In this paper,a feature selection method for determining input parameters in antenna modeling is proposed.In antenna modeling,the input feature of artificial neural network(ANN)is geometric parameters.The selection cr...In this paper,a feature selection method for determining input parameters in antenna modeling is proposed.In antenna modeling,the input feature of artificial neural network(ANN)is geometric parameters.The selection criteria contain correlation and sensitivity between the geometric parameter and the electromagnetic(EM)response.Maximal information coefficient(MIC),an exploratory data mining tool,is introduced to evaluate both linear and nonlinear correlations.The EM response range is utilized to evaluate the sensitivity.The wide response range corresponding to varying values of a parameter implies the parameter is highly sensitive and the narrow response range suggests the parameter is insensitive.Only the parameter which is highly correlative and sensitive is selected as the input of ANN,and the sampling space of the model is highly reduced.The modeling of a wideband and circularly polarized antenna is studied as an example to verify the effectiveness of the proposed method.The number of input parameters decreases from8 to 4.The testing errors of|S_(11)|and axis ratio are reduced by8.74%and 8.95%,respectively,compared with the ANN with no feature selection.展开更多
In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training s...In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training samples used to calculate the weight vector does not contain the jamming,then the jamming cannot be removed by adaptive spatial filtering.If the weight vector is constantly updated in the range dimension,the training data may contain target echo signals,resulting in signal cancellation effect.To cope with the situation that the training samples are contaminated by target signal,an iterative training sample selection method based on non-homogeneous detector(NHD)is proposed in this paper for updating the weight vector in entire range dimension.The principle is presented,and the validity is proven by simulation results.展开更多
Trade credit,as an effective tool for integrating and coordinating material,information,and financial flows in supply chain management,is becoming increasingly widespread.We explore how a manufacturer can design optim...Trade credit,as an effective tool for integrating and coordinating material,information,and financial flows in supply chain management,is becoming increasingly widespread.We explore how a manufacturer can design optimal trade credit contracts when a risk-averse retailer hides its sales cost information(adverse selection)and selling effort level(moral hazard).We develop incentive models for a risk-averse supply chain when adverse selection and moral hazard coexist,which are then compared with the results under single information asymmetry(moral hazard).Moreover,we analyze the effects of private information and risk-aversion coefficient on contract parameters,selling effort level and the profit or utility of the supply chain.The study shows that when the degree of retailer’s risk aversion is within a certain range,reasonable trade credit contracts designed by the manufacturer can effectively induce the retailer to report its real sales cost and encourage it to exert appropriate effort.Furthermore,we find that the optimal trade credit period,optimal transfer payment,and retailer’s optimal sales effort level under dual information asymmetry are less than those under single information asymmetry.Numerical analysis are conducted to demonstrate the effects of the parameters on decisions and profits.展开更多
This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod ...This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.展开更多
Soybean frogeye leaf spot(FLS) disease is a global disease affecting soybean yield, especially in the soybean growing area of Heilongjiang Province. In order to realize genomic selection breeding for FLS resistance of...Soybean frogeye leaf spot(FLS) disease is a global disease affecting soybean yield, especially in the soybean growing area of Heilongjiang Province. In order to realize genomic selection breeding for FLS resistance of soybean, least absolute shrinkage and selection operator(LASSO) regression and stepwise regression were combined, and a genomic selection model was established for 40 002 SNP markers covering soybean genome and relative lesion area of soybean FLS. As a result, 68 molecular markers controlling soybean FLS were detected accurately, and the phenotypic contribution rate of these markers reached 82.45%. In this study, a model was established, which could be used directly to evaluate the resistance of soybean FLS and to select excellent offspring. This research method could also provide ideas and methods for other plants to breeding in disease resistance.展开更多
In order to solve the problem of uncertainty and fuzzy information in the process of weapon equipment system selec-tion,a multi-attribute decision-making(MADM)method based on probabilistic hesitant fuzzy set(PHFS)is p...In order to solve the problem of uncertainty and fuzzy information in the process of weapon equipment system selec-tion,a multi-attribute decision-making(MADM)method based on probabilistic hesitant fuzzy set(PHFS)is proposed.Firstly,we introduce the concept of probability and fuzzy entropy to mea-sure the ambiguity,hesitation and uncertainty of probabilistic hesitant fuzzy elements(PHFEs).Sequentially,the expert trust network is constructed,and the importance of each expert in the network can be obtained by calculating the cumulative trust value under multiple trust propagation paths,so as to obtain the expert weight vector.Finally,we put forward an MADM method combining the probabilistic hesitant fuzzy entropy and grey rela-tion analysis(GRA)model,and an illustrative case is employed to prove the feasibility and effectiveness of the method when solving the weapon system selection decision-making problem.展开更多
The highly selective catalytic hydrogenation of halogenated nitroaromatics was achieved by employing Pd‑based catalysts that were co‑modified with organic and inorganic ligands.It was demonstrated that the catalysts c...The highly selective catalytic hydrogenation of halogenated nitroaromatics was achieved by employing Pd‑based catalysts that were co‑modified with organic and inorganic ligands.It was demonstrated that the catalysts contained Pd species in mixed valence states,with high valence Pd at the metal‑support interface and zero valence Pd at the metal surface.While the strong coordination of triphenylphosphine(PPh3)to Pd0 on the Pd surface prevents the adsorption of halogenated nitroaromatics and thus dehalogenation,the coordination of sodium metavanadate(NaVO3)to high‑valence Pd sites at the interface helps to activate H2 in a heterolytic pathway for the selective hydrogenation of nitro‑groups.The excellent catalytic performance of the interfacial active sites enables the selective hydrogenation of a wide range of halogenated nitroaromatics.展开更多
This paper investigates the selective maintenance o systems that perform multi-mission in succession. Selective maintenance is performed on systems with limited break time to improve the success of the next mission. I...This paper investigates the selective maintenance o systems that perform multi-mission in succession. Selective maintenance is performed on systems with limited break time to improve the success of the next mission. In general, the duration of the mission is stochastic. However, existing studies rarely take into account system availability and the repairpersons with different skill levels. To solve this problem, a new multi-mission selective maintenance and repairpersons assignment model with stochastic duration of the mission are developed. To maximize the minimum phase-mission reliability while meeting the minimum system availability, the model is transformed into an optimization problem subject to limited maintenance resources. The optimization is then realized using an analytical method based on a self-programming function and a Monte Carlo simulation method, respectively. Finally, the validity of the model and solution method approaches are verified by numerical arithmetic examples. Comparative and sensitivity analyses are made to provide proven recommendations for decision-makers.展开更多
Selective laser melting(SLM)is a cost-effective 3 D metal additive manufacturing(AM)process.However,AM 316 L stainless steel(SS)has different surface and microstructure properties as compared to conventional ones.Bori...Selective laser melting(SLM)is a cost-effective 3 D metal additive manufacturing(AM)process.However,AM 316 L stainless steel(SS)has different surface and microstructure properties as compared to conventional ones.Boriding process is one of the ways to modify and increase the surface properties.The aim of this study is to predict and understand the growth kinetic of iron boride layers on AM 316 L SS.In this study,the growth kinetic mechanism was evaluated for AM 316 L SS.Pack boriding was applied at 850,900 and 950℃,each for 2,4 and 6 h.The thickness of the boride layers ranged from(1.8±0.3)μm to(27.7±2.2)μm.A diffusion model based on error function solutions in Fick’s second law was proposed to quantitatively predict and elucidate the growth rate of FeB and Fe_(2)B phase layers.The activation energy(Q)values for boron diffusion in FeB layer,Fe_(2)B layer,and dual FeB+Fe_(2)B layer were found to be 256.56,161.61 and 209.014 kJ/mol,respectively,which were higher than the conventional 316 L SS.The findings might provide and open new directions and approaches for applications of additively manufactured steels.展开更多
In order to obtain high-density dual-scale ceramic particles(8.5 wt.%SiC+1.5 wt.%TiC)reinforced Al-Mg Sc-Zr composites with uniform microstructure,50 nm TiC and 7μm SiC particles were pre-dispersed into 15−53μm alum...In order to obtain high-density dual-scale ceramic particles(8.5 wt.%SiC+1.5 wt.%TiC)reinforced Al-Mg Sc-Zr composites with uniform microstructure,50 nm TiC and 7μm SiC particles were pre-dispersed into 15−53μm aluminum alloy powders by low-speed ball milling and mechanical mixing technology,respectively.Then,the effects of laser energy density,power and scanning rate on the density of the composites were investigated based on selective laser melting(SLM)technology.The effect of micron-sized SiC and nano-sized TiC particles on solidification structure,mechanical properties and fracture behaviors of the composites was revealed and analyzed in detail.Interfacial reaction and phase variations in the composites with varying reinforced particles were emphatically considered.Results showed that SiC-TiC particles could significantly improve forming quality and density of the SLMed composites,and the optimal relative density was up to 100%.In the process of laser melting,a strong chemical reaction occurs between SiC and aluminum matrix,and micron-scale acicular Al_(4)SiC_(4) bands were formed in situ.There was no interfacial reaction between TiC particles and aluminum matrix.TiC/Al semi-coherent interface had good bonding strength.Pinning effect of TiC particles in grain boundaries could prevent the equiaxial crystals from growing and transforming into columnar crystals,resulting in grain refinement.The optimal ultimate tensile strength(UTS),yield strength(YS),elongation(EL)and elastic modulus of the SiC-TiC/Al-Mg-Sc-Zr composite were~394 MPa,~262 MPa,~8.2%and~86 GPa,respectively.The fracture behavior of the composites included ductile fracture of Al matrix and brittle cleavage fracture of Al_(4)SiC_(4) phases.A large number of cross-distributed acicular Al_(4)SiC_(4) bands were the main factors leading to premature failure and fracture of SiC-TiC/Al-Mg-Sc-Zr composites.展开更多
In view of the difference in coordination capacity of the glycine ion(Gly−),a selective leaching process for treating with spent lithium-ion batteries(LIBs)in the alkaline glycinate system was proposed.The effects of ...In view of the difference in coordination capacity of the glycine ion(Gly−),a selective leaching process for treating with spent lithium-ion batteries(LIBs)in the alkaline glycinate system was proposed.The effects of retention time,leaching temperature,concentration of glycine ligand,liquid-solid ratio(L/S),pH,stirring speed,and H_(2)O_(2) dosage on the leaching efficiency of valuable metals and the dissolution of impurities were investigated.When the spent LIBs were leached in 3 mol/L glycine aqueous solution with pH of 8,L/S of 5 mL:1 g and H_(2)O_(2) dosage of 5 vol.%at 90℃and stirring speed of 400 r/min for 3 h,lithium,cobalt,nickel,and manganese recoveries were 96.31%,83.18%,91.56%,and 31.16%,respectively,but Ca,Al,Fe,and Cu were almost insoluble.Meanwhile,the kinetic study showed that the activation energies for the leaching of Li,Co,Ni,and Mn were all in the range of 45−61 kJ/mol.The results indicate that the leaching process is all controlled by chemical reactions.展开更多
The selective reduction of carbon dioxide(CO_(2))into high-value-added chemicals is one of the most effective means to solve the current energy and environmental problems,which could realize the utilization of CO_(2) ...The selective reduction of carbon dioxide(CO_(2))into high-value-added chemicals is one of the most effective means to solve the current energy and environmental problems,which could realize the utilization of CO_(2) and promote the balance of the carbon cycle.Formate is one of the most economical and practical products of all the electrochemical CO_(2) reduction products.Among the many metal-based electrocatalysts that can convert CO_(2) into formate,Sn-based catalysts have received a lot of attention because of their low-cost,non-toxic characteristics and high selectivity for formate.In this article,the most recent development of Sn-based electrocatalysts is comprehensively summarized by giving examples,which are mainly divided into monometallic Sn,alloyed Sn,Sn-based compounds and Sn composite catalysts.Finally,the current performance enhancement strategies and future directions of the field are summarized.展开更多
Because of an unfortunate mistake during the production of this article,the Acknowledgements have been omitted.The Acknowledgements are added as follows:Sasan YAZDANI would like to thank the Scientific and Technologic...Because of an unfortunate mistake during the production of this article,the Acknowledgements have been omitted.The Acknowledgements are added as follows:Sasan YAZDANI would like to thank the Scientific and Technological Research Council of Turkey(TÜB˙ITAK)for receiving financial support for this work through the 2221 Fellowship Program for Visiting Scientists and Scientists on Sabbatical Leave(Grant ID:E 21514107-115.02-228864).Sasan YAZDANI also expresses his gratitude to Sahand University of Technology for granting him sabbatical leave to facilitate the completion of this research.展开更多
The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results...The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal paraxneter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.展开更多
An adaptive approach to select analysis window param- eters for linear frequency modulated （LFM） signals is proposed to obtain the optimal 3 dB signal-to-noise ratio （SNR） in the short- time Fourier transform （S...An adaptive approach to select analysis window param- eters for linear frequency modulated （LFM） signals is proposed to obtain the optimal 3 dB signal-to-noise ratio （SNR） in the short- time Fourier transform （STFT） domain. After analyzing the instan- taneous frequency and instantaneous bandwidth to deduce the relation between the window length and deviation of the Gaus- sian window, high-order statistics is used to select the appropriate window length for STFT and get the optimal SNR with the right time-frequency resolution according to the signal characteristic under a fixed sampling rate. Computer simulations have verified the effectiveness of the new method.展开更多
An improved social force model based on exit selection is proposed to simulate pedestrians' microscopic behaviors in subway station. The modification lies in considering three factors of spatial distance, occupant...An improved social force model based on exit selection is proposed to simulate pedestrians' microscopic behaviors in subway station. The modification lies in considering three factors of spatial distance, occupant density and exit width. In addition, the problem of pedestrians selecting exit frequently is solved as follows: not changing to other exits in the affected area of one exit, using the probability of remaining preceding exit and invoking function of exit selection after several simulation steps. Pedestrians in subway station have some special characteristics, such as explicit destinations, different familiarities with subway station. Finally, Beijing Zoo Subway Station is taken as an example and the feasibility of the model results is verified through the comparison of the actual data and simulation data. The simulation results show that the improved model can depict the microscopic behaviors of pedestrians in subway station.展开更多
Suppliers＇ selection in supply chain management （SCM） has attracted considerable research interests in recent years. Recent literatures show that neural networks achieve better performance than traditional statisti...Suppliers＇ selection in supply chain management （SCM） has attracted considerable research interests in recent years. Recent literatures show that neural networks achieve better performance than traditional statistical methods. However, neural networks have inherent drawbacks, such as local optimization solution, lack generalization, and uncontrolled convergence. A relatively new machine learning technique, support vector machine （SVM）, which overcomes the drawbacks of neural networks, is introduced to provide a model with better explanatory power to select ideal supplier partners. Meanwhile, in practice, the suppliers＇ samples are very insufficient. SVMs are adaptive to deal with small samples＇ training and testing. The prediction accuracies for BPNN and SVM methods are compared to choose the appreciating suppliers. The actual examples illustrate that SVM methods are superior to BPNN.展开更多
Since most parameter control methods are based on prior knowledge, it is difficult for them to solve various problems.In this paper, an adaptive selection method used for operators and parameters is proposed and named...Since most parameter control methods are based on prior knowledge, it is difficult for them to solve various problems.In this paper, an adaptive selection method used for operators and parameters is proposed and named double adaptive selection(DAS) strategy. Firstly, some experiments about the operator search ability are given and the performance of operators with different donate vectors is analyzed. Then, DAS is presented by inducing the upper confidence bound strategy, which chooses suitable combination of operators and donates sets to optimize solutions without prior knowledge. Finally, the DAS is used under the framework of the multi-objective evolutionary algorithm based on decomposition, and the multi-objective evolutionary algorithm based on DAS(MOEA/D-DAS) is compared to state-of-the-art MOEAs. Simulation results validate that the MOEA/D-DAS could select the suitable combination of operators and donate sets to optimize problems and the proposed algorithm has better convergence and distribution.展开更多
A new antenna selection algorithm for multiple input multiple output （MIMO） wireless systems is proposed. The modified Tanimoto coefficient is used to compare the similarity of the rows/columns of the channel matrix...A new antenna selection algorithm for multiple input multiple output （MIMO） wireless systems is proposed. The modified Tanimoto coefficient is used to compare the similarity of the rows/columns of the channel matrix. Based on the calculated similarity, the proposed algorithm chooses the antenna subset, which has the maximum product of dissimilarity and Frobenius norm. The proposed algorithm requires low computational complexity as to the optimal selection but with comparative outage capacity and average signal to noise ratio （SNR） performance. It can improve both the outage capacity and the average SNR as compared to random selection. The simulation results are shown to validate our algorithm.展开更多
Sensor platforms with active sensing equipment such as radar may betray their existence, by emitting energy that will be intercepted by enemy surveillance sensors. The radar with less emission has more excellent perfo...Sensor platforms with active sensing equipment such as radar may betray their existence, by emitting energy that will be intercepted by enemy surveillance sensors. The radar with less emission has more excellent performance of the low probability of intercept（LPI）. In order to reduce the emission times of the radar, a novel sensor selection strategy based on an improved interacting multiple model particle filter（IMMPF） tracking method is presented. Firstly the IMMPF tracking method is improved by increasing the weight of the particle which is close to the system state and updating the model probability of every particle. Then a sensor selection approach for LPI takes use of both the target＇s maneuverability and the state＇s uncertainty to decide the radar＇s radiation time. The radar will work only when the target＇s maneuverability and the state＇s uncertainty exceed the control capability of the passive sensors. Tracking accuracy and LPI performance are demonstrated in the Monte Carlo simulations.展开更多
基金National Natural Science Foundation of China(62161048)Sichuan Science and Technology Program(2022NSFSC0547,2022ZYD0109)。
文摘In this paper,a feature selection method for determining input parameters in antenna modeling is proposed.In antenna modeling,the input feature of artificial neural network(ANN)is geometric parameters.The selection criteria contain correlation and sensitivity between the geometric parameter and the electromagnetic(EM)response.Maximal information coefficient(MIC),an exploratory data mining tool,is introduced to evaluate both linear and nonlinear correlations.The EM response range is utilized to evaluate the sensitivity.The wide response range corresponding to varying values of a parameter implies the parameter is highly sensitive and the narrow response range suggests the parameter is insensitive.Only the parameter which is highly correlative and sensitive is selected as the input of ANN,and the sampling space of the model is highly reduced.The modeling of a wideband and circularly polarized antenna is studied as an example to verify the effectiveness of the proposed method.The number of input parameters decreases from8 to 4.The testing errors of|S_(11)|and axis ratio are reduced by8.74%and 8.95%,respectively,compared with the ANN with no feature selection.
基金supported by the National Natural Science Foundation of China(62371049)。
文摘In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training samples used to calculate the weight vector does not contain the jamming,then the jamming cannot be removed by adaptive spatial filtering.If the weight vector is constantly updated in the range dimension,the training data may contain target echo signals,resulting in signal cancellation effect.To cope with the situation that the training samples are contaminated by target signal,an iterative training sample selection method based on non-homogeneous detector(NHD)is proposed in this paper for updating the weight vector in entire range dimension.The principle is presented,and the validity is proven by simulation results.
基金supported by the Plan Project of Shanghai Philosophy and Social Science(2017BGL014)the National Natural Science Foundation of China(71832001)the Fundamental Research Funds for the Central Universities(2232020B-04,2232018H-07).
文摘Trade credit,as an effective tool for integrating and coordinating material,information,and financial flows in supply chain management,is becoming increasingly widespread.We explore how a manufacturer can design optimal trade credit contracts when a risk-averse retailer hides its sales cost information(adverse selection)and selling effort level(moral hazard).We develop incentive models for a risk-averse supply chain when adverse selection and moral hazard coexist,which are then compared with the results under single information asymmetry(moral hazard).Moreover,we analyze the effects of private information and risk-aversion coefficient on contract parameters,selling effort level and the profit or utility of the supply chain.The study shows that when the degree of retailer’s risk aversion is within a certain range,reasonable trade credit contracts designed by the manufacturer can effectively induce the retailer to report its real sales cost and encourage it to exert appropriate effort.Furthermore,we find that the optimal trade credit period,optimal transfer payment,and retailer’s optimal sales effort level under dual information asymmetry are less than those under single information asymmetry.Numerical analysis are conducted to demonstrate the effects of the parameters on decisions and profits.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272257,12102292,12032006)the special fund for Science and Technology Innovation Teams of Shanxi Province(Nos.202204051002006).
文摘This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.
基金Supported by the National Key Research and Development Program of China(2021YFD1201103-01-05)。
文摘Soybean frogeye leaf spot(FLS) disease is a global disease affecting soybean yield, especially in the soybean growing area of Heilongjiang Province. In order to realize genomic selection breeding for FLS resistance of soybean, least absolute shrinkage and selection operator(LASSO) regression and stepwise regression were combined, and a genomic selection model was established for 40 002 SNP markers covering soybean genome and relative lesion area of soybean FLS. As a result, 68 molecular markers controlling soybean FLS were detected accurately, and the phenotypic contribution rate of these markers reached 82.45%. In this study, a model was established, which could be used directly to evaluate the resistance of soybean FLS and to select excellent offspring. This research method could also provide ideas and methods for other plants to breeding in disease resistance.
基金supported by the National Natural Science Foundation of China(71901214).
文摘In order to solve the problem of uncertainty and fuzzy information in the process of weapon equipment system selec-tion,a multi-attribute decision-making(MADM)method based on probabilistic hesitant fuzzy set(PHFS)is proposed.Firstly,we introduce the concept of probability and fuzzy entropy to mea-sure the ambiguity,hesitation and uncertainty of probabilistic hesitant fuzzy elements(PHFEs).Sequentially,the expert trust network is constructed,and the importance of each expert in the network can be obtained by calculating the cumulative trust value under multiple trust propagation paths,so as to obtain the expert weight vector.Finally,we put forward an MADM method combining the probabilistic hesitant fuzzy entropy and grey rela-tion analysis(GRA)model,and an illustrative case is employed to prove the feasibility and effectiveness of the method when solving the weapon system selection decision-making problem.
文摘The highly selective catalytic hydrogenation of halogenated nitroaromatics was achieved by employing Pd‑based catalysts that were co‑modified with organic and inorganic ligands.It was demonstrated that the catalysts contained Pd species in mixed valence states,with high valence Pd at the metal‑support interface and zero valence Pd at the metal surface.While the strong coordination of triphenylphosphine(PPh3)to Pd0 on the Pd surface prevents the adsorption of halogenated nitroaromatics and thus dehalogenation,the coordination of sodium metavanadate(NaVO3)to high‑valence Pd sites at the interface helps to activate H2 in a heterolytic pathway for the selective hydrogenation of nitro‑groups.The excellent catalytic performance of the interfacial active sites enables the selective hydrogenation of a wide range of halogenated nitroaromatics.
文摘This paper investigates the selective maintenance o systems that perform multi-mission in succession. Selective maintenance is performed on systems with limited break time to improve the success of the next mission. In general, the duration of the mission is stochastic. However, existing studies rarely take into account system availability and the repairpersons with different skill levels. To solve this problem, a new multi-mission selective maintenance and repairpersons assignment model with stochastic duration of the mission are developed. To maximize the minimum phase-mission reliability while meeting the minimum system availability, the model is transformed into an optimization problem subject to limited maintenance resources. The optimization is then realized using an analytical method based on a self-programming function and a Monte Carlo simulation method, respectively. Finally, the validity of the model and solution method approaches are verified by numerical arithmetic examples. Comparative and sensitivity analyses are made to provide proven recommendations for decision-makers.
文摘Selective laser melting(SLM)is a cost-effective 3 D metal additive manufacturing(AM)process.However,AM 316 L stainless steel(SS)has different surface and microstructure properties as compared to conventional ones.Boriding process is one of the ways to modify and increase the surface properties.The aim of this study is to predict and understand the growth kinetic of iron boride layers on AM 316 L SS.In this study,the growth kinetic mechanism was evaluated for AM 316 L SS.Pack boriding was applied at 850,900 and 950℃,each for 2,4 and 6 h.The thickness of the boride layers ranged from(1.8±0.3)μm to(27.7±2.2)μm.A diffusion model based on error function solutions in Fick’s second law was proposed to quantitatively predict and elucidate the growth rate of FeB and Fe_(2)B phase layers.The activation energy(Q)values for boron diffusion in FeB layer,Fe_(2)B layer,and dual FeB+Fe_(2)B layer were found to be 256.56,161.61 and 209.014 kJ/mol,respectively,which were higher than the conventional 316 L SS.The findings might provide and open new directions and approaches for applications of additively manufactured steels.
基金Project(2022J318)supported by the Natural Science Foundation of Ningbo,ChinaProject(2021A1515110525)supported by the Guangdong Basic and Applied Basic Research Foundation,ChinaProject(2022QN05023)supported by the Inner Mongolia Natural Science Foundation Youth Project,China。
文摘In order to obtain high-density dual-scale ceramic particles(8.5 wt.%SiC+1.5 wt.%TiC)reinforced Al-Mg Sc-Zr composites with uniform microstructure,50 nm TiC and 7μm SiC particles were pre-dispersed into 15−53μm aluminum alloy powders by low-speed ball milling and mechanical mixing technology,respectively.Then,the effects of laser energy density,power and scanning rate on the density of the composites were investigated based on selective laser melting(SLM)technology.The effect of micron-sized SiC and nano-sized TiC particles on solidification structure,mechanical properties and fracture behaviors of the composites was revealed and analyzed in detail.Interfacial reaction and phase variations in the composites with varying reinforced particles were emphatically considered.Results showed that SiC-TiC particles could significantly improve forming quality and density of the SLMed composites,and the optimal relative density was up to 100%.In the process of laser melting,a strong chemical reaction occurs between SiC and aluminum matrix,and micron-scale acicular Al_(4)SiC_(4) bands were formed in situ.There was no interfacial reaction between TiC particles and aluminum matrix.TiC/Al semi-coherent interface had good bonding strength.Pinning effect of TiC particles in grain boundaries could prevent the equiaxial crystals from growing and transforming into columnar crystals,resulting in grain refinement.The optimal ultimate tensile strength(UTS),yield strength(YS),elongation(EL)and elastic modulus of the SiC-TiC/Al-Mg-Sc-Zr composite were~394 MPa,~262 MPa,~8.2%and~86 GPa,respectively.The fracture behavior of the composites included ductile fracture of Al matrix and brittle cleavage fracture of Al_(4)SiC_(4) phases.A large number of cross-distributed acicular Al_(4)SiC_(4) bands were the main factors leading to premature failure and fracture of SiC-TiC/Al-Mg-Sc-Zr composites.
基金Projects(51974137,52274299)supported by the National Natural Science Foundation of ChinaProject(2023M733190)supported by the China Postdoctoral Science Foundation。
文摘In view of the difference in coordination capacity of the glycine ion(Gly−),a selective leaching process for treating with spent lithium-ion batteries(LIBs)in the alkaline glycinate system was proposed.The effects of retention time,leaching temperature,concentration of glycine ligand,liquid-solid ratio(L/S),pH,stirring speed,and H_(2)O_(2) dosage on the leaching efficiency of valuable metals and the dissolution of impurities were investigated.When the spent LIBs were leached in 3 mol/L glycine aqueous solution with pH of 8,L/S of 5 mL:1 g and H_(2)O_(2) dosage of 5 vol.%at 90℃and stirring speed of 400 r/min for 3 h,lithium,cobalt,nickel,and manganese recoveries were 96.31%,83.18%,91.56%,and 31.16%,respectively,but Ca,Al,Fe,and Cu were almost insoluble.Meanwhile,the kinetic study showed that the activation energies for the leaching of Li,Co,Ni,and Mn were all in the range of 45−61 kJ/mol.The results indicate that the leaching process is all controlled by chemical reactions.
基金Project(52204378)supported by the National Natural Science Foundation of China。
文摘The selective reduction of carbon dioxide(CO_(2))into high-value-added chemicals is one of the most effective means to solve the current energy and environmental problems,which could realize the utilization of CO_(2) and promote the balance of the carbon cycle.Formate is one of the most economical and practical products of all the electrochemical CO_(2) reduction products.Among the many metal-based electrocatalysts that can convert CO_(2) into formate,Sn-based catalysts have received a lot of attention because of their low-cost,non-toxic characteristics and high selectivity for formate.In this article,the most recent development of Sn-based electrocatalysts is comprehensively summarized by giving examples,which are mainly divided into monometallic Sn,alloyed Sn,Sn-based compounds and Sn composite catalysts.Finally,the current performance enhancement strategies and future directions of the field are summarized.
文摘Because of an unfortunate mistake during the production of this article,the Acknowledgements have been omitted.The Acknowledgements are added as follows:Sasan YAZDANI would like to thank the Scientific and Technological Research Council of Turkey(TÜB˙ITAK)for receiving financial support for this work through the 2221 Fellowship Program for Visiting Scientists and Scientists on Sabbatical Leave(Grant ID:E 21514107-115.02-228864).Sasan YAZDANI also expresses his gratitude to Sahand University of Technology for granting him sabbatical leave to facilitate the completion of this research.
基金the National Nature Science Foundation of China (60775047, 60402024)
文摘The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal paraxneter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.
基金supported by the National Natural Science Foundation of China(6107313361175053+8 种基金6127236960975019)the Heilongjiang Postdoctoral Grant(LRB08362)the Fundamental Research Funds for the Central Universities of China(2011QN0272011QN1262012QN0302011ZD010)the Science and Technology Planning Project of Dalian City(2011A17GX0732010E15SF153)
文摘An adaptive approach to select analysis window param- eters for linear frequency modulated （LFM） signals is proposed to obtain the optimal 3 dB signal-to-noise ratio （SNR） in the short- time Fourier transform （STFT） domain. After analyzing the instan- taneous frequency and instantaneous bandwidth to deduce the relation between the window length and deviation of the Gaus- sian window, high-order statistics is used to select the appropriate window length for STFT and get the optimal SNR with the right time-frequency resolution according to the signal characteristic under a fixed sampling rate. Computer simulations have verified the effectiveness of the new method.
基金Project(T14JB00200)supported by the Fundamental Research Funds for the Central UniversitiesChina+2 种基金Projects(RCS2012ZZ002RCS2012ZT003)supported by the State Key Laboratory of Rail Traffic Control and SafetyChina
文摘An improved social force model based on exit selection is proposed to simulate pedestrians' microscopic behaviors in subway station. The modification lies in considering three factors of spatial distance, occupant density and exit width. In addition, the problem of pedestrians selecting exit frequently is solved as follows: not changing to other exits in the affected area of one exit, using the probability of remaining preceding exit and invoking function of exit selection after several simulation steps. Pedestrians in subway station have some special characteristics, such as explicit destinations, different familiarities with subway station. Finally, Beijing Zoo Subway Station is taken as an example and the feasibility of the model results is verified through the comparison of the actual data and simulation data. The simulation results show that the improved model can depict the microscopic behaviors of pedestrians in subway station.
文摘Suppliers＇ selection in supply chain management （SCM） has attracted considerable research interests in recent years. Recent literatures show that neural networks achieve better performance than traditional statistical methods. However, neural networks have inherent drawbacks, such as local optimization solution, lack generalization, and uncontrolled convergence. A relatively new machine learning technique, support vector machine （SVM）, which overcomes the drawbacks of neural networks, is introduced to provide a model with better explanatory power to select ideal supplier partners. Meanwhile, in practice, the suppliers＇ samples are very insufficient. SVMs are adaptive to deal with small samples＇ training and testing. The prediction accuracies for BPNN and SVM methods are compared to choose the appreciating suppliers. The actual examples illustrate that SVM methods are superior to BPNN.
基金supported by the National Natural Science Foundation of China(7177121671701209)
文摘Since most parameter control methods are based on prior knowledge, it is difficult for them to solve various problems.In this paper, an adaptive selection method used for operators and parameters is proposed and named double adaptive selection(DAS) strategy. Firstly, some experiments about the operator search ability are given and the performance of operators with different donate vectors is analyzed. Then, DAS is presented by inducing the upper confidence bound strategy, which chooses suitable combination of operators and donates sets to optimize solutions without prior knowledge. Finally, the DAS is used under the framework of the multi-objective evolutionary algorithm based on decomposition, and the multi-objective evolutionary algorithm based on DAS(MOEA/D-DAS) is compared to state-of-the-art MOEAs. Simulation results validate that the MOEA/D-DAS could select the suitable combination of operators and donate sets to optimize problems and the proposed algorithm has better convergence and distribution.
文摘A new antenna selection algorithm for multiple input multiple output （MIMO） wireless systems is proposed. The modified Tanimoto coefficient is used to compare the similarity of the rows/columns of the channel matrix. Based on the calculated similarity, the proposed algorithm chooses the antenna subset, which has the maximum product of dissimilarity and Frobenius norm. The proposed algorithm requires low computational complexity as to the optimal selection but with comparative outage capacity and average signal to noise ratio （SNR） performance. It can improve both the outage capacity and the average SNR as compared to random selection. The simulation results are shown to validate our algorithm.
基金supported by the Fundamental Research Funds for the Central Universities(NJ20140010)the Scientific Research Start-up Funding from Jiangsu University of Science and Technology+1 种基金the Scienceand Technology on Electronic Information Control Laboratory Projectthe Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘Sensor platforms with active sensing equipment such as radar may betray their existence, by emitting energy that will be intercepted by enemy surveillance sensors. The radar with less emission has more excellent performance of the low probability of intercept（LPI）. In order to reduce the emission times of the radar, a novel sensor selection strategy based on an improved interacting multiple model particle filter（IMMPF） tracking method is presented. Firstly the IMMPF tracking method is improved by increasing the weight of the particle which is close to the system state and updating the model probability of every particle. Then a sensor selection approach for LPI takes use of both the target＇s maneuverability and the state＇s uncertainty to decide the radar＇s radiation time. The radar will work only when the target＇s maneuverability and the state＇s uncertainty exceed the control capability of the passive sensors. Tracking accuracy and LPI performance are demonstrated in the Monte Carlo simulations.
