minimum entropy autofocusing algorithm to autofocus this sparse representation signal. Next, the estimated PE is compensated for this sparse representation signal, and a more sparse representation signal is obtained. Hence, the non-PE complete echo can be reconstructed. Finally, the estimated complete echo can be used with classic imaging algorithms to obtain high-resolution imaging results under the PE-AMD condition. Simulation and real measured data have verified the effectiveness of the proposed SRA-AMDIA.

A Cross-Domain Generative Data Augmentation Framework for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a crucial fine-grained sentiment analysis task that aims to determine sentiment polarity in a specific aspect term. Recent research has advanced prediction accuracy by pre-training models on ABSA tasks. However, due to the lack of fine-grained data, those models cannot be trained effectively. In this paper, we propose the cross-domain generative data augmentation framework (CDGDA) that utilizes a generation model to produce in-domain, fine-grained sentences by learning from similar, coarse-grained datasets out-of-domain. To generate fine-grained sentences, we guide the generation model using two prompt methods: the aspect replacement and the aspect–sentiment pair replacement. We also refine the quality of generated sentences by an entropy minimization filter. Experimental results on three public datasets show that our framework outperforms most baseline methods and other data augmentation methods, thereby demonstrating its efficacy.

Period-refined CYCBD using time synchronous averaging for the feature extraction of bearing fault under heavy noise

Deconvolution methods have been widely used in machinery fault diagnosis. However, their application would be confined due to the heavy noise and complex interference since the fault feature in the measured signal becomes rather weak. Time synchronous averaging (TSA) can enhance the periodic components and suppress the others by the comb filter function. And in the iteration process of the deconvolution methods, the filtered signal after each iteration can be further processed using TSA, and the time delay with maximum Gini index value is refined as the iterative period for the next iteration. Benefitting from these advantages, a period-refined maximum second-order cyclostationarity blind deconvolution (PRCYCBD) using TSA is proposed for the weak fault detection of rolling element bearings (REBs) in this paper. Firstly, without any prior knowledge, the proposed method which can estimate the period more accurately is more suitable for the weak fault detection of REBs, especially incipient fault. Secondly, TSA is firstly applied to estimate the iterative period rather than just depending on the Signal Noise Ratio (SNR) of the filtered signal in the iterative process . Furthermore, the new improvement frame can be expanded to other deconvolution methods using iterative algorithms, especially under heavy noise. Finally, a simulation with a slight bearing fault as well as two real experimental data including the vibration signal with the wind turbine bearing fault and the acoustical signal with the locomotive wheel bearing fault is used to verify the superiority of the proposed PRCYCBD compared with the traditional minimum entropy deconvolution and the traditional autocorrelation-improved cyclostationarity blind deconvolution.

High-Speed Maneuvering Target Inverse Synthetic Aperture Radar Imaging and Motion Parameter Estimation Based on Fast Spare Bayesian Learning and Minimum Entropy

High-speed maneuvering target inverse synthetic aperture radar (ISAR) imaging is always a hot topic in signal processing. High-speed maneuvering targets often have high-order maneuvering characteristics, such as translational and rotational characteristics, which destroy the signal structure of stationary targets and make basic imaging processing methods such as range-Doppler (RD) algorithm no longer suitable. In this paper, a high-resolution imaging method for high-speed maneuvering targets is proposed, which uses the fast sparse Bayesian learning (SBL) algorithm and the minimum entropy algorithm for ISAR high-resolution imaging and motion parameter estimation, respectively. Because SBL makes full use of the characteristics of the target and the environment, it can obtain an ISAR high-resolution image of the maneuvering target. However, the high computational complexity caused by matrix inversion and some matrix operations in SBL iteration limit the practical application of SBL in ISAR imaging. In view of the special structure of the matrix required to be inverted, we propose a fast SBL algorithm, which uses a new decomposition method to obtain the decomposition formula of the inverse matrix. Based on the decomposition factors, the multiplication operation involving the inverse matrix can be quickly calculated using fast Fourier transform (FFT), which greatly improves the computational efficiency. Image entropy represents the sharpness and focusing degree of an image, and so the minimum entropy algorithm can estimate the motion parameters of maneuvering targets more accurately. We combine the minimum entropy algorithm with the fast SBL algorithm to realize phase error correction and high-resolution imaging, which has better noise sensitivity and can obtain the best focusing degree image. Finally, simulation results prove the effectiveness of this algorithm.

Learning the Finer Things: Bayesian Structure Learning at the Instantiation Level

Successful machine learning methods require a trade-off between memorization and generalization. Too much memorization and the model cannot generalize to unobserved examples. Too much over-generalization and we risk under-fitting the data. While we commonly measure their performance through cross validation and accuracy metrics, how should these algorithms cope in domains that are extremely under-determined where accuracy is always unsatisfactory? We present a novel probabilistic graphical model structure learning approach that can learn, generalize and explain in these elusive domains by operating at the random variable instantiation level. Using Minimum Description Length (MDL) analysis, we propose a new decomposition of the learning problem over all training exemplars, fusing together minimal entropy inferences to construct a final knowledge base. By leveraging Bayesian Knowledge Bases (BKBs), a framework that operates at the instantiation level and inherently subsumes Bayesian Networks (BNs), we develop both a theoretical MDL score and associated structure learning algorithm that demonstrates significant improvements over learned BNs on 40 benchmark datasets. Further, our algorithm incorporates recent off-the-shelf DAG learning techniques enabling tractable results even on large problems. We then demonstrate the utility of our approach in a significantly under-determined domain by learning gene regulatory networks on breast cancer gene mutational data available from The Cancer Genome Atlas (TCGA).

Joint ISAR imaging and azimuth scaling under low SNR using parameterized compensation and calibration method with entropy minimum criterion

In general, the method of conventional motion compensation for inverse synthetic aperture radar (ISAR) imaging is divided into translational motion compensation (TMC) and rotational motion compensation (RMC) in sequence. TMC is the premise of rotational compensation and the most critical procedure is range alignment. However, the deviation of echo correlation results in the poor performance of range alignment under low signal-to-noise ratio (SNR). Therefore, a new high-resolution ISAR imaging and azimuth scaling method under low SNR using parameterized compensation and calibration is proposed in this paper. Firstly, the target motion is modeled, in which translational motion is modeled as formula of the polynomial coefficient vector. In addition, entropy minimization corresponding to echo signal with compensation term based on coefficients is taken as objective function. Moreover, the particle swarm optimization (PSO) algorithm is utilized to search the global optimal parameters to be estimated precisely and efficiently to implement joint motion compensation and azimuth scaling. The experimental results from both simulated and real data verify the effectiveness and robustness of the method.

Water Cycle Health Assessment Using the Combined Weights and Relative Preference Relationship VIKOR Model: A Case Study in the Zheng-Bian-Luo Region, Henan Province

Both the natural and social water cycles form part of the regional water cycle, and the assessment of the health of the social water cycle provides useful recommendations for resource allocation, urban planning, and development. The Zheng-Bian-Luo region (Zhengzhou, Kaifeng, Luoyang city cluster in China) is used as an example in this study. The three-level “goal criterion index” is used to develop a water cycle index system based on deeper knowledge of the notion of the social water cycle. The system has four criterion layers that measure water quantity, utility, quality, and ecology, in addition to 22 index levels regarding the total water resources and drinking water compliance rate. By using this as a foundation, the minimum information entropy principle was applied to couple AHP (Analytic Hierarchy Process) and EFAST (Extended Fourier Amplitude Sensitivity Analysis) in order to calculate the comprehensive weights of the evaluation indicators and build a VIKOR (Intuitionistic Fuzzy Multi-attribute Decision Making Method) model of the relative preference relationship of the fused weights. This model was then compared to the conventional VIKOR model and the FCE (Fuzzy Comprehensive Evaluation Method) method in order to reflect on the objectivity of the evaluation results. The primary barriers preventing the improvement of water cycle health in the Zheng-Bian-Luo region were determined in this study by using the barrier degree model. The findings demonstrate that over the past 11 years, the overall water cycle health in the Zheng-Bian-Luo region has developed toward a healthy trend and that the water cycle health level in the region has gradually improved from the initial sub-pathological state to a healthy state. The results also demonstrate compliance with domestic drinking water sources, comprehensive water consumption per capita, the water consumption of CNY 10,000 of industrial-added value, the water consumption of CNY 10,000 of GDP, and the water consumption of CNY 10,000 for water. The primary barrier to the Zheng-Bian-Luo region’s improvement in water health is the water consumption ratio. The findings of this study can serve as a scientific foundation for creating a balanced urban water cycle and achieving long-term development in the area.

Thermal radiation impact on chemical reactive flow of micropolar nanomaterial subject to Brownian diffusion and thermophoresis phenomenon

Background and objectiveEntropy generation (a novel prospective) in different thermodynamical processes has usefulness in polymer process. Importance of entropy optimization is noticed in nuclear reactions, heat exchangers, biological system, electronic cooling, combustion, porous media, thermal systems, turbo machinery, turbine systems and many others. Entropy minimization has been utilized for assessment of thermal energy through thermodynamical processes. Entropy minimization rate is employed to boosts up efficiency of thermodynamical system. Therefore, present investigation is aimed to investigate optimized radiative flow of micropolar nanofluid due to exponential stretched curved surface. Heat expression with heat generation and radiation is explored. Moreover, interesting aspects of homogenous and heterogeneous chemical reactions are also presented. For nanofluid characteristics the Buongiorno's model is incorporated. Velocity and thermal slip conditions are explored. Mathematical modeling of flow expressions are presented through curvilinear coordinates. Entropy rate is modeled employing second law of thermodynamics. MethodologySuitable relations have been employed to get the dimensionless systems. Resultant dimensionless systems are computed through ND-solve technique. ResultsGraphical analysis for sundry variables on liquid flow, entropy generation, temperature, microrotation velocity and concentration are addressed. An opposite scenario for microrotation and velocity holds against material parameter. Heat transport rate augments in presence of slip constraint. Change in radiation constant and thermophoresis variable correspond to higher temperature. Entropy generation has enhancing trend for radiation parameter. Concentration has opposite scenario for random and thermophoresis diffusion variables. Larger thermal ratio parameter enhances entropy generation.

Compound fault diagnosis method for rolling bearings based on the multipoint kurtosis spectrum and AO-MOMDEA

In compound rolling bearing faults, different types of fault signals are coupled and cross-affected, which makes it difficult to separate and extract weak fault features, and making it easy to miss diagnoses or misdiagnose. Aiming at solving this problem, a compound fault diagnosis method for rolling bearings based on the multipoint kurtosis spectrum (MKS) and the aquila optimizer multipoint optimal minimum entropy deconvolution adjusted (AO-MOMEDA) method is proposed. Variational modal decomposition is used to reduce the noise of the original vibration signal and improve the recognition of the MKS so as to predict the fault type and preset the search range of the initial period value. A new fault feature strength index with relative amplitude difference is proposed and, combined with the kurtosis of the envelope spectrum, a multi-objective optimization function is constructed. Through the excellent optimization characteristics of the aquila optimizer, the optimal initial period value and filter length are automatically obtained. The optimized MOMEDA method is used for optimal deconvolution of different types of fault signals. Envelope demodulation of the deconvolution signal is carried out to obtain the 1.5-dimensional spectrum of the envelope signal. The types of rolling bearing composite faults are identified according to the fault features in the 1.5-dimensional spectrum. The feasibility of the proposed method is verified through measurements of the signals of rolling bearings with three different types of compound faults. The experimental results show that the proposed method can overcome the influence of strong fault signals and improve the accuracy of compound fault identification. Our results have application value in engineering practice.

THE COMPARISON OF THE EFFECTS OF THRESHOLDING METHODS ON SEGMENTATION USING THE MOTH FLAME OPTIMIZATION ALGORITHM

Segmentation is an important preprocessing step that directly affects the success in image processing applications. There are many methods and approaches used for the segmentation process. Thresholding is a frequently used approach among these methods. There are several suggested approaches to thresholding. In this study, six different thresholding approaches were used as the fitness functions using the moth flame algorithm and the results obtained from these approaches were compared. In experimental studies, seven different threshold levels of 10 different images were studied. In comparisons made with three different metrics, it was seen that the Otsu method was generally more successful. It has also been observed that the minimum cross entropy and Renyi entropies can be used as alternatives.

Entropy Minimization Versus Diversity Maximization for Domain Adaptation.

Entropy minimization has been widely used in unsupervised domain adaptation (UDA). However, existing works reveal that the use of entropy-minimization-only may lead to collapsed trivial solutions for UDA. In this article, we try to seek possible close-to-ideal UDA solutions by focusing on some intuitive properties of the ideal domain adaptation solution. In particular, we propose to introduce diversity maximization for further regulating entropy minimization. In order to achieve the possible minimum target risk for UDA, we show that diversity maximization should be elaborately balanced with entropy minimization, the degree of which can be finely controlled with the use of deep embedded validation in an unsupervised manner. The proposed minimal-entropy diversity maximization (MEDM) can be directly implemented by stochastic gradient descent without the use of adversarial learning. Empirical evidence demonstrates that MEDM outperforms the state-of-the-art methods on four popular domain adaptation datasets.

Augmented Complex Minimization of Error Entropy With Fiducial Points for Power System Frequency Estimation

Frequency estimation plays an important role in ensuring the stability of the power system. The augmented complex minimum error entropy (ACMEE) algorithm has achieved better performance in frequency estimation under impulsive noise environments. However, it has the problem of unknown error probability density function (PDF) in frequency tracking, the improved algorithms called the complex minimization of error entropy with fiducial points (CMEEF) and the augmented CMEEF (ACMEEF) are proposed in this paper. The ACMEEF algorithm based on widely linear modeling uses second-order statistical information to process the non-circular signal through the complex voltage signal. At the same time, it incorporates a location-sensitive cost into minimum error entropy (MEE) to automatically locate the peak of the error PDF at the origin, which can accurately estimate the grid frequency under the unbalanced system and interference of noise. The stability of the proposed algorithm and the computational complexity are analyzed. Finally, the enhanced frequency estimation performance of the proposed ACMEEF algorithm is verified through simulation results of different synthetic signals and real-world data.

Processing of Airborne Microwave Photonic SAR Raw Data With Inaccurate RSF

Due to system instability and other reasons, the actual range sampling frequency of the system may deviate from the ideal value for the microwave photonic synthetic aperture radar (SAR). This deviation may lead to severe residual range cell migration (RCM) and even range defocus after imaging, which can seriously affect the image quality. To resolve this problem, this paper proposes an airborne microwave photonic SAR imaging algorithm based on inaccurate system parameter estimation. Firstly, the algorithm estimates and compensates for the range spatial-variant motion error to eliminate the effect of this motion error on the remaining RCM and range defocus. Secondly, based on the minimum entropy criterion of the image, we use the optimization model to estimate the actual range sampling frequency (RSF). Finally, the existing wide-beam autofocus method is used to correct the azimuth spatial-variant motion error. The simulation data and the measured data processing results verify the effectiveness of the proposed method.

12-nm Stable Pre-Amplifier Physical Unclonable Function With Self-Destruct Capability

Physical unclonable functions (PUFs) produce full security keys without needing to store the key directly in nonvolatile memory (NVM) or publicly. PUFs rely on a stable entropy source across voltage, temperature, and lifetime. A high-gain preamplifier (Pre-Amp) bit cell was built into a dense 2-D array configured as 16 cells per bitline (BL) and 64 cells per wordline (WL). A hardware (HW) sample of 40 chips (1 Kb/chip) was manufactured in GLOBAL FOUNDRIES (GF) 12-nm (12 lp) CMOS technology for a total raw bit count of 40 960 bits. HW characterization was performed to support power supply ranging from 0.7 to 1.0 V and junction temperatures ranging from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$-$</tex-math> </inline-formula> 40 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\circ}$</tex-math> </inline-formula> C to 125 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\circ}$</tex-math> </inline-formula> C with a worst case bit error rate (BER) of 0.174% after stabilization. The entropy source array is complete for productization with control logic and analog power system block for process, voltage, and temperature (PVT) compensation. A stable bit identification and sensing circuit is designed to identify bit cells that are robust against varied test conditions. The NIST 800-90B test suite was run on the native array and stable entropy source bits with minimum entropy scores of 0.65/bit and 0.697/bit, respectively. This article then introduces for the first time ever the ability for a PUF key to be corrupted and physically destroyed, which can be utilized to stop a tamper event or to corrupt obsolete chips. In the self-destruct (SD) mode, the entropy source data can be irreversibly destroyed, blocking all future authentication attempts. HW data show the before and after SD bitmaps, where electromigration (EM) physically breaks the connection of the entropy source bit cells from the sensing circuits. A safety lock circuit is also included to prevent inadvertent SD.

Effect of entropy minimization and melting heat on gold-magnesium oxide hybrid nanofluid in squeezing channel with magnetic field with radiation

The principal aim of this investigation is to examine the gold and magnesium oxide hybrid nanoparticles in different physical capacities on the water-based hybrid nanofluid and elaborate on an erratic squeezing flow among both infinite plates. Melting effect and thermal radiation depict heat transfer features. Production of the Bejan number and entropy is upward. The explains the governing equations, and the standard transformation is supplied. The numerical solutions are carried out using an efficient finite element method. To analyze difference between heat transfer and its implication in industrial zone, the Nusselt number was arranged in a horizontal pattern. The results showed that a thicker thermal boundary layer increased the Eckert number and the volume fraction of gold nanoparticles. Anyhow, the heat transfer rate by Au–MgO/water was necessarily greater than the Au–water. The obtained results are excellent and vigorously oppose those that are applicable to the literature by the attached position. It was established for hybrid nanofluids to display higher entropy generation rates. The outcomes of this analysis were the consequence of the appraisal of the impact on few required form parameters in heat transfer and consequently on the expansion for industrial use.

Entropy minimization and domain adversarial training guided by label distribution similarity for domain adaptation

Entropy minimization and domain adversarial training guided by label distribution similarity for domain adaptation

Entropy-maximization oriented interpretable health indicators for locating informative fault frequencies for machine health monitoring

Machine health monitoring is an important domain to provide timely anomaly detection and diagnostic supports for condition based maintenance. Health indicator (HI) construction is an intuitive and efficient way to conduct continuous machine health monitoring. In this study, a maximization-entropy optimization oriented interpretable HI is proposed to locate informative fault frequencies for machine health monitoring. At first, a probabilistic HI is defined based on the sum of weighted spectral amplitudes in normalized square envelope spectrum (NSES) and its newly defined constraint. Next, different from maximization of negative entropy or minimization of entropy for extracting repetitive transients caused by localized faults, this paper provides a new perspective for integrating entropy maximization with maximum sparsity weights to propose a novel convex optimization degradation modeling methodology for construction of HIs. Besides the new perspective of entropy maximization, the novelty of this study lies in that optimized model weights can automatically locate fault characteristic frequencies in the NSES without any prior knowledge so that the probabilistic HI is sensitive to incipient abnormality. Based on the analysis of several run-to-failure bearing datasets, it is demonstrated the effectiveness and superiority of the proposed methodology for machine health monitoring and incipient fault diagnosis.

Multi-Dimensional Value Evaluation of Energy Storage Systems in New Power System Based on Multi-Criteria Decision-Making

The power sector may reduce carbon emissions and reach carbon neutrality by accelerating the energy transition and lowering its reliance on fossil fuels. However, there are limitations on the new power system’s ability to operate safely and steadily due to the randomness, volatility, and intermittent nature of renewable energy supply. The key to solving this issue is to harness the flexible resources that energy storage systems (ESSs) represent; however, ESSs have more than a value for providing system flexibility. Thus, this study suggested a flexible, technical, economic, and environmental value index system based on multi-criteria decision-making models for evaluating the multi-dimensional value of ESSs. First, the objective and subjective weights are obtained using the CRITIC model and best–worst method; then, the weights are combined using the minimum relative entropy model. The complete values of five typical ESSs are then evaluated using the TOPSIS model. Three scenarios that reflect the traits of the new power system are then created and analyzed. The outcome demonstrates the following: (1) as the new power system continues to grow, the value of ESSs’ flexibility has become ever more crucial; (2) as the need for system flexibility increases, the value of electrochemical ESSs gradually manifests.