ABSTRACTRecently, e-commerce has had the advantage of lower rental expenses. Convenient online payment has rapidly grown and provides a great business opportunity for enterprises. Inventory management plays a critical role in the competitiveness of most electronic business enterprises. Before the products hit the store shelves or are available online to customers, the quantity and quality of the products should be confirmed and inspected. Thus, in addition to the replenishment lead time, the preprocessing time has essential effects on inventory management. To reduce the inventory cost and determine a proper replenishment policy, we propose a multi-server inventory queue with a preprocessing time, which is seldom mentioned by the existing studies. The steady-state probabilities, system stability conditions, and expressions of several critical system characteristics are derived. Subsequently, the operating cost function is developed to determine the optimal reorder point and an appropriate number of servers with the minimum cost. The established numerical results and sensitivity analysis help managers identify critical variables and enhance operational efficiency.CO EDITOR-IN-CHIEF: Hsieh, Sun-Yuan, Pang, Ai-ChunASSOCIATE EDITOR: He, DebiaoKEYWORDS: Inventory queuematrix-geometric methodoptimal replenishment policypreprocessing time Nomenclature 0=the zero row vectorc=number of serversc∗,s∗,γ∗=the optimization solution at the minimum costCc=cost per serverCh=holding cost of each customer in the systemCI=holding cost per inventory in the systemCγ=cost for providing a specific processing ratee=the identity column vectorE[C]=the expected number of customers in the systemE[I]=the expected number of inventories in the systemF=the expected cost functionM=the replenishment quantityO=the zero matrixP=the steady-state vectorP[ID]=the probability of no customer or inventoryP[IDC]=the probability of an empty systemPi,jk=the steady-state probabilityQ=the transition matrixR=the rate matrixs=reorder point(S)=states for material preprocessing(W)=states for normal operatingx=the invariant probabilityα=the replenishment rateε=criteria for convergence determinationλ=the mean arrival rateμ=the mean service rateΠi=the steady-state sub-vectorγ=the preprocessing rateΩ=set of system statesΩS=set of system states for material preprocessingΩW=set of system states for normal operatingDisclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"Optimal analysis of a multi-server queue with preprocessing time and replenishment inventory","authors":"Chia-Huang Wu, Wen-Tso Huang, Jr-Fong Dang, Ming-Yang Yeh","doi":"10.1080/02533839.2023.2274089","DOIUrl":"https://doi.org/10.1080/02533839.2023.2274089","url":null,"abstract":"ABSTRACTRecently, e-commerce has had the advantage of lower rental expenses. Convenient online payment has rapidly grown and provides a great business opportunity for enterprises. Inventory management plays a critical role in the competitiveness of most electronic business enterprises. Before the products hit the store shelves or are available online to customers, the quantity and quality of the products should be confirmed and inspected. Thus, in addition to the replenishment lead time, the preprocessing time has essential effects on inventory management. To reduce the inventory cost and determine a proper replenishment policy, we propose a multi-server inventory queue with a preprocessing time, which is seldom mentioned by the existing studies. The steady-state probabilities, system stability conditions, and expressions of several critical system characteristics are derived. Subsequently, the operating cost function is developed to determine the optimal reorder point and an appropriate number of servers with the minimum cost. The established numerical results and sensitivity analysis help managers identify critical variables and enhance operational efficiency.CO EDITOR-IN-CHIEF: Hsieh, Sun-Yuan, Pang, Ai-ChunASSOCIATE EDITOR: He, DebiaoKEYWORDS: Inventory queuematrix-geometric methodoptimal replenishment policypreprocessing time Nomenclature 0=the zero row vectorc=number of serversc∗,s∗,γ∗=the optimization solution at the minimum costCc=cost per serverCh=holding cost of each customer in the systemCI=holding cost per inventory in the systemCγ=cost for providing a specific processing ratee=the identity column vectorE[C]=the expected number of customers in the systemE[I]=the expected number of inventories in the systemF=the expected cost functionM=the replenishment quantityO=the zero matrixP=the steady-state vectorP[ID]=the probability of no customer or inventoryP[IDC]=the probability of an empty systemPi,jk=the steady-state probabilityQ=the transition matrixR=the rate matrixs=reorder point(S)=states for material preprocessing(W)=states for normal operatingx=the invariant probabilityα=the replenishment rateε=criteria for convergence determinationλ=the mean arrival rateμ=the mean service rateΠi=the steady-state sub-vectorγ=the preprocessing rateΩ=set of system statesΩS=set of system states for material preprocessingΩW=set of system states for normal operatingDisclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":17313,"journal":{"name":"Journal of the Chinese Institute of Engineers","volume":"3 22","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135141751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-08DOI: 10.1080/02533839.2023.2274082
San-Shyan Lin, Chih-Yu Su, Chen En Chiang, Chwen-Huan Wang
ABSTRACTThis study presents a method that is conventionally used for interpretation of a head-down pile load test, which is modified and extended for a bi-directional pile load test. A parabolic function is used to simulate the load transfer curve along a depth above or below the load cell. The coefficients of the function are obtained by fitting the measured gauge data. Three bored piles are tested using a bi-directional load cell that is installed in the middle of the pile shaft and the results are interpreted using the method of this study. An equivalent head-down load-displacement curve that is obtained using the presented method produces results that are in good agreement with those that are obtained using the conventional method. The relationship between mobilized unit skin friction and displacement that is obtained using the measurement data and using the presented method shows similar trend.CO EDITOR-IN-CHIEF: Ou, Yu-ChenASSOCIATE EDITOR: Ou, Yu-ChenKEYWORDS: Bi-directional load testhead-down load testbored pileequivalent load-displacement curve Nomenclature A=Cross-sectional area of pile.Ac=Concrete cross-sectional area of pile.As=Steel cross-sectional area of pile.C=A centroid factor.Ds=Diameter of Pile.Ec=Elastic modulus of concrete.Ep=Elastic modulus of pile.Es=Elastic modulus of steel.ePR=Pile compression for the pile section below the load cell.ePS=Pile compression for the pile section above the load cell.fRz=Unit shaft resistance.k0=A constant that is determined by regression analysis of back analyzed curve.k1=A constant that is determined by regression analysis of back analyzed curve.L=Total pile length.LR=Pile length for the pile section below the load cell.Ls=Pile length for the pile section on the top of the load cell.Pb=Mobilized base resistance of pile.P0=Equivalent head-down load.PR=Mobilized shaft resistance for the pile section below load cell.Ps=Mobilized shaft resistance for the pile section above load cell.Pj=Pile axial force at any rebar strain gauge level j.Pz=Axial force in pile at depth z.qu=Uniaxial compressive strength of rock.U=Initial tangent modulus for the concrete in the pile.wz=Pile displacement at depth z.Wt=Weight of pile.α1=Constant coefficient.α2=Constant coefficient.α3=Constant coefficient.Δ=Additional pile compression between the head-down and the bi-directional load.Δb=Pile toe displacement.Δd=Pile compression induced by equivalent head-down load of P0.Δh=Pile compression induced by equivalent head-down load of Ps.Δo=Load cell location displacement.Δo1=Pile compression induced by upward load from load cell.Δo2=Pile compression induced by downward load from load cell.Δs=Average shaft displacement for the section above load cell.∈=Measured strain from rebar gauge.φ=International friction angle of soil.AcknowledgmentsThis study is part of a research project funded by the National Science and Technology Council (110-2221-E-019 -016 -), Taiwan. The principal author is grateful for this financial support.Di
{"title":"Interpretation of bi-directional pile load Tests using instrumented gauge data","authors":"San-Shyan Lin, Chih-Yu Su, Chen En Chiang, Chwen-Huan Wang","doi":"10.1080/02533839.2023.2274082","DOIUrl":"https://doi.org/10.1080/02533839.2023.2274082","url":null,"abstract":"ABSTRACTThis study presents a method that is conventionally used for interpretation of a head-down pile load test, which is modified and extended for a bi-directional pile load test. A parabolic function is used to simulate the load transfer curve along a depth above or below the load cell. The coefficients of the function are obtained by fitting the measured gauge data. Three bored piles are tested using a bi-directional load cell that is installed in the middle of the pile shaft and the results are interpreted using the method of this study. An equivalent head-down load-displacement curve that is obtained using the presented method produces results that are in good agreement with those that are obtained using the conventional method. The relationship between mobilized unit skin friction and displacement that is obtained using the measurement data and using the presented method shows similar trend.CO EDITOR-IN-CHIEF: Ou, Yu-ChenASSOCIATE EDITOR: Ou, Yu-ChenKEYWORDS: Bi-directional load testhead-down load testbored pileequivalent load-displacement curve Nomenclature A=Cross-sectional area of pile.Ac=Concrete cross-sectional area of pile.As=Steel cross-sectional area of pile.C=A centroid factor.Ds=Diameter of Pile.Ec=Elastic modulus of concrete.Ep=Elastic modulus of pile.Es=Elastic modulus of steel.ePR=Pile compression for the pile section below the load cell.ePS=Pile compression for the pile section above the load cell.fRz=Unit shaft resistance.k0=A constant that is determined by regression analysis of back analyzed curve.k1=A constant that is determined by regression analysis of back analyzed curve.L=Total pile length.LR=Pile length for the pile section below the load cell.Ls=Pile length for the pile section on the top of the load cell.Pb=Mobilized base resistance of pile.P0=Equivalent head-down load.PR=Mobilized shaft resistance for the pile section below load cell.Ps=Mobilized shaft resistance for the pile section above load cell.Pj=Pile axial force at any rebar strain gauge level j.Pz=Axial force in pile at depth z.qu=Uniaxial compressive strength of rock.U=Initial tangent modulus for the concrete in the pile.wz=Pile displacement at depth z.Wt=Weight of pile.α1=Constant coefficient.α2=Constant coefficient.α3=Constant coefficient.Δ=Additional pile compression between the head-down and the bi-directional load.Δb=Pile toe displacement.Δd=Pile compression induced by equivalent head-down load of P0.Δh=Pile compression induced by equivalent head-down load of Ps.Δo=Load cell location displacement.Δo1=Pile compression induced by upward load from load cell.Δo2=Pile compression induced by downward load from load cell.Δs=Average shaft displacement for the section above load cell.∈=Measured strain from rebar gauge.φ=International friction angle of soil.AcknowledgmentsThis study is part of a research project funded by the National Science and Technology Council (110-2221-E-019 -016 -), Taiwan. The principal author is grateful for this financial support.Di","PeriodicalId":17313,"journal":{"name":"Journal of the Chinese Institute of Engineers","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135392655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-07DOI: 10.1080/02533839.2023.2274085
Wei Qi, Junlin Pei, Xuwang Liu
ABSTRACTThe global economy is transitioning from product economy to service economy, and the optimization of product line with value-added services is one of the important issues faced by enterprises. Therefore, this paper studies the pricing optimization of product line with value-added services considering consumer choice behavior. First, we apply the multinomial logit (MNL) model to simulate consumer choice behavior, and establish a MNL-based product line pricing optimization model considering value-added services. Then, the optimal pricing strategies for products and services are solved for three different scenarios: single product with service, homogeneous products with services, and heterogeneous products with services. Finally, through numerical experiments, we analyze the changing trends of the optimal pricing, market share and profit of products line with value-added services with some impact factors, such as product quality, service level, consumer sensitivity, and number of products under three different scenarios. We also obtain some managerial implications when an enterprise develops product line with value-added service.CO EDITOR-IN-CHIEF: Kuo, Cheng-ChienASSOCIATE EDITOR: Wang, Wen-JuneKEYWORDS: Product linevalue-added serviceconsumer choice behaviormultinomial logit model Nomenclature bi=quality of product ic=total cost of products with value-added servicesci=fix cost of product ii=index of each productj=index of each consumerm=number of potential consumersn=number of productsp=price vectorpi=price of product iq=choice probability vectorqi=probability of a consumer choosing product iq0=probability of a consumer choosing outside productR=expected revenuesi=service level of product iuij=utility obtained by consumer j from purchasing product ivij=deterministic utility obtained by consumer j from purchasing product iαij=sensitivity degree of consumer j to the quality of product iβij=sensitivity degree of consumer j to the price of product iγij=sensitivity degree of consumer j to the service level of product iεij=random utility obtained by consumer j from purchasing product iπ=profitAcknowledgmentsThis work was supported by the National Natural Science Foundation of China [grant number 72001071]; National Social Science Foundation of China [grant number 22FGLB083]; Philosophy and Social Sciences Planning Project of Henan Province in China [grant number 2022BJJ031]; the Humanities and Social Sciences Research Program of the Ministry of Education of China [grant number 21YJA630061]; Program for Science &Technology Innovation Talents in Universities of Henan Province (Humanities and Social Sciences) [grant number 2021-CX-004]; Program of Higher Education Philosophy and Social Sciences Innovative Talents of Henan Province [grant number 2024-CXRC-02].Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported by the National Natural Science Foundation of China [7200
{"title":"Pricing optimization of product line with value-added services considering consumer choice behavior","authors":"Wei Qi, Junlin Pei, Xuwang Liu","doi":"10.1080/02533839.2023.2274085","DOIUrl":"https://doi.org/10.1080/02533839.2023.2274085","url":null,"abstract":"ABSTRACTThe global economy is transitioning from product economy to service economy, and the optimization of product line with value-added services is one of the important issues faced by enterprises. Therefore, this paper studies the pricing optimization of product line with value-added services considering consumer choice behavior. First, we apply the multinomial logit (MNL) model to simulate consumer choice behavior, and establish a MNL-based product line pricing optimization model considering value-added services. Then, the optimal pricing strategies for products and services are solved for three different scenarios: single product with service, homogeneous products with services, and heterogeneous products with services. Finally, through numerical experiments, we analyze the changing trends of the optimal pricing, market share and profit of products line with value-added services with some impact factors, such as product quality, service level, consumer sensitivity, and number of products under three different scenarios. We also obtain some managerial implications when an enterprise develops product line with value-added service.CO EDITOR-IN-CHIEF: Kuo, Cheng-ChienASSOCIATE EDITOR: Wang, Wen-JuneKEYWORDS: Product linevalue-added serviceconsumer choice behaviormultinomial logit model Nomenclature bi=quality of product ic=total cost of products with value-added servicesci=fix cost of product ii=index of each productj=index of each consumerm=number of potential consumersn=number of productsp=price vectorpi=price of product iq=choice probability vectorqi=probability of a consumer choosing product iq0=probability of a consumer choosing outside productR=expected revenuesi=service level of product iuij=utility obtained by consumer j from purchasing product ivij=deterministic utility obtained by consumer j from purchasing product iαij=sensitivity degree of consumer j to the quality of product iβij=sensitivity degree of consumer j to the price of product iγij=sensitivity degree of consumer j to the service level of product iεij=random utility obtained by consumer j from purchasing product iπ=profitAcknowledgmentsThis work was supported by the National Natural Science Foundation of China [grant number 72001071]; National Social Science Foundation of China [grant number 22FGLB083]; Philosophy and Social Sciences Planning Project of Henan Province in China [grant number 2022BJJ031]; the Humanities and Social Sciences Research Program of the Ministry of Education of China [grant number 21YJA630061]; Program for Science &Technology Innovation Talents in Universities of Henan Province (Humanities and Social Sciences) [grant number 2021-CX-004]; Program of Higher Education Philosophy and Social Sciences Innovative Talents of Henan Province [grant number 2024-CXRC-02].Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported by the National Natural Science Foundation of China [7200","PeriodicalId":17313,"journal":{"name":"Journal of the Chinese Institute of Engineers","volume":"101 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135476279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-03DOI: 10.1080/02533839.2023.2274084
D. Bharathy Priya, A. Sumathi
ABSTRACTSatisfying the growing energy demand with various power resources is one of the crucial areas. Most of the grid-based industries have come forward to install solar-based photovoltaic (PV) systems to lower energy costs. Due to its simple accessibility and streamlined panel structure of solar PV, this is widely used in many application environments. Maximum Power Point Controlling (MPPT) technique is one of the extensively used controlling techniques that use standard controls like Proportional Integral (PI) and Proportional Integral Derivative (PID) for power extraction. Some hybrid control mechanisms are used in traditional solar PV systems, limiting the issues of increased time consumption, reduced efficiency, and increased THD. For this purpose, this paper intends to develop a new controlling technique named Reverse Flow Transition Control (RFTC) with the MPPT technique to provide power output. Also, the Bidirectional Buck-Boost (BBB) is implemented to increase the efficiency of the controlling scheme. Moreover, this paper developed an optimization-based controlling technique for attaining a reliable converter, and controller design results are validated for the proposed controller technique.CO EDITOR-IN-CHIEF: KuoCheng-ChienASSOCIATE EDITOR: KuoCheng-ChienKEYWORDS: Photovoltaic (PV) systemreverse flow transition control (RFTC)bi-directional buck-boost (BBB) convertermaximum peak point Tracking (MPPT) Nomenclature AUPQS=Advanced Universal Power Quality Conditioning SystemBA=Bat algorithmBBB=Bidirectional Buck-BoostCCM=Continuous Conduction ModeFFT=Fast Fourier transformHVDC=High Voltage Direct CurrentLCL=Inductor-capacitor-inductorLLLAD=Leaky Least Logarithmic Absolute DifferenceMp=Maximum peak point of PVMPPT=Maximum Power Point ControllingP&O=Perturbation and ObservationPI=Proportional IntegralPID=Proportional Integral DerivativePOFO-SMC=Perturbation Observer-based Functional Order Sliding Mode ControllerPV=PhotovoltaicRES=Renewable Energy SourceRFTC=Reverse Flow Transition ControlSiC MOSFET=Silicon-Carbide Metal Oxide Semiconductor field-effect transistorSVPWM=Space Vector Pulse Width ModulationTHD=Total harmonic distortionVC1iC1=Voltage and current capacitanceVL1iL1=Voltage and current inductanceVpIp=Peak voltage and currentVpvipv=Photovoltaic voltage and currentVRsiRs=Voltage and current resistanceDisclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"Reverse flow transition control mechanism with maximum power point controlling (MPPT) technique for solar PV application","authors":"D. Bharathy Priya, A. Sumathi","doi":"10.1080/02533839.2023.2274084","DOIUrl":"https://doi.org/10.1080/02533839.2023.2274084","url":null,"abstract":"ABSTRACTSatisfying the growing energy demand with various power resources is one of the crucial areas. Most of the grid-based industries have come forward to install solar-based photovoltaic (PV) systems to lower energy costs. Due to its simple accessibility and streamlined panel structure of solar PV, this is widely used in many application environments. Maximum Power Point Controlling (MPPT) technique is one of the extensively used controlling techniques that use standard controls like Proportional Integral (PI) and Proportional Integral Derivative (PID) for power extraction. Some hybrid control mechanisms are used in traditional solar PV systems, limiting the issues of increased time consumption, reduced efficiency, and increased THD. For this purpose, this paper intends to develop a new controlling technique named Reverse Flow Transition Control (RFTC) with the MPPT technique to provide power output. Also, the Bidirectional Buck-Boost (BBB) is implemented to increase the efficiency of the controlling scheme. Moreover, this paper developed an optimization-based controlling technique for attaining a reliable converter, and controller design results are validated for the proposed controller technique.CO EDITOR-IN-CHIEF: KuoCheng-ChienASSOCIATE EDITOR: KuoCheng-ChienKEYWORDS: Photovoltaic (PV) systemreverse flow transition control (RFTC)bi-directional buck-boost (BBB) convertermaximum peak point Tracking (MPPT) Nomenclature AUPQS=Advanced Universal Power Quality Conditioning SystemBA=Bat algorithmBBB=Bidirectional Buck-BoostCCM=Continuous Conduction ModeFFT=Fast Fourier transformHVDC=High Voltage Direct CurrentLCL=Inductor-capacitor-inductorLLLAD=Leaky Least Logarithmic Absolute DifferenceMp=Maximum peak point of PVMPPT=Maximum Power Point ControllingP&O=Perturbation and ObservationPI=Proportional IntegralPID=Proportional Integral DerivativePOFO-SMC=Perturbation Observer-based Functional Order Sliding Mode ControllerPV=PhotovoltaicRES=Renewable Energy SourceRFTC=Reverse Flow Transition ControlSiC MOSFET=Silicon-Carbide Metal Oxide Semiconductor field-effect transistorSVPWM=Space Vector Pulse Width ModulationTHD=Total harmonic distortionVC1iC1=Voltage and current capacitanceVL1iL1=Voltage and current inductanceVpIp=Peak voltage and currentVpvipv=Photovoltaic voltage and currentVRsiRs=Voltage and current resistanceDisclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":17313,"journal":{"name":"Journal of the Chinese Institute of Engineers","volume":"33 15","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135868110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ABSTRACTGenerating a synergistic response by combining the advantages of different fiber types in concrete is of current scholarly interest. This study investigated the effects of different hooked-end steel fiber (SF) volume fractions up to 1.6% and three macro fiber types including polypropylene (PP), SF, and typical hybrid fibers (HF: 50% SF + 50% PP) at a fixed dosage of 1.6% on properties of high-strength high-performance concrete (HSHPC). A novel densified mixture design algorithm was used to incorporate a high quantity of fly ash and rice husk ash as an eco-binder. Results showed the specimens with added macro SF exhibited improved mechanical strength, dynamic modulus of elasticity and rigidity, and lower drying shrinkage while the ones with added PP exhibited reductions in compressive strength and dynamic modulus. Interestingly, the incorporation of macro fibers, regardless of fiber type and content, reduced electrical resistivity, ultrasonic pulse velocity and increased the total charge passed in the chloride ion penetration test, resulting in likely underestimation of the reinforcement corrosion resistance, especially for SF specimens. The findings also confirm that HF demonstrating synergistic response may be effective and creative in improving most of the concrete characteristics, contributing to the efficient use of HSHPC in real-world structures.CO EDITOR-IN-CHIEF: OuYu-ChenASSOCIATE EDITOR: OuYu-ChenKEYWORDS: Macro fiberhybrid fiberhigh-strength high-performance concreteengineering propertiesdurability performance Nomenclature ACI=American Concrete InstituteDMDA=Densified Mixture Design AlgorithmER=Electrical ResistivityFA=Fly AshFRC=Fiber-Reinforced ConcreteHPC=High-Performance ConcreteHF=Hybrid FiberHPFRC=High-Performance Fiber Reinforced ConcreteHSC=High-Strength ConcreteHSHPC=High-Strength High-Performance ConcreteOPC=Ordinary Portland CementPP=Polypropylene FiberRCPT=Rapid Chloride ion Penetration TestRHA=Rice Husk AshSCC=Self-Compacting ConcreteSCM=Supplementary Cementitious MaterialsSF=Steel FiberSP=SuperplasticizerUPV=Ultrasonic Pulse VelocityAcknowledgmentsThe samples used for this study were prepared at the Construction Material Research Laboratory of the National Taiwan University of Science and Technology with valuable assistance from Dr. Duy-Hai Vo.Disclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"High-strength high-performance concrete incorporating different macro fiber types and contents: Engineering and durability performance","authors":"Viet-Hung Vu, Minh-Hieu Nguyen, Chao-Lung Hwang, Trong-Phuoc Huynh","doi":"10.1080/02533839.2023.2274079","DOIUrl":"https://doi.org/10.1080/02533839.2023.2274079","url":null,"abstract":"ABSTRACTGenerating a synergistic response by combining the advantages of different fiber types in concrete is of current scholarly interest. This study investigated the effects of different hooked-end steel fiber (SF) volume fractions up to 1.6% and three macro fiber types including polypropylene (PP), SF, and typical hybrid fibers (HF: 50% SF + 50% PP) at a fixed dosage of 1.6% on properties of high-strength high-performance concrete (HSHPC). A novel densified mixture design algorithm was used to incorporate a high quantity of fly ash and rice husk ash as an eco-binder. Results showed the specimens with added macro SF exhibited improved mechanical strength, dynamic modulus of elasticity and rigidity, and lower drying shrinkage while the ones with added PP exhibited reductions in compressive strength and dynamic modulus. Interestingly, the incorporation of macro fibers, regardless of fiber type and content, reduced electrical resistivity, ultrasonic pulse velocity and increased the total charge passed in the chloride ion penetration test, resulting in likely underestimation of the reinforcement corrosion resistance, especially for SF specimens. The findings also confirm that HF demonstrating synergistic response may be effective and creative in improving most of the concrete characteristics, contributing to the efficient use of HSHPC in real-world structures.CO EDITOR-IN-CHIEF: OuYu-ChenASSOCIATE EDITOR: OuYu-ChenKEYWORDS: Macro fiberhybrid fiberhigh-strength high-performance concreteengineering propertiesdurability performance Nomenclature ACI=American Concrete InstituteDMDA=Densified Mixture Design AlgorithmER=Electrical ResistivityFA=Fly AshFRC=Fiber-Reinforced ConcreteHPC=High-Performance ConcreteHF=Hybrid FiberHPFRC=High-Performance Fiber Reinforced ConcreteHSC=High-Strength ConcreteHSHPC=High-Strength High-Performance ConcreteOPC=Ordinary Portland CementPP=Polypropylene FiberRCPT=Rapid Chloride ion Penetration TestRHA=Rice Husk AshSCC=Self-Compacting ConcreteSCM=Supplementary Cementitious MaterialsSF=Steel FiberSP=SuperplasticizerUPV=Ultrasonic Pulse VelocityAcknowledgmentsThe samples used for this study were prepared at the Construction Material Research Laboratory of the National Taiwan University of Science and Technology with valuable assistance from Dr. Duy-Hai Vo.Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":17313,"journal":{"name":"Journal of the Chinese Institute of Engineers","volume":"163 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135868107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ABSTRACTNatural fiber composites are capable of replacing synthetic fiber composites in aeronautics, transportation, architecture, and sports. This work used epoxy matrices to produce natural hybrid fiber composites from hemp, pineapple, and palm fiber. To choose the best material for a design or component, a thorough and successful approach is needed. Material selection judgments are best handled by multi-criteria decision-making (MCDM) procedures. Integrated MCDM approaches like AHP, TOPSIS, and MOORA rank epoxy-hemp-pineapple-palm fiber composites. TOPSIS and MOORA use AHP weights to rank objects. AHP weights are used to rank objects in both the TOPSIS and MOORA methods. Selection is based on a variety of properties of the generated composites, including water absorption and specific heat capacity, in addition to density, hardness, tensile strength, and toughness. The thermal degradation of fiber polymerization is assessed by thermo-gravimetric analysis (TGA). According to a thorough evaluation of MCDM methodologies, a hybrid composite made of palm, hemp, and pineapple performed best, followed by a hybrid composite made of palm and pineapple. This study found that a palm fiber composite has subpar results. Scanning electron microscopy (SEM) is used to examine fiber form and interfacial bonding in composite samples that have been exposed to tension fracture.CO EDITOR-IN-CHIEF: Hsiau, Shu-SanASSOCIATE EDITOR: Chen, Ping-HeiKEYWORDS: Hybrid epoxy natural fibers compositeTGAMCDMAHPTOPSIS and MOORA Nomenclature AHP=Analytical hierarchy processARAS=Additive ratio assessmentCI=Consistency indexCR=Consistency ratioDTG=Derivative of mass variationELECTRE=Elimination and choice translating realityFC1=Epoxy-hemp compositeFC2=Epoxy-pine apple compositeFC3=Epoxy-palm fiber compositeFC4=Epoxy-hemp-pine apple compositeFC5=Epoxy-pine apple-palm compositeFC6=Epoxy-palm-hemp fiber compositeFC7=Epoxy-palm-hemp-pine apple compositeMCDM=Multi-criteria decision-makingMOORA=Multi-objective optimization on the basis of ratio analysisRI=Random inconsistency indicesSEM=Scanning electron microscopySHC=Specific heat capacityTOPSIS=Technique for order preference by the resemblance to an ideal solutionTS=Tensile strengthTGA=Thermo gravimetric analysisWA=Water absorptionWPM=Weighted product modelWSM=Weighted sum modelWf=Wet weight of sample after time in water subtractedWi=Dry weight of sampleAcknowledgmentsThe authors esteem the insightful advice provided by all of the subject matter specialists. The authors are grateful to the Aditya Institute of Technology and Management in Tekkali, where they acquired access to the ARC laboratory and R&D laboratory funded by the European Centre for Mechatronics (APS GMBH), and Aachen Department of Science and Technology (DST), respectively.Disclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"A hybrid AHP-TOPSIS, MOORA technique for multi-objective optimization of thermal, mechanical, and water absorption behavior of epoxy/hemp, pine apple, and palm fiber composites","authors":"Sivasankara Raju, Srihari Palli, Pilla Devi Prasad, Venkata Ramana Menda, Bondala Ramakrishna","doi":"10.1080/02533839.2023.2274092","DOIUrl":"https://doi.org/10.1080/02533839.2023.2274092","url":null,"abstract":"ABSTRACTNatural fiber composites are capable of replacing synthetic fiber composites in aeronautics, transportation, architecture, and sports. This work used epoxy matrices to produce natural hybrid fiber composites from hemp, pineapple, and palm fiber. To choose the best material for a design or component, a thorough and successful approach is needed. Material selection judgments are best handled by multi-criteria decision-making (MCDM) procedures. Integrated MCDM approaches like AHP, TOPSIS, and MOORA rank epoxy-hemp-pineapple-palm fiber composites. TOPSIS and MOORA use AHP weights to rank objects. AHP weights are used to rank objects in both the TOPSIS and MOORA methods. Selection is based on a variety of properties of the generated composites, including water absorption and specific heat capacity, in addition to density, hardness, tensile strength, and toughness. The thermal degradation of fiber polymerization is assessed by thermo-gravimetric analysis (TGA). According to a thorough evaluation of MCDM methodologies, a hybrid composite made of palm, hemp, and pineapple performed best, followed by a hybrid composite made of palm and pineapple. This study found that a palm fiber composite has subpar results. Scanning electron microscopy (SEM) is used to examine fiber form and interfacial bonding in composite samples that have been exposed to tension fracture.CO EDITOR-IN-CHIEF: Hsiau, Shu-SanASSOCIATE EDITOR: Chen, Ping-HeiKEYWORDS: Hybrid epoxy natural fibers compositeTGAMCDMAHPTOPSIS and MOORA Nomenclature AHP=Analytical hierarchy processARAS=Additive ratio assessmentCI=Consistency indexCR=Consistency ratioDTG=Derivative of mass variationELECTRE=Elimination and choice translating realityFC1=Epoxy-hemp compositeFC2=Epoxy-pine apple compositeFC3=Epoxy-palm fiber compositeFC4=Epoxy-hemp-pine apple compositeFC5=Epoxy-pine apple-palm compositeFC6=Epoxy-palm-hemp fiber compositeFC7=Epoxy-palm-hemp-pine apple compositeMCDM=Multi-criteria decision-makingMOORA=Multi-objective optimization on the basis of ratio analysisRI=Random inconsistency indicesSEM=Scanning electron microscopySHC=Specific heat capacityTOPSIS=Technique for order preference by the resemblance to an ideal solutionTS=Tensile strengthTGA=Thermo gravimetric analysisWA=Water absorptionWPM=Weighted product modelWSM=Weighted sum modelWf=Wet weight of sample after time in water subtractedWi=Dry weight of sampleAcknowledgmentsThe authors esteem the insightful advice provided by all of the subject matter specialists. The authors are grateful to the Aditya Institute of Technology and Management in Tekkali, where they acquired access to the ARC laboratory and R&D laboratory funded by the European Centre for Mechatronics (APS GMBH), and Aachen Department of Science and Technology (DST), respectively.Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":17313,"journal":{"name":"Journal of the Chinese Institute of Engineers","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135820822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-31DOI: 10.1080/02533839.2023.2274103
Johnnie Hepziba R, Balaji G
ABSTRACT A Modified Hysteresis Current Controller (MHCC) with Dual Fundamental Component Extraction Algorithm (DFCEA) is presented in this paper. Its purpose is to improve the current harmonics mitigation performance of a three-level T-type inverter-based Shunt Active Power Filter (SHAPF) while operating under non-sinusoidal voltage conditions. The DFCEA is responsible for isolating the fundamental voltage and current components that are necessary for synchronizing each phase and producing the reference current. The existing dual fundamental component extraction technique is updated in order to create the optimized reference currents that are required by the proposed PV-linked T-Type inverter-based Shunt Active Power Filter (PV-SHAPF). This was done in order to meet the requirements of the PV-SHAPF. It is also anticipated that it is excellent at extracting reference currents even under situations of voltage that are not optimum. To maintain the dc-link capacitor voltage at 600 V, a control algorithm of the dc-link capacitor known as the ‘DC link voltage controller’ is used. Both control algorithms are tested in a three-phase T-Type inverter-based PV-SHAPF by simulating them in a MATLAB/Simulink environment. The performance of the MHCC-DFCEA that was designed to mitigate current harmonics was proven by the findings that were gathered.
{"title":"A modified hysteresis current controller with DFCEA for current harmonic mitigation using PV-SHAPF","authors":"Johnnie Hepziba R, Balaji G","doi":"10.1080/02533839.2023.2274103","DOIUrl":"https://doi.org/10.1080/02533839.2023.2274103","url":null,"abstract":"ABSTRACT A Modified Hysteresis Current Controller (MHCC) with Dual Fundamental Component Extraction Algorithm (DFCEA) is presented in this paper. Its purpose is to improve the current harmonics mitigation performance of a three-level T-type inverter-based Shunt Active Power Filter (SHAPF) while operating under non-sinusoidal voltage conditions. The DFCEA is responsible for isolating the fundamental voltage and current components that are necessary for synchronizing each phase and producing the reference current. The existing dual fundamental component extraction technique is updated in order to create the optimized reference currents that are required by the proposed PV-linked T-Type inverter-based Shunt Active Power Filter (PV-SHAPF). This was done in order to meet the requirements of the PV-SHAPF. It is also anticipated that it is excellent at extracting reference currents even under situations of voltage that are not optimum. To maintain the dc-link capacitor voltage at 600 V, a control algorithm of the dc-link capacitor known as the ‘DC link voltage controller’ is used. Both control algorithms are tested in a three-phase T-Type inverter-based PV-SHAPF by simulating them in a MATLAB/Simulink environment. The performance of the MHCC-DFCEA that was designed to mitigate current harmonics was proven by the findings that were gathered.","PeriodicalId":17313,"journal":{"name":"Journal of the Chinese Institute of Engineers","volume":"11 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135869900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-31DOI: 10.1080/02533839.2023.2274076
Handy Prayogo, I-Tung Yang, Kuo-Wei Liao
ABSTRACT The need for more resilient infrastructures entails an accurate and efficient structural reliability analysis. In recent years, Kriging-based meta-modeling has constantly been adopted to reduce the computational cost in estimating the probability of failure. Active learning models focus on most informative samples to increase computational efficiency. Nevertheless, the efficiency gained from the models may diminish when dealing with a rare event with a relatively small probability of failure. To resolve the difficulty, this study proposes a new method, Adaptive Kriging with Multi-layered Hyperball-based Importance Sampling (AK-MHIS), to estimate the structural probability of failure. AK-MHIS uses an adaptive multi-layered hyperball as its candidate sample pool with a new sample filtering mechanism and a more robust stopping condition for the active-learning phase. The performance of AK-MHIS method is validated in benchmark cases. The verification results confirm the superior performance of AK-MHIS to previous methods.
{"title":"Efficient estimation of structural probability of failure with adaptive kriging and multi-layered hyperball-based importance sampling","authors":"Handy Prayogo, I-Tung Yang, Kuo-Wei Liao","doi":"10.1080/02533839.2023.2274076","DOIUrl":"https://doi.org/10.1080/02533839.2023.2274076","url":null,"abstract":"ABSTRACT The need for more resilient infrastructures entails an accurate and efficient structural reliability analysis. In recent years, Kriging-based meta-modeling has constantly been adopted to reduce the computational cost in estimating the probability of failure. Active learning models focus on most informative samples to increase computational efficiency. Nevertheless, the efficiency gained from the models may diminish when dealing with a rare event with a relatively small probability of failure. To resolve the difficulty, this study proposes a new method, Adaptive Kriging with Multi-layered Hyperball-based Importance Sampling (AK-MHIS), to estimate the structural probability of failure. AK-MHIS uses an adaptive multi-layered hyperball as its candidate sample pool with a new sample filtering mechanism and a more robust stopping condition for the active-learning phase. The performance of AK-MHIS method is validated in benchmark cases. The verification results confirm the superior performance of AK-MHIS to previous methods.","PeriodicalId":17313,"journal":{"name":"Journal of the Chinese Institute of Engineers","volume":"308 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135871297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ABSTRACTThe twisting force arm (TFA) is an important part of the pillar landing gear, anti-fatigue optimization on its structure can improve the reliability of the landing gear. However, the optimization accuracy of the conventional optimization method is limited by the basic topological structure from the views of topological theory. Besides that, the optimization efficiency of the conventional method is also relatively low because of the high computational cost of the fatigue life estimation. In this paper, an anti-fatigue optimization method of the TFA was developed to improve the optimization accuracy and efficiency by using the approximate sequential optimization method after an optimal basic topological structure was obtained. To verify its effectiveness, the proposed method was introduced to the anti-fatigue optimization of a pillar landing gear TFA. The results show that the optimization accuracy of the proposed method is higher than the conventional method, and the computational cost can be reduced 82.35%. This indicates that the proposed method can improve the optimization accuracy and efficiency of the anti-fatigue optimization.CO EDITOR-IN-CHIEF: Jeng, Yeau-RenASSOCIATE EDITOR: Jeng, Yeau-RenKEYWORDS: Twisting force armlanding gearKrigingfatigue lifesequential optimization Nomenclature Dj=accumulative fatigue damage under the jth stage loadG0=weight of the TFA after topological optimizationG=weight after anti-fatigue optimizationNj=number of the stress cycles when the failure occurs under the jth stage loadN=fatigue life of the TFAnj=number of the stress cycles under the jth stage loadSE=total strain energy of the design domainSa=stress corresponding to the TFA Sa-N curveTFA=twisting force arm of the pillar landing gearxk=element density in the design domainxi=design variablesximin=low bounds of the design variablesximax=up bounds of the design variablesγ=scatter factor that considering the dispersion of the fatigue lifeωj=ratio that the number of the jth stage stress cycles to the gross number of the stress cyclesσa=stress corresponding to the material S-N curveσmax=maximum Von-Mises stressDisclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Natural Science Basic Research Plan in Shanxi Province of China, grant number: 2022JM-213, 2022JQ-412, 2021JQ-874.
{"title":"Anti-fatigue optimization of the twisting force arm of landing gear based on Kriging approximate sequential optimization method","authors":"Huan Xie, Guang Yang, Jianxiang Sun, Wei Sai, Wei Zeng","doi":"10.1080/02533839.2023.2274086","DOIUrl":"https://doi.org/10.1080/02533839.2023.2274086","url":null,"abstract":"ABSTRACTThe twisting force arm (TFA) is an important part of the pillar landing gear, anti-fatigue optimization on its structure can improve the reliability of the landing gear. However, the optimization accuracy of the conventional optimization method is limited by the basic topological structure from the views of topological theory. Besides that, the optimization efficiency of the conventional method is also relatively low because of the high computational cost of the fatigue life estimation. In this paper, an anti-fatigue optimization method of the TFA was developed to improve the optimization accuracy and efficiency by using the approximate sequential optimization method after an optimal basic topological structure was obtained. To verify its effectiveness, the proposed method was introduced to the anti-fatigue optimization of a pillar landing gear TFA. The results show that the optimization accuracy of the proposed method is higher than the conventional method, and the computational cost can be reduced 82.35%. This indicates that the proposed method can improve the optimization accuracy and efficiency of the anti-fatigue optimization.CO EDITOR-IN-CHIEF: Jeng, Yeau-RenASSOCIATE EDITOR: Jeng, Yeau-RenKEYWORDS: Twisting force armlanding gearKrigingfatigue lifesequential optimization Nomenclature Dj=accumulative fatigue damage under the jth stage loadG0=weight of the TFA after topological optimizationG=weight after anti-fatigue optimizationNj=number of the stress cycles when the failure occurs under the jth stage loadN=fatigue life of the TFAnj=number of the stress cycles under the jth stage loadSE=total strain energy of the design domainSa=stress corresponding to the TFA Sa-N curveTFA=twisting force arm of the pillar landing gearxk=element density in the design domainxi=design variablesximin=low bounds of the design variablesximax=up bounds of the design variablesγ=scatter factor that considering the dispersion of the fatigue lifeωj=ratio that the number of the jth stage stress cycles to the gross number of the stress cyclesσa=stress corresponding to the material S-N curveσmax=maximum Von-Mises stressDisclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Natural Science Basic Research Plan in Shanxi Province of China, grant number: 2022JM-213, 2022JQ-412, 2021JQ-874.","PeriodicalId":17313,"journal":{"name":"Journal of the Chinese Institute of Engineers","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136068123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-16DOI: 10.1080/02533839.2023.2262726
Yong-Fang Cai, Shuo-Ping Wang, Wei Ji
ABSTRACTTraditional 12-lead ECGs have the common problem of lead waveform crossover. Importantly, this problem leads to difficulty in extracting lead waveforms and causes signal distortion during the digitization of ECGs. In this paper, an ECG digitization method that combines density clustering and curve prediction is proposed, and it can effectively solve the issue of lead waveform crossover while also minimizing signal distortion. This method first uses the image preprocessing technique to remove the background mesh, and second, the density and prediction methods are used to solve the lead waveform crossover. Finally, morphological and vertical scanning methods are used to digitize the lead waveform. In addition, experimental verification is carried out on a large quantity of ECG records provided by Yifu Hospital, which is affiliated with Nanjing Medical University. Furthermore, five indicators are adopted for quantitative measurements and comparisons between the reconstructed signals and original waveforms. The comparison results show that the accuracy of the method is 95.5%, and this verifies the effectiveness of the algorithm.CO EDITOR-IN-CHIEF: Yuan, Shyan-MingASSOCIATE EDITOR: Yuan, Shyan-MingKEYWORDS: 12-lead ECGlead waveform crossoverdensity clusteringECG digitalization Nomenclature APR=average P-R intervalAQRS=average QRS intervalAQT=average Q-T intervalAR=average R waveD=lead waveform pixel point coordinate setECG=electrocardiogramHR=heart rateHSV=hue, saturation, valueIo=the calculated result of the original waveformIr=the calculated result of the reconstructed waveformMinPts=minimum number of samples in epsNRMSE=normalized root-mean-square errorPDVD=period distance vertical directionε=distance thresholdDisclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"Digitization of ECG paper records based on density and prediction methods","authors":"Yong-Fang Cai, Shuo-Ping Wang, Wei Ji","doi":"10.1080/02533839.2023.2262726","DOIUrl":"https://doi.org/10.1080/02533839.2023.2262726","url":null,"abstract":"ABSTRACTTraditional 12-lead ECGs have the common problem of lead waveform crossover. Importantly, this problem leads to difficulty in extracting lead waveforms and causes signal distortion during the digitization of ECGs. In this paper, an ECG digitization method that combines density clustering and curve prediction is proposed, and it can effectively solve the issue of lead waveform crossover while also minimizing signal distortion. This method first uses the image preprocessing technique to remove the background mesh, and second, the density and prediction methods are used to solve the lead waveform crossover. Finally, morphological and vertical scanning methods are used to digitize the lead waveform. In addition, experimental verification is carried out on a large quantity of ECG records provided by Yifu Hospital, which is affiliated with Nanjing Medical University. Furthermore, five indicators are adopted for quantitative measurements and comparisons between the reconstructed signals and original waveforms. The comparison results show that the accuracy of the method is 95.5%, and this verifies the effectiveness of the algorithm.CO EDITOR-IN-CHIEF: Yuan, Shyan-MingASSOCIATE EDITOR: Yuan, Shyan-MingKEYWORDS: 12-lead ECGlead waveform crossoverdensity clusteringECG digitalization Nomenclature APR=average P-R intervalAQRS=average QRS intervalAQT=average Q-T intervalAR=average R waveD=lead waveform pixel point coordinate setECG=electrocardiogramHR=heart rateHSV=hue, saturation, valueIo=the calculated result of the original waveformIr=the calculated result of the reconstructed waveformMinPts=minimum number of samples in epsNRMSE=normalized root-mean-square errorPDVD=period distance vertical directionε=distance thresholdDisclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":17313,"journal":{"name":"Journal of the Chinese Institute of Engineers","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136114606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}