Equalization Method Research Articles

Sound Quality Prediction Method of Dual-Phase Hy-Vo Chain Transmission System Based on MFCC-CNN and Fuzzy Generation

redict the sound quality of dual-phase Hy-Vo chain transmission system noise using a small sample size. Noise acquisition tests are conducted under various working conditions, followed by subjective evaluations using the equal interval direct one-dimensional method. Objective evaluations are performed using the Mel-frequency cepstral coefficient (MFCC). To understand the impact of the MFCC order and the frame number on prediction accuracy, MFCC feature maps of different specifications are analyzed. The dataset is expanded threefold using fuzzy generation with an appropriate membership degree. The convolutional neural network (CNN) is developed, utilizing MFCC feature maps as inputs and evaluation scores as outputs. Results indicate a positive correlation between the frame number and prediction accuracy, whereas higher MFCC orders introduce redundancy, reducing accuracy. The proposed CNN method outperforms three traditional machine learning approaches, demonstrating superior accuracy and resistance to overfitting.

Trait Association Studies to Determine Selection Indices in F5 Segregating Populations of Rice (Oryza sativa L.)

The experiment consisted of 38 promising F5 breeding lines developed and evaluated for correlation coefficient, path coefficient and selection indices studies with four check varieties viz., NVSR-2435, Sardar, Lalkada Gold and GR-18 in RBD with three replications during kharif-2023 at RRRS, Vyara, Gujarat. Genotypic correlation analysis revealed that grain yield per plant appeared to be positively and significantly correlated with productive tillers per plant, plant height, panicle length, straw yield per plant and 100 grain weight, which suggested that these characters can be improved simultaneously towards higher grain yield per plant by direct selection. Genotypic path analysis revealed that positive and direct effects on grain yield per plant were exhibited by days to maturity, productive tillers per plant, panicle length, 100 grain weight and straw yield per plant. Among these traits, panicle length, productive tillers per plant, 100 grain weight and straw yield per plant exhibited direct effect along with highly significant and positive association with grain yield per plant. Therefore, selection for such traits may be considered as most important yield attributing characters. The negligible residual effect of 0.037 was observed in path analysis, indicates that the model explained large proportion of variance. Thirty-one selection indices were constructed in all possible combinations of grain yield per plant (X1) along with four components viz., productive tiller per plant (X2), panicle length (X3), 100 grain weight (X4) and straw yield per plant (X5) through equal weight method. Among all the selection indices, the relative efficiency (%) and expected genetic advance (GA) was noted maximum with four characters selection index (I1245) compared to grain yield per plant (I1).

A multi-criteria measurement and assessment of human capital development in EU-27 countries: A 10-year perspective

In today's global economy and across various types of organizations, human capital is a fundamental resource that determines their existence and development. This is particularly important in the context of ongoing climate-technological transformation processes based on innovative solutions. The role and importance of personal resources are well understood in the EU-27 countries, which base their future on competent human capital. This paper addresses this critical and timely topic by assessing human capital in the EU-27 countries over the period 2013–2022. The research aims to evaluate these countries' human capital potential, distinguishing between the "new EU-13" and the "old EU-14" countries, and to measure the effectiveness of its development during the study period. A crucial part of the research was to determine the impact of human capital on selected indicators of economic growth, innovation, and sustainable development. To achieve these objectives, an original research methodology was developed, employing the COPRAS method to determine the human capital assessment index and the equal weights and CRITIC methods to establish the weights of indicators characterizing the studied human capital. The impact assessment of this capital, along with innovation and unemployment on economic growth, was conducted using an econometric model. Meanwhile, correlations between the human capital assessment index and the indicators characterizing innovation, economic growth, and sustainability were analyzed using Spearman's non-parametric tests. The results show significant variation in human capital between the EU-27 countries and between the "old EU-14" and "new EU-13" groups. The Netherlands, Denmark, and Sweden achieved the best results in terms of human capital development, while Greece, Romania, and Bulgaria had the weakest outcomes. In contrast, Cyprus, Malta, and Portugal recorded the greatest progress during the review period. These results offer extensive opportunities for multidimensional inference and can be used to assess the current state of the EU-27 economy. They should also inform the development of strategies for individual countries and the EU-27 as a whole.

Physical and numerical tests on adjustable–height structures of high–speed railway transition section

The disparity in stiffness and differential settlement between the subgrade and rigid structures can lead to track irregularities within transition sections, resulting in significant train vibrations and impacts. This study proposes an adjustable–height subgrade structure for transition sections by introducing a raft slab to divide the subgrade into upper and lower parts. When settlement occurs in the subgrade, the raft slab can be elevated using height–adjustment techniques to compensate for the settlement. To this end, large–scale physical model tests are conducted under train loading to verify the design's rationality and to elucidate the influence of settlement magnitude and train speed on the stress and deformation of the slab. The experimental results indicate that the maximum principal stress and maximum shear stress of the slab generally increase with settlement magnitude and train speed, and the area near the abutment end is the most unfavorable position for the deformation of the slab. However, it is still lower than the design compressive strength of the C40 concrete structure. Thus, the designed adjustable–height structure meets the load–bearing requirements under train loading. Additionally, a numerical simulation model of the adjustable–height structure is developed to investigate the recovery capabilities and stress distribution of the settled raft slab. The results indicated that whether the lifting operation employed linear load or equal load methods, the raft slab primarily endured compressive stress, and there is stress concentration at the lifting points. For the same lifting displacement, the linear load method proved to be more reasonable than the equal load method. These findings have significant practical and theoretical implications for ensuring the safe and smooth operation of high–speed railways, effectively addressing the post–construction settlement deformation control challenges in ballastless track transition sections.

Exponential stability and non-weighted L 2-gain analysis for switched neutral systems with unstable subsystems

Exponential stability and non-weighted L 2 -gain are studied for switched neutral systems with unstable subsystems. Initially, a time scheduling strategy is introduced to formulate switching-signal-based multiple discontinuous Lyapunov functions. Multiple discontinuous Lyapunov functions can be applied to each subsystem, thereby relaxing the limitations of traditional Lyapunov functions. Meanwhile, mode-dependent average dwell times are utilised to design fast and slow switching signals, ensuring stability in switched neutral systems where the switching between unstable and stable subsystems is arbitrary. Moreover, in the presence of external disturbances, the non-weighted L 2 -gain performance is discussed. Sufficient conditions are proposed to achieve the stability of switched neutral systems and non-weighted L 2 -gain performance using the linear matrix inequality method. The validity of this study is ultimately demonstrated through simulation examples.

Stabilized equal lower-order finite element methods for simulating Brinkman equations in porous media

This paper demonstrates the mixed formulation of the Brinkman problem using linear equal-order finite element methods in porous media modelling. We introduce Galerkin least-squares (GLS) and least-squares (LS) finite element methods to address the incompatibility of finite element spaces, treating velocity, pressure, and vorticity as independent variables. Theoretical analysis examines coercivity and continuity, providing error estimates. Demonstrating resilience in theoretical findings, these methods achieve optimal convergence rates in the L 2 norm by incorporating stabilization terms with low-order basis functions. Numerical experiments validate theoretical predictions, showing the effectiveness of the GLS method and addressing finite element space incompatibility. Additionally, the GLS method exhibits promising capabilities in handling the Brinkman equation at low permeability compared to the LS method. The study reveals an increase in the average pressure difference in the Brinkman problem compared to the Stokes equations as the inlet velocity rises, providing insights into the behaviour of Brinkman equations.

Natural Hazard Induced Coastal Vulnerability in Indian Sundarbans: A Village-level study by using Geospatial and Statistical Techniques

The Sundarbans, an area with a history dating back to the dawn of civilization, has faced numerous environmental hazards that have remarkably affected the lives and livelihoods of its natives. Strom surge and Tropical cyclones are the most substantial natural hazards, causing severe damage to local communities by affecting food security, the economy, shelter and health. By defining vulnerability as a function of exposure, sensitivity, and resilience capacity, we calculated a composite vulnerability index (CVI) using equal weight method (EWM) to assess the vulnerability of mouzas (small administrative units) to natural hazards in the Sundarbans, India. The vulnerability map has been drawn based on composite value of CVI which shows 30.50% of villages falling into the high vulnerability category and 12.06% in the very high vulnerability category. The mouza-level analysis also indicates that 22.62% of Sundarbans’s villages are highly exposed to natural hazards and 19.70 % of villages are classified as being at Very high sensitive. Only 7.07% villages have very high adopted capacity against these natural hazards. Villages in the southern parts and along the coast were found to be more vulnerable to storm surges. Conversely, those situated at higher elevations in the central area exhibited lower vulnerability. In the northern part of the region, several villages faced high to very high vulnerability due to low-lying, waterlogged wetlands. This study offers vital insights for decision-makers, government planners, and disaster management professionals, assisting in the identification of high-risk populations and areas that require immediate preservation efforts.

Robust Cooperative Control for Heterogeneous Uncertain Nonlinear High-Order Fully Actuated Multiagent Systems.

This research is intended to address a robust cooperative control problem of heterogeneous uncertain nonlinear high-order fully actuated multiagent systems (HUN-HOFAMASs). A nonlinear HOFA system model is used to describe the multiagent systems (MASs) with heterogeneous uncertain nonlinear dynamics, which is called the HUN-HOFAMASs. A predictive terminal sliding-mode control-based robust cooperative control scheme is presented to address this problem. In this scheme, heterogeneous nonlinear dynamics of original system are offset to establish a linear constant HOFA system with the help of full actuation feature. Then, a terminal sliding-mode variable for enhancing the system robustness is introduced to handle the uncertainties. Furthermore, a linear incremental prediction model is developed in a HOFA form by means of a Diophantine equation. According to this model, the multistep terminal sliding-mode predictions are yielded to optimize the robust cooperative control performance and compensate for the network-induced communication constraints in the feedback and forward channels. Based on a linear matrix inequality (LMI) method, a necessary and sufficient criterion is derived to discuss the simultaneous consensus and stability of closed-loop HUN-HOFAMASs. The simulation and comparison results of cooperative flying around of multiple spacecraft system are shown to illustrate the capability and advantage of the presented predictive terminal sliding-mode control for robust cooperative control.

Existence of global weak solution to tumor chemotaxis competition systems with loop and signal dependent sensitivity

This article examines the weak solution of a fully parabolic chemotaxis-competition system with loop and signal-dependent sensitivity. The system is subject to homogeneous Neumann boundary conditions within an open, bounded domain \(\Omega\subset\mathbb{R}^n\), where \(n\geq 1\) and \(\partial\Omega\) is smooth. We assume that the parameters in the system are positive constants. Additionally, the initial data \((u_{10}, u_{20}, v_{10}, v_{20})\in L^2(\Omega)\times L^2(\Omega) \times W^{1,2}(\Omega)\times W^{1,2}(\Omega)\) are non-negative. The existence of a weak solution to the problem is established using energy inequality method. For more information see https://ejde.math.txstate.edu/Volumes/2024/56/abstr.html

Effect of Electric Fields on the Decomposition of Phosphate Esters.

Phosphate esters decompose on metal surfaces and form protective polyphosphate films. For many applications, such as in lubricants for electric vehicles and wind turbines, an understanding of the effect of electric fields on molecular decomposition is urgently required. Experimental investigations have yielded contradictory results, with some suggesting that electric fields improve tribological performance, while others have reported the opposite effect. Here, we use nonequilibrium molecular dynamics (NEMD) simulations to study the decomposition of tri-n-butyl phosphate (TNBP) molecules nanoconfined between ferrous surfaces (iron and iron oxide) under electrostatic fields. The reactive force field (ReaxFF) method is used to model the effects of chemical bonding and molecular dissociation. We show that the charge transfer with the polarization current equalization (QTPIE) method gives more realistic behavior compared to the standard charge equilibration (QEq) method under applied electrostatic fields. The rate of TNBP decomposition via carbon-oxygen bond dissociation is faster in the nanoconfined systems than that in the bulk due to the catalytic action of the surfaces. In all cases, the application of an electric field accelerates TNBP decomposition. When electric fields are applied to the confined systems, the phosphate anions are pulled toward the surface with high electric potential, while the alkyl cations are pulled to the surface with lower potential, leading to asymmetric film growth. Analysis of the temperature- and electric field strength-dependent dissociation rate constants using the Arrhenius equation suggests that, on reactive iron surfaces, the increased reactivity under an applied electric field is driven mostly by an increase in the pre-exponential factor, which is linked to the number of molecule-surface collisions. Conversely, the accelerated decomposition of TNBP on iron oxide surfaces can be attributed to a reduction in the activation energy with increasing electric field strength. Single-molecule nudged-elastic band (NEB) calculations also show a linear reduction in the energy barrier for carbon-oxygen bond breaking with electric field strength, due to stabilization of the charged transition state. The simulation results are consistent with experimental observations of enhanced and asymmetric tribofilm growth under electrostatic fields.

Stabilization of fractional nonlinear systems with disturbances via sliding mode control

AbstractIn this article, the sliding mode control (SMC) of fractional nonlinear systems (FNSs) with disturbances is studied. First, a useful fractional power‐rate inequality for is extended to a more general form . Based on the generalized inequality, the method of a modified fractional integral SMC is completed, in which the parameter of the symbolic function is extended to and . This increases the degree of freedom of the sliding mode surface (SMS). Then, the SMC of FNSs is studied for the cases of known and unknown disturbances. In the case of known disturbance, it is proved by the generalized inequality and quadratic Lyapunov function method that the state of the FNSs can converge asymptotically to zero on the SMS. For the case of unknown disturbance, a fractional disturbance observer is used to estimate the disturbance by introducing auxiliary variables . The disturbance estimation error of the proposed FNSs is proved to be bounded. An equivalent global control law is obtained and asymptotic convergence of the FNSs under the action of the controller is proved. Finally, two numerical simulation examples are given to verify the feasibility of the method proposed in this article.

Oriented to a multi-learning mode: Establishing trend-fuzzy-granule-based LSTM neural networks for time series forecasting

In the construction of information granule based neural networks for time series multi-step forecasting, the existing works tend to focus on consecutive-learning mode while rarely explore multi-learning mode. In fact, only under the multi-learning mode can diversified associations among data collected over time granules be well learned. Also, the existing works exhibit limited time interpretability. Here the problem centers around how to endow information granule based neural networks with a multi-learning mode to learn diversified associations simultaneously, and well-articulated trend semantics. To solve these problems, the first originality of this paper stems from a scale equalization method for multilinear-trend fuzzy information granules to track complex trend changes of data in a more accurate and explainable manner from both global and local views. Furthermore, an adaptive rather than empirical or traversal method, which is trend-driven in nature, is tailored for mining diversified associations. The resulting model can give forecasts in the form of granules as well as numerical values, being interpretable and accurate in the sense that: (a) its inputs and output are granules which come with well-defined trend semantics under a customary time concept, and (b) a clump of data is considered in a concise granule whilst roles of diversified associations are ware of during forecasting, making the model less prone to cumulative errors. Appealing experimental results corroborate the effectiveness of the proposed model.

Failure rate analysis and maintenance plan optimization method for civil aircraft parts based on data fusion

In the face of data scarcity in the optimization of maintenance strategies for civil aircraft, traditional failure data-driven methods are encountering challenges owing to the increasing reliability of aircraft design. This study addresses this issue by presenting a novel combined data fusion algorithm, which serves to enhance the accuracy and reliability of failure rate analysis for a specific aircraft model by integrating historical failure data from similar models as supplementary information. Through a comprehensive analysis of two different maintenance projects, this study illustrates the application process of the algorithm. Building upon the analysis results, this paper introduces the innovative equal integral value method as a replacement for the conventional equal interval method in the context of maintenance schedule optimization. The Monte Carlo simulation example validates that the equivalent essential value method surpasses the traditional method by over 20% in terms of inspection efficiency ratio. This discovery indicates that the equal critical value method not only upholds maintenance efficiency but also substantially decreases workload and maintenance costs. The findings of this study open up novel perspectives for airlines grappling with data scarcity, offer fresh strategies for the optimization of aviation maintenance practices, and chart a new course toward achieving more efficient and cost-effective maintenance schedule optimization through refined data analysis.

An Automated Wavelet Scattering Network Classification Using Three Stages of Cataract Disease

إعتام عدسة العين وهو مرض يصيب العين حيث يسبب تشوهًا بصريًا ويمكن أن تؤدي المرحلة المتأخرة من هذا المرض إلى العمى. ويعتبر مرضًا صامتًا يمكن أن يحدث دون ظهور الأعراض. لذلك ، فإن الطريقة الأكثر فاعلية للكشف عن إعتام عدسة العين هي من خلال الكشف الدقيق في الوقت المناسب لمنع الأذى والعمى والعمليات المكلفة. ان الغرض من هذه البحث هو اقتراح نظام آلي يعتمد على شبكة نثر المويجات التي تصنف المرضى إلى أربع فئات: إعتام عدسة العين المبكر وإعتام عدسة العين المتوسط وإعتام عدسة العين المتأخر وعدم إعتام عدسة العين باستخدام 512 صورة لقاعدة البيانات ODIR (212 شخص مصاب و300 شخص غير مصاب). الخطوة الأولى في هذه التقنية هي خطوة المعالجة المسبقة لصورة الشبكية التي كانت عبارة عن مرشح المتوسط ، والذي تم استخدامه لتقليل ضوضاء الصورة. ثم بعد ذلك استخدام طريقة معادلة الرسم البياني التكيفية محدودة التباين (CLAHE) لتحسين مستوى عينات الصورة. بعد ذلك ، يمكن استخراج الخصائص منخفضة التباين من بيانات الصورة باستخدام شبكة تشتت المويجات لاستخدامها في تطبيقات التعلم العميق. في هذه الشبكة ، يتم استخدام مرشحات تحجيم تمرير منخفض وموجات موجية محددة مسبقًا. كان متوسط دقة الطريقة المقترحة 100٪ لأربع فئات (غير مصاب، مبكر، متوسط، شديد). علما ان النتائج موعودة مقارنة مع أعمال أخرى مماثلة.

Finite-time hybrid impulsive formation tracking control of multi-agent systems via aperiodic intermittent communication

This article studies the problem of formation tracking control in multi-agent systems, achieved in finite time, under challenging conditions such as strong nonlinearity, aperiodic intermittent communication, and time-delay effects, all within a hybrid impulsive framework. The impulses are categorized as either stabilizing control impulses or disruptive impulses. Furthermore, by integrating Lyapunov-based stability theory, graph theory, and the linear matrix inequality (LMI) method, new stability criteria are established. These criteria ensure finite-time intermittent formation tracking while considering weak Lyapunov inequality conditions, intermittent communication rates, and time-varying gain strengths. Additionally, the approach manages an indefinite number of impulsive moments and adjusts the control domain’s width based on the average impulsive interval and state-dependent control width. Numerical simulations are provided to validate the applicability and effectiveness of the proposed formation tracking control protocols.

K0.72Na1.71Ca5.79Si6O19 - the first oligosilicate based on [Si6O19]-hexamers and its stability compared to cyclosilicates.

Synthesis experiments were conducted in the quaternary system K2O-Na2O-CaO-SiO2, resulting in the formation of a previously unknown compound with the composition K0.72Na1.71Ca5.79Si6O19. Single crystals of sufficient size and quality were recovered from a starting mixture with a K2O:Na2O:CaO:SiO2 molar ratio of 1.5:0.5:2:3. The mixture was confined in a closed platinum tube and slowly cooled from 1150°C at a rate of 0.1°C min-1 to 700°C before being finally quenched in air. The structure has tetragonal symmetry and belongs to space group P4122 (No. 91), with a = 7.3659 (2), c = 32.2318 (18) Å, V = 1748.78 (12) Å3, and Z = 4. The silicate anion consists of highly puckered, unbranched six-membered oligomers with the composition [Si6O19] and point group symmetry 2 (C2). Although several thousands of natural and synthetic oxosilicates have been structurally characterized, this compound is the first representative of a catena-hexasilicate anion, to the best of our knowledge. Structural investigations were completed using Raman spectroscopy. The spectroscopic data was interpreted and the bands were assigned to certain vibrational species with the support of density functional theory at the HSEsol level of theory. To determine the stability properties of the novel oligosilicate compared to those of the chemically and structurally similar cyclosilicate combeite, we calculated the electronegativity of the respective structures using the electronegativity equalization method. The results showed that the molecular electronegativity of the cyclosilicate was significantly higher than that of the oligostructure due to the different connectivities of the oxygen atoms within the molecular units.

Finite-time quasi-projective synchronization of fractional-order reaction-diffusion delayed neural networks

This paper investigates the finite-time quasi-projective synchronization (FTQPS) issue of fractional-order reaction-diffusion neural networks (FORDNNs). To the best of our knowledge, this paper introduces the concept of FTQPS for the first time. First, an integral-type Lyapunov function is constructed relying on the characterization of the reaction-diffusion term and some inequality methods. Subsequently, the nonlinear feedback control strategy is designed to achieve the FTQPS goal and some sufficient conditions are obtained to guarantee FTQPS of FORDNNs. Further, the system's synchronization speed is measured by estimating the settling time. It should be noted that the above control strategy is also applicable to conventional integer-order reaction-diffusion neural networks with time delays. Finally, a numerical example is used to illustrate the validity of the theoretical analysis presented.

Optimization of IPOP-connected dissimilar power-rated converters: An equal incremental cost-based approach

As electric vehicles gain prominence, optimizing the efficiency of charging stations has become a critical issue. This paper explores the application of dual active bridge (DAB) converters arranged in an input-parallel-output-parallel (IPOP) configuration, offering a flexible solution for power conversion across varied power-rated loads. While previous research mainly focused on optimizing single DABs and establishing the system using identical converters, this study leverages dissimilar modules, enhancing flexibility and operational efficiency. We introduce a novel IPOP system incorporating diverse modules and corresponding switch regulators. Additionally, an efficiency optimization strategy featuring dynamic load allocation is proposed. Herein, each module or each group of modules is responsible for its corresponding power range. Based on curve-fitted power profiles, an equal incremental cost-based method is employed to formulate the path of power allocation. Moreover, a simulated annealing algorithm determines the optimal power output strategy to accommodate dynamic power flow requirements. Experimental comparisons with the conventional equal-power sharing scheme underscore the proposed method’s effectiveness and superiority.

Trajectory tracking control of unmanned surface vehicle based on optimized barrier Lyapunov function under real ocean wave modeling

The state-constrained trajectory tracking control problem of unmanned surface vehicle (USV) under real wave interference is studied in this paper. This method is closer to the actual situation. Firstly, the interference of real ocean waves measured by ocean wave experiments on USV is considered. The wave spectrum and the equal interval method are used to simulate ocean waves in combination with the classical long-crested wave model. Secondly, the simulated wave is innovatively transformed into wave interference force, and the interference of the real wave is applied to the state constraint control of USV. The disturbance observer is used to observe and compensate for the external real wave interference, and the input saturation problem in real situations is fully considered. Furthermore, this paper proposes an improved logarithmic constraint function, which pre-set the performance function to constrain the trajectory tracking position error of the USV within a reasonable range. Finally, the rationality and effectiveness of the control strategy are verified by simulation.

Equal Incremental Cost Method with Adjustable Gamma Control to Solve Generator Scheduling

Generator scheduling remains an intriguing issue within the energy industry. It relates to the optimization of production costs, where system operators must select the optimal combination of available resources to minimize production costs. This paper proposes an enhancement to the Equal Incremental Cost (EIC) Method using Adjustable Gamma Control (AGC) in generator scheduling. Iterations begin with an initial lambda value, then gradually increase with the application of the factor until power demand is met. A variable of 10% is used as an adjustment step in the optimization method. The proposed method is capable of achieving convergence with 100% accuracy, where the power generated by all generators precisely matches the load demand (2,650 MW), at a cost of USD 32,289.03. EIC-AGC ranks second-best after VLIM, albeit with the consequence of consuming 195 seconds. This method is expected to have a significant impact on designing highly accurate economic dispatch techniques. Thus, generator scheduling will lead to a reduction in operational costs compared to current practices.