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Journal articles on the topic 'Modified multi- variable A-function'
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Srimannarayana, N., Fredric Ayant, Y. Pragathi Kumar, D. K. Pavan Kumar, and B. Satyanarayana. "Multiple eulerian integrals with modified multivariable I-function having general arguments." Journal of Interdisciplinary Mathematics 26, no. 1 (2023): 77–94. http://dx.doi.org/10.47974/jim-1649.
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Many authors have recently provided a number of expressions for a general Eulerian integrals about the multi-variable H-functions. Inspired by these works, we evaluated here a general class of multi-variable Eulerian integrals with modified multi-variable I-function (MMIF) with general arguments, which has been defined in this article. Some of the particular cases have been discussed. These results will help to deduce numerous useful integrals.
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Ayant, F., Y. Pragathi Kumar, N. Srimannarayana, and B. Satyanarayana. "TRANSFORMATION FORMULAE FOR MODIFIED MULTI-VARIABLE I-FUNCTION OF PRASAD." Advances in Mathematics: Scientific Journal 9, no. 6 (2020): 3663–74. http://dx.doi.org/10.37418/amsj.9.6.44.
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Frédéric, Ayant, Kumar Prvindra, and Singh Harendra. "Multidimensional Laguerre Transform and Modified of generalized multivariable A-function." APPLIED SCIENCE PERIODICAL XXIII, no. 2, May 2021 (2021): 14–28. https://doi.org/10.5281/zenodo.6796596.
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<em> The object of this paper is to use the multidimensional Laguerre transforms involving the modified of </em> <em>generalized multivariable A-function. In the end, we shall see several corollaries and remarks.</em>
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Ma, Song-Hua, Yi-Pin Lu, Jian-Ping Fang, and Zhi-Jie Lv. "Time Evolution of Folded (2+1)-Dimensional Solitary Waves." Zeitschrift für Naturforschung A 64, no. 5-6 (2009): 309–14. http://dx.doi.org/10.1515/zna-2009-5-604.
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Abstract With an extended mapping approach and a linear variable separation approach, a series of solutions (including theWeierstrass elliptic function solutions, solitary wave solutions, periodic wave solutions and rational function solutions) of the (2+1)-dimensional modified dispersive water-wave system (MDWW) is derived. Based on the derived solutions and using some multi-valued functions, we find a few new folded solitary wave excitations.
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Wang, Fan, and Guige Gao. "Optimization of short-term wind power prediction of Multi-kernel Extreme Learning Machine based on Sparrow Search Algorithm." Journal of Physics: Conference Series 2527, no. 1 (2023): 012075. http://dx.doi.org/10.1088/1742-6596/2527/1/012075.
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Abstract Aiming at the problem that the single kernel function of kernel extreme learning machine (KELM) cannot adapt to the variable actual wind power. This paper proposes a modified prediction model which can increase the accuracy of prediction. The prediction model uses multiple kernel functions instead of a single kernel function and optimizes the kernel parameters by using a sparrow search algorithm (SSA). Finally, through the simulation and comparison experiments, the proposed prediction model has better prediction accuracy than the conventional prediction model.
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Asghar, Amber, Aamir Sanaullah, Muhammad Hanif, and Laila A. Al-Essa. "Enhancing Precision in Population Variance Vector Estimation: A Two-Phase Sampling Approach with Multi-Auxiliary Information." Sains Malaysiana 53, no. 7 (2024): 1693–702. http://dx.doi.org/10.17576/jsm-2024-5307-16.
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To enhance precision in estimating unknown population parameters, an auxiliary variable is often used. However, in scenarios where required information on an auxiliary variable is partially or fully unavailable, two-phase sampling is commonly employed. The challenge of estimating the variance vector using multi-auxiliary variables is a less explored area in current literature. This paper addresses the estimation of vector of unknown population variances for multiple study variables by using an estimated vector of variances derived from multi-auxiliary information. This approach is particularly relevant when population variances for the multi-auxiliary variables are not known prior to the survey. The paper introduces a generalized variance and a vector of biases for the proposed multivariate estimator. Special cases of the proposed multivariate variance estimator are provided, accompanied by expressions for mean square errors. Theoretical mathematical conditions are discussed to guide the preference for the proposed estimator. Through the analysis of real-world application-based data, the applicability and efficiency of the proposed multivariate variance estimator are demonstrated, outperforming modified versions of multivariate variance estimators. Additionally, a simulation study validates the superior performance of the proposed estimator compared to its modified estimators.
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Li, Qiaoping, and Sanyang Liu. "Dual-stage adaptive finite-time modified function projective multi-lag combined synchronization for multiple uncertain chaotic systems." Open Mathematics 15, no. 1 (2017): 1035–47. http://dx.doi.org/10.1515/math-2017-0087.
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Abstract In this paper, for multiple different chaotic systems with unknown bounded disturbances and fully unknown parameters, a more general synchronization method called modified function projective multi-lag combined synchronization is proposed. This new method covers almost all of the synchronization methods available. As an advantage of the new method, the drive system is a linear combination of multiple chaotic systems, which makes the signal hidden channels more abundant and the signal hidden methods more flexible. Based on the finite-time stability theory and the sliding mode variable structure control technique, a dual-stage adaptive variable structure control scheme is established to realize the finite-time synchronization and to tackle the parameters well. The detailed theoretical derivation and representative numerical simulation is put forward to demonstrate the correctness and effectiveness of the advanced scheme.
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Al-Suhili, Rafa H. "Multi-Sites Multi-Variables Forecasting Model for Hydrological Data using Genetic Algorithm Modeling." Journal of Engineering 21, no. 3 (2015): 54–72. http://dx.doi.org/10.31026/j.eng.2015.03.04.
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A two time step stochastic multi-variables multi-sites hydrological data forecasting model was developed and verified using a case study. The philosophy of this model is to use the cross-variables correlations, cross-sites correlations and the two steps time lag correlations simultaneously, for estimating the parameters of the model which then are modified using the mutation process of the genetic algorithm optimization model. The objective function that to be minimized is the Akiake test value. The case study is of four variables and three sites. The variables are the monthly air temperature, humidity, precipitation, and evaporation; the sites are Sulaimania, Chwarta, and Penjwin, which are located north Iraq. The model performance was checked by comparing it's results with the results of six forecasting models developed for the same data by Al-Suhili and khanbilvardi, 2014.The check of the performance of the new developed model was made for three forecasted series for each variable, using the Akaike test which indicates that the developed model is more successful, since it gave the minimum (AIC) values for (91.67 %) of the forecasted series. This indicates that the developed model had improved the forecasting performance. For the rest of cases (8.33%), other models gave the lowest AIC value, however it is slightly lower than that given by the developed model. Moreover the t-test for monthly means comparison between the models indicates that the developed model has the highest percent of succeed (100%).
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Chen, Liang, Xiaokai Chang, and Sanyang Liu. "A Three-Operator Splitting Perspective of a Three-Block ADMM for Convex Quadratic Semidefinite Programming and Beyond." Asia-Pacific Journal of Operational Research 37, no. 04 (2020): 2040009. http://dx.doi.org/10.1142/s0217595920400096.
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In recent years, several convergent variants of the multi-block alternating direction method of multipliers (ADMM) have been proposed for solving the convex quadratic semidefinite programming via its dual, which is inherently a [Formula: see text]-block separable convex optimization problem with coupled linear constraints. Among these multi-block ADMM-type algorithms, the modified [Formula: see text]-block ADMM in [Chang, XK, SY Liu and X Li (2016). Modified alternating direction method of multipliers for convex quadratic semidefinite programming. Neurocomputing, 214, 575–586] bears a peculiar feature that the augmented Lagrangian function is not necessarily to be minimized with respect to the block-variable corresponding to the quadratic term in the objective function. In this paper, we lay the theoretical foundation of this phenomenon by interpreting this modified [Formula: see text]-block ADMM as a special implementation of the Davis–Yin [Formula: see text]-operator splitting [Davis, D and WT Yin (2017). A three-operator splitting scheme and its optimization applications. Set-Valued and Variational Analysis, 25, 829–858]. Based on this perspective, we are able to extend this modified [Formula: see text]-block ADMM to a generalized [Formula: see text]-block ADMM, in the sense of [Eckstein, J and DP Bertsekas (1992). On the Douglas–Rachford splitting method and the proximal point algorithm for maximal monotone operators. Mathematical Programming, 55, 293–318], which not only applies to the more general convex composite quadratic programming problems but also admits the flexibility of achieving even better numerical performance.
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Nurdin, Muhammad Rafi Hasan, Muhammad Ohid Ullah, Adji Achmad Rinaldo Fernandes, Eni Sumarminingsih, and Solimun Solimun. "Development of Semiparametric Smoothing Spline Path Analysis on Cashless Society." CAUCHY: Jurnal Matematika Murni dan Aplikasi 10, no. 1 (2025): 53–71. https://doi.org/10.18860/cauchy.v10i1.29846.
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Path analysis requires assumptions to be met, particularly the linearity assumption, which can be tested using the Ramsey Regression Specification Error Test (RESET). Parametric path analysis is appropriate when all variable relationships are linear. For entirely non-linear relationships, a nonparametric model can be used, while a semiparametric model applies if there is a mix of linear and non-linear relationships. One nonparametric method is spline smoothing, which requires determining the spline polynomial order in estimating the nonparametric path function. Determining the spline polynomial order is challenging because there is no standard test for it. This study thus develops a modified Ramsey RESET to identify the optimal spline smoothing order. The development involves modifying the second regression equation with a nonparametric spline smoothing regression of orders 2 to 5. The modified Ramsey RESET algorithm is applied to cashless data, and the results are used to estimate a multi-group semiparametric smoothing spline function with a dummy variable approach. This estimation yields a goodness of fit of 94.14%, indicating that Product Quality and the Moderating Effect of Cashless Usage Frequency can explain Cashless User Satisfaction and Cashless User Loyalty by 94.14%, with the remaining 5.86% explained by variables outside the research model
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Zhang, Wen-Ting, Wei-Lu Chen, Li-Pu Zhang, and Chao-Qing Dai. "Interaction Behaviours Among Special Solitons in the (2+1)-Dimensional Modified Dispersive Water-Wave System." Zeitschrift für Naturforschung A 68, no. 6-7 (2013): 447–53. http://dx.doi.org/10.5560/zna.2013-0025.
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A modified mapping method is used to obtain variable separation solutions with two arbitrary functions of the (2+1)-dimensional modified dispersive water-wave system. Based on the variable separation solution and by selecting appropriate functions, we discuss interaction behaviours among special anti-solitons constructed by multi-valued functions. The analysis results exhibit that the interaction behaviours among special anti-dromion, dromion-like anti-peakon, and dromion-like anti-semifoldon are all non-completely elastic and phase shifts exist, while the interaction behaviour among dromionlike anti-semifoldons is completely elastic and without phase shifts.
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Godbole, P. N., M. N. Viladkar, and J. Noorzaei. "A modified frontal solver with multi-element and variable degrees of freedom features." Computers & Structures 39, no. 5 (1991): 525–34. http://dx.doi.org/10.1016/0045-7949(91)90061-p.
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Magnus, Robert. "A scaling approach to bumps and multi-bumps for nonlinear partial differential equations." Proceedings of the Royal Society of Edinburgh: Section A Mathematics 136, no. 3 (2006): 585–614. http://dx.doi.org/10.1017/s0308210500005072.
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The problem −Δu + F(V (εx), u) = 0 is considered in Rn. For small ε > 0, solutions are obtained that approach, as ε → 0, a linear combination of specified functions, mutually translated by O(1/ε). These are the so-called multi-bump solutions. The method involves a rescaling of the variables and the use of a modified implicit function theorem. The usual implicit function theorem is inapplicable, owing to lack of convergence of the derivative of the nonlinear Hilbert space operator, obtained after an appropriate rescaling, in the operator-norm topology. An asymptotic formula for the solution for small ε is obtained.
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Tang, Chenqi, Lingen Chen, Huijun Feng, and Yanlin Ge. "Four-Objective Optimizations for an Improved Irreversible Closed Modified Simple Brayton Cycle." Entropy 23, no. 3 (2021): 282. http://dx.doi.org/10.3390/e23030282.
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An improved irreversible closed modified simple Brayton cycle model with one isothermal heating process is established in this paper by using finite time thermodynamics. The heat reservoirs are variable-temperature ones. The irreversible losses in the compressor, turbine, and heat exchangers are considered. Firstly, the cycle performance is optimized by taking four performance indicators, including the dimensionless power output, thermal efficiency, dimensionless power density, and dimensionless ecological function, as the optimization objectives. The impacts of the irreversible losses on the optimization results are analyzed. The results indicate that four objective functions increase as the compressor and turbine efficiencies increase. The influences of the latter efficiency on the cycle performances are more significant than those of the former efficiency. Then, the NSGA-II algorithm is applied for multi-objective optimization, and three different decision methods are used to select the optimal solution from the Pareto frontier. The results show that the dimensionless power density and dimensionless ecological function compromise dimensionless power output and thermal efficiency. The corresponding deviation index of the Shannon Entropy method is equal to the corresponding deviation index of the maximum ecological function.
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Company, Rafael, Vera N. Egorova, Lucas Jodar, and Fazlollah Soleymani. "A Local Radial Basis Function Method for High-Dimensional American Option Pricing Problems." Mathematical Modelling and Analysis 23, no. 1 (2018): 117–38. http://dx.doi.org/10.3846/mma.2018.008.
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In this work, we apply the local Wendland radial basis function (RBF) for solving the time-dependent multi dimensional option pricing nonlinear PDEs. Firstly, cross derivative terms of the PDE are removed with a change of spatial variables based in LDLT factorization of the diffusion matrix. Then, it is discussed that the valuation of a multi-asset option up to 4D can be computed using a modified shape parameter algorithm. In fact, several experiments containing of three and four assets are worked out showing that the results of the presented method are in good agreement with the literature and could be much more accurate once the shape parameter is chosen carefully.
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Ahmed, M. S., and Atsu S. S. Dorvlo. "A GENERAL CLASS OF ESTIMATORS UNDER MULTI PHASE SAMPLING." Statistics in Transition new series 10, no. 2 (2009): 183–92. http://dx.doi.org/10.59170/stattrans-2009-015.
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This paper derives the general estimators for finite population mean using multivariate auxiliary information under multiphase sampling. Here a number of auxiliary variables are considered in each phase under general sampling design. The properties of these estimators are studied and the results are presented for simple random sampling without replacement (SRSWOR) scheme. Using a modified cost function the optimum sample sizes are also derived.
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Liu, Jian, Zhi-heng Gong, Cheng-dong Wu, and En-yang Gao. "A Multi-angle Face Recognition Algorithm Based on Modified Gaussian Process Latent Variable Mode." Journal of Electronics & Information Technology 35, no. 9 (2014): 2033–39. http://dx.doi.org/10.3724/sp.j.1146.2013.00412.
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Li, Jiaqi, Zhaoyi He, Le Yu, Lian He, and Zuzhen Shen. "Multi-Objective Optimization and Performance Characterization of Asphalt Modified by Nanocomposite Flame-Retardant Based on Response Surface Methodology." Materials 14, no. 16 (2021): 4367. http://dx.doi.org/10.3390/ma14164367.
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In order to improve the safety of the tunnel asphalt pavement in the event of a fire, and reduce the deterioration of the low temperature crack resistance of the asphalt by the flame retardant. The research uses aluminum hydroxide (ATH) as a smoke suppressant, diethyl aluminum hypophosphite (ADP) as a flame retardant, and halloysite nanotubes (HNTs) as a synergist to modified styrene-butadiene-styrene block copolymer (SBS) modified asphalt (MA). First, the content of ATH, ADP, and HNTs was used as the response variable. The physical properties (Penetration, Softening point, Ductility) and static flame retardant properties (Limiting oxygen index meter, Ignition point) of the asphalt modified by nanocomposite flame-retardant (HNTs-CFRMA) were the response variables. The response surface methodology was used to design the test, and regression models were established to analyze the influence of flame retardants on the performance of asphalt. Then, comprehensively considering the effects of physical properties and flame retardant properties, the normalized desirability function was used to perform a multi-objective optimization design on the components of the nanocomposite flame retardant modifier to obtain the best flame retardant formula. Finally, the rheological properties of MA, conventional flame-retardant modified asphalt (CFRMA), and HNTs-CFRMA were tested based on Dynamic shear rheometer, Multiple stress creep test, Force ductility tester, and Bending beam rheometer. The performance of flame-retardant and smoke suppression were tested by the Cone calorimeter tests. The result shows that ATH, ADP, and HNTs can enhance the high temperature performance of asphalt, reduce the penetration. The addition of HNTs can increase significantly the softening point and reduce the deteriorating effect of flame retardants on the low temperature performance of asphalt; the addition of ATH and HNTs can improve significantly the flame retardancy of asphalt. Based on the desirability function of power exponent, the formulation of the nanocomposite flame retardant with better physical properties and flame retardant properties is ATH:ADP:HNTs = 3:5:1, and the total content is 9 wt%. Nanocomposite flame retardants can improve obviously the high temperature rheological properties of asphalt. The rutting factor and the cracking factor of HNTs-CFRMA improve markedly, and the irrecoverable creep compliance is reduced, compared with MA and CFRMA. Nanocomposite flame retardant can make up for the deterioration of conventional flame retardants on asphalt’s low temperature performance. At the same time, it has better flame-retardant performance and smoke suppression performance.
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Huang, Mingqing, Lin Chen, Ming Zhang, and Shulin Zhan. "Multi-Objective Function Optimization of Cemented Neutralization Slag Backfill Strength Based on RSM-BBD." Materials 15, no. 4 (2022): 1585. http://dx.doi.org/10.3390/ma15041585.
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Tailings produced in the beneficiation of Carlin-type gold deposits are characterized by fine particle size and high mud content. When neutralized with wasted acid generated by pressurized pre-oxidation, the tailings turn to neutralized slag and perform as a novel backfill material. To understand the influential behavior of variable factors on the strength and its optimization of cemented neutralization slag backfill, RMS-BBD design test was carried out with 56–60% slurry mass fraction, 12.5–25% cement/(neutralization slag + waste rock) (i.e., C/(S+R)) and 30–40% waste rock content. A modified three-dimensional quadratic regression model was proposed to predict the strength of cemented neutralization slag backfill. The results showed that backfill strength predicted by the modified ternary quadratic regression model was in high coincidence with the data of backfill mixture tests. C/(S+R) was predominant in backfill strength with regard to every single influential factor throughout the curing age, and the mass fraction of slurry had a significant effect on the later strength. From the perspective of economic and engineering operation, a multi-objective function method was further introduced to optimize the backfill strength. The optimal mixture proportion of cemented neutralized slag backfill slurry was: 58.4% slurry mass fraction, 32.2% waste rock content, and 20.1% C/(S+R). The backfill strength of this mixture proportion on days 7, 28 and 56 was verified as 0.42, 0.64 and 0.85 MPa, respectively. RSM-BBD design and multi-objective function optimization proposed a reliable way to evaluate and optimize the strength of neutralized slag backfill with high mud content.
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Dan-Isa, Ado, and D. P. Atherton. "A Computational Method for Obtaining the z-Transform from the s-Transform." Transactions of the Institute of Measurement and Control 17, no. 3 (1995): 107–11. http://dx.doi.org/10.1177/014233129501700301.
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This paper presents a method for the numerical determination of the z-transform and the modified z-transform of a proper rational function expressed in the Laplace transform complex variables. The method is easy to implement on a computer using any high-level language and does not necessarily require the poles of the s-domain function to be found. The method is equally applicable to single-loop and multi-loop systems.
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Uporn, Rapeepat, and Pongchanun Luangpaiboon. "Multi-Response Surface Optimization of a Cryogenic Freezing Process via Variable Neighborhood Modified Simplex Search." Advanced Science Letters 19, no. 10 (2013): 2970–73. http://dx.doi.org/10.1166/asl.2013.5056.
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Jiang, Zhanyuan, Jianquan Ge, Qiangqiang Xu, and Tao Yang. "Impact Time Control Cooperative Guidance Law Design Based on Modified Proportional Navigation." Aerospace 8, no. 8 (2021): 231. http://dx.doi.org/10.3390/aerospace8080231.
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The paper proposes a two-dimensional impact time control cooperative guidance law under constant velocity and a three-dimensional impact time control cooperative guidance law under time-varying velocity, which can both improve the penetration ability and combat effectiveness of multi-missile systems and adapt to the complex and variable future warfare. First, a more accurate time-to-go estimation method is proposed, and based on which a modified proportional navigational guidance (MPNG) law with impact time constraint is designed in this paper, which is also effective when the initial leading angle is zero. Second, adopting cooperative guidance architecture with centralized coordination, using the MPNG law as the local guidance, and the desired impact time as the coordination variables, a two-dimensional impact time control cooperative guidance law under constant velocity is designed. Finally, a method of solving the expression of velocity is derived, and the analytic function of velocity with respect to time is given, a three-dimensional impact time control cooperative guidance law under time-varying velocity based on desired impact time is designed. Numerical simulation results verify the feasibility and applicability of the methods.
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Jiang, Yingdan, Yang Yu, Lu Tang, Junhao Yang, Yujia Lu, and Zongguang Yu. "A Low Jitter, Wideband Clock Generator for Multi-Protocol Data Communications Applications." Electronics 12, no. 14 (2023): 3196. http://dx.doi.org/10.3390/electronics12143196.
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This paper presents a charge-pump phase-locked loop (PLL) frequency-synthesizer-based low-jitter wideband clock generator for multi-protocol data communications applications. Automatic frequency calibration (AFC) using linear variable time window technology and modified multi-modulus dividers (MMD) based on sub-multi-modulus dividers (SMMD) are developed for faster locking, lower jitter, and implementation of multi-protocol data communications applications. The clock generator is fabricated in 0.18 μm CMOS technology. The measured division ratio of the multi-modulus divider ranges from 1.875 to 25, and the output frequency is 46.875~625 MHz. The lock time does not exceed 30 μs, while jitter is less than 500 fs.
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Eyad, Radwan, Salih Khalil, Awada Emad, and Nour Mutasim. "Modified phase locked loop for grid connected single phase inverter." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 3934–43. https://doi.org/10.11591/ijece.v9i5.pp3934-3943.
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Connecting a single-phase or three-phase inverter to the grid in distributed generation applications requires synchronization with the grid. Synchronization of an inverter-connected distributed generation units in its basic form necessitates accurate information about the frequency and phase angle of the utility grid. Phase Locked Loop (PLL) circuit is usually used for the purpose of synchronization. However, deviation in the grid frequency from nominal value will cause errors in the PLL estimated outputs, and that’s a major drawback. Moreover, if the grid is heavily distorted with low order harmonics the estimation of the grid phase angle deteriorates resulting in higher oscillations (errors) appearing in the synchronization voltage signals. This paper proposes a modified time delay PLL (MTDPLL) technique that continuously updates a variable time delay unit to keep track of the variation in the grid frequency. The MTDPLL is implemented along a Multi-Harmonic Decoupling Cell (MHDC) to overcome the effects of distortion caused by gird lower order harmonics. The performance of the proposed MTDPLL is verified by simulation and compared in terms of performance and accuracy with recent PLL techniques.
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Xiao, Zi-Jian, Bo Tian, and Yan Sun. "Soliton interactions and Bäcklund transformation for a (2+1)-dimensional variable-coefficient modified Kadomtsev-Petviashvili equation in fluid dynamics." Modern Physics Letters B 32, no. 02 (2018): 1750170. http://dx.doi.org/10.1142/s0217984917501706.
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In this paper, we investigate a (2[Formula: see text]+[Formula: see text]1)-dimensional variable-coefficient modified Kadomtsev-Petviashvili (mKP) equation in fluid dynamics. With the binary Bell-polynomial and an auxiliary function, bilinear forms for the equation are constructed. Based on the bilinear forms, multi-soliton solutions and Bell-polynomial-type Bäcklund transformation for such an equation are obtained through the symbolic computation. Soliton interactions are presented. Based on the graphic analysis, Parametric conditions for the existence of the shock waves, elevation solitons and depression solitons are given, and it is shown that under the condition of keeping the wave vectors invariable, the change of [Formula: see text] and [Formula: see text] can lead to the change of the solitonic velocities, but the shape of each soliton remains unchanged, where [Formula: see text] and [Formula: see text] are the variable coefficients in the equation. Oblique elastic interactions can exist between the (i) two shock waves, (ii) two elevation solitons, and (iii) elevation and depression solitons. However, oblique interactions between (i) shock waves and elevation solitons, (ii) shock waves and depression solitons are inelastic.
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Alhrshy, Laurence, Alexander Lippke, and Clemens Jauch. "Variable Blade Inertia in State-of-the-Art Wind Turbine Structural-Dynamics Models." Energies 16, no. 16 (2023): 6061. http://dx.doi.org/10.3390/en16166061.
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This paper presents a comparison of two methods to represent variable blade inertia in two codes for aero-servo-elastic simulations of wind turbines: the nonlinear aeroelastic multi-body model HAWC2 and the nonlinear geometrically exact beam model BeamDyn for OpenFAST. The main goal is to enable these tools to simulate the dynamic behavior of a wind turbine with variable blade inertia. However, current state-of-the-art load simulation tools for wind turbines cannot simulate variable blade inertia, so the source code of these tools must be modified. The validity of the modified codes is proven based on a simple beam model. The validation shows very good agreement between the modified codes of HAWC2, BeamDyn and an analytical calculation. The add-on of variable blade inertias is applied to reduce the mechanical loads of a 5-megawatt reference wind turbine with an integrated hydraulic-pneumatic flywheel in its rotor blades.
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Webb, G. M., J. F. McKenzie, and G. P. Zank. "Multi-symplectic magnetohydrodynamics." Journal of Plasma Physics 80, no. 5 (2014): 707–43. http://dx.doi.org/10.1017/s0022377814000257.
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AbstractA multi-symplectic formulation of ideal magnetohydrodynamics (MHD) is developed based on the Clebsch variable variational principle in which the Lagrangian consists of the kinetic minus the potential energy of the MHD fluid modified by constraints using Lagrange multipliers that ensure mass conservation, entropy advection with the flow, the Lin constraint, and Faraday's equation (i.e. the magnetic flux is Lie dragged with the flow). The analysis is also carried out using the magnetic vector potentialÃwhere α=Ã⋅dxis Lie dragged with the flow, andB=∇×Ã. The multi-symplectic conservation laws give rise to the Eulerian momentum and energy conservation laws. The symplecticity or structural conservation laws for the multi-symplectic system corresponds to the conservation of phase space. It corresponds to taking derivatives of the momentum and energy conservation laws and combining them to producen(n−1)/2 extra conservation laws, wherenis the number of independent variables. Noether's theorem for the multi-symplectic MHD system is derived, including the case of non-Cartesian space coordinates, where the metric plays a role in the equations.
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Wang, Zhendong, Jianlan Wang, Dahai Li, and Donglin Zhu. "A Multi-Strategy Sparrow Search Algorithm with Selective Ensemble." Electronics 12, no. 11 (2023): 2505. http://dx.doi.org/10.3390/electronics12112505.
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Aiming at the deficiencies of the sparrow search algorithm (SSA), such as being easily disturbed by the local optimal and deficient optimization accuracy, a multi-strategy sparrow search algorithm with selective ensemble (MSESSA) is proposed. Firstly, three novel strategies in the strategy pool are proposed: variable logarithmic spiral saltation learning enhances global search capability, neighborhood-guided learning accelerates local search convergence, and adaptive Gaussian random walk coordinates exploration and exploitation. Secondly, the idea of selective ensemble is adopted to select an appropriate strategy in the current stage with the aid of the priority roulette selection method. In addition, the modified boundary processing mechanism adjusts the transgressive sparrows’ locations. The random relocation method is for discoverers and alerters to conduct global search in a large range, and the relocation method based on the optimal and suboptimal of the population is for scroungers to conduct better local search. Finally, MSESSA is tested on CEC 2017 suites. The function test, Wilcoxon test, and ablation experiment results show that MSESSA achieves better comprehensive performance than 13 other advanced algorithms. In four engineering optimization problems, the stability, effectiveness, and superiority of MSESSA are systematically verified, which has significant advantages and can reduce the design cost.
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Khurana, M., and H. Winarto. "Development and validation of an efficient direct numerical optimisation approach for aerofoil shape design." Aeronautical Journal 114, no. 1160 (2010): 611–28. http://dx.doi.org/10.1017/s0001924000004097.
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Abstract Intelligent shape optimisation architecture is developed, validated and applied in the design of high-altitude long endurance aerofoil (HALE). The direct numeric optimisation (DNO) approach integrating a geometrical shape parameterisation model coupled to a validated flow solver and a population based search algorithm are applied in the design process. The merit of the DNO methodology is measured by computational time efficiency and feasibility of the optimal solution. Gradient based optimisers are not suitable for multi-modal solution topologies. Thus, a novel particle swarm optimiser with adaptive mutation (AM-PSO) is developed. The effect of applying the PARSEC and a modified variant of the original function, as a shape parameterisation model on the global optimal is verified. Optimisation efficiency is addressed by mapping the solution topology for HALE aerofoil designs and by computing the sensitivity of aerofoil shape variables on the objective function. Variables with minimal influence are identified and eliminated from shape optimisation simulations. Variable elimination has a negligible effect on the aerodynamics of the global optima, with a significant reduction in design iterations to convergence. A novel data-mining technique is further applied to verify the accuracy of the AM-PSO solutions. The post-processing analysis, to swarm optimisation solutions, indicates a hybrid optimisation methodology with the integration of global and local gradient based search methods, yields a true optima. The findings are consistent for single and multi-point designs.
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Shanahan, J. W., A. C. Cole, M. J. Semmens, and T. M. LaPara. "Acetate and ammonium diffusivity in membrane-aerated biofilms: improving model predictions using experimental results." Water Science and Technology 52, no. 7 (2005): 121–26. http://dx.doi.org/10.2166/wst.2005.0190.
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Membrane-aerated biofilm reactors (MABRs) are advantageous for wastewater treatment because of their ability to achieve both nitrification and denitrification in a single bioreactor. The stratification of membrane aerated biofilms, however, needs to be better understood so that MABRs can be properly designed and implemented. In this study, we present a modified multi-population model that accounts for variation in effective diffusivity in biofilms of variable biomass density. For biofilms grown at a low fluid velocity (2 cm s−1), the variation in effective diffusivity had a profound effect on the predicted stratification and activity of bacterial populations. For biofilms grown at a high fluid velocity (14 cm s−1), biomass density was relatively constant as a function of depth and thus there was less substantial variation in effective diffusivity; our modified model, therefore, predicted a population stratification that was similar to its original version under these conditions.
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Li, Weiyang, Yixin Nie, and Fan Yang. "Multi-Variable Transformer-Based Meta-Learning for Few-Shot Fault Diagnosis of Large-Scale Systems." Sensors 25, no. 9 (2025): 2941. https://doi.org/10.3390/s25092941.
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Fault diagnosis in large-scale systems presents significant challenges due to the complexity and high dimensionality of data, as well as the scarcity of labeled fault data, which are hard to obtain during the practical operation process. This paper proposes a novel approach, called Multi-Variable Meta-Transformer (MVMT), to tackle these challenges. In order to deal with the multi-variable time series data, we modify the Transformer model, which is the currently most popular model on feature extraction of time series. To enable the Transformer model to simultaneously receive continuous and state inputs, we introduced feature layers before the encoder to better integrate the characteristics of both continuous and state variables. Then, we adopt the modified model as the base model for meta-learning—more specifically, the Model-Agnostic Meta-Learning (MAML) strategy. The proposed method leverages the power of Transformers for handling multi-variable time series data and employs meta-learning to enable few-shot learning capabilities. The case studies conducted on the Tennessee Eastman Process database and a Power-Supply System database demonstrate the exceptional performance of fault diagnosis in few-shot scenarios, whether based on continuous-only data or a combination of continuous and state variables.
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Zeng, Xiang, Leheng Huang, Xiaoguang Fan, et al. "Multi-Mode Damage and Fracture Mechanisms of Thin-Walled Tubular Parts with Cross Inner Ribs Manufactured via Flow Forming." Materials 17, no. 7 (2024): 1576. http://dx.doi.org/10.3390/ma17071576.
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In order to study the multi-mode damage and fracture mechanisms of thin-walled tubular parts with cross inner ribs (longitudinal and transverse inner ribs, LTIRs), the Gurson–Tvergaard–Needleman (GTN) model was modified with a newly proposed stress state function. Thus, tension damage and shear damage were unified by the new stress state function, which was asymmetric with respect to stress triaxiality. Tension damage dominated the modification, which coupled with the shear damage variable, ensured the optimal prediction of fractures of thin-walled tubular parts with LTIRs by the modified GTN model. This included fractures occurring at the non-rib zone (NRZ), the longitudinal rib (LIR) and the interface between the transverse rib (TIR) and the NRZ. Among them, the stripping of material from the outer surface of the tubular part was mainly caused by the shearing of built-up material in front of the rollers under a large wall thickness reduction (ΔT). Shear and tension deformation were the causes of fractures occurring at the NRZ, while axial tension under a large TIR interval (l) mainly resulted in fractures on LIRs. Fractures at the interface between the TIR and NRZ were due to the shearing applied by rib grooves and radial tension during the formation of ribs. This study can provide guidance for the manufacturing of high-performance aluminum alloy thin-walled tubular components with complex inner ribs.
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Suhad, Muhajer Kareem, and Monem S. Rahma Abdul. "A new multi-level key block cypher based on the Blowfish algorithm." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 2 (2020): 685–94. https://doi.org/10.12928/TELKOMNIKA.v18i2.13556.
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Blowfish is a block cypher algorithm used in many applications to enhance security, but it includes several drawbacks. For example, the mix between the key and data is limited. This paper presents a new modification to the Blowfish algorithm to overcome such problems realised through a multi-state operation instead of an XOR. Our proposed algorithm uses three keys in the encryption and decryption processes instead of one for controlling the variable block bits sizes (1, 2, 4, and 8) bits and for determining the state table numbers. These tables are formed from the addition in a Galois field GF (2n ) based on block bit size to increase the complexity of the proposed algorithm. Results are evaluated based on the criteria of complexity, time encryption, throughout, and histogram, and show that the original Blowfish, those modified by other scholars, and our proposed algorithm are similar in time computation. Our algorithm is demonstrated to be the most complex compared with other well-known and modified algorithms. This increased complexity score for our proposed Blowfish makes it more resistant against attempts to break the keys.
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Liang, Chunyu, Xin Xu, Heping Chen, et al. "Machine Learning Approach to Develop a Novel Multi-Objective Optimization Method for Pavement Material Proportion." Applied Sciences 11, no. 2 (2021): 835. http://dx.doi.org/10.3390/app11020835.
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Asphalt mixture proportion design is one of the most important steps in asphalt pavement design and application. This study proposes a novel multi-objective particle swarm optimization (MOPSO) algorithm employing the Gaussian process regression (GPR)-based machine learning (ML) method for multi-variable, multi-level optimization problems with multiple constraints. First, the GPR-based ML method is proposed to model the objective and constraint functions without the explicit relationships between variables and objectives. In the optimization step, the metaheuristic algorithm based on adaptive weight multi-objective particle swarm optimization (AWMOPSO) is used to achieve the global optimal solution, which is very efficient for the objectives and constraints without mathematical relationships. The results showed that the optimal GPR model could describe the relationship between variables and objectives well in terms of root-mean-square error (RMSE) and R2. After the optimization by the proposed GPR-AWMOPSO algorithm, the comprehensive pavement performances were enhanced in terms of the permanent deformation resistance at high temperature, crack resistance at low temperature as well as moisture stability. Therefore, the proposed GPR-AWMOPSO algorithm is the best option and efficient for maximizing the performances of composite modified asphalt mixture. The GPR-AWMOPSO algorithm has advantages of less computational time and fewer samples, higher accuracy, etc. over traditional laboratory-based experimental methods, which can serve as guidance for the proportion optimization design of asphalt pavement.
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Song, Xudong, Yuedong Zhao, Zihao Li, et al. "A Dual-Loop Modified Active Disturbance Rejection Control Scheme for a High-Purity Distillation Column." Processes 13, no. 5 (2025): 1359. https://doi.org/10.3390/pr13051359.
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High-purity distillation columns typically give rise to multi-variable, strongly coupled nonlinear systems with substantial time delay and significant inertia. The control performance of high-purity distillation columns crucially influences the purity of the final product. Taking into account the process of a high-purity distillation column, this article puts forward a dual-loop modified active disturbance rejection control (MADRC) scheme to improve the control of product purity. During the stable operation of the distillation process, the structures of two control loops are, respectively, approximated by two linear transfer function models via open-loop experiments. Subsequently, the compensation part of the MADRC scheme is designed, respectively, for each approximate model. Furthermore, this paper employs singular perturbation theory to prove the stability of MADRC. The performance of the dual-loop MADRC scheme (MADRC) is compared with that of a proportional–integral–derivative (PID) control scheme, a cascade PID control scheme (CPID), and a regular ADRC scheme (ADRC). The simulations demonstrate that the dual-loop MADRC scheme is capable of efficiently tracking the reference value and exhibits optimal disturbance rejection capabilities. Additionally, the superiority of the dual-loop MADRC scheme is validated through Monte Carlo trials.
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Wang, Yuhong, Xu Zhou, Yunxiang Shi, et al. "Transmission Network Expansion Planning Considering Wind Power and Load Uncertainties Based on Multi-Agent DDQN." Energies 14, no. 19 (2021): 6073. http://dx.doi.org/10.3390/en14196073.
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This paper presents a multi-agent Double Deep Q Network (DDQN) based on deep reinforcement learning for solving the transmission network expansion planning (TNEP) of a high-penetration renewable energy source (RES) system considering uncertainty. First, a K-means algorithm that enhances the extraction quality of variable wind and load power uncertain characteristics is proposed. Its clustering objective function considers the cumulation and change rate of operation data. Then, based on the typical scenarios, we build a bi-level TNEP model that includes comprehensive cost, electrical betweenness, wind curtailment and load shedding to evaluate the stability and economy of the network. Finally, we propose a multi-agent DDQN that predicts the construction value of each line through interaction with the TNEP model, and then optimizes the line construction sequence. This training mechanism is more traceable and interpretable than the heuristic-based methods. Simultaneously, the experience reuse characteristic of multi-agent DDQN can be implemented in multi-scenario TNEP tasks without repeated training. Simulation results obtained in the modified IEEE 24-bus system and New England 39-bus system verify the effectiveness of the proposed method.
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Chakrabortt, Ripon K., Alireza Abbasi, and Michael J. Ryan. "A Modified Variable Neighbourhood Search Heuristic for the Multi‐Mode Resource Constrained Project Scheduling Problem with A Case Study." INCOSE International Symposium 29, no. 1 (2019): 1124–39. http://dx.doi.org/10.1002/j.2334-5837.2019.00657.x.
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Arjasakusuma, Sanjiwana, Sandiaga Swahyu Kusuma, Raihan Rafif, Siti Saringatin, and Pramaditya Wicaksono. "Combination of Landsat 8 OLI and Sentinel-1 SAR Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia." ISPRS International Journal of Geo-Information 9, no. 11 (2020): 663. http://dx.doi.org/10.3390/ijgi9110663.
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The rise of Google Earth Engine, a cloud computing platform for spatial data, has unlocked seamless integration for multi-sensor and multi-temporal analysis, which is useful for the identification of land-cover classes based on their temporal characteristics. Our study aims to employ temporal patterns from monthly-median Sentinel-1 (S1) C-band synthetic aperture radar data and cloud-filled monthly spectral indices, i.e., Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Built-up Index (NDBI), from Landsat 8 (L8) OLI for mapping rice cropland areas in the northern part of Central Java Province, Indonesia. The harmonic function was used to fill the cloud and cloud-masked values in the spectral indices from Landsat 8 data, and smile Random Forests (RF) and Classification And Regression Trees (CART) algorithms were used to map rice cropland areas using a combination of monthly S1 and monthly harmonic L8 spectral indices. An additional terrain variable, Terrain Roughness Index (TRI) from the SRTM dataset, was also included in the analysis. Our results demonstrated that RF models with 50 (RF50) and 80 (RF80) trees yielded better accuracy for mapping the extent of paddy fields, with user accuracies of 85.65% (RF50) and 85.75% (RF80), and producer accuracies of 91.63% (RF80) and 93.48% (RF50) (overall accuracies of 92.10% (RF80) and 92.47% (RF50)), respectively, while CART yielded a user accuracy of only 84.83% and a producer accuracy of 80.86%. The model variable importance in both RF50 and RF80 models showed that vertical transmit and horizontal receive (VH) polarization and harmonic-fitted NDVI were identified as the top five important variables, and the variables representing February, April, June, and December contributed more to the RF model. The detection of VH and NDVI as the top variables which contributed up to 51% of the Random Forest model indicated the importance of the multi-sensor combination for the identification of paddy fields.
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Matin, A. O., and F. Misagh. "A MODIFIED MCDM ALGORITHM WITH CUMULATIVE ENTROPY WEIGHTS FOR SELECTING THE WINNER OF THE TENDER." Strategic decisions and risk management, no. 1 (May 2, 2019): 46–51. http://dx.doi.org/10.17747/2618-947x-2019-1-46-51.
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The aim of this research is to evaluate the proposed bids using impartial and entropy weights in a multi-criteria decision-making model. We use matrix data for hypothetical bidding involving nine criteria, with the presence of four domestic and two foreign contractors. Then, using cumulative entropy function, we estimate the entropy weights and use it in a multi-criteria decision-making model. The criteria of experience and knowledge in the field, good history and satisfaction in previous projects, financial and support capabilities, localization of the contractor, having the experience at the site of the project, availability and readiness of equipment and machines, the adequacy of technical staff, the work quality system, the efficient management and appropriate management system, creativity and innovation in similar tasks are the input variables of the decision model. After analyzing them, the proposals are prioritized through a multi-criteria decision-making model. The research findings include Shannon entropy and cumulative entropy-based weights for evaluation criteria and after applying the specific weight for the proposed quotation, the utility rate of each contractor is calculated. The results showed that the use of modified multi-dimensional decision-making method is more advantageous than traditional methods of evaluating bidding proposals in selecting the winner of a tender, and also using cumulative entropy weights in comparison with Shannon's leads to a more realistic choice of contractors.
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Lu, Yang, Chunzhu Wei, Matthew F. McCabe, and Justin Sheffield. "Multi-variable assimilation into a modified AquaCrop model for improved maize simulation without management or crop phenology information." Agricultural Water Management 266 (May 2022): 107576. http://dx.doi.org/10.1016/j.agwat.2022.107576.
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Huang, Song, Na Tian, and Zhicheng Ji. "Particle swarm optimization with variable neighborhood search for multiobjective flexible job shop scheduling problem." International Journal of Modeling, Simulation, and Scientific Computing 07, no. 03 (2016): 1650024. http://dx.doi.org/10.1142/s1793962316500240.
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The simulation on benchmarks is a very simple and efficient method to evaluate the performance of the algorithm for solving flexible job shop scheduling model. Due to the assignment and scheduling decisions, flexible job shop scheduling problem (FJSP) becomes extremely hard to solve for production management. A discrete multi-objective particle swarm optimization (PSO) and simulated annealing (SA) algorithm with variable neighborhood search is developed for FJSP with three criteria: the makespan, the total workload and the critical machine workload. Firstly, a discrete PSO is designed and then SA algorithm performs variable neighborhood search integrating two neighborhoods on public critical block to enhance the search ability. Finally, the selection strategy of the personal-best individual and global-best individual from the external archive is developed in multi-objective optimization. Through the experimental simulation on matlab, the tests on Kacem instances, Brdata instances and BCdata instances show that the modified discrete multi-objective PSO algorithm is a promising and valid method for optimizing FJSP with three criteria.
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Xue, Weifeng, Fang Li, Xuemei Li, and Ying Liu. "A Support Vector Machine-Assisted Metabolomics Approach for Non-Targeted Screening of Multi-Class Pesticides and Veterinary Drugs in Maize." Molecules 29, no. 13 (2024): 3026. http://dx.doi.org/10.3390/molecules29133026.
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The contamination risks of plant-derived foods due to the co-existence of pesticides and veterinary drugs (P&VDs) have not been fully understood. With an increasing number of unexpected P&VDs illegally added to foods, it is essential to develop a non-targeted screening method for P&VDs for their comprehensive risk assessment. In this study, a modified support vector machine (SVM)-assisted metabolomics approach by screening eligible variables to represent marker compounds of 124 multi-class P&VDs in maize was developed based on the results of high-performance liquid chromatography–tandem mass spectrometry. Principal component analysis and orthogonal partial least squares discriminant analysis indicate the existence of variables with obvious inter-group differences, which were further investigated by S-plot plots, permutation tests, and variable importance in projection to obtain eligible variables. Meanwhile, SVM recursive feature elimination under the radial basis function was employed to obtain the weight-squared values of all the variables ranging from large to small for the screening of eligible variables as well. Pairwise t-tests and fold changes of concentration were further employed to confirm these eligible variables to represent marker compounds. The results indicate that 120 out of 124 P&VDs can be identified by the SVM-assisted metabolomics method, while only 109 P&VDs can be found by the metabolomics method alone, implying that SVM can promote the screening accuracy of the metabolomics method. In addition, the method’s practicability was validated by the real contaminated maize samples, which provide a bright application prospect in non-targeted screening of contaminants. The limits of detection for 120 P&VDs in maize samples were calculated to be 0.3~1.5 µg/kg.
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Liu, Yun, Quanxing Liu, Guofu Yin, and Xiaofeng Luo. "Research on the Operational Modal Prediction of Dry Gas Seal System Based on Response Surface Method." Journal of Physics: Conference Series 2101, no. 1 (2021): 012040. http://dx.doi.org/10.1088/1742-6596/2101/1/012040.
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Abstract The cross power spectrum function is used to realize the operational modal analysis and identification of the dry gas seal device system through the multi-reference point least squares complex frequency domain method. The steady state diagram and mathematical indicators MAC, MPD, MPC, MOV and MIF are used to verify the modal results. At the same time, based on the response surface method, with two different operating conditions of medium pressure and rotating speed, modal direction and modal order as the response surface variables, a time-varying modal recognition model is established. Through the Full Factorial experiment design, Box-Behnken experiment design and Central Composite experiment design, the suitable variable sample points are formed. A complete quadratic polynomial response surface model of the system operational modal parameters is established. The complex correlation coefficient, the modified complex correlation coefficient and the root mean square error are used to verify the effectiveness of the response surface model. It provides new method and technical support for realizing time-varying modal identification in this paper.
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Kuehn, Bernhard, Marc Taylor, and Alexander Kempf. "Using machine learning to link spatiotemporal information to biological processes in the ocean: a case study for North Sea cod recruitment." Marine Ecology Progress Series 664 (April 15, 2021): 1–22. https://doi.org/10.3354/meps13689.
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Marine organisms are subject to environmental variability on various temporal and spatial scales, which affect processes related to growth and mortality of different life stages. Marine scientists are often faced with the challenge of identifying environmental variables that best explain these processes, which, given the complexity of the interactions, can be like searching for a needle in the proverbial haystack. Even after initial hypothesis-based variable selection, a large number of potential candidate variables can remain if different lagged and seasonal influences are considered. To tackle this problem, we propose a machine learning framework that incorporates important steps in model building, ranging from environmental signal extraction to automated variable selection and model validation. Its modular structure allows for the inclusion of both parametric and machine learning models, like random forest. Unsupervised feature extractions via empirical orthogonal functions (EOFs) or self-organising maps (SOMs) are demonstrated as a way to summarize spatiotemporal fields for inclusion in predictive models. The proposed framework offers a robust way to reduce model complexity through a multi-objective genetic algorithm (NSGA-II) combined with rigorous cross-validation. We applied the framework to recruitment of the North Sea cod stock and investigated the effects of sea surface temperature (SST), salinity and currents on the stock via a modified version of random forest. The best model (5-fold CV r<sup>2</sup> = 0.69) incorporated spawning stock biomass and EOF-derived time series of SST and salinity anomalies acting through different seasons, likely relating to differing environmental effects on specific life-history stages during the recruitment year.
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Ram, Prakash Ponraj, Ganeshprabhu Devadharshini, Balaji Haripriya, Ganesan Hemadharshini, and Dhanabalan Keerthana. "Modified Multi Input Multilevel DC-DC Boost Converter for Hybrid Energy Systems." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 1067–72. https://doi.org/10.35940/ijeat.D7854.049420.
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DC-DC converters are playing an important role in designing of Electric Vehicles, integration of solar cells and other DC applications. Contemporary high power applications use multilevel converters that have multi stage outputs for integrating low voltage sources. Conventional DC-DC converters use single source and have complex structure while using for Hybrid Energy Systems. This paper proposes a multi-input, multi-output DC-DC converter to produce constant output voltage at different input voltage conditions. This topology is best suitable for hybrid power systems where the output voltage is variable due to environmental conditions. It reduces the requirement of magnetic components in the circuit and also reduces the switching losses. The proposed topology has two parts namely multi-input boost converter and level-balancing circuit. Boost converter increases the input voltage and Level Balancing Circuit produce Multi output. Equal values of capacitors are used in Level Balancing Circuit to ensure the constant output voltage at all output stages. The operating modes of each part are given and the design parameters of each part are calculated. Performance of the proposed topology is verified using MATLAB/Simulink simulation which shows the correctness of the analytical approach. Hardware is also presented to evaluate the simulation results.
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Oliveira, Hugo S., and Helder P. Oliveira. "Transformers for Energy Forecast." Sensors 23, no. 15 (2023): 6840. http://dx.doi.org/10.3390/s23156840.
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Forecasting energy consumption models allow for improvements in building performance and reduce energy consumption. Energy efficiency has become a pressing concern in recent years due to the increasing energy demand and concerns over climate change. This paper addresses the energy consumption forecast as a crucial ingredient in the technology to optimize building system operations and identifies energy efficiency upgrades. The work proposes a modified multi-head transformer model focused on multi-variable time series through a learnable weighting feature attention matrix to combine all input variables and forecast building energy consumption properly. The proposed multivariate transformer-based model is compared with two other recurrent neural network models, showing a robust performance while exhibiting a lower mean absolute percentage error. Overall, this paper highlights the superior performance of the modified transformer-based model for the energy consumption forecast in a multivariate step, allowing it to be incorporated in future forecasting tasks, allowing for the tracing of future energy consumption scenarios according to the current building usage, playing a significant role in creating a more sustainable and energy-efficient building usage.
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Wan, Chenxia, Xianing Chang, and Qinghui Zhang. "Improvement of Road Instance Segmentation Algorithm Based on the Modified Mask R-CNN." Electronics 12, no. 22 (2023): 4699. http://dx.doi.org/10.3390/electronics12224699.
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Although the Mask region-based convolutional neural network (R-CNN) model possessed a dominant position for complex and variable road scene segmentation, some problems still existed, including insufficient feature expressive ability and low segmentation accuracy. To address these problems, a novel road scene segmentation algorithm based on the modified Mask R-CNN was proposed. The multi-scale backbone network, Res2Net, was utilized to replace the ResNet network, and aimed to improve the feature extraction capability. The soft non-maximum suppression algorithm with attenuation function (soft-NMS) was adopted to improve detection efficiency in the case of a higher overlap rate. The comparison analyses of partition accuracy for various models were performed on the adopted Cityscapes dataset. The results demonstrated that the modified Mask R-CNN effectively increased the segmentation accuracy, especially for small and highly overlapping objects. The adopted Res2Net and soft-NMS can effectively enhance the feature extraction and improve segmentation performance. The average accuracy of the modified Mask R-CNN model reached up to 0.321, and was 0.054 higher than Mask R-CNN. This work provides important guidance to design a more efficient road scene instance segmentation algorithm for further promoting the actual application in automatic driving systems.
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Abderrahmane, Ouchatti, Majdoul Redouane, Moutabir Ahmed, Taouni Abderrahim, and Touati Abdelouahed. "Modified T-type topology of three-phase multi-level inverter for photovoltaic systems." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 262–68. https://doi.org/10.11591/ijece.v12i1.pp262-268.
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In this article, a three-phase multilevel neutral-point-clamped inverter with a modified t-type structure of switches is proposed. A pulse width modulation (PWM) scheme of the proposed inverter is also developed. The proposed topology of the multilevel inverter has the advantage of being simple, on the one hand since it does contain only semiconductors in reduced number (corresponding to the number of required voltage levels), and no other components such as switching or flying capacitors, and on the other hand, the control scheme is much simpler and more suitable for variable frequency and voltage control. The performances of this inverter are analyzed through simulations carried out in the MATLAB/Simulink environment on a threephase inverter with 9 levels. In all simulations, the proposed topology is connected with R-load or RL-load without any output filter.
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Tian, Zhuoqun. "An Optimized Deep Learning Model for Stock Price Prediction Using Bi-Directional LSTM with Multi-Inputs and Multi-Steps." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 1079–86. http://dx.doi.org/10.54097/fyg9kh89.
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Predicting stock prices accurately is an inherently challenging task due to the dynamic and fluctuating nature of various influencing factors. However, with the advent and implementation of deep learning, achieving precise stock predictions has become feasible. This study employs Bi-Directional Long Short-Term Memory (LSTM) models to forecast the closing stock price of Tesla for the following day. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) are selected as indicators to show methods' performance. A new variable has been created through open and close stock price. The original method uses close stock prices as the only input for prediction. In contrast, the modified method uses both the open stock price and the created new variable for calculating close stock price. Both methods' parameters are firstly trained and adjusted on training and validation dataset for their best performance. Finally, both methods are applied to test dataset and the value of both indicators depict their prediction performance.
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Jana, Dipak Kumar, Sutapa Pramanik, and Manoranjan Maiti. "A Parametric Programming Method on Gaussian Type-2 Fuzzy Set and Its Application to a Multilevel Supply Chain." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 24, no. 03 (2016): 451–77. http://dx.doi.org/10.1142/s0218488516500239.
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The transportation problem is an important and relevant supply chain optimization problem in the traffic engineering. This paper minimizes shipping costs of a three channel distribution system comprised of plants, distribution centers, and customers. Plants manufacture several products that are delivered to distribution centers. If a distribution center is used then fixed cost is charged. Customers are replenished by an only one distribution center. To characterize the uncertainty that typically occurs in many practical decision environments, this paper considers the supply capacities, demands as Gaussian type-2 fuzzy variables. To provide a modelling framework for optimization problems with multi-fold uncertainty, different reduction methods are proposed to transform a Gaussian type-2 fuzzy variable into a type-1 fuzzy variable by mean reduction method. Then the transportation problem is reformulated as a chance-constrained expected value model enlightened by the credibility optimization method. The deterministic models are then solved using two different soft computing techniques (i) Generalized Reduced Gradient (Lingo-14.0), and (ii) modified Particle Swarm Optimization(PSO), where the position of each particle is adjusted according to its own experience and that of its neighbors. The numerical experiments illustrate the application and effectiveness of the proposed solution approaches.