Mead Method Research Articles

The impact of the social adaptability of different groups on the human capital level in an uncertain environment: Panel data analysis based on 35 urban life satisfaction.

To explore the role of the social adaptability of different groups in human capital in an uncertain environment, this paper empirically tests the relationship between social adaptation and human capital by using a panel quantile regression model (QRPD) with the life satisfaction of 35 cities in China as an indicator. By selecting Nelder Mead and adaptive MCMC estimation methods, this paper finds that (1) the higher the level of social adaptation is, the more obvious the promotion effect on the level of human capital; (2) for eastern cities, social adaptation can promote the improvement of the human capital level; for central or western cities, the relationship between social adaptation and human capital is not obvious and depends on the method selected for estimation. To enhance the social adaptability of individuals, the government leadership should actively create conditions, carry out targeted social adaptation training and active adaptation skills exchange for different social groups in order to strengthen social adaptability and enhance human capital. At the same time, policymakers need to consider the differences between the eastern, central and western regions and choose the most suitable human capital improvement action framework for the region to manage environmental uncertainty and make regional human capital sustainable competitive in the era of uncertainty.

Comparative Study of Electrically Excited Conventional and Homopolar Synchronous Motors for the Traction Drive of a Mining Dump Truck Operating in a Wide Speed Range in Field-Weakening Region

A synchronous homopolar motor (SHM) has a salient pole passive rotor, an excitation winding located on the stator, and no permanent magnets, which ensures high reliability and makes this type of motor a good alternative to motors traditionally used in traction drives. However, there is no comparison between SHMs and conventional brushed synchronous machines for traction applications in the literature. In this paper, the performances of a wound rotor synchronous machine (WRSM) and SHM are theoretically compared at the operating points of a 370 kW dump mining truck drive traction curve that has a 10:1 constant power range in the field weakening region. The nine-phase motors under comparison have the same outer diameter of the stator lamination. Before comparison, both motor designs are optimized using the Nelder–Mead method to minimize the semiconductor inverter rated current and the operating cycle power loss. The main advantages of the WRSM, which was designed, are reduction in stator length, smaller losses, and smaller inverter. The reduction in the total stator length was by 1.23 times taking into account the winding end parts as well. Losses were reduced by 1.21 times for the same radius of the stator lamination. Finally, the cost of power modules of the inverter was decreased by 1.4 times. SHM is more reliable since its rotor does not have an excitation winding and a diode rectifier, as in a WRSM with a brushless exciter. In addition, SHM provides lower consumption of copper, which reduces the total mass and cost of active materials. This article also introduces a new term, “inverter utilization factor”, which can be useful, more informative than motor power factor, when comparing traction drives with different types of motors.

Magnetotelluric Sounding in the Arctic Using a Drifting Station on an Ice Floe (Numerical Experiment)

Abstract —The magnetotelluric sounding (MTS) method implemented on drifting ice floes in the Arctic is suitable for detection of 3D inhomogeneities in crustal conductivity while recording the transverse magnetic (TM) mode potential of the electromagnetic field. Highconductivity layers of seawater and sediments shield the underlying 3D inhomogeneity. Their presence virtually does not affect changes in the standard responses of the medium used in MTS but is quite noticeable in the characteristics of the TM mode. To register them, one can use a circular electric dipole (CED) located at the surface of an ice floe. During the drift, the electric field can be measured on the ice floe using electrodes in seawater. We propose to lower the magnetic sensors beneath the ice, in seawater, because ice deformations interfere with the magnetic-field component measurements. The coordinates of the observation station during MT soundings on the ice floe in the Arctic (similarly to earlier observations at the North Pole stations) can change significantly. To take into account the effect of horizontal movements of the drifting station, we propose to complement all the recorded time series with the coordinates of measurement points. We have developed a technique for processing such data to take into account nonplane-wave effects, which can occur in the Arctic because of the proximity of ionospheric current jets. We carry out the synchronization of all observations in the investigated area, using a model of spatial and temporal field variations and data accumulation. To test our approach, we use the synthetic experimental data for the model that considers the existence of seawater, sediment, resistive crust, crustal object, and underlying mantle. We determine the crustal 3D object parameters with account of the TM-mode potential distributions at the seawater surface restored from the synthetic experimental data obtained at the drifting station during the drift. We use the Nelder–Mead method for optimization of the object characteristics. The parameters of the object become highly similar to their test values if the trajectory of the drifting station passes through an object, covering it most fully.

Artificial Bee Colony Algorithm with Nelder–Mead Method to Solve Nurse Scheduling Problem

The nurse scheduling problem (NSP) is an NP-Hard combinatorial optimization scheduling problem that allocates a set of shifts to the group of nurses concerning the schedule period subject to the constraints. The objective of the NSP is to create a schedule that satisfies both hard and soft constraints suggested by the healthcare management. This work explores the meta-heuristic approach to an artificial bee colony algorithm with the Nelder–Mead method (NM-ABC) to perform efficient nurse scheduling. Nelder–Mead (NM) method is used as a local search in the onlooker bee phase of ABC to enhance the intensification process of ABC. Thus, the author proposed an improvised solution strategy at the onlooker bee phase with the benefits of the NM method. The proposed algorithm NM-ABC is evaluated using the standard dataset NSPLib, and the experiments are performed on various-sized NSP instances. The performance of the NM-ABC is measured using eight performance metrics: best time, standard deviation, least error rate, success percentage, cost reduction, gap, and feasibility analysis. The results of our experiment reveal that the proposed NM-ABC algorithm attains highly significant achievements compared to other existing algorithms. The cost of our algorithm is reduced by 0.66%, and the gap percentage to move towards the optimum value is 94.30%. Instances have been successfully solved to obtain the best deal with the known optimal value recorded in NSPLib.

Hydrodeoxygenation of stearic acid to produce diesel–like hydrocarbons: kinetic modeling, parameter estimation and simulation

• Different assumptions for the adsorption step were tested to develop a suitable kinetic model for the hydrodeoxygenation of stearic acid. • Reaction rates constants, equilibrium adsorption constants and activation energies were estimated. • The model based on non-dissociate H 2 and single-site adsorption provided the best fitting quality. • The Peng-Robinson equation of state had better performance than the Predictive-Soave-Redlich-Kwong equation to calculate hydrogen solubilities in the reaction mixture. There has been an increasing search for renewable sources to produce alternative biofuels due to the growth in the global energy demand and the environmental impacts caused by fossil fuels. Green diesel obtained via hydrotreatment of vegetable oils and fats is a promising option, mainly due to the possibility of being integrated into an existing refinery. Here, we investigate the kinetics of hydrodeoxygenation of stearic acid as a model compound of triglyceride raw materials over NiMo/Al 2 O 3 catalyst to obtain hydrocarbons in the diesel range. Kinetic models based on different assumptions to the adsorption step, such as Eley–Rideal and Langmuir–Hinshelwood mechanisms, were evaluated through nonlinear regression to parameter estimation using a hybrid algorithm combining Particle Swarm Optimization and Nelder–Mead method. All proposed models fitted well to the experimental data, presenting correlation coefficients higher than 90% for the fatty acid and the final hydrocarbons. However, the surface reaction limited model based on competitive adsorption with non-dissociate H 2 adsorbed showed the best results and was used to simulate the process.

Subject-specific optimization of background suppression for arterial spin labeling magnetic resonance imaging using a feedback loop on the scanner.

Background suppression (BGS) in arterial spin labeling (ASL) magnetic resonance imaging leads to a higher temporal signal‐to‐noise ratio (tSNR) of the perfusion images compared with ASL without BGS. The performance of the BGS, however, depends on the tissue relaxation times and on inhomogeneities of the scanner's magnetic fields, which differ between subjects and are unknown at the moment of scanning. Therefore, we developed a feedback loop (FBL) mechanism that optimizes the BGS for each subject in the scanner during acquisition. We implemented the FBL for 2D pseudo‐continuous ASL scans with an echo‐planar imaging readout. After each dynamic scan, the acquired ASL images were automatically sent to an external computer and processed with a Python processing tool. Inversion times were optimized on the fly using 80 iterations of the Nelder–Mead method, by minimizing the signal intensity in the label image while maximizing the signal intensity in the perfusion image. The performance of this method was first tested in a four‐component phantom. The regularization parameter was then tuned in six healthy subjects (three males, three females, age 24–62 years) and set as λ = 4 for all other experiments. The resulting ASL images, perfusion images, and tSNR maps obtained from the last 20 iterations of the FBL scan were compared with those obtained without BGS and with standard BGS in 12 healthy volunteers (five males, seven females, age 24–62 years) (including the six volunteers used for tuning of λ). The FBL resulted in perfusion images with a statistically significantly higher tSNR (2.20) compared with standard BGS (1.96) ( p<5x10−3, two‐sided paired t‐test). Minimizing signal in the label image furthermore resulted in control images, from which approximate changes in perfusion signal can directly be appreciated. This could be relevant to ASL applications that require a high temporal resolution. Future work is needed to minimize the number of initial acquisitions during which the performance of BGS is reduced compared with standard BGS, and to extend the technique to 3D ASL.

Comparison of Interior Permanent Magnet and Synchronous Homopolar Motors for a Mining Dump Truck Traction Drive Operated in Wide Constant Power Speed Range

Synchronous homopolar motors (SHMs) with an excitation winding located at the stator and a toothed salient pole rotor are a good alternative to motors traditionally used in traction applications such as induction motors or interior permanent magnet synchronous motors (IPMSM). This study presents the results of a theoretical comparison between an IPMSM and an SHM in a traction application with a constant power speed range of 1:10, which is specific to the mining truck drives, and with a rated power of 370 kW. The considered IPMSM and SHM have the same number of phases, poles and stator slots, and the same outer diameter of the stator lamination. The IPMSM design is optimized using the Nelder–Mead method. The main objectives of optimization are to minimize the average losses in the operating cycle and to limit the required power of the semiconductor inverter. The performance of the optimized IPMSM is compared with the previously obtained performance of the SHM optimized by the same method. Although the average losses in the operating cycle in the compared motors are approximately equal, the losses at high speed for the IPMSM are about two times greater than at low speed with maximum torque, which means that there is a need to intensify the IPMSM cooling system and there is deterioration of reliability. The advantage of the IPMSM is the reduction in the length of the active part by 30%. The advantage of the SHM is that there is 4.6 times lower cost of active materials. In addition, the SHM is more reliable than the IPMSM, as there is no risk of overheating, demagnetization or degradation of permanent magnets over time.

Experimental and Theoretical Investigation of the Acoustic Absorbers Fabricated from Natural Kenaf Fibres and Rice Husk

ABSTRACT Fabrication of acoustic absorbers from a combination of natural waste materials is a novel approach in the field of noise control. The aim of this study was to investigate the acoustic absorption performance of several sample sound absorbers made from natural kenaf fibers and rice husk. A new method of fabricating these sample sound absorbers was also employed to study the usability as well as performance improvement via the approach fibro-granular sound absorbers (FGSA). The values of acoustic absorption performance, porosity, and airflow resistivity of the samples were used in the experimental design. The Dunn and Davern (D-D) theoretical model as well as the reversed D-D model with Nelder–Mead Simplex were applied to evaluate the sound absorption as well. According to the obtained values, it was found that at low- and mid-frequency range, the sound absorption value of FGSA is higher than other acoustic materials. The FGSA (with a density of 150 kg/m3) increased noise reduction coefficient value by 31.25% compared to samples fabricated to the rice husk and also by 10.53% compared to kenaf. Based on comparison of the theoretical and experimental results, the model optimized with the Nelder–Mead method had higher accuracy and fitting in predicting the sound absorption values than the D-D model. Finally, it was found that improvement in the sound absorption performance of the natural acoustic absorbers might be achieved through adopting the approach of combining natural materials and fabrication of FGSA.

Enhanced Brain Storm Optimization Algorithm Based on Modified Nelder–Mead and Elite Learning Mechanism

Brain storm optimization algorithm (BSO) is a popular swarm intelligence algorithm. A significant part of BSO is to divide the population into different clusters with the clustering strategy, and the blind disturbance operator is used to generate offspring. However, this mechanism is easy to lead to premature convergence due to lacking effective direction information. In this paper, an enhanced BSO algorithm based on modified Nelder–Mead and elite learning mechanism (BSONME) is proposed to improve the performance of BSO. In the proposed BSONEM algorithm, the modified Nelder–Mead method is used to explore the effective evolutionary direction. The elite learning mechanism is used to guide the population to exploit the promising region, and the reinitialization strategy is used to alleviate the population stagnation caused by individual homogenization. CEC2014 benchmark problems and two engineering management prediction problems are used to assess the performance of the proposed BSONEM algorithm. Experimental results and statistical analyses show that the proposed BSONEM algorithm is competitive compared with several popular improved BSO algorithms.

Employing the MCMC technique to compute the projection depth in high dimensions

Projection depth (PD), one of the prevailing location depth notions, is a powerful and favored tool for multivariate nonparametric analysis. It permits the extension of the univariate median and weighted means to a multivariate setting. The multidimensional projection depth median (PM) and depth weighted means, including the Stahel–Donoho (SD) estimator are highly robust and affine equivariant. PM has the highest finite sample breakdown point robustness among affine equivariant location estimators.However, the computation of PD remains a challenge because its exact computation is only feasible for a data set with a dimension that is theoretically no higher than eight but practically no higher than three. Approximate algorithms such as random direction procedure or simulated annealing (SA) algorithm, are time-consuming in high-dimensional cases. Here, we present an efficient SA algorithm and its extension for the computation of PD. Simulated and real data examples indicate that the proposed algorithms outperform their competitors, including the Nelder–Mead method, and the SA algorithm, in high-dimensional cases and can obtain highly accurate results compared with those of the exact algorithm in low-dimensional cases.

Applicability of Common Algorithms in Species–Area Relationship Model Fitting

The species–area relationship (SAR) describes a law of species richness changes as the sampling area varies. SAR has been studied for more than 100 years and is of great significance in the fields of biogeography, population ecology, and conservation biology. Accordingly, there are many algorithms available for fitting the SARs, but their applicability is not numerically evaluated yet. Here, we have selected three widely used algorithms, and discuss three aspects of their applicability: the number of iterations, the time consumption, and the sensitivity to the initial parameter setting. Our results showed that, the Gauss–Newton method and the Levenberg–Marquardt method require relatively few iterative steps and take less time. In contrast, the Nelder–Mead method requires relatively more iteration steps and consumes the most time. Regarding the sensitivity of the algorithm to the initial parameters, the Gauss–Newton and the Nelder–Mead methods are more sensitive to the choice of initial parameters, while the Levenberg–Marquardt method is relatively insensitive to the choice of initial parameters. Considering that the Gauss–Newton method and the Levenberg–Marquardt method can only be used to fit smooth SAR models, we concluded that the Levenberg–Marquardt model is the best choice to fit the smooth SARs, while the Nelder–Mead method is the best choice to fit the non-smooth SARs.

The effects of noise-induced hair cell lesions on cochlear electromechanical responses: A computational approach using a biophysical model.

A biophysically inspired signal processing model of the human cochlea is deployed to simulate the effects of specific noise‐induced inner hair cell (IHC) and outer hair cell (OHC) lesions on hearing thresholds, cochlear compression, and the spectral and temporal features of the auditory nerve (AN) coding. The model predictions were evaluated by comparison with corresponding data from animal studies as well as human clinical observations. The hearing thresholds were simulated for specific OHC and IHC damages and the cochlear nonlinearity was assessed at 0.5 and 4 kHz. The tuning curves were estimated at 1 kHz and the contributions of the OHC and IHC pathologies to the tuning curve were distinguished by the model. Furthermore, the phase locking of AN spikes were simulated in quiet and in presence of noise. The model predicts that the phase locking drastically deteriorates in noise indicating the disturbing effect of background noise on the temporal coding in case of hearing impairment. Moreover, the paper presents an example wherein the model is inversely configured for diagnostic purposes using a machine learning optimization technique (Nelder–Mead method). Accordingly, the model finds a specific pattern of OHC lesions that gives the audiometric hearing loss measured in a group of noise‐induced hearing impaired humans.

Best-Practice Aspects of Quantum-Computer Calculations: A Case Study of the Hydrogen Molecule

Quantum computers are reaching one crucial milestone after another. Motivated by their progress in quantum chemistry, we performed an extensive series of simulations of quantum-computer runs that were aimed at inspecting the best-practice aspects of these calculations. In order to compare the performance of different setups, the ground-state energy of the hydrogen molecule was chosen as a benchmark for which the exact solution exists in the literature. Applying the variational quantum eigensolver (VQE) to a qubit Hamiltonian obtained by the Bravyi–Kitaev transformation, we analyzed the impact of various computational technicalities. These included (i) the choice of the optimization methods, (ii) the architecture of the quantum circuits, as well as (iii) the different types of noise when simulating real quantum processors. On these, we eventually performed a series of experimental runs as a complement to our simulations. The simultaneous perturbation stochastic approximation (SPSA) and constrained optimization by linear approximation (COBYLA) optimization methods clearly outperformed the Nelder–Mead and Powell methods. The results obtained when using the variational form were better than those obtained when the form was used. The choice of an optimum entangling layer was sensitively interlinked with the choice of the optimization method. The circular entangling layer was found to worsen the performance of the COBYLA method, while the full-entangling layer improved it. All four optimization methods sometimes led to an energy that corresponded to an excited state rather than the ground state. We also show that a similarity analysis of measured probabilities can provide a useful insight.

Application of the Nelder-Mead method to optimize the selecting of the Likhachev-Volkov model constants

A new method was proposed for selecting constants of the modified Likhachev - Volkov microstructural model to describe the reversible strain variation during isothermal martensitic transformation in Ti49Ni51 alloy. The experimental data were approximated by solving the optimization problem using the Nelder - Mead method. A set of model parameters was found that provided the best approximation. Using the selected constants set, model qualitatively and quantitatively describe the strain variation during cooling and heating of the model sample, as well as during isothermal holding under constant stress. The proposed method of selecting constants allowed performing the smallest number of numerical experiments without requiring additional real experiments.

Energy-saving optimization of refrigeration system in a data centre based on Nelder-Mead method

In order to improve the cooling efficiency of the data centre HVAC (Heating Ventilation and Air Conditioning) system and save costs, this paper proposes a control strategy that combines traditional control method with Nelder and Mead method, in order to optimize equipment operating parameters. Firstly, this paper selected a data centre in Northeast China as the research object. Moreover, the central air-conditioning system, as the control object, was simulated on TRNSYS, whose result was compared with the actual operating parameters to verify the reliability of the simulation model. Finally, the optimal control strategy was applied to the simulation model to analyze the changes of energy consumption of the system before and after optimization. The results showed that in August, the optimal control strategy is able to meet the extremity of cooling load demand, and the total energy consumption of the system is 269,612kwh, which is 10% less than the energy consumption before optimization. The overall energy consumption of the system is significantly reduced, and the proportion of energy consumption of each part tends to be reasonable, which will effectively improve the cooling efficiency of HVAC.

Inverter Volt-Ampere Capacity Reduction by Optimization of the Traction Synchronous Homopolar Motor

The synchronous homopolar motor (SHM) with an excitation winding on the stator and a toothed rotor is a good alternative to traction induction motors for hybrid mining trucks. The main problem in the design of the SHM electric drives is that the magnetic flux forms three-dimensional loops and, as a result, the lack of high-quality optimization methods, which leads to the need to overrate the installed power of the inverter. This article discusses the procedure and results of optimization of a commercially available 370 kW traction SHM using the Nelder–Mead method. The objective function is composed to mainly improve the following characteristics of the traction SHM: total motor power loss and maximum armature winding current. In addition, terms are introduced into the objective function to make it possible to limit the voltage, the loss in the excitation winding, and the maximum magnetic flux density in the non-laminated sections of the magnetic core. As a result of the optimization, the motor losses and the maximum current required by the motor from the inverter were significantly reduced. The achieved reduction in the maximum current allows the cost of the IGBT modules of the inverter to be reduced by 1.4 times (by $ 2295), and also allows the AC component of the DC-link current to be reduced by the same amount.

Design of an Optimized GMV Controller Based on Data-Driven Approach

This paper presents a data-driven scheme that can obtain the optimized Generalized Minimum Variance (GMV) control parameters by applying the Nelder—Mead (NM) method based on Proportional-Integral Derivative (PID) controller for linear system. An adjustable λ is included in the GMV-PID controller. In the existing GMV-PID controller, the PID parameters are calculated by simply changing λ manually. Therefore, it is hard to get desirable control performance. The NM method is introduced to improve the control performance. The application of NM method can optimize the calculation of λ. However, the objective function in NM method needs the output calculation. In other words, the model information is inevitably to obtain output. To achieve model-free design scheme, the estimation of closed-loop response method is introduced in database-driven approach. Using obtained closed-loop data can predict the output and then substitute it into the objective function without any model information of the process. The effectiveness is verified by experiment.

Effective Power Congestion Management Technique Using Hybrid Nelder–Mead – Grey Wolf Optimizer (HNMGWO) in Deregulated Power System

The aim of this article is to apply Hybrid Nelder–Mead – Grey Wolf Optimizer (HNMGWO) in congestion management problem with optimal congestion cost by rescheduling generators in the power system. When the transmission power lines are overloaded in the power system, the following strategies are followed: generator rescheduling and installation of FACTS devices on the lines and load curtailment. But, load curtailment is not performed as the deregulated system encourages customer satisfaction. Thus, generator rescheduling is selected for problem solution as it does not involve the creation of any new infrastructure. Grey Wolf Optimizer (GWO) is one of the recent optimization techniques which work based on the leadership hierarchy and the hunting technique of grey wolves. The effective local search is performed by the Nelder–Mead (NM) method and the output is used to initialize the population for GWO, which searches global best value. In this paper, the HNMGWO is attempted based on the positive outcome of both NM and GWO. This proposed algorithm has been applied and results have been discussed related to the tasks of Independent System Operator. The proposed HNMGWO is applied in a standard IEEE 30 bus and IEEE 118 bus systems and thereby results are compared with standard GWO, Particle Swarm Optimization (PSO), fuzzy adaptive PSO, genetic algorithm, and bacterial foraging algorithms. The result shows that the proposed work is more effective by consuming minimum congestion cost in reduced rescheduled power and power loss.

Comparison of Methods for Assessing the Assimilation Capacity of the Kazakhstani Sector of the Ili River

A mixed inverse problem for determining the biochemical oxygen demand of water ( L 0 ) and the rate of biochemical oxygen consumption ( k 0 ), which are important indicators of water quality, has been formulated and numerically solved based on real experimental data. The inverse problem is reduced to the optimization problem consisting in minimization of the deviation of the calculated values from the experimental data, which is solved numerically using the Nelder–Mead method (zero order) and the gradient method (first order). A number of examples of processing both model experimental data and field experimental data provided by hydrological stations monitoring pollutants in the Kazakhstani part of the Ili River basin are presented. A mathematical model that adequately describes the processes in the river system has been constructed.

Identification and evaluation of cracks in electrostatically actuated resonant gas sensors using Harris Hawk / Nelder Mead and perturbation methods

In this paper we study the static deflection, natural frequency, primary resonance of an electrostatically actuated cracked gas sensor. Besides, a novel hybrid metaheuristic algorithm is proposed to detect the location and depth of possible crack on the microcantilever systems. The gas sensor configuration consists of a microcantilever with a rigid plate attached to its end. The nonlinear effects of the electrostatic force and fringing field are taken into account in the mathematical model. The crack is represented by a rotational spring. In the first part, the effect of crack on the static and dynamic pull-in instability are studied. The equations of motion are solved by the application of the perturbation methods. Next, an inverse problem is formulated to predict the location and depth of the crack in the gas sensor. For that purpose, the weighted squared difference of the analytical and predicted frequency response is considered as the objective function. The location and depth of the crack in the microsystem are determined using the hybrid Harris Hawk and Nelder Mead optimization algorithms. The accuracy and efficiency of the proposed algorithm are compared with the HHO, DA, GOA, and WOA algorithms. Taguchi design of experiments method is used in order to tune the parameters of optimization algorithms systematically. It is shown that the proposed algorithm can predict the exact location and depth of the open-edge crack on an electrostatically actuated microbeam with proof mass.