Integrated classification method of tight sandstone reservoir based on principal component analysise simulated annealing genetic algorithmefuzzy cluster means

In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.

Cascade refrigeration system synthesis based on hybrid simulated annealing and particle swarm optimization algorithm

Cascade refrigeration system(CRS)can meet a wider range of refrigeration temperature requirements and is more energy efficient than single-refrigerant refrigeration system,making it more widely used in low-temperature industry processes.The synthesis of a CRS with simultaneous consideration of heat integration between refrigerant and process streams is challenging but promising for significant cost saving and reduction of carbon emission.This study presented a stochastic optimization method for the synthesis of CRS.An MINLP model was formulated based on the superstructure developed for the CRS,and an optimization framework was proposed,where simulated annealing algorithm was used to evolve the numbers of pressure/temperature levels for all sub-refrigeration systems,and particle swarm optimization algorithm was employed to optimize the continuous variables.The effectiveness of the proposed methodology was verified by a case study of CRS optimization in an ethylene plant with 21.89%the total annual cost saving.

An Improved Multi-Objective Hybrid Genetic-Simulated Annealing Algorithm for AGV Scheduling under Composite Operation Mode

This paper presents an improved hybrid algorithm and a multi-objective model to tackle the scheduling problem of multiple Automated Guided Vehicles(AGVs)under the composite operation mode.The multi-objective model aims to minimize the maximum completion time,the total distance covered by AGVs,and the distance traveled while empty-loaded.The improved hybrid algorithm combines the improved genetic algorithm(GA)and the simulated annealing algorithm(SA)to strengthen the local search ability of the algorithm and improve the stability of the calculation results.Based on the characteristics of the composite operation mode,the authors introduce the combined coding and parallel decoding mode and calculate the fitness function with the grey entropy parallel analysis method to solve the multi-objective problem.The grey entropy parallel analysis method is a combination of the grey correlation analysis method and the entropy weighting method to solve multi-objective solving problems.A task advance evaluation strategy is proposed in the process of crossover and mutation operator to guide the direction of crossover and mutation.The computational experiments results show that the improved hybrid algorithm is better than the GA and the genetic algorithm with task advance evaluation strategy(AEGA)in terms of convergence speed and solution results,and the effectiveness of the multi-objective solution is proved.All three objectives are optimized and the proposed algorithm has an optimization of 7.6%respectively compared with the GA and 3.4%compared with the AEGA in terms of the objective of maximum completion time.

Imbalanced Data Classification Using SVM Based on Improved Simulated Annealing Featuring Synthetic Data Generation and Reduction

Imbalanced data classification is one of the major problems in machine learning.This imbalanced dataset typically has significant differences in the number of data samples between its classes.In most cases,the performance of the machine learning algorithm such as Support Vector Machine(SVM)is affected when dealing with an imbalanced dataset.The classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class samples.In this paper,a hybrid approach combining data pre-processing technique andSVMalgorithm based on improved Simulated Annealing(SA)was proposed.Firstly,the data preprocessing technique which primarily aims at solving the resampling strategy of handling imbalanced datasets was proposed.In this technique,the data were first synthetically generated to equalize the number of samples between classes and followed by a reduction step to remove redundancy and duplicated data.Next is the training of a balanced dataset using SVM.Since this algorithm requires an iterative process to search for the best penalty parameter during training,an improved SA algorithm was proposed for this task.In this proposed improvement,a new acceptance criterion for the solution to be accepted in the SA algorithm was introduced to enhance the accuracy of the optimization process.Experimental works based on ten publicly available imbalanced datasets have demonstrated higher accuracy in the classification tasks using the proposed approach in comparison with the conventional implementation of SVM.Registering at an average of 89.65%of accuracy for the binary class classification has demonstrated the good performance of the proposed works.

THD Reduction for Permanent Magnet Synchronous Motor Using Simulated Annealing

Any nonlinear behavior of the system is analyzed by a useful way of Total Harmonic Distortion(THD)technique.Reduced THD achieves lower peak current,higher efficiency and longer equipment life span.Simulated annealing(SA)is applied due to the effectiveness of locating solutions that are close to ideal and to challenge large-scale combinatorial optimization for Permanent Magnet Synchronous Machine(PMSM).The parameters of direct torque controllers(DTC)for the drive are automatically adjusted by the optimization algorithm.Advantages of the PI-Fuzzy-SA algorithm are retained when used together.It also improves the rate of system convergence.Speed response improvement and har-monic reduction is achieved with SA-based DTC for PMSM.This mechanism is known to be faster than other algorithms.Also,it is observed that as compared to other algorithms,the projected algorithm yields a reduced total harmonic distor-tion.As a result of the employment of Space Vector Modulation(SVM)techni-que,the system is resistant to changes in motor specifications and load torque.Through MATLAB&Simulink simulation,the experiment is done and the per-formance is calculated for the controller.

Simulated Annealing with Deep Learning Based Tongue Image Analysis for Heart Disease Diagnosis

Tongue image analysis is an efficient and non-invasive technique to determine the internal organ condition of a patient in oriental medicine,for example,traditional Chinese medicine(TCM),Japanese traditional herbal medicine,and traditional Korean medicine(TKM).The diagnosis procedure is mainly based on the expert’s knowledge depending upon the visual inspec-tion comprising color,substance,coating,form,and motion of the tongue.But conventional tongue diagnosis has limitations since the procedure is inconsistent and subjective.Therefore,computer-aided tongue analyses have a greater potential to present objective and more consistent health assess-ments.This manuscript introduces a novel Simulated Annealing with Transfer Learning based Tongue Image Analysis for Disease Diagnosis(SADTL-TIADD)model.The presented SADTL-TIADD model initially pre-processes the tongue image to improve the quality.Next,the presented SADTL-TIADD technique employed an EfficientNet-based feature extractor to generate useful feature vectors.In turn,the SA with the ELM model enhances classification efficiency for disease detection and classification.The design of SA-based parameter tuning for heart disease diagnosis shows the novelty of the work.A wide-ranging set of simulations was performed to ensure the improved performance of the SADTL-TIADD algorithm.The experimental outcomes highlighted the superior of the presented SADTL-TIADD system over the compared methods with maximum accuracy of 99.30%.

Improving Performance of Recurrent Neural Networks Using Simulated Annealing for Vertical Wind Speed Estimation

An accurate vertical wind speed(WS)data estimation is required to determine the potential for wind farm installation.In general,the vertical extrapolation of WS at different heights must consider different parameters fromdifferent locations,such as wind shear coefficient,roughness length,and atmospheric conditions.The novelty presented in this article is the introduction of two steps optimization for the Recurrent Neural Networks(RNN)model to estimate WS at different heights using measurements from lower heights.The first optimization of the RNN is performed to minimize a differentiable cost function,namely,mean squared error(MSE),using the Broyden-Fletcher-Goldfarb-Shanno algorithm.Secondly,the RNN is optimized to reduce a non-differentiable cost function using simulated annealing(RNN-SA),namely mean absolute error(MAE).Estimation ofWS vertically at 50 m height is done by training RNN-SA with the actualWS data a 10–40 m heights.The estimatedWS at height of 50 m and the measured WS at 10–40 heights are further used to train RNN-SA to obtain WS at 60 m height.This procedure is repeated continuously until theWS is estimated at a height of 180 m.The RNN-SA performance is compared with the standard RNN,Multilayer Perceptron(MLP),Support Vector Machine(SVM),and state of the art methods like convolutional neural networks(CNN)and long short-term memory(LSTM)networks to extrapolate theWS vertically.The estimated values are also compared with realWS dataset acquired using LiDAR and tested using four error metrics namely,mean squared error(MSE),mean absolute percentage error(MAPE),mean bias error(MBE),and coefficient of determination(R2).The numerical experimental results show that the MSE values between the estimated and actualWS at 180mheight for the RNN-SA,RNN,MLP,and SVM methods are found to be 2.09,2.12,2.37,and 2.63,respectively.

Location and Capacity Determination Method of Electric Vehicle Charging Station Based on Simulated Annealing Immune Particle Swarm Optimization

As the number of electric vehicles(EVs)continues to grow and the demand for charging infrastructure is also increasing,how to improve the charging infrastructure has become a bottleneck restricting the development of EVs.In other words,reasonably planning the location and capacity of charging stations is important for development of the EV industry and the safe and stable operation of the power system.Considering the construction and maintenance of the charging station,the distribution network loss of the charging station,and the economic loss on the user side of the EV,this paper takes the node and capacity of charging station planning as control variables and the minimum cost of system comprehensive planning as objective function,and thus proposes a location and capacity planning model for the EV charging station.Based on the problems of low efficiency and insufficient global optimization ability of the current algorithm,the simulated annealing immune particle swarm optimization algorithm(SA-IPSO)is adopted in this paper.The simulated annealing algorithm is used in the global update of the particle swarm optimization(PSO),and the immune mechanism is introduced to participate in the iterative update of the particles,so as to improve the speed and efficiency of PSO.Voronoi diagram is used to divide service area of the charging station,and a joint solution process of Voronoi diagram and SA-IPSO is proposed.By example analysis,the results show that the optimal solution corresponding to the optimisation method proposed in this paper has a low overall cost,while the average charging waiting time is only 1.8 min and the charging pile utilisation rate is 75.5%.The simulation comparison verifies that the improved algorithm improves the operational efficiency by 18.1%and basically does not fall into local convergence.

RFID Network Planning Optimization Using a Genetic-Simulated Annealing Combined Algorithm

The main purpose of this paper is to present and apply a genetic and simulated annealing combined algorithm to solve an optimization problem of Radio Frequency Identification(RFID)network planning in an emergency department of a hospital.Accordingly,though genetic algorithm(GA)and simulated annealing(SA)have advantages and disadvantages,but they are also complementary.Hence,the combined algorithm not only takes advantages of the two methods,but also avoids their disadvantages.The simulation results in an emergency department of a hospital present that the proposed method provides minimum total cost and maximum RFID network coverage in a simultaneous way with the efficient use of multi-antenna RFID readers.Besides,the results of comparison of two scenarios of the model with the results of other existing models in the relevant literature show that the proposed model has better outcomes.

Vehicle recognition and tracking based on simulated annealing chaotic particle swarm optimization-Gauss particle filter algorithm

Target recognition and tracking is an important research filed in the surveillance industry.Traditional target recognition and tracking is to track moving objects, however, for the detected moving objects the specific content can not be determined.In this paper, a multi-target vehicle recognition and tracking algorithm based on YOLO v5 network architecture is proposed.The specific content of moving objects are identified by the network architecture, furthermore, the simulated annealing chaotic mechanism is embedded in particle swarm optimization-Gauss particle filter algorithm.The proposed simulated annealing chaotic particle swarm optimization-Gauss particle filter algorithm(SA-CPSO-GPF) is used to track moving objects.The experiment shows that the algorithm has a good tracking effect for the vehicle in the monitoring range.The root mean square error(RMSE), running time and accuracy of the proposed method are superior to traditional methods.The proposed algorithm has very good application value.

Achieving High Strength and Tensile Ductility in Pure Nickel by Cryorolling with Subsequent Low-Temperature Short-Time Annealing

Ultra fine-grained pure metals and their alloys have high strength and low ductility.In this study,cryorolling under different strains followed by low-temperature short-time annealing was used to fabricate pure nickel sheets combining high strength with good ductility.The results show that,for different cryorolling strains,the uniform elongation was greatly increased without sacrificing the strength after annealing.A yield strength of 607 MPa and a uniform elongation of 11.7%were obtained after annealing at a small cryorolling strain(ε=0.22),while annealing at a large cryorolling strain(ε=1.6)resulted in a yield strength of 990 MPa and a uniform elongation of 6.4%.X-ray diffraction(XRD),transmission electron microscopy(TEM),scanning electron microscopy(SEM),and electron backscattered diffraction(EBSD)were used to characterize the microstructure of the specimens and showed that the high strength could be attributed to strain hardening during cryorolling,with an additional contribution from grain refinement and the formation of dislocation walls.The high ductility could be attributed to annealing twins and micro-shear bands during stretching,which improved the strain hardening capacity.The results show that the synergistic effect of strength and ductility can be regulated through low-temperature short-time annealing with different cryorolling strains,which provides a new reference for the design of future thermo-mechanical processes.

Achievable hole concentration at room temperature as a function of Mg concentration for MOCVD-grown p-GaN after sufficient annealing

Relationship between the hole concentration at room temperature and the Mg doping concentration in p-GaN grown by MOCVD after sufficient annealing was studied in this paper.Different annealing conditions were applied to obtain sufficient activation for p-GaN samples with different Mg doping ranges.Hole concentration,resistivity and mobility were characterized by room-temperature Hall measurements.The Mg doping concentration and the residual impurities such as H,C,O and Si were measured by secondary ion mass spectroscopy,confirming negligible compensations by the impurities.The hole concentration,resistivity and mobility data are presented as a function of Mg concentration,and are compared with literature data.The appropriate curve relating the Mg doping concentration to the hole concentration is derived using a charge neutrality equation and the ionized-acceptor-density[N-(A)^(-)](cm^(−3))dependent ionization energy of Mg acceptor was determined asE_(A)^(Mg)=184−2.66×10^(−5)×[N_(A)^(-)]1/3 meV.

Residual stress modeling of mitigated fused silica damage sites with CO_(2)laser annealing

A numerical model based on measured fictive temperature distributions is explored to evaluate the residual stress fields of CO_(2)laser-annealed mitigated fused silica damage sites.The proposed model extracts the residual strain from the differences in thermoelastic contraction of fused silica with different fictive temperatures from the initial frozen-in temperatures to ambient temperature.The residual stress fields of mitigated damage sites for the CO_(2)laser-annealed case are obtained by a finite element analysis of equilibrium equations and constitutive equations.The simulated results indicate that the proposed model can accurately evaluate the residual stress fields of laser-annealed mitigated damage sites with a complex thermal history.The calculated maximum hoop stress is in good agreement with the reported experimental result.The estimated optical retardance profiles from the calculated radial and hoop stress fields are consistent with the photoelastic measurements.These results provide sufficient evidence to demonstrate the suitability of the proposed model for describing the residual stresses of mitigated fused silica damage sites after CO_(2)laser annealing.

Effect of annealing on the electrical performance of N-polarity GaN Schottky barrier diodes

A nitrogen-polarity(N-polarity)GaN-based high electron mobility transistor(HEMT)shows great potential for high-fre-quency solid-state power amplifier applications because its two-dimensional electron gas(2DEG)density and mobility are mini-mally affected by device scaling.However,the Schottky barrier height(SBH)of N-polarity GaN is low.This leads to a large gate leakage in N-polarity GaN-based HEMTs.In this work,we investigate the effect of annealing on the electrical characteristics of N-polarity GaN-based Schottky barrier diodes(SBDs)with Ni/Au electrodes.Our results show that the annealing time and tem-perature have a large influence on the electrical properties of N-polarity GaN SBDs.Compared to the N-polarity SBD without annealing,the SBH and rectification ratio at±5 V of the SBD are increased from 0.51 eV and 30 to 0.77 eV and 7700,respec-tively,and the ideal factor of the SBD is decreased from 1.66 to 1.54 after an optimized annealing process.Our analysis results suggest that the improvement of the electrical properties of SBDs after annealing is mainly due to the reduction of the inter-face state density between Schottky contact metals and N-polarity GaN and the increase of barrier height for the electron emis-sion from the trap state at low reverse bias.

Frontogenesis and Frontolysis of a Cold Filament Driven by the Cross-Filament Wind and Wave Fields Simulated by a Large Eddy Simulation

The variations of the frontogenetic trend of a cold filament induced by the cross-filament wind and wave fields are studied by a non-hydrostatic large eddy simulation. Five cases with different strengths of wind and wave fields are studied.The results show that the intense wind and wave fields further break the symmetries of submesoscale flow fields and suppress the levels of filament frontogenesis. The changes of secondary circulation directions—that is, the conversion between the convergence and divergence of the surface cross-filament currents with the downwelling and upwelling jets in the filament center—are associated with the inertial oscillation. The filament frontogenesis and frontolysis caused by the changes of secondary circulation directions may periodically sharpen and smooth the gradient of submesoscale flow fields.The lifecycle of the cold filament may include multiple stages of filament frontogenesis and frontolysis.

Evolution of Biofilm and Its Effect on Microstructure of Mortar Surfaces in Simulated Seawater

To explore the role of biofilm formation on the corrosion of marine concrete structures, we investigated the attachment of biofilm on mortar surfaces in simulated seawater and the influence of biofilm on the microstructure of mortar surfaces. The results show that the evolution of biofilm on mortar surfaces in simulated seawater is closely related to the corrosion suffered by the mortar, and the process of biofilm attachment and shedding is continuous and cyclical. It is found that the specimens in the absence of biofilm attachment are more severely eroded internally by the corrosive medium in simulated seawater than those in the presence of biofilm attachment. For the specimens without biofilm attachment, after 60 days, gypsum forms,and after 120 days, the number of pores in the mortar is reduced. In contrast, for the specimens in the presence of biofilm attachment, gypsum could only be detected after 90 days, and fewer pores are filled. Therefore, the formation of biofilm could delay the invasion of the corrosive medium into the interior of mortar during the evolution of biofilm on mortar surfaces, mitigating the corrosion of mortars in seawater.

Shallow Convection Dataset Simulated by Three Different Large Eddy Models

Shallow convection plays an important role in transporting heat and moisture from the near-surface to higher altitudes,yet its parameterization in numerical models remains a great challenge,partly due to the lack of high-resolution observations.This study describes a large eddy simulation(LES)dataset for four shallow convection cases that differ primarily in inversion strength,which can be used as a surrogate for real data.To reduce the uncertainty in LES modeling,three different large eddy models were used,including SAM(System for Atmospheric Modeling),WRF(Weather Research and Forecasting model),and UCLA-LES.Results show that the different models generally exhibit similar behavior for each shallow convection case,despite some differences in the details of the convective structure.In addition to grid-averaged fields,conditionally sampled variables,such as in-cloud moisture and vertical velocity,are also provided,which are indispensable for calculation of the entrainment/detrainment rate.Considering the essentiality of the entraining/detraining process in the parameterization of cumulus convection,the dataset presented in this study is potentially useful for validation and improvement of the parameterization of shallow convection.

Simulated patient methodology as a“gold standard”in community pharmacy practice:Response to criticism

The simulated patient methodology(SPM)is considered the“gold standard”as covert participatory observation.SPM is attracting increasing interest for the investigation of community pharmacy practice;however,there is criticism that SPM can only show a small picture of everyday pharmacy practice and therefore has limited external validity.On the one hand,a certain design and application of the SPM goes hand in hand with an increase in external validity.Even if,on the other hand,this occurs at the expense of internal validity due to the trade-off situation,the justified criticism of the SPM for investigating community pharmacy practice can be countered.

A SIMULATED ANNEALING METHOD FOR FAULT TREE ANALYSIS

采用模拟退火方法解最大的最小割集发生概率，同时在寻优过程中找出近优的最小割集，最后确定基本事件的重要性顺序．通过计算机模拟，本算法得到较满意的结果．

A Parallel Genetic Simulated Annealing Hybrid Algorithm for Task Scheduling

In this paper combined with the advantages of genetic algorithm and simulated annealing, brings forward a parallel genetic simulated annealing hybrid algorithm （PGSAHA） and applied to solve task scheduling problem in grid computing. It first generates a new group of individuals through genetic operation such as reproduction, crossover, mutation, etc, and than simulated anneals independently all the generated individuals respectively. When the temperature in the process of cooling no longer falls, the result is the optimal solution on the whole. From the analysis and experiment result, it is concluded that this algorithm is superior to genetic algorithm and simulated annealing.