A low utilization rate of public chargers and unmatched deployment of public charging sta-tions(CSs)are partly attributed to inappropriate modeling of charging behavior and biased charging demand estimation.This study...A low utilization rate of public chargers and unmatched deployment of public charging sta-tions(CSs)are partly attributed to inappropriate modeling of charging behavior and biased charging demand estimation.This study proposes an optimization methodology for public CS deployment,considering real charging behavior and interactions between battery elec-tric vehicle(BEV)users and CSs.Realistic charging choice behavior is modeled based on surveys,and a dynamic charging decision chain is simulated,allowing interactions between BEV users and CSs through an agent-based modeling(ABM)approach.The charging-related activities are triggered by state of charge(SOC)levels randomly generated from distributions derived from real BEV operating data,including the random SOC levels at the start of a trip,the SOC level that prompts the user to charge the BEV,and the SOC level at which the user stops charging the BEV.A bi-level programming model is proposed to optimize the deployment schemes for building new CSs considering the existing CSs,to determine the location and the capacity of new CSs.The objective is to minimize the total time cost per BEV user,including travel time,charging time and waiting time in the queue.An application is conducted,for the deployment of fast CSs in Washington State,USA.The results show that our method could provide effective guidance for allocating new CSs that are good supplements to the existing heavy-load CSs to share their charging load and relieve their serious queuing problems.The optimized deployment scheme can efficiently alleviate long waiting times at existing CSs,leading to a more balanced utilization among CSs.The proposed approach is expected to contribute to better planning and deployment of public CSs,satisfaction of the booming charging demand,and increased utilization of pub-lic CSs.展开更多
Autonomous vehicles rely heavily on accurate and efficient scene segmentation for safe navigation and efficient operations.Traditional Bird’s Eye View(BEV)methods on semantic scene segmentation,which leverage multimo...Autonomous vehicles rely heavily on accurate and efficient scene segmentation for safe navigation and efficient operations.Traditional Bird’s Eye View(BEV)methods on semantic scene segmentation,which leverage multimodal sensor fusion,often struggle with noisy data and demand high-performance GPUs,leading to sensor misalignment and performance degradation.This paper introduces an Enhanced Channel Attention BEV(ECABEV),a novel approach designed to address the challenges under insufficient GPU memory conditions.ECABEV integrates camera and radar data through a de-noise enhanced channel attention mechanism,which utilizes global average and max pooling to effectively filter out noise while preserving discriminative features.Furthermore,an improved fusion approach is proposed to efficiently merge categorical data across modalities.To reduce computational overhead,a bilinear interpolation layer normalizationmethod is devised to ensure spatial feature fidelity.Moreover,a scalable crossentropy loss function is further designed to handle the imbalanced classes with less computational efficiency sacrifice.Extensive experiments on the nuScenes dataset demonstrate that ECABEV achieves state-of-the-art performance with an IoU of 39.961,using a lightweight ViT-B/14 backbone and lower resolution(224×224).Our approach highlights its cost-effectiveness and practical applicability,even on low-end devices.The code is publicly available at:https://github.com/YYF-CQU/ECABEV.git.展开更多
Bird's-eye-view(BEV)perception is a core technology for autonomous driving systems.However,existing solutions face the dilemma of high costs associated with multimodal methods and limited performance of vision-onl...Bird's-eye-view(BEV)perception is a core technology for autonomous driving systems.However,existing solutions face the dilemma of high costs associated with multimodal methods and limited performance of vision-only approaches.To address this issue,this paper proposes a framework named“a lightweight pure visual BEV perception method based on dual distillation of spatial-temporal knowledge”.This framework innovatively designs a lightweight vision-only student model based on Res Net,which leverages a dual distillation mechanism to learn from a powerful teacher model that integrates temporal information from both image and light detection and ranging(LiDAR)modalities.Specifically,we distill efficient multi-modal feature extraction and spatial fusion capabilities from the BEVFusion model,and distill advanced temporal information fusion and spatiotemporal attention mechanisms from the BEVFormer model.This dual distillation strategy enables the student model to achieve perception performance close to that of multi-modal models without relying on Li DAR.Experimental results on the nu Scenes dataset demonstrate that the proposed model significantly outperforms classical vision-only algorithms,achieves comparable performance to current state-of-the-art vision-only methods on the nu Scenes detection leaderboard in terms of both mean average precision(mAP)and the nu Scenes detection score(NDS)metrics,and exhibits notable advantages in inference computational efficiency.Although the proposed dual-teacher paradigm incurs higher offline training costs compared to single-model approaches,it yields a streamlined and highly efficient student model suitable for resource-constrained real-time deployment.This provides an effective pathway toward low-cost,high-performance autonomous driving perception systems.展开更多
Since its discovery in the 1980s,the insect cell-baculovirus expression vector system(IC-BEVS)has been widely used in biomedical applications,such as recombinant protein expression,drug screening,vaccine development,g...Since its discovery in the 1980s,the insect cell-baculovirus expression vector system(IC-BEVS)has been widely used in biomedical applications,such as recombinant protein expression,drug screening,vaccine development,gene therapy and so on[1].As a eukaryotic system,IC-BEVS has great development prospects due to its advantages such as high safety,simple operation,simultaneous expression of multi-subunit proteins,and suitability for large-scale cultivation[2].展开更多
为了解我国牛肠道病毒(BEV)流行现状,为其防控提供理论依据,本试验从四川省成都市某牛场的腹泻病牛粪便样本中分离得到1株病毒,将其命名为SC-726并进行后续研究。将SC-726接种牛肾细胞(MDBK)后观察细胞病变效应(CPE),计算病毒含量,使用...为了解我国牛肠道病毒(BEV)流行现状,为其防控提供理论依据,本试验从四川省成都市某牛场的腹泻病牛粪便样本中分离得到1株病毒,将其命名为SC-726并进行后续研究。将SC-726接种牛肾细胞(MDBK)后观察细胞病变效应(CPE),计算病毒含量,使用透射电子显微镜观察该病毒的形态特征,分析其理化特性、核酸型和细胞嗜性,绘制一步生长曲线,最后对该分离株进行5′非翻译区(5′UTR)基因测序以分析其遗传演化。结果显示,SC-726分离株感染MDBK细胞后,细胞发生明显的CPE;病毒最高滴度为1×10^(6.2) TCID_(50)/0.1 m L;电镜下观察到直径约30 nm的无囊膜球形粒子,符合传统小RNA病毒形态学特征;理化特性鉴定结果显示,该分离株几乎不受有机溶剂(乙醚、氯仿)和胰蛋白酶的影响,同时具有一系列与BEV相符的特征,如耐酸、不耐强碱、热敏感;DNA抑制剂阿糖胞苷(Ara-C)对该病毒滴度无影响,判定为RNA病毒;SC-726株能够在MDBK、乳仓鼠肾细胞(BHK-21)、猪肾细胞(PK-15)、非洲绿猴胚胎肾细胞(Marc-145)和犬肾细胞(MDCK)等多种动物细胞上增殖;遗传进化分析结果显示,该分离株为F型牛肠道病毒(BEV-F)。本试验从腹泻牛粪便样本中成功分离出1株BEV-F,进一步丰富了我国BEV资料库,为该病毒病的防治提供了理论依据。展开更多
基金supported by the National Natural Science Foundation of China(No.71971162)Key Research Project from Shanxi Transportation Holdings Group(No.20-JKKJ-1).
文摘A low utilization rate of public chargers and unmatched deployment of public charging sta-tions(CSs)are partly attributed to inappropriate modeling of charging behavior and biased charging demand estimation.This study proposes an optimization methodology for public CS deployment,considering real charging behavior and interactions between battery elec-tric vehicle(BEV)users and CSs.Realistic charging choice behavior is modeled based on surveys,and a dynamic charging decision chain is simulated,allowing interactions between BEV users and CSs through an agent-based modeling(ABM)approach.The charging-related activities are triggered by state of charge(SOC)levels randomly generated from distributions derived from real BEV operating data,including the random SOC levels at the start of a trip,the SOC level that prompts the user to charge the BEV,and the SOC level at which the user stops charging the BEV.A bi-level programming model is proposed to optimize the deployment schemes for building new CSs considering the existing CSs,to determine the location and the capacity of new CSs.The objective is to minimize the total time cost per BEV user,including travel time,charging time and waiting time in the queue.An application is conducted,for the deployment of fast CSs in Washington State,USA.The results show that our method could provide effective guidance for allocating new CSs that are good supplements to the existing heavy-load CSs to share their charging load and relieve their serious queuing problems.The optimized deployment scheme can efficiently alleviate long waiting times at existing CSs,leading to a more balanced utilization among CSs.The proposed approach is expected to contribute to better planning and deployment of public CSs,satisfaction of the booming charging demand,and increased utilization of pub-lic CSs.
基金funded by the National Natural Science Foundation of China,grant number 62262045the Fundamental Research Funds for the Central Universities,grant number 2023CDJYGRH-YB11the Open Funding of SUGON Industrial Control and Security Center,grant number CUIT-SICSC-2025-03.
文摘Autonomous vehicles rely heavily on accurate and efficient scene segmentation for safe navigation and efficient operations.Traditional Bird’s Eye View(BEV)methods on semantic scene segmentation,which leverage multimodal sensor fusion,often struggle with noisy data and demand high-performance GPUs,leading to sensor misalignment and performance degradation.This paper introduces an Enhanced Channel Attention BEV(ECABEV),a novel approach designed to address the challenges under insufficient GPU memory conditions.ECABEV integrates camera and radar data through a de-noise enhanced channel attention mechanism,which utilizes global average and max pooling to effectively filter out noise while preserving discriminative features.Furthermore,an improved fusion approach is proposed to efficiently merge categorical data across modalities.To reduce computational overhead,a bilinear interpolation layer normalizationmethod is devised to ensure spatial feature fidelity.Moreover,a scalable crossentropy loss function is further designed to handle the imbalanced classes with less computational efficiency sacrifice.Extensive experiments on the nuScenes dataset demonstrate that ECABEV achieves state-of-the-art performance with an IoU of 39.961,using a lightweight ViT-B/14 backbone and lower resolution(224×224).Our approach highlights its cost-effectiveness and practical applicability,even on low-end devices.The code is publicly available at:https://github.com/YYF-CQU/ECABEV.git.
基金supported by the National Natural Science Foundation of China(42476084,62203456,42276199)the Stable Support Project of National Key Laboratory(WDZC 20245250302)the National Key R&D Program of China(2024YFC2813502,2024YFC2813302)。
文摘Bird's-eye-view(BEV)perception is a core technology for autonomous driving systems.However,existing solutions face the dilemma of high costs associated with multimodal methods and limited performance of vision-only approaches.To address this issue,this paper proposes a framework named“a lightweight pure visual BEV perception method based on dual distillation of spatial-temporal knowledge”.This framework innovatively designs a lightweight vision-only student model based on Res Net,which leverages a dual distillation mechanism to learn from a powerful teacher model that integrates temporal information from both image and light detection and ranging(LiDAR)modalities.Specifically,we distill efficient multi-modal feature extraction and spatial fusion capabilities from the BEVFusion model,and distill advanced temporal information fusion and spatiotemporal attention mechanisms from the BEVFormer model.This dual distillation strategy enables the student model to achieve perception performance close to that of multi-modal models without relying on Li DAR.Experimental results on the nu Scenes dataset demonstrate that the proposed model significantly outperforms classical vision-only algorithms,achieves comparable performance to current state-of-the-art vision-only methods on the nu Scenes detection leaderboard in terms of both mean average precision(mAP)and the nu Scenes detection score(NDS)metrics,and exhibits notable advantages in inference computational efficiency.Although the proposed dual-teacher paradigm incurs higher offline training costs compared to single-model approaches,it yields a streamlined and highly efficient student model suitable for resource-constrained real-time deployment.This provides an effective pathway toward low-cost,high-performance autonomous driving perception systems.
文摘Since its discovery in the 1980s,the insect cell-baculovirus expression vector system(IC-BEVS)has been widely used in biomedical applications,such as recombinant protein expression,drug screening,vaccine development,gene therapy and so on[1].As a eukaryotic system,IC-BEVS has great development prospects due to its advantages such as high safety,simple operation,simultaneous expression of multi-subunit proteins,and suitability for large-scale cultivation[2].
文摘为了解我国牛肠道病毒(BEV)流行现状,为其防控提供理论依据,本试验从四川省成都市某牛场的腹泻病牛粪便样本中分离得到1株病毒,将其命名为SC-726并进行后续研究。将SC-726接种牛肾细胞(MDBK)后观察细胞病变效应(CPE),计算病毒含量,使用透射电子显微镜观察该病毒的形态特征,分析其理化特性、核酸型和细胞嗜性,绘制一步生长曲线,最后对该分离株进行5′非翻译区(5′UTR)基因测序以分析其遗传演化。结果显示,SC-726分离株感染MDBK细胞后,细胞发生明显的CPE;病毒最高滴度为1×10^(6.2) TCID_(50)/0.1 m L;电镜下观察到直径约30 nm的无囊膜球形粒子,符合传统小RNA病毒形态学特征;理化特性鉴定结果显示,该分离株几乎不受有机溶剂(乙醚、氯仿)和胰蛋白酶的影响,同时具有一系列与BEV相符的特征,如耐酸、不耐强碱、热敏感;DNA抑制剂阿糖胞苷(Ara-C)对该病毒滴度无影响,判定为RNA病毒;SC-726株能够在MDBK、乳仓鼠肾细胞(BHK-21)、猪肾细胞(PK-15)、非洲绿猴胚胎肾细胞(Marc-145)和犬肾细胞(MDCK)等多种动物细胞上增殖;遗传进化分析结果显示,该分离株为F型牛肠道病毒(BEV-F)。本试验从腹泻牛粪便样本中成功分离出1株BEV-F,进一步丰富了我国BEV资料库,为该病毒病的防治提供了理论依据。
