In this paper,self-designed multi-hollow needle electrodes are used as a high-voltage electrode in a packed bed dielectric barrier discharge reactor to facilitate fast gas flow through the active discharge area and ac...In this paper,self-designed multi-hollow needle electrodes are used as a high-voltage electrode in a packed bed dielectric barrier discharge reactor to facilitate fast gas flow through the active discharge area and achieve large-volume stable discharge.The dynamic characteristics of the plasma,the generated active species,and the energy transfer mechanisms in both positive discharge(PD)and negative discharge(ND)are investigated by using fast-exposure intensified charge coupled device(ICCD)images and time-resolved optical emission spectra.The experimental results show that the discharge intensity,number of discharge channels,and discharge volume are obviously enhanced when the multi-needle electrode is replaced by a multihollow needle electrode.During a single voltage pulse period,PD mainly develops in a streamer mode,which results in a stronger discharge current,luminous intensity,and E/N compared with the diffuse mode observed in ND.In PD,as the gap between dielectric beads changes from 0 to250μm,the discharge between the dielectric bead gap changes from a partial discharge to a standing filamentary micro-discharge,which allows the plasma to leave the local area and is conducive to the propagation of surface streamers.In ND,the discharge only appears as a diffusionlike mode between the gap of dielectric beads,regardless of whether there is a discharge gap.Moreover,the generation of excited states N_(2)^(+)(B^(2)∑_(u)^(+))and N2(C^(3)Π_(u))is mainly observed in PD,which is attributed to the higher E/N in PD than that in ND.However,the generation of the OH(A^(2)∑^(+))radical in ND is higher than in PD.It is not directly dominated by E/N,but mainly by the resonant energy transfer process between metastable N_(2)(A^(3)∑_(u)^(+))and OH(X^(2)Π).Furthermore,both PD and ND demonstrate obvious energy relaxation processes of electron-to-vibration and vibration-to-vibration,and no vibration-to-rotation energy relaxation process is observed.展开更多
Thermal Energy Storage is becoming a necessary component of sustainable energy production systems as it helps alleviate intrinsic limitations of Renewable Energy Sources, such as intermittent use and mismatch between ...Thermal Energy Storage is becoming a necessary component of sustainable energy production systems as it helps alleviate intrinsic limitations of Renewable Energy Sources, such as intermittent use and mismatch between power demand and supply. This paper discusses a packed bed thermocline tank as a thermal energy storage solution. Firstly, this paper presents the development of a numerical model calculating heat transfers within the tank, based on a discretization over several nodes and the nodal formulation of the heat balance equation. The model considers a filler material and a heat transferring fluid and uses the finite difference method to calculate the temperature evolution of the two media across the tank. The model was validated with two different packed bed systems from the literature during a discharging process, presenting a good fit with the experimental results. Secondly, the experimental packed bed is presented and characterized for a charging cycle from ambient temperature to approximately 180?C. The charging experiment was accurately reproduced with the numerical model requiring minimal computational time. Two additional charging modes were simulated with different inlet HTF conditions: constant temperature and varying temperature following the profile produced by a thermal solar collector field. The temperature profiles obtained from the three charging modes were analysed and compared to each other. The proposed numerical and experimental tools will be used in future studies for a better understanding of the design and operating conditions of packed bed thermal energy storage systems.展开更多
Rotating packed bed(RPB) is one of the most effective gas–liquid mass transfer enhancement reactors, its effective specific mass transfer area(ae) is critical to understand the mass transfer process. By using the NaO...Rotating packed bed(RPB) is one of the most effective gas–liquid mass transfer enhancement reactors, its effective specific mass transfer area(ae) is critical to understand the mass transfer process. By using the NaOH–CO_(2) chemical absorption method, the aevalues of three RPB reactors with different rotor sizes were measured under different operation conditions. The results showed that the high gravity factor and liquid flow rate were major affecting factors, while the gas flow rate exhibited minor influence.The radius of packing is the dominant equipment factor to affect aevalue. The results indicated that the contact area depends on the dispersion of the liquid phase, thus the centrifugal force of rotating packed bed greatly influenced the aevalue. Moreover, the measured ae/ap(effective specific mass transfer area/specific surface area of packing) values were fitted with dimensionless correlation formulas. The unified correlation formula with dimensionless bed size parameter can well predict the experimental data and the prediction errors were within 15%.展开更多
The liquid-cooled battery energy sto rage system(LCBESS) has gained significant attention due to its superior thermal management capacity.However,liquid-cooled battery pack(LCBP) usually has a high sealing level above...The liquid-cooled battery energy sto rage system(LCBESS) has gained significant attention due to its superior thermal management capacity.However,liquid-cooled battery pack(LCBP) usually has a high sealing level above IP65,which can trap flammable and explosive gases from battery thermal runaway and cause explosions.This poses serious safety risks and challenges for LCBESS.In this study,we tested overcharged battery inside a commercial LCBP and found that the conventionally mechanical pressure relief valve(PRV) on the LCBP had a delayed response and low-pressure relief efficiency.A realistic 20-foot model of an energy storage cabin was constructed using the Flacs finite element simulation software.Comparative studies were conducted to evaluate the pressure relief efficiency and the influence on neighboring battery packs in case of internal explosions,considering different sizes and installation positions of the PRV.Here,a newly developed electric-controlled PRV integrated with battery fault detection is introduced,capable of starting within 50 ms of the battery safety valve opening.Furthermore,the PRV was integrated with the battery management system and changed the battery charging and discharging strategy after the PRV was opened.Experimental tests confirmed the efficacy of this method in preventing explosions.This paper addresses the safety concerns associated with LCBPs and proposes an effective solution for explosion relief.展开更多
Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion batteries.Despite numerous works on battery capacity estimation using...Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion batteries.Despite numerous works on battery capacity estimation using laboratory datasets,most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle(EV)battery packs.The challenges intensify for large-sized EV battery packs,where unpredictable operating profiles and low-quality data acquisition hinder precise capacity estimation.To fill the gap,this study introduces a novel data-driven battery pack capacity estimation method grounded in field data.The proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral,open circuit voltage-based,and resistance-based correction methods.Then,multiple health features are extracted from incremental capacity curves,voltage curves,equivalent circuit model parameters,and operating temperature to thoroughly characterize battery aging behavior.A feature selection procedure is performed to determine the optimal feature set based on the Pearson correlation coefficient.Moreover,a convolutional neural network and bidirectional gated recurrent unit,enhanced by an attention mechanism,are employed to estimate the battery pack capacity in real-world EV applications.Finally,the proposed method is validated with a field dataset from two EVs,covering approximately 35,000 kilometers.The results demonstrate that the proposed method exhibits better estimation performance with an error of less than 1.1%compared to existing methods.This work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data,which provides significant insights into reliable labeled capacity calculation,effective features extraction,and machine learning-enabled health diagnosis.展开更多
Designing novel nonfullerene acceptors(NFAs)is of vital importance for the development of organic solar cells(OSC).Modification on the side chain and end group are two powerful tools to construct efficient NFAs.Here,b...Designing novel nonfullerene acceptors(NFAs)is of vital importance for the development of organic solar cells(OSC).Modification on the side chain and end group are two powerful tools to construct efficient NFAs.Here,based on the high-performance L8BO,we selected 3-ethylheptyl to substitute the inner chain of 2-ethylhexyl,obtaining the backbone of BON3.Then we introduced different halogen atoms of fluorine and chlorine on 2-(3-oxo-2,3-dihydro-1Hinden-1-ylidene)malononitrile end group(EG)to construct efficient NFAs named BON3-F and BON3-Cl,respectively.Polymer donor D18 was chosen to combine with two novel NFAs to construct OSC devices.Impressively,D18:BON3-Cl-based device shows a remarkable power conversion efficiency(PCE)of 18.57%,with a high open-circuit voltage(V_(OC))of 0.907 V and an excellent fill factor(FF)of 80.44%,which is one of the highest binary PCE of devices based on D18 as the donor.However,BON3-F-based device shows a relatively lower PCE of 17.79%with a decreased FF of 79.05%.The better photovoltaic performance is mainly attributed to the red-shifted absorption,higher electron and hole mobilities,reduced charge recombination,and enhanced molecular packing in the D18:BON3-Cl films.Also,we performed stability tests on two binary systems;the D18:BON3-Cl and D18:BON3-F devices maintain 88.1%and 85.5%of their initial efficiencies after 169 h of storage at 85°C in an N2-filled glove box,respectively.Our work demonstrates the importance of selecting halogen atoms on EG and provides an efficient binary system of D18:BON3-Cl for further improvement of PCE.展开更多
Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.Ho...Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.However,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation.To address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data.Unlike traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory resources.The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation.Using only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating conditions.Its high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.展开更多
Fixed-bed reactors are generally considered the optimal choice for numerous multi-phase catalytic reactions due to their excellent performance and stability.However,conventional fixed beds often encounter challenges r...Fixed-bed reactors are generally considered the optimal choice for numerous multi-phase catalytic reactions due to their excellent performance and stability.However,conventional fixed beds often encounter challenges related to inadequate mass transfer and a high pressure drop caused by the non-uniform void fraction distribution.To enhance the overall performance of fixed beds,the impact of different packing configurations on performance was investigated.Experimental and simulation methods were used to investigate the fluid flow and mass transfer performances of various packed beds under different flow rates.It was found that structured beds exhibited a significantly lower pressure drop per unit length than conventional packed beds.Furthermore,the packing configurations had a critical role in improving the overall performance of fixed beds.Specifically,structured packed beds,particularly the H-2 packing configuration,effectively reduced the pressure drop per unit length and improved the mass transfer efficiency.The H-2 packing configuration consisted of two parallel strips of particles in each layer,with strips arranged perpendicularly between adjacent layers,and the spacing between the strips varied from layer to layer.展开更多
Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly women.Thus identifying BAC could provide an expense,and be inaccurate.Recently Deep Learning(DL)method...Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly women.Thus identifying BAC could provide an expense,and be inaccurate.Recently Deep Learning(DL)methods have been introduced for automatic BAC detection and quantification with increased accuracy.Previously,classification with deep learning had reached higher efficiency,but designing the structure of DL proved to be an extremely challenging task due to overfitting models.It also is not able to capture the patterns and irregularities presented in the images.To solve the overfitting problem,an optimal feature set has been formed by Enhanced Wolf Pack Algorithm(EWPA),and their irregularities are identified by Dense-kUNet segmentation.In this paper,Dense-kUNet for segmentation and optimal feature has been introduced for classification(severe,mild,light)that integrates DenseUNet and kU-Net.Longer bound links exist among adjacent modules,allowing relatively rough data to be sent to the following component and assisting the system in finding higher qualities.The major contribution of the work is to design the best features selected by Enhanced Wolf Pack Algorithm(EWPA),and Modified Support Vector Machine(MSVM)based learning for classification.k-Dense-UNet is introduced which combines the procedure of Dense-UNet and kU-Net for image segmentation.Longer bound associations occur among nearby sections,allowing relatively granular data to be sent to the next subsystem and benefiting the system in recognizing smaller characteristics.The proposed techniques and the performance are tested using several types of analysis techniques 826 filled digitized mammography.The proposed method achieved the highest precision,recall,F-measure,and accuracy of 84.4333%,84.5333%,84.4833%,and 86.8667%when compared to other methods on the Digital Database for Screening Mammography(DDSM).展开更多
【目的】了解广东省某猪群中猪圆环病毒2型(Porcine circovirus type 2,PCV2)流行毒株的遗传进化情况,丰富PCV2分子流行病学数据,为当地PCV2疫苗候选株的选用和研发提供参考。【方法】使用qPCR方法对疑似PCV2的样品进行检测,发现1株具...【目的】了解广东省某猪群中猪圆环病毒2型(Porcine circovirus type 2,PCV2)流行毒株的遗传进化情况,丰富PCV2分子流行病学数据,为当地PCV2疫苗候选株的选用和研发提供参考。【方法】使用qPCR方法对疑似PCV2的样品进行检测,发现1株具有高病毒载量的PCV2毒株,命名为GD222858。通过PCR方法进行全基因组分子克隆及遗传进化分析。使用MegAlign软件将该毒株ORF1、ORF2基因编码的氨基酸序列与PCV2同亚型参考毒株进行比对,分析氨基酸序列的相似性;采用DNAStar预测该毒株的Cap蛋白二级结构及B细胞表位,并与4株疫苗株DBN-SX07-2(HM641752)、LG(HM038034)、SH(HM038027)、ZJ(AY686764)的Cap蛋白抗原指数进行比对分析。【结果】GD222858毒株基因组长度为1767 bp。遗传进化分析表明该毒株属于PCV2d亚型。与国内外82株参考毒株的核苷酸相似性为91.4%~99.6%,与越南毒株Han8(GenBank登录号:JQ181600)的亲缘关系最近。在ORF1编码的Rep蛋白处发现多个特异性突变位点F70Y、F77L、W202R、N256S;ORF2编码的Cap蛋白相对保守。Protean预测Cap蛋白的氨基酸第5~18、24~25、39~41、48~49、57~65、99、101、112~114、139~140、145~150、162~165、175~181、188~189、205~211、227~232位置处均可能存在潜在的B细胞表位。GD222858毒株的Cap蛋白抗原指数与4株疫苗株均有差异,在氨基酸45~57、124~132、223~233位置处抗原指数明显高于4株疫苗株,且与疫苗株HM038034差异最大。【结论】GD222858毒株感染猪群的原因可能是Rep蛋白多个位点发生特异性突变及疫苗株选用不当所致。展开更多
基金supported by National Natural Science Foundations of China(Nos.51977023 and 52077026)the Fundamental Research Funds for the Central Universities(No.DUT23YG227)。
文摘In this paper,self-designed multi-hollow needle electrodes are used as a high-voltage electrode in a packed bed dielectric barrier discharge reactor to facilitate fast gas flow through the active discharge area and achieve large-volume stable discharge.The dynamic characteristics of the plasma,the generated active species,and the energy transfer mechanisms in both positive discharge(PD)and negative discharge(ND)are investigated by using fast-exposure intensified charge coupled device(ICCD)images and time-resolved optical emission spectra.The experimental results show that the discharge intensity,number of discharge channels,and discharge volume are obviously enhanced when the multi-needle electrode is replaced by a multihollow needle electrode.During a single voltage pulse period,PD mainly develops in a streamer mode,which results in a stronger discharge current,luminous intensity,and E/N compared with the diffuse mode observed in ND.In PD,as the gap between dielectric beads changes from 0 to250μm,the discharge between the dielectric bead gap changes from a partial discharge to a standing filamentary micro-discharge,which allows the plasma to leave the local area and is conducive to the propagation of surface streamers.In ND,the discharge only appears as a diffusionlike mode between the gap of dielectric beads,regardless of whether there is a discharge gap.Moreover,the generation of excited states N_(2)^(+)(B^(2)∑_(u)^(+))and N2(C^(3)Π_(u))is mainly observed in PD,which is attributed to the higher E/N in PD than that in ND.However,the generation of the OH(A^(2)∑^(+))radical in ND is higher than in PD.It is not directly dominated by E/N,but mainly by the resonant energy transfer process between metastable N_(2)(A^(3)∑_(u)^(+))and OH(X^(2)Π).Furthermore,both PD and ND demonstrate obvious energy relaxation processes of electron-to-vibration and vibration-to-vibration,and no vibration-to-rotation energy relaxation process is observed.
文摘Thermal Energy Storage is becoming a necessary component of sustainable energy production systems as it helps alleviate intrinsic limitations of Renewable Energy Sources, such as intermittent use and mismatch between power demand and supply. This paper discusses a packed bed thermocline tank as a thermal energy storage solution. Firstly, this paper presents the development of a numerical model calculating heat transfers within the tank, based on a discretization over several nodes and the nodal formulation of the heat balance equation. The model considers a filler material and a heat transferring fluid and uses the finite difference method to calculate the temperature evolution of the two media across the tank. The model was validated with two different packed bed systems from the literature during a discharging process, presenting a good fit with the experimental results. Secondly, the experimental packed bed is presented and characterized for a charging cycle from ambient temperature to approximately 180?C. The charging experiment was accurately reproduced with the numerical model requiring minimal computational time. Two additional charging modes were simulated with different inlet HTF conditions: constant temperature and varying temperature following the profile produced by a thermal solar collector field. The temperature profiles obtained from the three charging modes were analysed and compared to each other. The proposed numerical and experimental tools will be used in future studies for a better understanding of the design and operating conditions of packed bed thermal energy storage systems.
基金the support from the National Natural Science Foundation of China (22008157,21978178)。
文摘Rotating packed bed(RPB) is one of the most effective gas–liquid mass transfer enhancement reactors, its effective specific mass transfer area(ae) is critical to understand the mass transfer process. By using the NaOH–CO_(2) chemical absorption method, the aevalues of three RPB reactors with different rotor sizes were measured under different operation conditions. The results showed that the high gravity factor and liquid flow rate were major affecting factors, while the gas flow rate exhibited minor influence.The radius of packing is the dominant equipment factor to affect aevalue. The results indicated that the contact area depends on the dispersion of the liquid phase, thus the centrifugal force of rotating packed bed greatly influenced the aevalue. Moreover, the measured ae/ap(effective specific mass transfer area/specific surface area of packing) values were fitted with dimensionless correlation formulas. The unified correlation formula with dimensionless bed size parameter can well predict the experimental data and the prediction errors were within 15%.
基金sponsored by the Science and Technology Program of State Grid Corporation of China(4000-202355090A-1-1ZN)。
文摘The liquid-cooled battery energy sto rage system(LCBESS) has gained significant attention due to its superior thermal management capacity.However,liquid-cooled battery pack(LCBP) usually has a high sealing level above IP65,which can trap flammable and explosive gases from battery thermal runaway and cause explosions.This poses serious safety risks and challenges for LCBESS.In this study,we tested overcharged battery inside a commercial LCBP and found that the conventionally mechanical pressure relief valve(PRV) on the LCBP had a delayed response and low-pressure relief efficiency.A realistic 20-foot model of an energy storage cabin was constructed using the Flacs finite element simulation software.Comparative studies were conducted to evaluate the pressure relief efficiency and the influence on neighboring battery packs in case of internal explosions,considering different sizes and installation positions of the PRV.Here,a newly developed electric-controlled PRV integrated with battery fault detection is introduced,capable of starting within 50 ms of the battery safety valve opening.Furthermore,the PRV was integrated with the battery management system and changed the battery charging and discharging strategy after the PRV was opened.Experimental tests confirmed the efficacy of this method in preventing explosions.This paper addresses the safety concerns associated with LCBPs and proposes an effective solution for explosion relief.
基金supported in part by the National Key Research and Development Program of China(No.2022YFB3305403)Project of basic research funds for central universities(2022CDJDX006)+1 种基金Talent Plan Project of Chongqing(No.cstc2021ycjhbgzxm0295)National Natural Science Foundation of China(No.52111530194)。
文摘Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion batteries.Despite numerous works on battery capacity estimation using laboratory datasets,most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle(EV)battery packs.The challenges intensify for large-sized EV battery packs,where unpredictable operating profiles and low-quality data acquisition hinder precise capacity estimation.To fill the gap,this study introduces a novel data-driven battery pack capacity estimation method grounded in field data.The proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral,open circuit voltage-based,and resistance-based correction methods.Then,multiple health features are extracted from incremental capacity curves,voltage curves,equivalent circuit model parameters,and operating temperature to thoroughly characterize battery aging behavior.A feature selection procedure is performed to determine the optimal feature set based on the Pearson correlation coefficient.Moreover,a convolutional neural network and bidirectional gated recurrent unit,enhanced by an attention mechanism,are employed to estimate the battery pack capacity in real-world EV applications.Finally,the proposed method is validated with a field dataset from two EVs,covering approximately 35,000 kilometers.The results demonstrate that the proposed method exhibits better estimation performance with an error of less than 1.1%compared to existing methods.This work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data,which provides significant insights into reliable labeled capacity calculation,effective features extraction,and machine learning-enabled health diagnosis.
基金supported by the National Natural Science Foundation of China(No.U21A20331)the National Science Fund for Distinguished Young Scholars(No.21925506)+3 种基金Zhejiang Provincial Natural Science Foundation of China(No.LQ22E030013)Ningbo Key Scientific and Technological Project(2022Z117)Ningbo Public Welfare Science and Technology Planning Project(2021S149)ZBTI Scientific Research Innovation Team(KYTD202105).
文摘Designing novel nonfullerene acceptors(NFAs)is of vital importance for the development of organic solar cells(OSC).Modification on the side chain and end group are two powerful tools to construct efficient NFAs.Here,based on the high-performance L8BO,we selected 3-ethylheptyl to substitute the inner chain of 2-ethylhexyl,obtaining the backbone of BON3.Then we introduced different halogen atoms of fluorine and chlorine on 2-(3-oxo-2,3-dihydro-1Hinden-1-ylidene)malononitrile end group(EG)to construct efficient NFAs named BON3-F and BON3-Cl,respectively.Polymer donor D18 was chosen to combine with two novel NFAs to construct OSC devices.Impressively,D18:BON3-Cl-based device shows a remarkable power conversion efficiency(PCE)of 18.57%,with a high open-circuit voltage(V_(OC))of 0.907 V and an excellent fill factor(FF)of 80.44%,which is one of the highest binary PCE of devices based on D18 as the donor.However,BON3-F-based device shows a relatively lower PCE of 17.79%with a decreased FF of 79.05%.The better photovoltaic performance is mainly attributed to the red-shifted absorption,higher electron and hole mobilities,reduced charge recombination,and enhanced molecular packing in the D18:BON3-Cl films.Also,we performed stability tests on two binary systems;the D18:BON3-Cl and D18:BON3-F devices maintain 88.1%and 85.5%of their initial efficiencies after 169 h of storage at 85°C in an N2-filled glove box,respectively.Our work demonstrates the importance of selecting halogen atoms on EG and provides an efficient binary system of D18:BON3-Cl for further improvement of PCE.
基金supported by the National Outstanding Youth Science Fund Project of National Natural Science Foundation of China[Grant No.52222708]the Natural Science Foundation of Beijing Municipality[Grant No.3212033]。
文摘Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.However,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation.To address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data.Unlike traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory resources.The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation.Using only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating conditions.Its high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.
文摘Fixed-bed reactors are generally considered the optimal choice for numerous multi-phase catalytic reactions due to their excellent performance and stability.However,conventional fixed beds often encounter challenges related to inadequate mass transfer and a high pressure drop caused by the non-uniform void fraction distribution.To enhance the overall performance of fixed beds,the impact of different packing configurations on performance was investigated.Experimental and simulation methods were used to investigate the fluid flow and mass transfer performances of various packed beds under different flow rates.It was found that structured beds exhibited a significantly lower pressure drop per unit length than conventional packed beds.Furthermore,the packing configurations had a critical role in improving the overall performance of fixed beds.Specifically,structured packed beds,particularly the H-2 packing configuration,effectively reduced the pressure drop per unit length and improved the mass transfer efficiency.The H-2 packing configuration consisted of two parallel strips of particles in each layer,with strips arranged perpendicularly between adjacent layers,and the spacing between the strips varied from layer to layer.
文摘Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly women.Thus identifying BAC could provide an expense,and be inaccurate.Recently Deep Learning(DL)methods have been introduced for automatic BAC detection and quantification with increased accuracy.Previously,classification with deep learning had reached higher efficiency,but designing the structure of DL proved to be an extremely challenging task due to overfitting models.It also is not able to capture the patterns and irregularities presented in the images.To solve the overfitting problem,an optimal feature set has been formed by Enhanced Wolf Pack Algorithm(EWPA),and their irregularities are identified by Dense-kUNet segmentation.In this paper,Dense-kUNet for segmentation and optimal feature has been introduced for classification(severe,mild,light)that integrates DenseUNet and kU-Net.Longer bound links exist among adjacent modules,allowing relatively rough data to be sent to the following component and assisting the system in finding higher qualities.The major contribution of the work is to design the best features selected by Enhanced Wolf Pack Algorithm(EWPA),and Modified Support Vector Machine(MSVM)based learning for classification.k-Dense-UNet is introduced which combines the procedure of Dense-UNet and kU-Net for image segmentation.Longer bound associations occur among nearby sections,allowing relatively granular data to be sent to the next subsystem and benefiting the system in recognizing smaller characteristics.The proposed techniques and the performance are tested using several types of analysis techniques 826 filled digitized mammography.The proposed method achieved the highest precision,recall,F-measure,and accuracy of 84.4333%,84.5333%,84.4833%,and 86.8667%when compared to other methods on the Digital Database for Screening Mammography(DDSM).
文摘【目的】了解广东省某猪群中猪圆环病毒2型(Porcine circovirus type 2,PCV2)流行毒株的遗传进化情况,丰富PCV2分子流行病学数据,为当地PCV2疫苗候选株的选用和研发提供参考。【方法】使用qPCR方法对疑似PCV2的样品进行检测,发现1株具有高病毒载量的PCV2毒株,命名为GD222858。通过PCR方法进行全基因组分子克隆及遗传进化分析。使用MegAlign软件将该毒株ORF1、ORF2基因编码的氨基酸序列与PCV2同亚型参考毒株进行比对,分析氨基酸序列的相似性;采用DNAStar预测该毒株的Cap蛋白二级结构及B细胞表位,并与4株疫苗株DBN-SX07-2(HM641752)、LG(HM038034)、SH(HM038027)、ZJ(AY686764)的Cap蛋白抗原指数进行比对分析。【结果】GD222858毒株基因组长度为1767 bp。遗传进化分析表明该毒株属于PCV2d亚型。与国内外82株参考毒株的核苷酸相似性为91.4%~99.6%,与越南毒株Han8(GenBank登录号:JQ181600)的亲缘关系最近。在ORF1编码的Rep蛋白处发现多个特异性突变位点F70Y、F77L、W202R、N256S;ORF2编码的Cap蛋白相对保守。Protean预测Cap蛋白的氨基酸第5~18、24~25、39~41、48~49、57~65、99、101、112~114、139~140、145~150、162~165、175~181、188~189、205~211、227~232位置处均可能存在潜在的B细胞表位。GD222858毒株的Cap蛋白抗原指数与4株疫苗株均有差异,在氨基酸45~57、124~132、223~233位置处抗原指数明显高于4株疫苗株,且与疫苗株HM038034差异最大。【结论】GD222858毒株感染猪群的原因可能是Rep蛋白多个位点发生特异性突变及疫苗株选用不当所致。
