Prediction and optimization of flue pressure in sintering process based on SHAP

Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.

Enhanced Multi-Object Dwarf Mongoose Algorithm for Optimization Stochastic Data Fusion Wireless Sensor Network Deployment

Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic models,and there is a significant gap between the research results and actual wireless sensor networks.Some scholars have now modeled data fusion networks to make them more suitable for practical applications.This paper will explore the deployment problem of a stochastic data fusion wireless sensor network(SDFWSN),a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in actual sensor networks for information collection.The deployment problem of SDFWSN is modeled as a multi-objective optimization problem.The network life cycle,spatiotemporal coverage,detection rate,and false alarm rate of SDFWSN are used as optimization objectives to optimize the deployment of network nodes.This paper proposes an enhanced multi-objective mongoose optimization algorithm(EMODMOA)to solve the deployment problem of SDFWSN.First,to overcome the shortcomings of the DMOA algorithm,such as its low convergence and tendency to get stuck in a local optimum,an encircling and hunting strategy is introduced into the original algorithm to propose the EDMOA algorithm.The EDMOA algorithm is designed as the EMODMOA algorithm by selecting reference points using the K-Nearest Neighbor(KNN)algorithm.To verify the effectiveness of the proposed algorithm,the EMODMOA algorithm was tested at CEC 2020 and achieved good results.In the SDFWSN deployment problem,the algorithm was compared with the Non-dominated Sorting Genetic Algorithm II(NSGAII),Multiple Objective Particle Swarm Optimization(MOPSO),Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D),and Multi-Objective Grey Wolf Optimizer(MOGWO).By comparing and analyzing the performance evaluation metrics and optimization results of the objective functions of the multi-objective algorithms,the algorithm outperforms the other algorithms in the SDFWSN deployment results.To better demonstrate the superiority of the algorithm,simulations of diverse test cases were also performed,and good results were obtained.

GPU-Enabled Isogometric Topology Optimization with Bezier Element Stiffness Mapping

Due to the high-order B-spline basis functions utilized in isogeometric analysis(IGA)and the repeatedly updating global stiffness matrix of topology optimization,Isogeometric topology optimization(ITO)intrinsically suffers from the computationally demanding process.In this work,we address the efficiency problem existing in the assembling stiffness matrix and sensitivity analysis using B˙ezier element stiffness mapping.The Element-wise and Interaction-wise parallel computing frameworks for updating the global stiffness matrix are proposed for ITO with B˙ezier element stiffness mapping,which differs from these ones with the traditional Gaussian integrals utilized.Since the explicit stiffness computation formula derived from B˙ezier element stiffness mapping possesses a typical parallel structure,the presented GPU-enabled ITO method can greatly accelerate the computation speed while maintaining its high memory efficiency unaltered.Numerical examples demonstrate threefold speedup:1)the assembling stiffness matrix is accelerated by 10×maximumly with the proposed GPU strategy;2)the solution efficiency of a sparse linear system is enhanced by up to 30×with Eigen replaced by AMGCL;3)the efficiency of sensitivity analysis is promoted by 100×with GPU applied.Therefore,the proposed method is a promising way to enhance the numerical efficiency of ITO for both single-patch and multiple-patch design problems.

Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.

Optimization Design of the Multi-Layer Cross-Sectional Layout of An Umbilical Based on the GA-GLM

Marine umbilical is one of the key equipment for subsea oil and gas exploitation,which is usually integrated by a great number of different functional components with multi-layers.The layout of these components directly affects manufacturing,operation and storage performances of the umbilical.For the multi-layer cross-sectional layout design of the umbilical,a quantifiable multi-objective optimization model is established according to the operation and storage requirements.Considering the manufacturing factors,the multi-layering strategy based on contact point identification is introduced for a great number of functional components.Then,the GA-GLM global optimization algorithm is proposed combining the genetic algorithm and the generalized multiplier method,and the selection operator of the genetic algorithm is improved based on the steepest descent method.Genetic algorithm is used to find the optimal solution in the global space,which can converge from any initial layout to the feasible layout solution.The feasible layout solution is taken as the initial value of the generalized multiplier method for fast and accurate solution.Finally,taking umbilicals with a great number of components as examples,the results show that the cross-sectional performance of the umbilical obtained by optimization algorithm is better and the solution efficiency is higher.Meanwhile,the multi-layering strategy is effective and feasible.The design method proposed in this paper can quickly obtain the optimal multi-layer cross-sectional layout,which replaces the manual design,and provides useful reference and guidance for the umbilical industry.

Design and optimization of a greener sinomenine hydrochloride preparation process considering variations among different batches of the medicinal herb

The current methods used to industrially produce sinomenine hydrochloride involve several issues,including high solvent toxicity,long process flow,and low atomic utilization efficiency,and the greenness scores of the processes are below 65 points.To solve these problems,a new process using anisole as the extractant was proposed.Anisole exhibits high selectivity for sinomenine and can be connected to the subsequent water-washing steps.After alkalization of the medicinal material,heating extraction,water washing,and acidification crystallization were carried out.The process was modeled and optimized.The design space was constructed.The recommended operating ranges for the critical process parameters were 3.0–4.0 h for alkalization time,60.0–80.0℃ for extraction temperature,2.0–3.0(volume ratio)for washing solution amount,and 2.0–2.4 mol·L^(-1) for hydrochloric acid concentration.The new process shows good robustness because different batches of medicinal materials did not greatly impact crystal purity or sinomenine transfer rate.The sinomenine transfer rate was about 20%higher than that of industrial processes.The greenness score increased to 90 points since the novel process proposed in this research solves the problems of long process flow,high solvent toxicity,and poor atomic economy,better aligning with the concept of green chemistry.

Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s lifetime.Many studies have been conducted for homogeneous networks,but few have been performed for heterogeneouswireless sensor networks.This paper utilizes Rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial energies.The proposed algorithms lack algorithm-specific parameters and metaphorical connotations.The proposed algorithms examine the search space based on the relations of the population with the best,worst,and randomly assigned solutions.The proposed algorithms can be evaluated using any routing protocol,however,we have chosen the well-known routing protocols in the literature:Low Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Gathering in Sensor Information Systems(PEAGSIS),Partitioned-based Energy-efficient LEACH(PE-LEACH),and the Power-Efficient Gathering in Sensor Information Systems Neural Network(PEAGSIS-NN)recent routing protocol.We compare our optimized method with the Jaya,the Particle Swarm Optimization-based Energy Efficient Clustering(PSO-EEC)protocol,and the hybrid Harmony Search Algorithm and PSO(HSA-PSO)algorithms.The efficiencies of our proposed algorithms are evaluated by conducting experiments in terms of the network lifetime(first dead node,half dead nodes,and last dead node),energy consumption,packets to cluster head,and packets to the base station.The experimental results were compared with those obtained using the Jaya optimization algorithm.The proposed algorithms exhibited the best performance.The proposed approach successfully prolongs the network lifetime by 71% for the PEAGSIS protocol,51% for the LEACH protocol,10% for the PE-LEACH protocol,and 73% for the PEGSIS-NN protocol;Moreover,it enhances other criteria such as energy conservation,fitness convergence,packets to cluster head,and packets to the base station.

Deployment optimization for target perpetual coverage in energy harvesting wireless sensor network

Energy limitation of traditional Wireless Sensor Networks(WSNs)greatly confines the network lifetime due to generating and processing massive sensing data with a limited battery.The energy harvesting WSN is a novel network architecture to address the limitation of traditional WSN.However,existing coverage and deployment schemes neglect the environmental correlation of sensor nodes and external energy with respect to physical space.Comprehensively considering the spatial correlation of the environment and the uneven distribution of energy in energy harvesting WSN,we investigate how to deploy a collection of sensor nodes to save the deployment cost while ensuring the target perpetual coverage.The Confident Information Coverage(CIC)model is adopted to formulate the CIC Minimum Deployment Cost Target Perpetual Coverage(CICMTP)problem to minimize the deployed sensor nodes.As the CICMTP is NP-hard,we devise two approximation algorithms named Local Greedy Threshold Algorithm based on CIC(LGTA-CIC)and Overall Greedy Search Algorithm based on CIC(OGSA-CIC).The LGTA-CIC has a low time complexity and the OGSA-CIC has a better approximation rate.Extensive simulation results demonstrate that the OGSA-CIC is able to achieve lower deployment cost and the performance of the proposed algorithms outperforms GRNP,TPNP and EENP algorithms.

Comparison of debris flow susceptibility assessment methods:support vector machine,particle swarm optimization,and feature selection techniques

The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.

Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network optimization.This study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic RESs.The primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss mitigation.Additionally,a carbon tax is included in the objective function to reduce carbon emissions.Thorough scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal solutions.Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution quality.Notably,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)effectively.This research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima.GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

一种面向智能移动终端的iPDR/GNSS组合导航方法

针对现有行人航位推算(PDR)方法存在误差积累并且定位与航向精度较低,从而导致PDR/全球导航卫星系统(GNSS)组合导航精度下降的问题,提出了一种面向智能移动终端的改进的PDR(iPDR)/GNSS组合导航方法。首先,设计了一种iPDR定位方法,将GNSS载波相位历元差分技术计算的航向角引入PDR航向估计中,减小由陀螺漂移引起的航向累积误差。其次,基于因子图优化将GNSS绝对位置与iPDR定位结果相融合。最后,利用智能手机采集的实测数据对所提方法进行验证。实验结果表明:与传统PDR方法相比,iPDR方法的定位精度提高了62.0%,航向精度提高了33.7%;与基于卡尔曼滤波的iPDR/GNSS组合方法相比,基于因子图优化的iPDR/GNSS方法在定位精度上提高了39.8%,有效提高了组合导航系统的精度。

Urban Electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric vehicles.The demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of recharge.The establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this issue.Nevertheless,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of service.Modelling the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging infrastructure.It is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic patterns.When vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering system.Initially,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature selection.Subsequently,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic congestion.Based on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is provided.The results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algorithm(GA),to the theoretical median that would be expected that there is no difference.

Environmental,economic and exergy analysis of separation of ternary azeotrope by variable pressure extractive distillation based on multi-objective optimization

In this work,the ternary azeotrope of tert-butyl alcohol/ethyl acetate/water is separated by extractive distillation(ED)to recover the available constituents and protect the environment.Based on the conductor like shielding model and relative volatility method,ethylene glycol was selected as the extractant in the separation process.In addition,in view of the characteristic that the relative volatility between components changes with pressure,the multi-objective optimization method based on nondominated sorting genetic algorithm II optimizes the pressure and the amount of solvent cooperatively to avoid falling into the optimal local solution.Based on the optimal process parameters,the proposed heat-integrated process can reduce the gas emissions by 29.30%.The heat-integrated ED,further coupled with the pervaporation process,can reduce gas emission by 42.36%and has the highest exergy efficiency of 47.56%.In addition,based on the heat-integrated process,the proposed two heat pump assisted heat-integrated ED processes show good economic and environmental performance.The double heat pump assisted heat-integrated ED can reduce the total annual cost by 28.78%and the gas emissions by 55.83%compared with the basis process,which has a good application prospect.This work provides a feasible approach for the separation of ternary azeotropes.

Distributed Robust Scheduling Optimization of Wind-Thermal-Storage System Based on Hybrid Carbon Trading and Wasserstein Fuzzy Set

A robust scheduling optimization method for wind–fire storage system distribution based on the mixed carbon trading mechanism is proposed to improve the rationality of carbon emission quota allocation while reducing the instability of large-scale wind power access systems.A hybrid carbon trading mechanism that combines shortterm and long-term carbon trading is constructed,and a fuzzy set based onWasserstein measurement is proposed to address the uncertainty of wind power access.Moreover,a robust scheduling optimization method for wind–fire storage systems is formed.Results of the multi scenario comparative analysis of practical cases show that the proposed method can deal with the uncertainty of large-scale wind power access and can effectively reduce operating costs and carbon emissions.

Pareto Optimization as the Basic for Selecting Robotic Mechanic Assembly Technologies

The task of selecting robotic mechanic assembly technologies (RMAT) is considered as a multi-criteria optimization task, which in this formulation is solved on the set of previously obtained solutions regarding the selection of RMAT. The purpose of the paper is to increase the efficiency of technological preparation of robotic mechanical assembly production of machine and instrument engineering due to a new approach to the selection of RMAT using Pareto optimization and the peculiarities of the selection task formulation. The novelty consists in the further development of a science-based approach to solving multi-criteria selection task, based on the first proposed formalisms of the specified process, which reflect the peculiarities of the selection task formulation, its meaningful essence and the content of the Pareto optimization method. The practical value of the research lies in the proposed engineering-acceptable approach to solving applied multi-criteria selection tasks on the example of RMAT selection, which is invariant to the statement of the selection task, the dimension of the task, and its meaningful essence. The methods of discrete optimization, fuzzy multi-criteria selection of alternatives, and the Pareto optimization method were used for the research. The main results of this work consist of the development of formalisms and the demonstration of the efficiency of the proposed approach for the applied task of RMAT selection. The peculiarity of the developed approach is the combination of Pareto optimization, performed on a discrete set of local criteria. Directions for further research are presented.

Hybrid Seagull and Whale Optimization Algorithm-Based Dynamic Clustering Protocol for Improving Network Longevity in Wireless Sensor Networks

Energy efficiency is the prime concern in Wireless Sensor Networks(WSNs) as maximized energy consumption without essentially limits the energy stability and network lifetime. Clustering is the significant approach essential for minimizing unnecessary transmission energy consumption with sustained network lifetime. This clustering process is identified as the Non-deterministic Polynomial(NP)-hard optimization problems which has the maximized probability of being solved through metaheuristic algorithms.This adoption of hybrid metaheuristic algorithm concentrates on the identification of the optimal or nearoptimal solutions which aids in better energy stability during Cluster Head(CH) selection. In this paper,Hybrid Seagull and Whale Optimization Algorithmbased Dynamic Clustering Protocol(HSWOA-DCP)is proposed with the exploitation benefits of WOA and exploration merits of SEOA to optimal CH selection for maintaining energy stability with prolonged network lifetime. This HSWOA-DCP adopted the modified version of SEagull Optimization Algorithm(SEOA) to handle the problem of premature convergence and computational accuracy which is maximally possible during CH selection. The inclusion of SEOA into WOA improved the global searching capability during the selection of CH and prevents worst fitness nodes from being selected as CH, since the spiral attacking behavior of SEOA is similar to the bubble-net characteristics of WOA. This CH selection integrates the spiral attacking principles of SEOA and contraction surrounding mechanism of WOA for improving computation accuracy to prevent frequent election process. It also included the strategy of levy flight strategy into SEOA for potentially avoiding premature convergence to attain better trade-off between the rate of exploration and exploitation in a more effective manner. The simulation results of the proposed HSWOADCP confirmed better network survivability rate, network residual energy and network overall throughput on par with the competitive CH selection schemes under different number of data transmission rounds.The statistical analysis of the proposed HSWOA-DCP scheme also confirmed its energy stability with respect to ANOVA test.

Study on trifluoromethanesulfonic acid-promoted synthesis of daidzein: Process optimization and reaction mechanism

Daidzein has been widely used in pharmaceuticals,nutraceuticals,cosmetics,feed additives,etc.Its preparation process and related reaction mechanism need to be further investigated.A cost-effective process for synthesizing daidzein was developed in this work.In this article,a two-step synthesis of daidzein(Friedel–Crafts acylation and[5+1]cyclization)was developed via the employment of trifluoromethanesulfonic acid(TfOH)as an effective promoting reagent.The effect of reaction conditions such as solvent,the amount of TfOH,reaction temperature,and reactant ratio on the conversion rate and the yield of the reaction,respectively,was systematically investigated,and daidzein was obtained in 74.0%isolated yield under optimal conditions.Due to the facilitating effect of TfOH,the Friedel–Crafts acylation was completed within 10 min at 90℃ and the[5+1]cyclization was completed within 180 min at 25℃.In addition,a possible reaction mechanism for this process was proposed.The results of the study may provide useful guidance for industrial production of daidzein on a large scale.

Far and Near Optimization:A New Simple and Effective Metaphor-LessOptimization Algorithm for Solving Engineering Applications

In this article,a novel metaheuristic technique named Far and Near Optimization(FNO)is introduced,offeringversatile applications across various scientific domains for optimization tasks.The core concept behind FNO lies inintegrating global and local search methodologies to update the algorithm population within the problem-solvingspace based on moving each member to the farthest and nearest member to itself.The paper delineates the theoryof FNO,presenting a mathematical model in two phases:(i)exploration based on the simulation of the movementof a population member towards the farthest member from itself and(ii)exploitation based on simulating themovement of a population member towards the nearest member from itself.FNO’s efficacy in tackling optimizationchallenges is assessed through its handling of the CEC 2017 test suite across problem dimensions of 10,30,50,and 100,as well as to address CEC 2020.The optimization results underscore FNO’s adeptness in exploration,exploitation,and maintaining a balance between them throughout the search process to yield viable solutions.Comparative analysis against twelve established metaheuristic algorithms reveals FNO’s superior performance.Simulation findings indicate FNO’s outperformance of competitor algorithms,securing the top rank as the mosteffective optimizer across a majority of benchmark functions.Moreover,the outcomes derived by employing FNOon twenty-two constrained optimization challenges from the CEC 2011 test suite,alongside four engineering designdilemmas,showcase the effectiveness of the suggested method in tackling real-world scenarios.

Deep learning CNN-APSO-LSSVM hybrid fusion model for feature optimization and gas-bearing prediction

Conventional machine learning(CML)methods have been successfully applied for gas reservoir prediction.Their prediction accuracy largely depends on the quality of the sample data;therefore,feature optimization of the input samples is particularly important.Commonly used feature optimization methods increase the interpretability of gas reservoirs;however,their steps are cumbersome,and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data efficiently.In contrast to CML methods,deep learning(DL)methods can directly extract the important features of targets from raw data.Therefore,this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network(CNN)and an adaptive particle swarm optimization-least squares support vector machine(APSO-LSSVM).This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes,considerably simplifying the feature optimization.A CNN was used for feature optimization to highlight sensitive gas reservoir information.APSO-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction results.The constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction,giving full play to the advantages of DL and CML methods.The prediction results obtained are better than those of a single CNN model or APSO-LSSVM model.In the feature optimization process of multicomponent seismic attribute data,CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization methods.In the prediction process,the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction accuracy.The constructed CNN-APSO-LSSVM model had lower errors and a better fit on the test dataset than the other individual models.This method proves the effectiveness of DL technology for the feature extraction of gas reservoirs and provides a feasible way to combine DL and CML technologies to predict gas reservoirs.

Ballistic performance of additive manufacturing 316l stainless steel projectiles based on topology optimization method

Material and structure made by additive manufacturing(AM)have received much attention lately due to their flexibility and ability to customize complex structures.This study first implements multiple objective topology optimization simulations based on a projectile perforation model,and a new topologic projectile is obtained.Then two types of 316L stainless steel projectiles(the solid and the topology)are printed in a selective laser melt(SLM)machine to evaluate the penetration performance of the projectiles by the ballistic test.The experiment results show that the dimensionless specific kinetic energy value of topologic projectiles is higher than that of solid projectiles,indicating the better penetration ability of the topologic projectiles.Finally,microscopic studies(scanning electron microscope and X-ray micro-CT)are performed on the remaining projectiles to investigate the failure mechanism of the internal structure of the topologic projectiles.An explicit dynamics simulation was also performed,and the failure locations of the residual topologic projectiles were in good agreement with the experimental results,which can better guide the design of new projectiles combining AM and topology optimization in the future.