In this paper,we propose a parameterization transfer algorithm for planar domains bounded by B-spline curves,where the shapes of the planar domains are similar.The domain geometries are considered to be similar if the...In this paper,we propose a parameterization transfer algorithm for planar domains bounded by B-spline curves,where the shapes of the planar domains are similar.The domain geometries are considered to be similar if their simplified skeletons have the same structures.One domain we call source domain,and it is parameterized using multi-patch B-spline surfaces.The resulting parameterization is C1 continuous in the regular region and G1 continuous around singular points regardless of whether the parameterization of the source domain is C1/G1 continuous or not.In this algorithm,boundary control points of the source domain are extracted from its parameterization as sequential points,and we establish a correspondence between sequential boundary control points of the source domain and the target boundary through discrete sampling and fitting.Transfer of the parametrization satisfies C1/G1 continuity under discrete harmonic mapping with continuous constraints.The new algorithm has a lower calculation cost than a decomposition-based parameterization with a high-quality parameterization result.We demonstrate that the result of the parameterization transfer in this paper can be applied in isogeometric analysis.Moreover,because of the consistency of the parameterization for the two models,this method can be applied in many other geometry processing algorithms,such as morphing and deformation.展开更多
Knowledge of the oxygen mass transfer of aerators under operational conditions in a full-scale wastewater treatment plant （WWTP） is meaningful for the optimization of WWTP, however, scarce to best of our knowledge. ...Knowledge of the oxygen mass transfer of aerators under operational conditions in a full-scale wastewater treatment plant （WWTP） is meaningful for the optimization of WWTP, however, scarce to best of our knowledge. Through analyzing a plug flow aeration tank in the Lucun WWTP, in Wuxi, China, the oxygenation capacity of fine-bubble aerators under process conditions have been measured in- situ using the off-gas method and the non-steady-state method. The off-gas method demonstrated that the aerators in different corridors in the aeration tank of WWTP had significantly different oxygen transfer performance; furthermore, the aerators in the same corridor shared almost equal oxygen transfer performance over the course of a day. Results measured by the two methods showed that the oxygen transfer performance of fine-bubble aerators in the aeration tank decreased dramatically compared with that in the clean water. The loss of oxygen transfer coefficient was over 50% under low-aeration conditions （aeration amount 〈 0.67 Nm 3 /hr）. However, as the aeration amount reached 0.96 Nm 3 /hr, the discrepancy of oxygen transfer between the process condition and clean water was negligible. The analysis also indicated that the non-steady-state and off-gas methods resulted in comparable estimates of oxygen transfer parameters for the aerators under process conditions.展开更多
Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering applications.However,the various working conditions would degrade the diagnostic performance and m...Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering applications.However,the various working conditions would degrade the diagnostic performance and make gear fault diagnosis(GFD)more and more challenging.In this paper,a novel model parameter transfer(NMPT)is proposed to boost the performance of GFD under varying working conditions.Based on the previous transfer strategy that controls empirical risk of source domain,this method further integrates the superiorities of multi-task learning with the idea of transfer learning(TL)to acquire transferable knowledge by minimizing the discrepancies of separating hyperplanes between one specific working condition(target domain)and another(source domain),and then transferring both commonality and specialty parameters over tasks to make use of source domain samples to assist target GFD task when sufficient labeled samples from target domain are unavailable.For NMPT implementation,insufficient target domain features and abundant source domain features with supervised information are fed into NMPT model to train a robust classifier for target GFD task.Related experiments prove that NMPT is expected to be a valuable technology to boost practical GFD performance under various working conditions.The proposed methods provides a transfer learning-based framework to handle the problem of insufficient training samples in target task caused by variable operation conditions.展开更多
A single potential step chronoabsorptometric method for the determination of ki- netic parameters of simple quasi-reversible reactions is described.It is verified by determining the kinetic parameters for the electror...A single potential step chronoabsorptometric method for the determination of ki- netic parameters of simple quasi-reversible reactions is described.It is verified by determining the kinetic parameters for the electroreduction of ferricyanide.A long-optical-path electro- chemical cell with a plug-in electrode is used.The thickness of solution layer is 0.55 mm展开更多
This paper implemented cooling configuration design on certain gas turbine HP rotor using parameterized method.It is convenient for complicated gas turbine blade modeling using parameters and also benefit for the geom...This paper implemented cooling configuration design on certain gas turbine HP rotor using parameterized method.It is convenient for complicated gas turbine blade modeling using parameters and also benefit for the geometry modify in later period.Parameterized modeling is the foundation of air cooling turbine blade design method engineering application.Mesh quality can be awarded when generated complicated cooling configuration blade grids,and also the increase of calculation error can arise by many mesh blocks.Film cooling and serpentine passage can effectively enhance the cooling effectiveness and protect blade.展开更多
Osteosarcoma is a type of malignant bone tumor that is reported across the globe.Recent advancements in Machine Learning(ML)and Deep Learning(DL)models enable the detection and classification of malignancies in biomed...Osteosarcoma is a type of malignant bone tumor that is reported across the globe.Recent advancements in Machine Learning(ML)and Deep Learning(DL)models enable the detection and classification of malignancies in biomedical images.In this regard,the current study introduces a new Biomedical Osteosarcoma Image Classification using Elephant Herd Optimization and Deep Transfer Learning(BOIC-EHODTL)model.The presented BOIC-EHODTL model examines the biomedical images to diagnose distinct kinds of osteosarcoma.At the initial stage,Gabor Filter(GF)is applied as a pre-processing technique to get rid of the noise from images.In addition,Adam optimizer with MixNet model is also employed as a feature extraction technique to generate feature vectors.Then,EHOalgorithm is utilized along with Adaptive Neuro-Fuzzy Classifier(ANFC)model for recognition and categorization of osteosarcoma.EHO algorithm is utilized to fine-tune the parameters involved in ANFC model which in turn helps in accomplishing improved classification results.The design of EHO with ANFC model for classification of osteosarcoma is the novelty of current study.In order to demonstrate the improved performance of BOIC-EHODTL model,a comprehensive comparison was conducted between the proposed and existing models upon benchmark dataset and the results confirmed the better performance of BOIC-EHODTL model over recent methodologies.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.62072148 and U22A2033)the National Key R&D Program of China(Grant Nos.2022YFB3303000 and 2020YFB1709402)+2 种基金the Zhejiang Provincial Science and Technology Program in China(Grant No.2021C01108)the NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization(Grant No.U1909210)the Fundamental Research Funds for the Provincial Universities of Zhejiang(Grant No.490 GK219909299001-028).
文摘In this paper,we propose a parameterization transfer algorithm for planar domains bounded by B-spline curves,where the shapes of the planar domains are similar.The domain geometries are considered to be similar if their simplified skeletons have the same structures.One domain we call source domain,and it is parameterized using multi-patch B-spline surfaces.The resulting parameterization is C1 continuous in the regular region and G1 continuous around singular points regardless of whether the parameterization of the source domain is C1/G1 continuous or not.In this algorithm,boundary control points of the source domain are extracted from its parameterization as sequential points,and we establish a correspondence between sequential boundary control points of the source domain and the target boundary through discrete sampling and fitting.Transfer of the parametrization satisfies C1/G1 continuity under discrete harmonic mapping with continuous constraints.The new algorithm has a lower calculation cost than a decomposition-based parameterization with a high-quality parameterization result.We demonstrate that the result of the parameterization transfer in this paper can be applied in isogeometric analysis.Moreover,because of the consistency of the parameterization for the two models,this method can be applied in many other geometry processing algorithms,such as morphing and deformation.
基金supported by the Major Water Project of the National Science and Technology (No.2011ZX07319-001-004, 2011ZX07301-002)
文摘Knowledge of the oxygen mass transfer of aerators under operational conditions in a full-scale wastewater treatment plant （WWTP） is meaningful for the optimization of WWTP, however, scarce to best of our knowledge. Through analyzing a plug flow aeration tank in the Lucun WWTP, in Wuxi, China, the oxygenation capacity of fine-bubble aerators under process conditions have been measured in- situ using the off-gas method and the non-steady-state method. The off-gas method demonstrated that the aerators in different corridors in the aeration tank of WWTP had significantly different oxygen transfer performance; furthermore, the aerators in the same corridor shared almost equal oxygen transfer performance over the course of a day. Results measured by the two methods showed that the oxygen transfer performance of fine-bubble aerators in the aeration tank decreased dramatically compared with that in the clean water. The loss of oxygen transfer coefficient was over 50% under low-aeration conditions （aeration amount 〈 0.67 Nm 3 /hr）. However, as the aeration amount reached 0.96 Nm 3 /hr, the discrepancy of oxygen transfer between the process condition and clean water was negligible. The analysis also indicated that the non-steady-state and off-gas methods resulted in comparable estimates of oxygen transfer parameters for the aerators under process conditions.
基金Supported by National Natural Science Foundation of China(Grant No.51835009).
文摘Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering applications.However,the various working conditions would degrade the diagnostic performance and make gear fault diagnosis(GFD)more and more challenging.In this paper,a novel model parameter transfer(NMPT)is proposed to boost the performance of GFD under varying working conditions.Based on the previous transfer strategy that controls empirical risk of source domain,this method further integrates the superiorities of multi-task learning with the idea of transfer learning(TL)to acquire transferable knowledge by minimizing the discrepancies of separating hyperplanes between one specific working condition(target domain)and another(source domain),and then transferring both commonality and specialty parameters over tasks to make use of source domain samples to assist target GFD task when sufficient labeled samples from target domain are unavailable.For NMPT implementation,insufficient target domain features and abundant source domain features with supervised information are fed into NMPT model to train a robust classifier for target GFD task.Related experiments prove that NMPT is expected to be a valuable technology to boost practical GFD performance under various working conditions.The proposed methods provides a transfer learning-based framework to handle the problem of insufficient training samples in target task caused by variable operation conditions.
文摘A single potential step chronoabsorptometric method for the determination of ki- netic parameters of simple quasi-reversible reactions is described.It is verified by determining the kinetic parameters for the electroreduction of ferricyanide.A long-optical-path electro- chemical cell with a plug-in electrode is used.The thickness of solution layer is 0.55 mm
基金Sponsored by the National Natural Science Foundation of China(Grant No. 50476028)
文摘This paper implemented cooling configuration design on certain gas turbine HP rotor using parameterized method.It is convenient for complicated gas turbine blade modeling using parameters and also benefit for the geometry modify in later period.Parameterized modeling is the foundation of air cooling turbine blade design method engineering application.Mesh quality can be awarded when generated complicated cooling configuration blade grids,and also the increase of calculation error can arise by many mesh blocks.Film cooling and serpentine passage can effectively enhance the cooling effectiveness and protect blade.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number(42/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R151)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4340237DSR16).
文摘Osteosarcoma is a type of malignant bone tumor that is reported across the globe.Recent advancements in Machine Learning(ML)and Deep Learning(DL)models enable the detection and classification of malignancies in biomedical images.In this regard,the current study introduces a new Biomedical Osteosarcoma Image Classification using Elephant Herd Optimization and Deep Transfer Learning(BOIC-EHODTL)model.The presented BOIC-EHODTL model examines the biomedical images to diagnose distinct kinds of osteosarcoma.At the initial stage,Gabor Filter(GF)is applied as a pre-processing technique to get rid of the noise from images.In addition,Adam optimizer with MixNet model is also employed as a feature extraction technique to generate feature vectors.Then,EHOalgorithm is utilized along with Adaptive Neuro-Fuzzy Classifier(ANFC)model for recognition and categorization of osteosarcoma.EHO algorithm is utilized to fine-tune the parameters involved in ANFC model which in turn helps in accomplishing improved classification results.The design of EHO with ANFC model for classification of osteosarcoma is the novelty of current study.In order to demonstrate the improved performance of BOIC-EHODTL model,a comprehensive comparison was conducted between the proposed and existing models upon benchmark dataset and the results confirmed the better performance of BOIC-EHODTL model over recent methodologies.
