In this paper,we focus on combining the theories of fuzzy soft sets with Γ-modules,and establishing a new framework for fuzzy soft Γ-submodules.The main contributions of the paper are 3-fold.First,we present the con...In this paper,we focus on combining the theories of fuzzy soft sets with Γ-modules,and establishing a new framework for fuzzy soft Γ-submodules.The main contributions of the paper are 3-fold.First,we present the concepts of（R,S）-bi-Γ-submodules,quasi-Γ-submodules and regular Γ-modules.Meanwhile,some illustrative examples are given to show the rationality of the definitions introduced in this paper.Second,several new kinds of generalized fuzzy soft Γ-submodules are proposed,and related properties and mutual relationships are also investigated.Third,we discover some intrinsic connections between the generalized fuzzy soft Γ-submodules presented in this paper and crisp Γ-submodules,and describe the relationships between regular Γ-modules and the generalized fuzzy soft Γ-submodules presented in this paper.展开更多
光伏故障检测对光伏电站智能运维具有重要意义。针对光伏组件红外图像中热斑目标小、难检测的问题,研究了基于改进Faster R CNN的光伏组件红外热斑故障检测模型。将Swin Transformer作为Faster R CNN模型中的特征提取模块,捕获图像的全...光伏故障检测对光伏电站智能运维具有重要意义。针对光伏组件红外图像中热斑目标小、难检测的问题,研究了基于改进Faster R CNN的光伏组件红外热斑故障检测模型。将Swin Transformer作为Faster R CNN模型中的特征提取模块,捕获图像的全局信息,建立特征之间的依赖关系,提高模型的建模能力;进一步利用BiFPN进行特征融合,改善了热斑故障由于目标小和特征不明显容易被模型忽略掉的问题;同时为了抑制光伏红外图像中背景和噪声的干扰,加入轻量级注意力模块CBAM,使模型更加关注重要通道和关键区域,提高对热斑故障检测精度。在自建光伏组件图像数据集上进行实验,热斑故障检测精度高达915,验证了本文模型对光伏组件热斑故障检测的有效性。展开更多
针对RGB(Red Green Blue)模态与热度模态信息表征形式不一致,特征信息无法有效挖掘、融合问题,提出了一种新的联合注意力强化网络-FCNet(Feature Sharpening and Cross-modal Feature Fusion Net)。首先,通过双维度注意力机制提升图像...针对RGB(Red Green Blue)模态与热度模态信息表征形式不一致,特征信息无法有效挖掘、融合问题,提出了一种新的联合注意力强化网络-FCNet(Feature Sharpening and Cross-modal Feature Fusion Net)。首先,通过双维度注意力机制提升图像特征映射能力;然后,利用跨模态特征融合机制捕获目标区域;最后,利用逐层解码结构消除背景干扰,优化检测目标。实验结果表明,该优化改进算法运算参数更少、运算时间更短,且模型整体检测性能均优于现有多模态检测模型性能。展开更多
基金Supported by the National Natural Science Foundation of China (61175055)the Innovation Term of Higher Education of Hubei Province,China (T201109)+1 种基金the Natural Science Foundation of Hubei Province (2012FFB01101)the Natural Science Foundation of Education Committee of Hubei Province (D20131903)
文摘In this paper,we focus on combining the theories of fuzzy soft sets with Γ-modules,and establishing a new framework for fuzzy soft Γ-submodules.The main contributions of the paper are 3-fold.First,we present the concepts of（R,S）-bi-Γ-submodules,quasi-Γ-submodules and regular Γ-modules.Meanwhile,some illustrative examples are given to show the rationality of the definitions introduced in this paper.Second,several new kinds of generalized fuzzy soft Γ-submodules are proposed,and related properties and mutual relationships are also investigated.Third,we discover some intrinsic connections between the generalized fuzzy soft Γ-submodules presented in this paper and crisp Γ-submodules,and describe the relationships between regular Γ-modules and the generalized fuzzy soft Γ-submodules presented in this paper.
文摘光伏故障检测对光伏电站智能运维具有重要意义。针对光伏组件红外图像中热斑目标小、难检测的问题,研究了基于改进Faster R CNN的光伏组件红外热斑故障检测模型。将Swin Transformer作为Faster R CNN模型中的特征提取模块,捕获图像的全局信息,建立特征之间的依赖关系,提高模型的建模能力;进一步利用BiFPN进行特征融合,改善了热斑故障由于目标小和特征不明显容易被模型忽略掉的问题;同时为了抑制光伏红外图像中背景和噪声的干扰,加入轻量级注意力模块CBAM,使模型更加关注重要通道和关键区域,提高对热斑故障检测精度。在自建光伏组件图像数据集上进行实验,热斑故障检测精度高达915,验证了本文模型对光伏组件热斑故障检测的有效性。
文摘针对RGB(Red Green Blue)模态与热度模态信息表征形式不一致,特征信息无法有效挖掘、融合问题,提出了一种新的联合注意力强化网络-FCNet(Feature Sharpening and Cross-modal Feature Fusion Net)。首先,通过双维度注意力机制提升图像特征映射能力;然后,利用跨模态特征融合机制捕获目标区域;最后,利用逐层解码结构消除背景干扰,优化检测目标。实验结果表明,该优化改进算法运算参数更少、运算时间更短,且模型整体检测性能均优于现有多模态检测模型性能。
