Holder’s inequality, its refinement, and reverse have received considerable attention in the theory of mathematical analysis and differential equations. In this paper, we give some refinements of Holder’s inequality...Holder’s inequality, its refinement, and reverse have received considerable attention in the theory of mathematical analysis and differential equations. In this paper, we give some refinements of Holder’s inequality and its reverse using a simple analytical technique of algebra and calculus. Our results show many results related to holder’s inequality as special cases of the inequalities presented.展开更多
The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud,particularly in credit card transactions.Advanced technologies such as machin...The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud,particularly in credit card transactions.Advanced technologies such as machine learning have been widely employed to enhance the early detection and prevention of losses arising frompotentially fraudulent activities.However,a prevalent approach in existing literature involves the use of extensive data sampling and feature selection algorithms as a precursor to subsequent investigations.While sampling techniques can significantly reduce computational time,the resulting dataset relies on generated data and the accuracy of the pre-processing machine learning models employed.Such datasets often lack true representativeness of realworld data,potentially introducing secondary issues that affect the precision of the results.For instance,undersampling may result in the loss of critical information,while over-sampling can lead to overfitting machine learning models.In this paper,we proposed a classification study of credit card fraud using fundamental machine learning models without the application of any sampling techniques on all the features present in the original dataset.The results indicate that Support Vector Machine(SVM)consistently achieves classification performance exceeding 90%across various evaluation metrics.This discovery serves as a valuable reference for future research,encouraging comparative studies on original dataset without the reliance on sampling techniques.Furthermore,we explore hybrid machine learning techniques,such as ensemble learning constructed based on SVM,K-Nearest Neighbor(KNN)and decision tree,highlighting their potential advancements in the field.The study demonstrates that the proposed machine learning models yield promising results,suggesting that pre-processing the dataset with sampling algorithm or additional machine learning technique may not always be necessary.This research contributes to the field of credit card fraud detection by emphasizing the potential of employing machine learning models directly on original datasets,thereby simplifying the workflow and potentially improving the accuracy and efficiency of fraud detection systems.展开更多
This paper investigates the effects of tool holder materials on chatter vibration in turning operations.The study uses a complex dynamic turning model with two degrees of freedom for the orthogonal cutting system.Tool...This paper investigates the effects of tool holder materials on chatter vibration in turning operations.The study uses a complex dynamic turning model with two degrees of freedom for the orthogonal cutting system.Tool holders made from different materials,including Al 5083,Al 6082,Al 7012,and a standard 4140 material,were subjected to chatter vibration to investigate their process damping capabilities.The study found that the standard tool holder 4140 allows for higher stable depths of cut and produces similar process damping values compared to the other tool holders.Finite element analyses(FEA)were performed to verify the experimental results,and the modal and FEA analyses produced very similar results.The study concludes that future research should investigate the effects of tool holders made from high alloy steel alloys on process damping.Overall,this paper provides important insights into the effects of tool hold-er materials on chatter vibration and process damping in turning operations,which can help in the design of more effi-cient and effective cutting systems.展开更多
A large aspect ratio vibration reducing tool holder based on passive damping vibration reduction technology is designed to solve the vibration problem that occurs in deep hole machining of vertical lathes.A dynamic mo...A large aspect ratio vibration reducing tool holder based on passive damping vibration reduction technology is designed to solve the vibration problem that occurs in deep hole machining of vertical lathes.A dynamic model of passive damping vibration reduction tool holder was established,and the optimal damping ratio,optimal frequency ratio,and maximum relative amplitude were derived.Modal analysis of the passive damping vibrationreduction tool holder was conducted using software.The results showed that the maximum response amplitude of the passive damping vibration reduction tool holder decreased significantly compared to the original one.展开更多
文摘Holder’s inequality, its refinement, and reverse have received considerable attention in the theory of mathematical analysis and differential equations. In this paper, we give some refinements of Holder’s inequality and its reverse using a simple analytical technique of algebra and calculus. Our results show many results related to holder’s inequality as special cases of the inequalities presented.
文摘The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud,particularly in credit card transactions.Advanced technologies such as machine learning have been widely employed to enhance the early detection and prevention of losses arising frompotentially fraudulent activities.However,a prevalent approach in existing literature involves the use of extensive data sampling and feature selection algorithms as a precursor to subsequent investigations.While sampling techniques can significantly reduce computational time,the resulting dataset relies on generated data and the accuracy of the pre-processing machine learning models employed.Such datasets often lack true representativeness of realworld data,potentially introducing secondary issues that affect the precision of the results.For instance,undersampling may result in the loss of critical information,while over-sampling can lead to overfitting machine learning models.In this paper,we proposed a classification study of credit card fraud using fundamental machine learning models without the application of any sampling techniques on all the features present in the original dataset.The results indicate that Support Vector Machine(SVM)consistently achieves classification performance exceeding 90%across various evaluation metrics.This discovery serves as a valuable reference for future research,encouraging comparative studies on original dataset without the reliance on sampling techniques.Furthermore,we explore hybrid machine learning techniques,such as ensemble learning constructed based on SVM,K-Nearest Neighbor(KNN)and decision tree,highlighting their potential advancements in the field.The study demonstrates that the proposed machine learning models yield promising results,suggesting that pre-processing the dataset with sampling algorithm or additional machine learning technique may not always be necessary.This research contributes to the field of credit card fraud detection by emphasizing the potential of employing machine learning models directly on original datasets,thereby simplifying the workflow and potentially improving the accuracy and efficiency of fraud detection systems.
基金This study was supported by the Scientific Research Coordination Unit of Pamukkale University under the project number 2011BSP020.
文摘This paper investigates the effects of tool holder materials on chatter vibration in turning operations.The study uses a complex dynamic turning model with two degrees of freedom for the orthogonal cutting system.Tool holders made from different materials,including Al 5083,Al 6082,Al 7012,and a standard 4140 material,were subjected to chatter vibration to investigate their process damping capabilities.The study found that the standard tool holder 4140 allows for higher stable depths of cut and produces similar process damping values compared to the other tool holders.Finite element analyses(FEA)were performed to verify the experimental results,and the modal and FEA analyses produced very similar results.The study concludes that future research should investigate the effects of tool holders made from high alloy steel alloys on process damping.Overall,this paper provides important insights into the effects of tool hold-er materials on chatter vibration and process damping in turning operations,which can help in the design of more effi-cient and effective cutting systems.
基金Science and Technology Research Project of Liaoning Provincial Department of Education:Research on the Application of Damping and Vibration Reduction Technology in Key Components of Machine Tools(JYTMS20230066)The Fund of Liaoning Provincial Natural Science and Technology Foundation Regional Joint(No.2021-YKLH-08).
文摘A large aspect ratio vibration reducing tool holder based on passive damping vibration reduction technology is designed to solve the vibration problem that occurs in deep hole machining of vertical lathes.A dynamic model of passive damping vibration reduction tool holder was established,and the optimal damping ratio,optimal frequency ratio,and maximum relative amplitude were derived.Modal analysis of the passive damping vibrationreduction tool holder was conducted using software.The results showed that the maximum response amplitude of the passive damping vibration reduction tool holder decreased significantly compared to the original one.
