Optical switch fabric plays an important role in building multiple-user optical quantum communication networks.Owing to its self-routing property and low complexity, a banyan network is widely used for building switch...Optical switch fabric plays an important role in building multiple-user optical quantum communication networks.Owing to its self-routing property and low complexity, a banyan network is widely used for building switch fabric. While,there is no efficient way to remove internal blocking in a banyan network in a classical way, quantum state fusion, by which the two-dimensional internal quantum states of two photons could be combined into a four-dimensional internal state of a single photon, makes it possible to solve this problem. In this paper, we convert the output mode of quantum state fusion from spatial-polarization mode into time-polarization mode. By combining modified quantum state fusion and quantum state fission with quantum Fredkin gate, we propose a practical scheme to build an optical quantum switch unit which is block free. The scheme can be extended to building more complex units, four of which are shown in this paper.展开更多
An adaptive outlier controlling multirate model based on Hong’s multirate kinetic model was represented in order to resist the outliers and utilize their useful information. Wavelet transform was introduced to detect...An adaptive outlier controlling multirate model based on Hong’s multirate kinetic model was represented in order to resist the outliers and utilize their useful information. Wavelet transform was introduced to detect and control the outliers. The multirate information extraction and the controlling of outliers were properly integrated to establish an adaptive outlier controlling multirate model. The proposed model was applied to multisensor state fusion with interacting multiple model (IMM), and a robust interacting multisensor state fusion algorithm was established based on adaptive outlier controlling multirate model. The Monte-Carlo simulation shows that it could improve the accuracy of fusion estimation by 70% compared to Hong’s algorithm and at least 14% to Xiao’s algorithm.展开更多
In order to make full use of heterogeneous multi-sensor data to serve urban intelligent transportation systems, a real-time urban traffic state fusion model was proposed, named federated evidence fusion model. The mod...In order to make full use of heterogeneous multi-sensor data to serve urban intelligent transportation systems, a real-time urban traffic state fusion model was proposed, named federated evidence fusion model. The model improves conventional D-S evidence theory in temporal domain, such that it can satisfy the requirement of real-time processing and utilize traffic detection information more efficaciously. The model frame and computational procedures are given. In addition, a generalized reliability weight matrix of evidence is also presented to increase the accuracy of estimation. After that, a simulation test is presented to explain the advantage of the proposed method in comparison with conventional D-S evidence theory. Besides, the validity of the model is proven by the use of the data of loop detectors and GPS probe vehicles collected from an urban link in Shanghai. Results of the experiment show that the proposed approach can well embody and track traffic state at character level in real-time conditions.展开更多
Change detection(CD)plays a crucial role in numerous fields,where both convolutional neural networks(CNNs)and Transformers have demonstrated exceptional performance in CD tasks.However,CNNs suffer from limited recepti...Change detection(CD)plays a crucial role in numerous fields,where both convolutional neural networks(CNNs)and Transformers have demonstrated exceptional performance in CD tasks.However,CNNs suffer from limited receptive fields,hindering their ability to capture global features,while Transformers are constrained by high computational complexity.Recently,Mamba architecture,which is based on state space models(SSMs),has shown powerful global modeling capabilities while achieving linear computational complexity.Although some researchers have incorporated Mamba into CD tasks,the existing Mamba⁃based remote sensing CD methods struggle to effectively perceive the inherent locality of changed regions when flattening and scanning remote sensing images,leading to limitations in extracting change features.To address these issues,we propose a novel Mamba⁃based CD method termed difference feature fusion Mamba model(DFFMamba)by mitigating the loss of feature locality caused by traditional Mamba⁃style scanning.Specifically,two distinct difference feature extraction modules are designed:Difference Mamba(DMamba)and local difference Mamba(LDMamba),where DMamba extracts difference features by calculating the difference in coefficient matrices between the state⁃space equations of the bi⁃temporal features.Building upon DMamba,LDMamba combines a locally adaptive state⁃space scanning(LASS)strategy to enhance feature locality so as to accurately extract difference features.Additionally,a fusion Mamba(FMamba)module is proposed,which employs a spatial⁃channel token modeling SSM(SCTMS)unit to integrate multi⁃dimensional spatio⁃temporal interactions of change features,thereby capturing their dependencies across both spatial and channel dimensions.To verify the effectiveness of the proposed DFFMamba,extensive experiments are conducted on three datasets of WHU⁃CD,LEVIR⁃CD,and CLCD.The results demonstrate that DFFMamba significantly outperforms state⁃of⁃the⁃art CD methods,achieving intersection over union(IoU)scores of 90.67%,85.04%,and 66.56%on the three datasets,respectively.展开更多
磷酸铁锂(LiFePO_(4),LFP)与镍钴锰酸锂(LiNi_(x)Co_(y)Mn_(2)O_(2),NCM)电池串联构建的混合动力电池系统,是突破传统单一化学体系瓶颈的关键技术。然而,混装电池包中LFP电池具有平坦的电压平台特性,导致全工作区间的荷电状态(state of ...磷酸铁锂(LiFePO_(4),LFP)与镍钴锰酸锂(LiNi_(x)Co_(y)Mn_(2)O_(2),NCM)电池串联构建的混合动力电池系统,是突破传统单一化学体系瓶颈的关键技术。然而,混装电池包中LFP电池具有平坦的电压平台特性,导致全工作区间的荷电状态(state of charge,SOC)估算精度受限,且在多算法切换时易出现SOC跳变现象。为此,本工作提出一种基于开路电压(open circuit voltage,OCV)曲线区间自适应划分的分段融合SOC估算方法。首先,考虑到LFP电池OCV斜率变化特征,设计了分段平滑策略,在高斜率区保持电压特征,在平台区增强平滑效果,并根据平滑OCV曲线的一阶差分斜率,设定自适应斜率阈值,将放电区间划分为前端高斜率区、中间平台区与后端高斜率区,为SOC算法选择提供明确依据;其次,构建分段估算框架:在高斜率区采用改进自适应扩展卡尔曼滤波进行SOC动态跟踪,在平台区则利用混合包中NCM电池的SOC进行映射估算。针对算法切换点SOC跳变问题,进一步提出梯度敏感的S型融合算法(gradient-sensitive adaptive blending,GSAB),该算法通过量化切换点邻域的SOC梯度差异,动态调整融合函数参数以生成平滑过渡权重,抑制切换点的SOC跳变。结果表明,改进自适应扩展卡尔曼滤波算法在NCM电池上的均方根误差相较于传统扩展卡尔曼滤波算法降低63.70%;GSAB策略有效消除了算法切换时的SOC突变,使过渡区波动降低72.42%。最终,在城市道路循环工况下,LFP电池全区间SOC估算的平均绝对误差与均方根误差分别降至1.08%和1.31%,验证了所提方法能有效提升LFP电池SOC全区间估算精度。展开更多
We present a qubit-loss-free(QLF)fusion scheme for generating large-scale atom W states in cavity quantum electrodynamics(QED)system.Compared to the most current fusion schemes which are conditioned on the case where ...We present a qubit-loss-free(QLF)fusion scheme for generating large-scale atom W states in cavity quantum electrodynamics(QED)system.Compared to the most current fusion schemes which are conditioned on the case where one particle can be extracted from each initial W state to the fusion process,our scheme will access one or two particles from each W state.Based on the atom–cavity-field detuned interaction,three jWin+m+t states can be generated from the jWin,jWim,and jWit states with the help of two auxiliary atoms,and three jWin+m+t+q states can be generated from jWin,jWim,jWit,and a jWiq state with the help of three auxiliary atoms.Comparing the numerical simulations of the resource cost of fusing three small-size W states based on the previous schemes,our fusion scheme seems to be more efficient.This QLF fusion scheme can be generalized to the case of fusing k different or identical particle W states.Furthermore,with no qubit loss,it greatly reduces the number of fusion steps and prepares W states with larger particle numbers.展开更多
随着新能源汽车保有量的快速增长,动力电池荷电状态(State of Charge,SOC)的精确估算已成为电池管理系统(Battery Management System,BMS)的核心技术瓶颈。传统估算法在复杂工况波动、宽温域环境及电池老化场景中易出现累积误差与精度衰...随着新能源汽车保有量的快速增长,动力电池荷电状态(State of Charge,SOC)的精确估算已成为电池管理系统(Battery Management System,BMS)的核心技术瓶颈。传统估算法在复杂工况波动、宽温域环境及电池老化场景中易出现累积误差与精度衰减,直接影响车辆续航里程可信度及电池安全边界控制。因此,本文探索构建具备强环境适应性的SOC估算模型,设计高低温台架测试与实车验证。研究证实,优化策略能有效提升极端工况下SOC估算的稳定性,显著改善电量预警的可靠性。展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61372076 and 61301171)the 111 Project(Grant No.B08038)+1 种基金the Fundamental Research Funds for the Central Universities(Grant No.K5051201021)the Scholarship from China Scholarship Council(Grant No.201308615037)
文摘Optical switch fabric plays an important role in building multiple-user optical quantum communication networks.Owing to its self-routing property and low complexity, a banyan network is widely used for building switch fabric. While,there is no efficient way to remove internal blocking in a banyan network in a classical way, quantum state fusion, by which the two-dimensional internal quantum states of two photons could be combined into a four-dimensional internal state of a single photon, makes it possible to solve this problem. In this paper, we convert the output mode of quantum state fusion from spatial-polarization mode into time-polarization mode. By combining modified quantum state fusion and quantum state fission with quantum Fredkin gate, we propose a practical scheme to build an optical quantum switch unit which is block free. The scheme can be extended to building more complex units, four of which are shown in this paper.
基金The National Natural Science Foundation ofChina (No60304007)The China Aviation Science Foundation (No 03F57003 )The QMX Project of Shanghai Science and Technology Development Foundation ( No04QMX1410)
文摘An adaptive outlier controlling multirate model based on Hong’s multirate kinetic model was represented in order to resist the outliers and utilize their useful information. Wavelet transform was introduced to detect and control the outliers. The multirate information extraction and the controlling of outliers were properly integrated to establish an adaptive outlier controlling multirate model. The proposed model was applied to multisensor state fusion with interacting multiple model (IMM), and a robust interacting multisensor state fusion algorithm was established based on adaptive outlier controlling multirate model. The Monte-Carlo simulation shows that it could improve the accuracy of fusion estimation by 70% compared to Hong’s algorithm and at least 14% to Xiao’s algorithm.
文摘In order to make full use of heterogeneous multi-sensor data to serve urban intelligent transportation systems, a real-time urban traffic state fusion model was proposed, named federated evidence fusion model. The model improves conventional D-S evidence theory in temporal domain, such that it can satisfy the requirement of real-time processing and utilize traffic detection information more efficaciously. The model frame and computational procedures are given. In addition, a generalized reliability weight matrix of evidence is also presented to increase the accuracy of estimation. After that, a simulation test is presented to explain the advantage of the proposed method in comparison with conventional D-S evidence theory. Besides, the validity of the model is proven by the use of the data of loop detectors and GPS probe vehicles collected from an urban link in Shanghai. Results of the experiment show that the proposed approach can well embody and track traffic state at character level in real-time conditions.
基金supported by the National Natural Science Foundation of China(Nos.42371449,41801386).
文摘Change detection(CD)plays a crucial role in numerous fields,where both convolutional neural networks(CNNs)and Transformers have demonstrated exceptional performance in CD tasks.However,CNNs suffer from limited receptive fields,hindering their ability to capture global features,while Transformers are constrained by high computational complexity.Recently,Mamba architecture,which is based on state space models(SSMs),has shown powerful global modeling capabilities while achieving linear computational complexity.Although some researchers have incorporated Mamba into CD tasks,the existing Mamba⁃based remote sensing CD methods struggle to effectively perceive the inherent locality of changed regions when flattening and scanning remote sensing images,leading to limitations in extracting change features.To address these issues,we propose a novel Mamba⁃based CD method termed difference feature fusion Mamba model(DFFMamba)by mitigating the loss of feature locality caused by traditional Mamba⁃style scanning.Specifically,two distinct difference feature extraction modules are designed:Difference Mamba(DMamba)and local difference Mamba(LDMamba),where DMamba extracts difference features by calculating the difference in coefficient matrices between the state⁃space equations of the bi⁃temporal features.Building upon DMamba,LDMamba combines a locally adaptive state⁃space scanning(LASS)strategy to enhance feature locality so as to accurately extract difference features.Additionally,a fusion Mamba(FMamba)module is proposed,which employs a spatial⁃channel token modeling SSM(SCTMS)unit to integrate multi⁃dimensional spatio⁃temporal interactions of change features,thereby capturing their dependencies across both spatial and channel dimensions.To verify the effectiveness of the proposed DFFMamba,extensive experiments are conducted on three datasets of WHU⁃CD,LEVIR⁃CD,and CLCD.The results demonstrate that DFFMamba significantly outperforms state⁃of⁃the⁃art CD methods,achieving intersection over union(IoU)scores of 90.67%,85.04%,and 66.56%on the three datasets,respectively.
基金the National Natural Science Foun-dation of China(Grant No.12204311)the Jiangxi Natural Science Foundation(Grant No.20224BAB211025).
文摘We present a qubit-loss-free(QLF)fusion scheme for generating large-scale atom W states in cavity quantum electrodynamics(QED)system.Compared to the most current fusion schemes which are conditioned on the case where one particle can be extracted from each initial W state to the fusion process,our scheme will access one or two particles from each W state.Based on the atom–cavity-field detuned interaction,three jWin+m+t states can be generated from the jWin,jWim,and jWit states with the help of two auxiliary atoms,and three jWin+m+t+q states can be generated from jWin,jWim,jWit,and a jWiq state with the help of three auxiliary atoms.Comparing the numerical simulations of the resource cost of fusing three small-size W states based on the previous schemes,our fusion scheme seems to be more efficient.This QLF fusion scheme can be generalized to the case of fusing k different or identical particle W states.Furthermore,with no qubit loss,it greatly reduces the number of fusion steps and prepares W states with larger particle numbers.
文摘随着新能源汽车保有量的快速增长,动力电池荷电状态(State of Charge,SOC)的精确估算已成为电池管理系统(Battery Management System,BMS)的核心技术瓶颈。传统估算法在复杂工况波动、宽温域环境及电池老化场景中易出现累积误差与精度衰减,直接影响车辆续航里程可信度及电池安全边界控制。因此,本文探索构建具备强环境适应性的SOC估算模型,设计高低温台架测试与实车验证。研究证实,优化策略能有效提升极端工况下SOC估算的稳定性,显著改善电量预警的可靠性。
