为了提高多场景应用的技术经济性,本文对电池储能系统状态估计进行了综述。首先,分析了电池性能衰减的机理,介绍了目前常用的物理建模和数据建模方法,进而对荷电状态(state of charge,SOC)和健康状态(state of health,SOH)进行了定义与...为了提高多场景应用的技术经济性,本文对电池储能系统状态估计进行了综述。首先,分析了电池性能衰减的机理,介绍了目前常用的物理建模和数据建模方法,进而对荷电状态(state of charge,SOC)和健康状态(state of health,SOH)进行了定义与关联性分析,并对电池及其系统的状态估计方法进行了汇总;其次,为了获取更多精确的电池运行数据,重点介绍了能够刻画电池内部演化机理的原位/非原位表征技术,进而分析了嵌入式电池管理系统(battery management system,BMS)实际应用的主流开发路线;第三,提出了基于联邦学习的电池储能系统状态估计方法,基于轻量化模型在本地进行电池储能系统SOC的估计以保证控制实时性,基于大数据驱动策略在云中心进行其SOH估计以保证容量可信度,由此实现云边的交互与协同;最后,对电池储能系统未来可能的发展方向和研究重点进行了预测。研究结果表明:活性锂损失是锂离子电池容量衰退的主要原因,高温、低温、过充放等滥用也会加速电池性能衰减;数据驱动在电池系统级建模与状态评估方面具有较大优势;利用原位/非原位表征技术可以获取更多的电池内部状态数据,基于FPGA的BMS轻量化建模更易实现,基于联邦学习的状态评估方法能够提高电池储能系统的智慧化运维水平。展开更多
在小样本分类任务中,每个类别可供训练的样本数量非常有限.因此在特征空间中同类样本分布稀疏,异类样本间边界模糊.提出一种新的基于特征变换和度量网络(Feature transformation and metric networks,FTMN)的小样本学习算法用于小样本...在小样本分类任务中,每个类别可供训练的样本数量非常有限.因此在特征空间中同类样本分布稀疏,异类样本间边界模糊.提出一种新的基于特征变换和度量网络(Feature transformation and metric networks,FTMN)的小样本学习算法用于小样本分类任务.算法通过嵌入函数将样本映射到特征空间,并计算输入该样本与所属类别中心的特征残差.构造一个特征变换函数对该残差进行学习,使特征空间内的样本特征经过该函数后向同类样本中心靠拢.利用变换后的样本特征更新类别中心,使各类别中心间的距离增大.算法进一步构造了一种新的度量函数,对样本特征中每个局部特征点的度量距离进行联合表达,该函数能够同时对样本特征间的夹角和欧氏距离进行优化.算法在小样本分类任务常用数据集上的优秀表现证明了算法的有效性和泛化性.展开更多
Person image generation aims to generate images that maintain the original human appearance in different target poses.Recent works have revealed that the critical element in achieving this task is the alignment of app...Person image generation aims to generate images that maintain the original human appearance in different target poses.Recent works have revealed that the critical element in achieving this task is the alignment of appearance domain and pose domain.Previous alignment methods,such as appearance flow warping,correspondence learning and cross attention,often encounter challenges when it comes to producing fine texture details.These approaches suffer from limitations in accurately estimating appearance flows due to the lack of global receptive field.Alternatively,they can only perform cross-domain alignment on high-level feature maps with small spatial dimensions since the computational complexity increases quadratically with larger feature sizes.In this article,the significance of multi-scale alignment,in both low-level and high-level domains,for ensuring reliable cross-domain alignment of appearance and pose is demonstrated.To this end,a novel and effective method,named Multi-scale Crossdomain Alignment(MCA)is proposed.Firstly,MCA adopts global context aggregation transformer to model multi-scale interaction between pose and appearance inputs,which employs pair-wise window-based cross attention.Furthermore,leveraging the integrated global source information for each target position,MCA applies flexible flow prediction head and point correlation to effectively conduct warping and fusing for final transformed person image generation.Our proposed MCA achieves superior performance on two popular datasets than other methods,which verifies the effectiveness of our approach.展开更多
Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have b...Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.展开更多
Background External knowledge representations play an essential role in knowledge-based visual question and answering to better understand complex scenarios in the open world.Recent entity-relationship embedding appro...Background External knowledge representations play an essential role in knowledge-based visual question and answering to better understand complex scenarios in the open world.Recent entity-relationship embedding approaches are deficient in representing some complex relations,resulting in a lack of topic-related knowledge and redundancy in topic-irrelevant information.Methods To this end,we propose MKEAH:Multimodal Knowledge Extraction and Accumulation on Hyperplanes.To ensure that the lengths of the feature vectors projected onto the hyperplane compare equally and to filter out sufficient topic-irrelevant information,two losses are proposed to learn the triplet representations from the complementary views:range loss and orthogonal loss.To interpret the capability of extracting topic-related knowledge,we present the Topic Similarity(TS)between topic and entity-relations.Results Experimental results demonstrate the effectiveness of hyperplane embedding for knowledge representation in knowledge-based visual question answering.Our model outperformed state-of-the-art methods by 2.12%and 3.24%on two challenging knowledge-request datasets:OK-VQA and KRVQA,respectively.Conclusions The obvious advantages of our model in TS show that using hyperplane embedding to represent multimodal knowledge can improve its ability to extract topic-related knowledge.展开更多
文摘为了提高多场景应用的技术经济性,本文对电池储能系统状态估计进行了综述。首先,分析了电池性能衰减的机理,介绍了目前常用的物理建模和数据建模方法,进而对荷电状态(state of charge,SOC)和健康状态(state of health,SOH)进行了定义与关联性分析,并对电池及其系统的状态估计方法进行了汇总;其次,为了获取更多精确的电池运行数据,重点介绍了能够刻画电池内部演化机理的原位/非原位表征技术,进而分析了嵌入式电池管理系统(battery management system,BMS)实际应用的主流开发路线;第三,提出了基于联邦学习的电池储能系统状态估计方法,基于轻量化模型在本地进行电池储能系统SOC的估计以保证控制实时性,基于大数据驱动策略在云中心进行其SOH估计以保证容量可信度,由此实现云边的交互与协同;最后,对电池储能系统未来可能的发展方向和研究重点进行了预测。研究结果表明:活性锂损失是锂离子电池容量衰退的主要原因,高温、低温、过充放等滥用也会加速电池性能衰减;数据驱动在电池系统级建模与状态评估方面具有较大优势;利用原位/非原位表征技术可以获取更多的电池内部状态数据,基于FPGA的BMS轻量化建模更易实现,基于联邦学习的状态评估方法能够提高电池储能系统的智慧化运维水平。
文摘在小样本分类任务中,每个类别可供训练的样本数量非常有限.因此在特征空间中同类样本分布稀疏,异类样本间边界模糊.提出一种新的基于特征变换和度量网络(Feature transformation and metric networks,FTMN)的小样本学习算法用于小样本分类任务.算法通过嵌入函数将样本映射到特征空间,并计算输入该样本与所属类别中心的特征残差.构造一个特征变换函数对该残差进行学习,使特征空间内的样本特征经过该函数后向同类样本中心靠拢.利用变换后的样本特征更新类别中心,使各类别中心间的距离增大.算法进一步构造了一种新的度量函数,对样本特征中每个局部特征点的度量距离进行联合表达,该函数能够同时对样本特征间的夹角和欧氏距离进行优化.算法在小样本分类任务常用数据集上的优秀表现证明了算法的有效性和泛化性.
基金National Natural Science Foundation of China,Grant/Award Number:62274142Hangzhou Major Technology Innovation Project of Artificial Intelligence,Grant/Award Number:2022AIZD0060。
文摘Person image generation aims to generate images that maintain the original human appearance in different target poses.Recent works have revealed that the critical element in achieving this task is the alignment of appearance domain and pose domain.Previous alignment methods,such as appearance flow warping,correspondence learning and cross attention,often encounter challenges when it comes to producing fine texture details.These approaches suffer from limitations in accurately estimating appearance flows due to the lack of global receptive field.Alternatively,they can only perform cross-domain alignment on high-level feature maps with small spatial dimensions since the computational complexity increases quadratically with larger feature sizes.In this article,the significance of multi-scale alignment,in both low-level and high-level domains,for ensuring reliable cross-domain alignment of appearance and pose is demonstrated.To this end,a novel and effective method,named Multi-scale Crossdomain Alignment(MCA)is proposed.Firstly,MCA adopts global context aggregation transformer to model multi-scale interaction between pose and appearance inputs,which employs pair-wise window-based cross attention.Furthermore,leveraging the integrated global source information for each target position,MCA applies flexible flow prediction head and point correlation to effectively conduct warping and fusing for final transformed person image generation.Our proposed MCA achieves superior performance on two popular datasets than other methods,which verifies the effectiveness of our approach.
基金the National Natural Science Foundation of China(62003298,62163036)the Major Project of Science and Technology of Yunnan Province(202202AD080005,202202AH080009)the Yunnan University Professional Degree Graduate Practice Innovation Fund Project(ZC-22222770)。
文摘Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.
基金Supported by National Nature Science Foudation of China(61976160,61906137,61976158,62076184,62076182)Shanghai Science and Technology Plan Project(21DZ1204800)。
文摘Background External knowledge representations play an essential role in knowledge-based visual question and answering to better understand complex scenarios in the open world.Recent entity-relationship embedding approaches are deficient in representing some complex relations,resulting in a lack of topic-related knowledge and redundancy in topic-irrelevant information.Methods To this end,we propose MKEAH:Multimodal Knowledge Extraction and Accumulation on Hyperplanes.To ensure that the lengths of the feature vectors projected onto the hyperplane compare equally and to filter out sufficient topic-irrelevant information,two losses are proposed to learn the triplet representations from the complementary views:range loss and orthogonal loss.To interpret the capability of extracting topic-related knowledge,we present the Topic Similarity(TS)between topic and entity-relations.Results Experimental results demonstrate the effectiveness of hyperplane embedding for knowledge representation in knowledge-based visual question answering.Our model outperformed state-of-the-art methods by 2.12%and 3.24%on two challenging knowledge-request datasets:OK-VQA and KRVQA,respectively.Conclusions The obvious advantages of our model in TS show that using hyperplane embedding to represent multimodal knowledge can improve its ability to extract topic-related knowledge.
