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An energy consumption prediction approach of die casting machines driven by product parameters

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 868-886 doi: 10.1007/s11465-021-0656-0

Abstract: The energy consumption prediction of die casting machines can support energy consumption quota, process, there is still a lack of an approach for energy consumption prediction that can provide support forTo fill this gap, this paper proposes an energy consumption prediction approach for die casting machinesFirstly, the system boundary of energy consumption prediction is defined, and subsequently, based onConsequently, a systematic energy consumption prediction approach for die casting machines, involving

Keywords： die casting machine     energy consumption prediction     product parameters    

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Analysis and prediction of the influence of energy utilization on air quality in Beijing

LI Lin, HAO Jiming, HU Jingnan

Frontiers of Environmental Science & Engineering 2007, Volume 1, Issue 3,   Pages 339-344 doi: 10.1007/s11783-007-0058-5

Abstract: This work evaluates the influence of energy consumption on the future air quality in Beijing, using 2000It establishes the emission inventory of primary PM, SO and NO related to energy utilization in eightThe industrial sector contributed above 40% of primary PM and SO resulting from energy consumption, whileareas could become better in 2008 when the average concentrations of primary PM, SO and NO related to energy
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Modeling, simulation, and prediction of global energy indices: a differential approach

Stephen Ndubuisi NNAMCHI, Onyinyechi Adanma NNAMCHI, Janice Desire BUSINGYE, Maxwell Azubuike IJOMAH, Philip Ikechi OBASI

Frontiers in Energy 2022, Volume 16, Issue 2,   Pages 375-392 doi: 10.1007/s11708-021-0723-6

Abstract: Modeling, simulation, and prediction of global energy indices remain veritable tools for econometric,engineering, analysis, and prediction of energy indices.Furthermore, the simulated data were upgraded for extrapolative prediction of energy indices by introducingThe innovative model yielded a trendy prediction data for energy consumption, gross domestic product,Clearly, this paper has accomplished interpolative and extrapolative prediction of energy indices and

Keywords： energy indices     differential model     normalization     simulation     inflation/deflation     predictive factor andprediction rate    

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A model for creep life prediction of thin tube using strain energy density as a function of stress triaxiality

Tahir MAHMOOD, Sangarapillai KANAPATHIPILLAI, Mahiuddin CHOWDHURY

Frontiers of Mechanical Engineering 2013, Volume 8, Issue 2,   Pages 181-186 doi: 10.1007/s11465-013-0257-7

Abstract: The model employs strain energy density and assumes that the uniaxial strain energy density of a componentcan be easily calculated and can be converted to multi-axial strain energy density by multiplying itelastic-creep and elastic-plastic-creep finite element analysis (FEA) is performed to get multi-axial strain energydensity of the component which is compared with the calculated strain energy density for both cases.

Keywords： elastic-creep     elastic-plastic-creep     stress triaxiality     life prediction     pressure vessels     finite element    

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Big Data to support sustainable urban energy planning: The EvoEnergy project

Moulay Larbi CHALAL, Benachir MEDJDOUB, Nacer BEZAI, Raid SHRAHILY

Frontiers of Engineering Management 2020, Volume 7, Issue 2,   Pages 287-300 doi: 10.1007/s42524-019-0081-9

Abstract: Energy sustainability is a complex problem that needs to be tackled holistically by equally addressingconsumption where we developed a 3D urban energy prediction system (EvoEnergy) using the old UK panelTo achieve this aim, the household transition and energy prediction modules of EvoEnergy have been testedThe analysis of the results advised that EvoEnergy remains a reliable prediction system and had a goodprediction accuracy (MAPE  5%) when compared to actual energy performance certificate data.

Keywords： urban energy planning     sustainable planning     Big Data     household transition     energy prediction    

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Spatial prediction of soil contamination based on machine learning: a review

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1693-1

Abstract:

● A review of machine learning (ML) for spatial prediction of soil

Keywords： Soil contamination     Machine learning     Prediction     Spatial distribution    

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Universal Method for the Prediction of Abrasive Waterjet Performance in Mining

Eugene Averin

Engineering 2017, Volume 3, Issue 6,   Pages 888-891 doi: 10.1016/j.eng.2017.12.004

Abstract: This problem can be solved using the energy conservation approach, which states the proportionality betweenthe material removal volume and the kinetic energy of AWJs.

Keywords： Abrasive waterjet     Energy conservation approach     Depth of cut     Fracture mechanics     Threshold velocity     Mining    

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Frontiers of Medicine 2022, Volume 16, Issue 3,   Pages 496-506 doi: 10.1007/s11684-021-0828-7

Abstract: The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients’ physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.

Keywords： XGBoost     deep neural network     healthcare     risk prediction    

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Position-varying surface roughness prediction method considering compensated acceleration in milling

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 855-867 doi: 10.1007/s11465-021-0649-z

Abstract: Aiming at surface roughness prediction in the machining process, this paper proposes a position-varyingsurface roughness prediction method based on compensated acceleration by using regression analysis.i>R-square proving the effectiveness of the filtering features, is selected as the input of the predictionMoreover, the prediction curve matches and agrees well with the actual surface state, which verifies

Keywords： surface roughness prediction     compensated acceleration     milling     thin-walled workpiece    

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Improved prediction of pile bending moment and deflection due to adjacent braced excavation

Frontiers of Structural and Civil Engineering doi: 10.1007/s11709-023-0961-2

Abstract: Deep excavations in dense urban areas have caused damage to nearby existing structures in numerous past construction cases. Proper assessment is crucial in the initial design stages. This study develops equations to predict the existing pile bending moment and deflection produced by adjacent braced excavations. Influential parameters (i.e., the excavation geometry, diaphragm wall thickness, pile geometry, strength and small-strain stiffness of the soil, and soft clay thickness) were considered and employed in the developed equations. It is practically unfeasible to obtain measurement data; hence, artificial data for the bending moment and deflection of existing piles were produced from well-calibrated numerical analyses of hypothetical cases, using the three-dimensional finite element method. The developed equations were established through a multiple linear regression analysis of the artificial data, using the transformation technique. In addition, the three-dimensional nature of the excavation work was characterized by considering the excavation corner effect, using the plane strain ratio parameter. The estimation results of the developed equations can provide satisfactory pile bending moment and deflection data and are more accurate than those found in previous studies.

Keywords： pile responses     excavation     prediction     deflection     bending moments    

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Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

Frontiers in Energy 2016, Volume 10, Issue 4,   Pages 479-488 doi: 10.1007/s11708-016-0425-7

Abstract: In this paper a novel method for reliability prediction and validation of nuclear power units in serviceThe accuracy of the reliability prediction can be evaluated according to the comparison between the predictedFurthermore, the reliability prediction method is validated using the nuclear power units in North American

Keywords： nuclear power units in service     reliability     reliability prediction     equivalent availability factors    

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Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

Frontiers of Mechanical Engineering 2010, Volume 5, Issue 2,   Pages 171-175 doi: 10.1007/s11465-009-0091-0

Abstract: Trend prediction technology is the key technology to achieve condition-based maintenance of mechanicalTo ensure the normal operation of units and save maintenance costs, trend prediction technology is studiedThe main methods of the technology are given, the trend prediction method based on neural network isThe industrial site verification shows that the proposed trend prediction technology can reflect the

Keywords： water injection units     condition-based maintenance     trend prediction    

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Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Frontiers of Structural and Civil Engineering   Pages 994-1010 doi: 10.1007/s11709-023-0942-5

Abstract: Developing prediction models to support drivers in performing rectifications in advance can effectivelysubsequently, they are preprocessed and used to establish GRU-based multivariate multistep-ahead direct predictionIn addition, the effects of the activation function and input time-step length on the prediction performance

Keywords： dynamic prediction     moving trajectory     pipe jacking     GRU     deep learning    

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Prediction of the shear wave velocity

Amoroso SARA

Frontiers of Structural and Civil Engineering 2014, Volume 8, Issue 1,   Pages 83-92 doi: 10.1007/s11709-013-0234-6

Abstract: The paper examines the correlations to obtain rough estimates of the shear wave velocity from non-seismic dilatometer tests (DMT) and cone penetration tests (CPT). While the direct measurement of is obviously preferable, these correlations may turn out useful in various circumstances. The experimental results at six international research sites suggest that the DMT predictions of from the parameters (material index), (horizontal stress index), (constrained modulus) are more reliable and consistent than the CPT predictions from (cone resistance), presumably because of the availability, by DMT, of the stress history index .

Keywords： horizontal stress index     shear wave velocity     flat dilatometer test     cone penetration test    

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Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 1,   Pages 72-82 doi: 10.1007/s11709-013-0185-y

Abstract: A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibilityThis paper examines the potential of SVM model in prediction of liquefaction using actual field coneUsing cone resistance ( ) and cyclic stress ratio ( ), model has been developed for prediction of liquefactionto simplify the model, requiring only two parameters ( and maximum horizontal acceleration ), for predictionThe study shows that SVM can be used as a practical tool for prediction of liquefaction potential, based

Keywords： earthquake     cone penetration test     liquefaction     support vector machine (SVM)     prediction    

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Title Author Date Type Operation

An energy consumption prediction approach of die casting machines driven by product parameters

Journal Article

Analysis and prediction of the influence of energy utilization on air quality in Beijing

LI Lin, HAO Jiming, HU Jingnan

Journal Article

Modeling, simulation, and prediction of global energy indices: a differential approach

Stephen Ndubuisi NNAMCHI, Onyinyechi Adanma NNAMCHI, Janice Desire BUSINGYE, Maxwell Azubuike IJOMAH, Philip Ikechi OBASI

Journal Article

A model for creep life prediction of thin tube using strain energy density as a function of stress triaxiality

Tahir MAHMOOD, Sangarapillai KANAPATHIPILLAI, Mahiuddin CHOWDHURY

Journal Article

Big Data to support sustainable urban energy planning: The EvoEnergy project

Moulay Larbi CHALAL, Benachir MEDJDOUB, Nacer BEZAI, Raid SHRAHILY

Journal Article

Spatial prediction of soil contamination based on machine learning: a review

Journal Article

Universal Method for the Prediction of Abrasive Waterjet Performance in Mining

Eugene Averin

Journal Article

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Journal Article

Position-varying surface roughness prediction method considering compensated acceleration in milling

Journal Article

Improved prediction of pile bending moment and deflection due to adjacent braced excavation

Journal Article

Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

Journal Article

Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

Journal Article

Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Journal Article

Prediction of the shear wave velocity

Amoroso SARA

Journal Article

Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Journal Article