As our understanding of ecology deepens and modeling techniques advance,species distribution models have grown increasingly sophisticated,enhancing both their fitting and predictive capabilities.However,the dependabil...As our understanding of ecology deepens and modeling techniques advance,species distribution models have grown increasingly sophisticated,enhancing both their fitting and predictive capabilities.However,the dependability of predictive accuracy remains a critical issue,as the precision of these predictions largely hinges on the quality of the base data.We developed models using both field survey and remote sensing data from 2016 to 2020 to evaluate the impact of different data sources on the accuracy of predictions for Scomber japonicus distributions.Our research findings indicate that the variability of water temperature and salinity data from field suvery is significantly greater than that from remote sensing data.Within the same season,we found that the relationship between the abundance of S.japonicus and environmental factors varied significantly depending on the data source.Models using field survey data were able to more accurately reflect the complex relationships between resource distribution and environmental factors.Additionally,in terms of model predictive performance,models based on field survey data demonstrated greater accuracy in predicting the abundance of S.japonicus compared to those based on remote sensing data,allowing for more accurate mastery of their spatial distribution characteristics.This study highlights the significant impact of data sources on the accuracy of species distribution models and offers valuable insights for fisheries resources management.展开更多
The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-lear...The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-learning(DL)-driven CV in four key areas of materials science:microstructure-based performance prediction,microstructure information generation,microstructure defect detection,and crystal structure-based property prediction.The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction.Moreover,recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments.The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data,thereby allowing for the discovery and innovation of next-generation materials.Finally,the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.展开更多
The current highly competitive environment has driven industries to operate with increasingly restricted profit margins. Thus, it is imperative to optimize production processes. Faced with this scenario, multivariable...The current highly competitive environment has driven industries to operate with increasingly restricted profit margins. Thus, it is imperative to optimize production processes. Faced with this scenario, multivariable predictive control of processes has been presented as a powerful alternative to achieve these goals. Moreover, the rationale for implementation of advanced control and subsequent analysis of its post-match performance also focus on the benefits that this tool brings to the plant. It is therefore essential to establish a methodology for analysis, based on clear and measurable criteria. Currently, there are different methodologies available in the market to assist with such analysis. These tools can have a quantitative or qualitative focus. The aim of this study is to evaluate three of the best current main performance assessment technologies： Minimum Variance Control-Harris Index; Statistical Process Control （Cp and Cpk）; and the Qin and Yu Index. These indexes were studied for an alumina plant controlled by three MPC （model predictive control） algorithms （GPC （generalized predictive control）, RMPCT （robust multivariable predictive control technology） and ESSMPC （extended state space model predictive controller）） with different results.展开更多
Conical picks are by far the most widely used drag type cutting tools employed on partial face rock excavation machines.The cutting force and specific energy are two important design parameters for the conical pick pe...Conical picks are by far the most widely used drag type cutting tools employed on partial face rock excavation machines.The cutting force and specific energy are two important design parameters for the conical pick performance,and the rock cutting testing is considered as the promising tool for determining these parameters.In the absence of an instrumented cutting rig,researchers generally rely on empirical predictive plots.For this,this paper suggests predictive plots for estimating the cutting force and specific energy,in consideration of the cutting depth to define the cuttability with conical picks.In this context,rock cutting tests were carried out on six volcanic rock samples with varying cutting depths using the unrelieved and relieved cutting modes.The cutting force and specific energy were correlated with the uniaxial compressive strength,Brazilian tensile strength,elasticity modulus,and plasticity index.Predictive plots were proposed for different cutting depths in the unrelieved and relieved cutting modes and exponential relationships were obtained among the cuttability parameters,and mechanical and elastoplastic properties of rocks.展开更多
Polyimides(PIs)are widely used in the microelectronics field due to their excellent comprehensive performance and the diversity and designability of their structures.In flexible substrate applications,designing the mo...Polyimides(PIs)are widely used in the microelectronics field due to their excellent comprehensive performance and the diversity and designability of their structures.In flexible substrate applications,designing the molecular structure to balance thermodynamic and optical properties is the most critical part of the PI design process.To accelerate the discovery of high-performance PIs,we established predictive models for glass transition temperature(T_(g)),cut-off wavelength(CW),and coefficient of thermal expansion(CTE)using various machine learning algorithms.The optimal predictive models for the three properties demonstrated high accuracy and stability in both test set predictions and cross-validation results.Additionally,the interpretability of the three optimal models was analyzed using the SHAP method,and the accuracy and generalization ability of the models were validated using several novel PIs.By combining the three models,predictions were made for multiple PIs,leading to the selection and synthesis of PIs with excellent comprehensive performance.135 novel PIs were designed and their key properties were obtained without the need for experimental verification.The predictive models established in this study can assist researchers in quickly determining the T_(g),CW and CTE of PIs,thereby facilitating the swift identification of promising candidates for further development.展开更多
The reverse operation of existing centrifugal pumps,commonly referred to as“Pump as Turbine”(PAT),is a key approach for recovering liquid pressure energy.As a type of hydraulic machinery characterized by a simple st...The reverse operation of existing centrifugal pumps,commonly referred to as“Pump as Turbine”(PAT),is a key approach for recovering liquid pressure energy.As a type of hydraulic machinery characterized by a simple structure and user-friendly operation,PAT holds significant promise for application in industrial waste energy recovery systems.This paper reviews recent advancements in this field,with a focus on pump type selection,performance prediction,and optimization design.First,the advantages of various prototype pumps,including centrifugal,axial-flow,mixed-flow,screw,and plunger pumps,are examined in specific application scenarios while analyzing their suitability for turbine operation.Next,performance prediction techniques for PATs are discussed,encompassing theoretical calculations,numerical simulations,and experimental testing.Special emphasis is placed on the crucial role of Computational Fluid Dynamics(CFD)and internal flow field testing technologies in analyzing PAT internal flow characteristics.Additionally,the impact of multi-objective optimization methods and the application of advanced materials on PAT performance enhancement is addressed.Finally,based on current research findings and existing technical challenges,this review also indicates future development directions;in particular,four key breakthrough areas are identified:advanced materials,innovative design methodologies,internal flow diagnostics,and in-depth analysis of critical components.展开更多
Predicting player performance in sports is a critical challenge with significant implications for team success,fan engagement,and financial outcomes.Although,inMajor League Baseball(MLB),statistical methodologies such...Predicting player performance in sports is a critical challenge with significant implications for team success,fan engagement,and financial outcomes.Although,inMajor League Baseball(MLB),statistical methodologies such as sabermetrics have been widely used,the dynamic nature of sports makes accurate performance prediction a difficult task.Enhanced forecasts can provide immense value to team managers by aiding strategic player contract and acquisition decisions.This study addresses this challenge by employing the temporal fusion transformer(TFT),an advanced and cutting-edge deep learning model for complex data,to predict pitchers’earned run average(ERA),a key metric in baseball performance analysis.The performance of the TFT model is evaluated against recurrent neural network-based approaches and existing projection systems.In experimental results,the TFT based model consistently outperformed its counterparts,demonstrating superior accuracy in pitcher performance prediction.By leveraging the advanced capabilities of TFT,this study contributes to more precise player evaluations and improves strategic planning in baseball.展开更多
Performance prediction in preliminary design stages of several turbomachinery components is a critical task in order to bring the design processes of these devices to a successful conclusion. In this paper, a review a...Performance prediction in preliminary design stages of several turbomachinery components is a critical task in order to bring the design processes of these devices to a successful conclusion. In this paper, a review and analysis of the major loss mechanisms and loss models, used to determine the efficiency of a single stage centrifugal compressor, and a subsequent examination to determine an appropriate loss correlation set for estimating the isentropic efficiency in preliminary design stages of centrifugal compressors, were developed. Several semi-empirical correlations,commonly used to predict the efficiency of centrifugal compressors, were implemented in FORTRAN code and then were compared with experimental results in order to establish a loss correlation set to determine, with good approximation, the isentropic efficiency of single stage compressor.The aim of this study is to provide a suitable loss correlation set for determining the isentropic efficiency of a single stage centrifugal compressor, because, with a large amount of loss mechanisms and correlations available in the literature, it is difficult to ascertain how many and which correlations to employ for the correct prediction of the efficiency in the preliminary stage design of a centrifugal compressor. As a result of this study, a set of correlations composed by nine loss mechanisms for single stage centrifugal compressors, conformed by a rotor and a diffuser, are specified.展开更多
A pplication o f m echanical excavators is one o f th e m o st com m only used excavation m eth o d s because itcan bring th e p ro ject m ore productivity, accuracy and safety. A m ong th e m echanical excavators, ro...A pplication o f m echanical excavators is one o f th e m o st com m only used excavation m eth o d s because itcan bring th e p ro ject m ore productivity, accuracy and safety. A m ong th e m echanical excavators, roadhead ers are m echanical m iners w h ich have b een extensively u se d in tu n n elin g , m ining an d civil indu stries. Perform ance pred ictio n is an im p o rta n t issue for successful ro a d h e a d e r application andgenerally deals w ith m achine selection, p ro d u ctio n rate an d b it consu m p tio n . The m ain aim o f thisresearch is to investigate th e c u ttin g p erfo rm an ce （in stan tan eo u s c u ttin g rates （ICRs）） o f m ed iu m -d u tyro ad h ead ers by using artificial neural n etw o rk （ANN） approach. T here are d ifferent categories forANNs, b u t based o n train in g alg o rith m th e re are tw o m ain k in d s： supervised and u n su p erv ised . Them u lti-lay er p ercep tro n （MLP） an d K ohonen self-organizing feature m ap （KSOFM） are th e m o st w idelyused neu ral netw o rk s for supervised an d u n su p erv ised ones, respectively. For gaining this goal, ad atab ase w as prim arily provided from ro ad h e a d e rs＇ p erfo rm an ce an d geom echanical characteristics o frock form ations in tu n n els and d rift galleries in Tabas coal m ine, th e larg est an d th e only fullymech an ized coal m ine in Iran. T hen th e datab ase w as analyzed in o rd e r to yield th e m ost im p o rtan tfactor for ICR by using relatively im p o rta n t factor in w hich G arson eq u atio n w as utilized. The MLPn etw o rk w as train ed by 3 in p u t p ara m e te rs including rock m ass pro p erties, rock quality d esignation（RQD）, in tact rock p ro p erties such as uniaxial com pressive stre n g th （UCS） an d Brazilian ten sile stren g th（BTS）, and o n e o u tp u t p a ra m e te r （ICR）. In o rd e r to have m ore v alidation o n MLP o u tp u ts, KSOFM visualizationw as applied. The m ean square e rro r （MSE） an d regression coefficient （R ） o f MLP w e re found tobe 5.49 an d 0.97, respectively. M oreover, KSOFM n etw o rk has a m ap size o f 8 x 5 and final qu an tizatio nan d topographic erro rs w e re 0.383 an d 0.032, respectively. The results show th a t MLP neural n etw orkshave a strong capability to p red ict an d ev alu ate th e perfo rm an ce o f m ed iu m -d u ty ro ad h ead ers in coalm easu re rocks. Furtherm ore, it is concluded th a t KSOFM neural n etw o rk is an efficient w ay for u n d e rstand in g system beh av io r an d know ledge extraction. Finally, it is indicated th a t UCS has m ore influenceo n ICR b y applying th e b e st train ed MLP n etw o rk w eig h ts in G arson eq u atio n w h ich is also confirm ed byKSOFM.展开更多
MapReduce has emerged as a popular computing model used in datacenters to process large amount of datasets.In the map phase,hash partitioning is employed to distribute data that sharing the same key across data center...MapReduce has emerged as a popular computing model used in datacenters to process large amount of datasets.In the map phase,hash partitioning is employed to distribute data that sharing the same key across data center-scale cluster nodes.However,we observe that this approach can lead to uneven data distribution,which can result in skewed loads among reduce tasks,thus hamper performance of MapReduce systems.Moreover,worker nodes in MapReduce systems may differ in computing capability due to(1) multiple generations of hardware in non-virtualized data centers,or(2) co-location of virtual machines in virtualized data centers.The heterogeneity among cluster nodes exacerbates the negative effects of uneven data distribution.To improve MapReduce performance in heterogeneous clusters,we propose a novel load balancing approach in the reduce phase.This approach consists of two components:(1) performance prediction for reducers that run on heterogeneous nodes based on support vector machines models,and(2) heterogeneity-aware partitioning(HAP),which balances skewed data for reduce tasks.We implement this approach as a plug-in in current MapReduce system.Experimental results demonstrate that our proposed approach distributes work evenly among reduce tasks,and improves MapReduce performance with little overhead.展开更多
Evaluating the adaptability of cantilever boring machine(CBM) through in-depth excavation and analysis of tunnel excavation data and rock mass parameters is the premise of mechanical design and efficient excavation in...Evaluating the adaptability of cantilever boring machine(CBM) through in-depth excavation and analysis of tunnel excavation data and rock mass parameters is the premise of mechanical design and efficient excavation in the field of underground space engineering.This paper presented a case study of tunnelling performance prediction method of CBM in sedimentary hard-rock tunnel of Karst landform type by using tunneling data and surrounding rock parameters.The uniaxial compressive strength(UCS),rock integrity factor(Kv),basic quality index([BQ]),rock quality index RQD,brazilian tensile strength(BTS) and brittleness index(BI) were introduced to construct a performance prediction database based on the hard-rock tunnel of Guiyang Metro Line 1 and Line 3,and then established the performance prediction model of cantilever boring machine.Then the deep belief network(DBN) was introduced into the performance prediction model,and the reliability of performance prediction model was verified by combining with engineering data.The study showed that the influence degree of surrounding rock parameters on the tunneling performance of the cantilever boring machine is UCS > [BQ] > BTS >RQD > Kv > BI.The performance prediction model shows that the instantaneous cutting rate(ICR) has a good correlation with the surrounding rock parameters,and the predicting model accuracy is related to the reliability of construction data.The prediction of limestone and dolomite sections of Line 3 based on the DBN performance prediction model shows that the measured ICR and predicted ICR is consistent and the built performance prediction model is reliable.The research results have theoretical reference significance for the applicability analysis and mechanical selection of cantilever boring machine for hard rock tunnel.展开更多
Steels are widely used as structural materials,making them essential for supporting our lives and industries.However,further improving the comprehensive properties of steel through traditional trial-and-error methods ...Steels are widely used as structural materials,making them essential for supporting our lives and industries.However,further improving the comprehensive properties of steel through traditional trial-and-error methods becomes challenging due to the continuous development and numerous processing parameters involved in steel production.To address this challenge,the application of machine learning methods becomes crucial in establishing complex relationships between manufacturing processes and steel performance.This review begins with a general overview of machine learning methods and subsequently introduces various performance predictions in steel materials.The classification of performance pre-diction was used to assess the current application of machine learning model-assisted design.Several important issues,such as data source and characteristics,intermediate features,algorithm optimization,key feature analysis,and the role of environmental factors,were summarized and analyzed.These insights will be beneficial and enlightening to future research endeavors in this field.展开更多
Two new binary near-azeotropic mixtures named M1 and M2 were developed as the refrigerants of the high-temperature heat pump(HTHP).The experimental research was used to analyze and compare the performance of M1 and M2...Two new binary near-azeotropic mixtures named M1 and M2 were developed as the refrigerants of the high-temperature heat pump(HTHP).The experimental research was used to analyze and compare the performance of M1 and M2-based in the HTHP in different running conditions.The results demonstrated the feasibility and reliability of M1 and M2 as new high-temperature refrigerants.Additionally,the exploration and analyses of the support vector machine(SVM)and back propagation(BP)neural network models were made to find a practical way to predict the performance of HTHP system.The results showed that SVM-Linear,SVM-RBF and BP models shared the similar ability to predict the heat capacity and power input with high accuracy.SVM-RBF demonstrated better stability for coefficient of performance prediction.Finally,the proposed SVM model was used to assess the potential of the M1 and M2.The results indicated that the HTHP system using M1 could produce heat at the temperature of 130°C with good performance.展开更多
The duration of tunneling projects mostly depends on the performance of boring machines. The performance of boring machines is a function of advance rate, which depends on the machine characterizations and geomechanic...The duration of tunneling projects mostly depends on the performance of boring machines. The performance of boring machines is a function of advance rate, which depends on the machine characterizations and geomechanical properties of rock mass. There were various theoretical and empirical models for estimating the advance rate. In this paper, after determining the geomechanical properties of rock mass encountered in the Isfahan metro tunnel, the performance of the roadheader and tunnel boring machine （TBM） were then evaluated using various models. The calculation results show that the average instantaneous cutting rate of the roadheader in sandstone and shale are 42.8 and 74.5 m^3/h respectively. However the actual values in practice are 34.2 and 51.3 m^3/h. The operational cutting rate of the roadheader in sandstone and shale are 8.2 and 9.7 m^3/h respectively, but the actual values are 6.5 and 6.7 m^3/h. The penetration rate of the TBM in shale is predicted to be 50-60 mm/round.展开更多
A novel configuration performance prediction approach with combination of principal component analysis(PCA) and support vector machine(SVM) was proposed.This method can estimate the performance parameter values of a n...A novel configuration performance prediction approach with combination of principal component analysis(PCA) and support vector machine(SVM) was proposed.This method can estimate the performance parameter values of a newly configured product through soft computing technique instead of practical test experiments,which helps to evaluate whether or not the product variant can satisfy the customers' individual requirements.The PCA technique was used to reduce and orthogonalize the module parameters that affect the product performance.Then,these extracted features were used as new input variables in SVM model to mine knowledge from the limited existing product data.The performance values of a newly configured product can be predicted by means of the trained SVM models.This PCA-SVM method can ensure that the performance prediction is executed rapidly and accurately,even under the small sample conditions.The applicability of the proposed method was verified on a family of plate electrostatic precipitators.展开更多
In conventional chromite beneficiation plant, huge quantity of chromite is used to loss in the form of tailing. For recovery these valuable mineral, a gravity concentrator viz. wet shaking table was used.Optimisation ...In conventional chromite beneficiation plant, huge quantity of chromite is used to loss in the form of tailing. For recovery these valuable mineral, a gravity concentrator viz. wet shaking table was used.Optimisation along with performance prediction of the unit operation is necessary for efficient recovery.So, in this present study, an artificial neural network(ANN) modeling approach was attempted for predicting the performance of wet shaking table in terms of grade(%) and recovery(%). A three layer feed forward neural network(3:3–11–2:2) was developed by varying the major operating parameters such as wash water flow rate(L/min), deck tilt angle(degree) and slurry feed rate(L/h). The predicted value obtained by the neural network model shows excellent agreement with the experimental values.展开更多
Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machin...Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machine(RELM)is one of them.However,the regularization parameter determination of RELM consumes computational resources,which makes it unsuitable in the field of aeroengine performance parameter prediction with a large amount of data.This paper uses the forward and backward segmentation(FBS)algorithms to improve the RELM performance,and introduces an adaptive step size determination method and an improved solution mechanism to obtain a new machine learning algorithm.While maintaining good generalization,the new algorithm is not sensitive to regularization parameters,which greatly saves computing resources.The experimental results on the public data sets prove the above conclusions.Finally,the new algorithm is applied to the prediction of aero-engine performance parameters,and the excellent prediction performance is achieved.展开更多
The classical momentum-blade element theory is improved by using the empirical formula while part of rotor blades enters into the turbulent wake state, and the performance of a horizontal-axis wind turbine (HAWT) at a...The classical momentum-blade element theory is improved by using the empirical formula while part of rotor blades enters into the turbulent wake state, and the performance of a horizontal-axis wind turbine (HAWT) at all speed ratios can be predicted. By using an improved version of the so-called secant method, the convergent solutions of the system of two-dimensional equations concerning the induced velocity factors a and a' are guaranteed. Besides, a solving method of multiple solutions for a and a' is proposed and discussed. The method provided in this paper can be used for computing the aerodynamic performance of HAWTs both ofrlow solidity and of high solidity. The calculated results coincide well with the experimental data.展开更多
This paper proposes a performance prediction model for grid computing model ServiceBSP to support developing high quality applications in grid environment. In ServiceBSP model, the agents carrying computing tasks are ...This paper proposes a performance prediction model for grid computing model ServiceBSP to support developing high quality applications in grid environment. In ServiceBSP model, the agents carrying computing tasks are dispatched to the local domain of the selected computation services. By using the IP （integer program） approach, the Service Selection Agent selects the computation services with global optimized QoS （quality of service） consideration. The performance of a ServiceBSP application can be predicted according to the performance prediction model based on the QoS of the selected services. The performance prediction model can help users to analyze their applications and improve them by optimized the factors which affects the performance. The experiment shows that the Service Selection Agent can provide ServiceBSP users with satisfied QoS of applications.展开更多
Water-based aerosol is widely used as an effective strategy in electro-optical countermeasure on the battlefield used to the preponderance of high efficiency,low cost and eco-friendly.Unfortunately,the stability of th...Water-based aerosol is widely used as an effective strategy in electro-optical countermeasure on the battlefield used to the preponderance of high efficiency,low cost and eco-friendly.Unfortunately,the stability of the water-based aerosol is always unsatisfactory due to the rapid evaporation and sedimentation of the aerosol droplets.Great efforts have been devoted to improve the stability of water-based aerosol by using additives with different composition and proportion.However,the lack of the criterion and principle for screening the effective additives results in excessive experimental time consumption and cost.And the stabilization time of the aerosol is still only 30 min,which could not meet the requirements of the perdurable interference.Herein,to improve the stability of water-based aerosol and optimize the complex formulation efficiently,a theoretical calculation method based on thermodynamic entropy theory is proposed.All the factors that influence the shielding effect,including polyol,stabilizer,propellant,water and cosolvent,are considered within calculation.An ultra-stable water-based aerosol with long duration over 120 min is obtained with the optimal fogging agent composition,providing enough time for fighting the electro-optic weapon.Theoretical design guideline for choosing the additives with high phase transition temperature and low phase transition enthalpy is also proposed,which greatly improves the total entropy change and reduce the absolute entropy change of the aerosol cooling process,and gives rise to an enhanced stability of the water-based aerosol.The theoretical calculation methodology contributes to an abstemious time and space for sieving the water-based aerosol with desirable performance and stability,and provides the powerful guarantee to the homeland security.展开更多
基金The Research Project of China Yangtze River Three Gorges Group Limited under contract No.201903173the Zhejiang Mariculture Research Institute of China under contract No.325000。
文摘As our understanding of ecology deepens and modeling techniques advance,species distribution models have grown increasingly sophisticated,enhancing both their fitting and predictive capabilities.However,the dependability of predictive accuracy remains a critical issue,as the precision of these predictions largely hinges on the quality of the base data.We developed models using both field survey and remote sensing data from 2016 to 2020 to evaluate the impact of different data sources on the accuracy of predictions for Scomber japonicus distributions.Our research findings indicate that the variability of water temperature and salinity data from field suvery is significantly greater than that from remote sensing data.Within the same season,we found that the relationship between the abundance of S.japonicus and environmental factors varied significantly depending on the data source.Models using field survey data were able to more accurately reflect the complex relationships between resource distribution and environmental factors.Additionally,in terms of model predictive performance,models based on field survey data demonstrated greater accuracy in predicting the abundance of S.japonicus compared to those based on remote sensing data,allowing for more accurate mastery of their spatial distribution characteristics.This study highlights the significant impact of data sources on the accuracy of species distribution models and offers valuable insights for fisheries resources management.
基金financially supported by the National Science Fund for Distinguished Young Scholars,China(No.52025041)the National Natural Science Foundation of China(Nos.52450003,U2341267,and 52174294)+1 种基金the National Postdoctoral Program for Innovative Talents,China(No.BX20240437)the Fundamental Research Funds for the Central Universities,China(Nos.FRF-IDRY-23-037 and FRF-TP-20-02C2)。
文摘The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-learning(DL)-driven CV in four key areas of materials science:microstructure-based performance prediction,microstructure information generation,microstructure defect detection,and crystal structure-based property prediction.The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction.Moreover,recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments.The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data,thereby allowing for the discovery and innovation of next-generation materials.Finally,the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.
文摘The current highly competitive environment has driven industries to operate with increasingly restricted profit margins. Thus, it is imperative to optimize production processes. Faced with this scenario, multivariable predictive control of processes has been presented as a powerful alternative to achieve these goals. Moreover, the rationale for implementation of advanced control and subsequent analysis of its post-match performance also focus on the benefits that this tool brings to the plant. It is therefore essential to establish a methodology for analysis, based on clear and measurable criteria. Currently, there are different methodologies available in the market to assist with such analysis. These tools can have a quantitative or qualitative focus. The aim of this study is to evaluate three of the best current main performance assessment technologies： Minimum Variance Control-Harris Index; Statistical Process Control （Cp and Cpk）; and the Qin and Yu Index. These indexes were studied for an alumina plant controlled by three MPC （model predictive control） algorithms （GPC （generalized predictive control）, RMPCT （robust multivariable predictive control technology） and ESSMPC （extended state space model predictive controller）） with different results.
文摘Conical picks are by far the most widely used drag type cutting tools employed on partial face rock excavation machines.The cutting force and specific energy are two important design parameters for the conical pick performance,and the rock cutting testing is considered as the promising tool for determining these parameters.In the absence of an instrumented cutting rig,researchers generally rely on empirical predictive plots.For this,this paper suggests predictive plots for estimating the cutting force and specific energy,in consideration of the cutting depth to define the cuttability with conical picks.In this context,rock cutting tests were carried out on six volcanic rock samples with varying cutting depths using the unrelieved and relieved cutting modes.The cutting force and specific energy were correlated with the uniaxial compressive strength,Brazilian tensile strength,elasticity modulus,and plasticity index.Predictive plots were proposed for different cutting depths in the unrelieved and relieved cutting modes and exponential relationships were obtained among the cuttability parameters,and mechanical and elastoplastic properties of rocks.
基金supported by the National Key Research and Development Program of China(2022YFB3603100).
文摘Polyimides(PIs)are widely used in the microelectronics field due to their excellent comprehensive performance and the diversity and designability of their structures.In flexible substrate applications,designing the molecular structure to balance thermodynamic and optical properties is the most critical part of the PI design process.To accelerate the discovery of high-performance PIs,we established predictive models for glass transition temperature(T_(g)),cut-off wavelength(CW),and coefficient of thermal expansion(CTE)using various machine learning algorithms.The optimal predictive models for the three properties demonstrated high accuracy and stability in both test set predictions and cross-validation results.Additionally,the interpretability of the three optimal models was analyzed using the SHAP method,and the accuracy and generalization ability of the models were validated using several novel PIs.By combining the three models,predictions were made for multiple PIs,leading to the selection and synthesis of PIs with excellent comprehensive performance.135 novel PIs were designed and their key properties were obtained without the need for experimental verification.The predictive models established in this study can assist researchers in quickly determining the T_(g),CW and CTE of PIs,thereby facilitating the swift identification of promising candidates for further development.
基金supported by Science and Technology Project of Quzhou(Nos.2023K256,2023NC08,2022K41)Research Grants Program of Department of Education of Zhejiang Province(Nos.Y202455709,Y202456243)Hunan Province Key Field R&D Plan Project(No.2022GK2068).
文摘The reverse operation of existing centrifugal pumps,commonly referred to as“Pump as Turbine”(PAT),is a key approach for recovering liquid pressure energy.As a type of hydraulic machinery characterized by a simple structure and user-friendly operation,PAT holds significant promise for application in industrial waste energy recovery systems.This paper reviews recent advancements in this field,with a focus on pump type selection,performance prediction,and optimization design.First,the advantages of various prototype pumps,including centrifugal,axial-flow,mixed-flow,screw,and plunger pumps,are examined in specific application scenarios while analyzing their suitability for turbine operation.Next,performance prediction techniques for PATs are discussed,encompassing theoretical calculations,numerical simulations,and experimental testing.Special emphasis is placed on the crucial role of Computational Fluid Dynamics(CFD)and internal flow field testing technologies in analyzing PAT internal flow characteristics.Additionally,the impact of multi-objective optimization methods and the application of advanced materials on PAT performance enhancement is addressed.Finally,based on current research findings and existing technical challenges,this review also indicates future development directions;in particular,four key breakthrough areas are identified:advanced materials,innovative design methodologies,internal flow diagnostics,and in-depth analysis of critical components.
基金supported by SKKU Global Research Platform Research Fund,Sungkyunkwan University,2024-2025.
文摘Predicting player performance in sports is a critical challenge with significant implications for team success,fan engagement,and financial outcomes.Although,inMajor League Baseball(MLB),statistical methodologies such as sabermetrics have been widely used,the dynamic nature of sports makes accurate performance prediction a difficult task.Enhanced forecasts can provide immense value to team managers by aiding strategic player contract and acquisition decisions.This study addresses this challenge by employing the temporal fusion transformer(TFT),an advanced and cutting-edge deep learning model for complex data,to predict pitchers’earned run average(ERA),a key metric in baseball performance analysis.The performance of the TFT model is evaluated against recurrent neural network-based approaches and existing projection systems.In experimental results,the TFT based model consistently outperformed its counterparts,demonstrating superior accuracy in pitcher performance prediction.By leveraging the advanced capabilities of TFT,this study contributes to more precise player evaluations and improves strategic planning in baseball.
文摘Performance prediction in preliminary design stages of several turbomachinery components is a critical task in order to bring the design processes of these devices to a successful conclusion. In this paper, a review and analysis of the major loss mechanisms and loss models, used to determine the efficiency of a single stage centrifugal compressor, and a subsequent examination to determine an appropriate loss correlation set for estimating the isentropic efficiency in preliminary design stages of centrifugal compressors, were developed. Several semi-empirical correlations,commonly used to predict the efficiency of centrifugal compressors, were implemented in FORTRAN code and then were compared with experimental results in order to establish a loss correlation set to determine, with good approximation, the isentropic efficiency of single stage compressor.The aim of this study is to provide a suitable loss correlation set for determining the isentropic efficiency of a single stage centrifugal compressor, because, with a large amount of loss mechanisms and correlations available in the literature, it is difficult to ascertain how many and which correlations to employ for the correct prediction of the efficiency in the preliminary stage design of a centrifugal compressor. As a result of this study, a set of correlations composed by nine loss mechanisms for single stage centrifugal compressors, conformed by a rotor and a diffuser, are specified.
文摘A pplication o f m echanical excavators is one o f th e m o st com m only used excavation m eth o d s because itcan bring th e p ro ject m ore productivity, accuracy and safety. A m ong th e m echanical excavators, roadhead ers are m echanical m iners w h ich have b een extensively u se d in tu n n elin g , m ining an d civil indu stries. Perform ance pred ictio n is an im p o rta n t issue for successful ro a d h e a d e r application andgenerally deals w ith m achine selection, p ro d u ctio n rate an d b it consu m p tio n . The m ain aim o f thisresearch is to investigate th e c u ttin g p erfo rm an ce （in stan tan eo u s c u ttin g rates （ICRs）） o f m ed iu m -d u tyro ad h ead ers by using artificial neural n etw o rk （ANN） approach. T here are d ifferent categories forANNs, b u t based o n train in g alg o rith m th e re are tw o m ain k in d s： supervised and u n su p erv ised . Them u lti-lay er p ercep tro n （MLP） an d K ohonen self-organizing feature m ap （KSOFM） are th e m o st w idelyused neu ral netw o rk s for supervised an d u n su p erv ised ones, respectively. For gaining this goal, ad atab ase w as prim arily provided from ro ad h e a d e rs＇ p erfo rm an ce an d geom echanical characteristics o frock form ations in tu n n els and d rift galleries in Tabas coal m ine, th e larg est an d th e only fullymech an ized coal m ine in Iran. T hen th e datab ase w as analyzed in o rd e r to yield th e m ost im p o rtan tfactor for ICR by using relatively im p o rta n t factor in w hich G arson eq u atio n w as utilized. The MLPn etw o rk w as train ed by 3 in p u t p ara m e te rs including rock m ass pro p erties, rock quality d esignation（RQD）, in tact rock p ro p erties such as uniaxial com pressive stre n g th （UCS） an d Brazilian ten sile stren g th（BTS）, and o n e o u tp u t p a ra m e te r （ICR）. In o rd e r to have m ore v alidation o n MLP o u tp u ts, KSOFM visualizationw as applied. The m ean square e rro r （MSE） an d regression coefficient （R ） o f MLP w e re found tobe 5.49 an d 0.97, respectively. M oreover, KSOFM n etw o rk has a m ap size o f 8 x 5 and final qu an tizatio nan d topographic erro rs w e re 0.383 an d 0.032, respectively. The results show th a t MLP neural n etw orkshave a strong capability to p red ict an d ev alu ate th e perfo rm an ce o f m ed iu m -d u ty ro ad h ead ers in coalm easu re rocks. Furtherm ore, it is concluded th a t KSOFM neural n etw o rk is an efficient w ay for u n d e rstand in g system beh av io r an d know ledge extraction. Finally, it is indicated th a t UCS has m ore influenceo n ICR b y applying th e b e st train ed MLP n etw o rk w eig h ts in G arson eq u atio n w h ich is also confirm ed byKSOFM.
基金The authors would like to thank the reviewers for their detailed reviews and constructive comments, which have helped improve the quality of this paper. This work is support- ed by National High-Tech Research and Development Plan of China under grants NO.2011AA01A204, and 2012AA01A306, National Natural Science Foundation of China under grant NO. 61202041, and NO.91330117.
文摘MapReduce has emerged as a popular computing model used in datacenters to process large amount of datasets.In the map phase,hash partitioning is employed to distribute data that sharing the same key across data center-scale cluster nodes.However,we observe that this approach can lead to uneven data distribution,which can result in skewed loads among reduce tasks,thus hamper performance of MapReduce systems.Moreover,worker nodes in MapReduce systems may differ in computing capability due to(1) multiple generations of hardware in non-virtualized data centers,or(2) co-location of virtual machines in virtualized data centers.The heterogeneity among cluster nodes exacerbates the negative effects of uneven data distribution.To improve MapReduce performance in heterogeneous clusters,we propose a novel load balancing approach in the reduce phase.This approach consists of two components:(1) performance prediction for reducers that run on heterogeneous nodes based on support vector machines models,and(2) heterogeneity-aware partitioning(HAP),which balances skewed data for reduce tasks.We implement this approach as a plug-in in current MapReduce system.Experimental results demonstrate that our proposed approach distributes work evenly among reduce tasks,and improves MapReduce performance with little overhead.
基金National Natural Science Foundation of China (Grant No.52178393)the Science and Technology Innovation Team of Shaanxi Innovation Capability Support Plan (Grant No.2020TD005)Science and Technology Innovation Project of China Railway Construction Bridge Engineering Bureau Group Co.,Ltd.(Grant No.DQJ-2020-B07)。
文摘Evaluating the adaptability of cantilever boring machine(CBM) through in-depth excavation and analysis of tunnel excavation data and rock mass parameters is the premise of mechanical design and efficient excavation in the field of underground space engineering.This paper presented a case study of tunnelling performance prediction method of CBM in sedimentary hard-rock tunnel of Karst landform type by using tunneling data and surrounding rock parameters.The uniaxial compressive strength(UCS),rock integrity factor(Kv),basic quality index([BQ]),rock quality index RQD,brazilian tensile strength(BTS) and brittleness index(BI) were introduced to construct a performance prediction database based on the hard-rock tunnel of Guiyang Metro Line 1 and Line 3,and then established the performance prediction model of cantilever boring machine.Then the deep belief network(DBN) was introduced into the performance prediction model,and the reliability of performance prediction model was verified by combining with engineering data.The study showed that the influence degree of surrounding rock parameters on the tunneling performance of the cantilever boring machine is UCS > [BQ] > BTS >RQD > Kv > BI.The performance prediction model shows that the instantaneous cutting rate(ICR) has a good correlation with the surrounding rock parameters,and the predicting model accuracy is related to the reliability of construction data.The prediction of limestone and dolomite sections of Line 3 based on the DBN performance prediction model shows that the measured ICR and predicted ICR is consistent and the built performance prediction model is reliable.The research results have theoretical reference significance for the applicability analysis and mechanical selection of cantilever boring machine for hard rock tunnel.
基金supported by the National Natural Science Foundation of China (No.51701061)the Natural Science Foundation of Hebei Province (Nos.E2023202047 and E2021202075)+1 种基金the Key-Area R&D Program of Guangdong Province (No.2020B0101340004)Guangdong Academy of Science (2021GDASYL-20210102002).
文摘Steels are widely used as structural materials,making them essential for supporting our lives and industries.However,further improving the comprehensive properties of steel through traditional trial-and-error methods becomes challenging due to the continuous development and numerous processing parameters involved in steel production.To address this challenge,the application of machine learning methods becomes crucial in establishing complex relationships between manufacturing processes and steel performance.This review begins with a general overview of machine learning methods and subsequently introduces various performance predictions in steel materials.The classification of performance pre-diction was used to assess the current application of machine learning model-assisted design.Several important issues,such as data source and characteristics,intermediate features,algorithm optimization,key feature analysis,and the role of environmental factors,were summarized and analyzed.These insights will be beneficial and enlightening to future research endeavors in this field.
基金Project (2015CB251403) supported by the National Key Basic Research Program of China(973)
文摘Two new binary near-azeotropic mixtures named M1 and M2 were developed as the refrigerants of the high-temperature heat pump(HTHP).The experimental research was used to analyze and compare the performance of M1 and M2-based in the HTHP in different running conditions.The results demonstrated the feasibility and reliability of M1 and M2 as new high-temperature refrigerants.Additionally,the exploration and analyses of the support vector machine(SVM)and back propagation(BP)neural network models were made to find a practical way to predict the performance of HTHP system.The results showed that SVM-Linear,SVM-RBF and BP models shared the similar ability to predict the heat capacity and power input with high accuracy.SVM-RBF demonstrated better stability for coefficient of performance prediction.Finally,the proposed SVM model was used to assess the potential of the M1 and M2.The results indicated that the HTHP system using M1 could produce heat at the temperature of 130°C with good performance.
文摘The duration of tunneling projects mostly depends on the performance of boring machines. The performance of boring machines is a function of advance rate, which depends on the machine characterizations and geomechanical properties of rock mass. There were various theoretical and empirical models for estimating the advance rate. In this paper, after determining the geomechanical properties of rock mass encountered in the Isfahan metro tunnel, the performance of the roadheader and tunnel boring machine （TBM） were then evaluated using various models. The calculation results show that the average instantaneous cutting rate of the roadheader in sandstone and shale are 42.8 and 74.5 m^3/h respectively. However the actual values in practice are 34.2 and 51.3 m^3/h. The operational cutting rate of the roadheader in sandstone and shale are 8.2 and 9.7 m^3/h respectively, but the actual values are 6.5 and 6.7 m^3/h. The penetration rate of the TBM in shale is predicted to be 50-60 mm/round.
基金Project(9140A18010210KG01) supported by the Departmental Pre-Research Fund of China
文摘A novel configuration performance prediction approach with combination of principal component analysis(PCA) and support vector machine(SVM) was proposed.This method can estimate the performance parameter values of a newly configured product through soft computing technique instead of practical test experiments,which helps to evaluate whether or not the product variant can satisfy the customers' individual requirements.The PCA technique was used to reduce and orthogonalize the module parameters that affect the product performance.Then,these extracted features were used as new input variables in SVM model to mine knowledge from the limited existing product data.The performance values of a newly configured product can be predicted by means of the trained SVM models.This PCA-SVM method can ensure that the performance prediction is executed rapidly and accurately,even under the small sample conditions.The applicability of the proposed method was verified on a family of plate electrostatic precipitators.
文摘In conventional chromite beneficiation plant, huge quantity of chromite is used to loss in the form of tailing. For recovery these valuable mineral, a gravity concentrator viz. wet shaking table was used.Optimisation along with performance prediction of the unit operation is necessary for efficient recovery.So, in this present study, an artificial neural network(ANN) modeling approach was attempted for predicting the performance of wet shaking table in terms of grade(%) and recovery(%). A three layer feed forward neural network(3:3–11–2:2) was developed by varying the major operating parameters such as wash water flow rate(L/min), deck tilt angle(degree) and slurry feed rate(L/h). The predicted value obtained by the neural network model shows excellent agreement with the experimental values.
文摘Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machine(RELM)is one of them.However,the regularization parameter determination of RELM consumes computational resources,which makes it unsuitable in the field of aeroengine performance parameter prediction with a large amount of data.This paper uses the forward and backward segmentation(FBS)algorithms to improve the RELM performance,and introduces an adaptive step size determination method and an improved solution mechanism to obtain a new machine learning algorithm.While maintaining good generalization,the new algorithm is not sensitive to regularization parameters,which greatly saves computing resources.The experimental results on the public data sets prove the above conclusions.Finally,the new algorithm is applied to the prediction of aero-engine performance parameters,and the excellent prediction performance is achieved.
文摘The classical momentum-blade element theory is improved by using the empirical formula while part of rotor blades enters into the turbulent wake state, and the performance of a horizontal-axis wind turbine (HAWT) at all speed ratios can be predicted. By using an improved version of the so-called secant method, the convergent solutions of the system of two-dimensional equations concerning the induced velocity factors a and a' are guaranteed. Besides, a solving method of multiple solutions for a and a' is proposed and discussed. The method provided in this paper can be used for computing the aerodynamic performance of HAWTs both ofrlow solidity and of high solidity. The calculated results coincide well with the experimental data.
基金Supported by the National Natural Science Foundation of China (60573109)Shanghai Municipal Committee of Science and Tech-nology (05dz15005)Shanghai High Institution Grid Project
文摘This paper proposes a performance prediction model for grid computing model ServiceBSP to support developing high quality applications in grid environment. In ServiceBSP model, the agents carrying computing tasks are dispatched to the local domain of the selected computation services. By using the IP （integer program） approach, the Service Selection Agent selects the computation services with global optimized QoS （quality of service） consideration. The performance of a ServiceBSP application can be predicted according to the performance prediction model based on the QoS of the selected services. The performance prediction model can help users to analyze their applications and improve them by optimized the factors which affects the performance. The experiment shows that the Service Selection Agent can provide ServiceBSP users with satisfied QoS of applications.
基金supported by the Preparation and Characterization of Fogging Agents,Cooperative Project of China(Grant No.1900030040)Preparation and Test of Fogging Agents,Cooperative Project of China(Grant No.2200030085)。
文摘Water-based aerosol is widely used as an effective strategy in electro-optical countermeasure on the battlefield used to the preponderance of high efficiency,low cost and eco-friendly.Unfortunately,the stability of the water-based aerosol is always unsatisfactory due to the rapid evaporation and sedimentation of the aerosol droplets.Great efforts have been devoted to improve the stability of water-based aerosol by using additives with different composition and proportion.However,the lack of the criterion and principle for screening the effective additives results in excessive experimental time consumption and cost.And the stabilization time of the aerosol is still only 30 min,which could not meet the requirements of the perdurable interference.Herein,to improve the stability of water-based aerosol and optimize the complex formulation efficiently,a theoretical calculation method based on thermodynamic entropy theory is proposed.All the factors that influence the shielding effect,including polyol,stabilizer,propellant,water and cosolvent,are considered within calculation.An ultra-stable water-based aerosol with long duration over 120 min is obtained with the optimal fogging agent composition,providing enough time for fighting the electro-optic weapon.Theoretical design guideline for choosing the additives with high phase transition temperature and low phase transition enthalpy is also proposed,which greatly improves the total entropy change and reduce the absolute entropy change of the aerosol cooling process,and gives rise to an enhanced stability of the water-based aerosol.The theoretical calculation methodology contributes to an abstemious time and space for sieving the water-based aerosol with desirable performance and stability,and provides the powerful guarantee to the homeland security.
