Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational s...Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational scenarios,considering the large amount of historical operational snapshot data.Specifically,DTSAs analyse the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios.A Gramian angular summation field-based operational scenario image encoder was designed to convert operational scenario sequences into highdimensional spaces.This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models.The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots.Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional feature-screening methods.In addition,experiments with different new energy access ratios were conducted to verify the robustness of the proposed method.DTSAs enable dispatchers to master the operation experience of the power system in advance,and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy.展开更多
In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinfor...In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes. We introduce features of the states of the original problem, and we formulate a smaller "aggregate" Markov decision problem, whose states relate to the features. We discuss properties and possible implementations of this type of aggregation, including a new approach to approximate policy iteration. In this approach the policy improvement operation combines feature-based aggregation with feature construction using deep neural networks or other calculations. We argue that the cost function of a policy may be approximated much more accurately by the nonlinear function of the features provided by aggregation, than by the linear function of the features provided by neural networkbased reinforcement learning, thereby potentially leading to more effective policy improvement.展开更多
Rain streaks introduced by atmospheric precipitation significantly degrade image quality and impair the reliability of high-level vision tasks.We present a novel image deraining framework built on a three-stage dual-r...Rain streaks introduced by atmospheric precipitation significantly degrade image quality and impair the reliability of high-level vision tasks.We present a novel image deraining framework built on a three-stage dual-residual architecture that progressively restores rain-degraded content while preserving fine structural details.Each stage begins with a multi-scale feature extractor and a channel attention module that adaptively emphasizes informative representations for rain removal.The core restoration is achieved via enhanced dual-residual blocks,which stabilize training and mitigate feature degradation across layers.To further refine representations,we integrate crossdimensional spatial attention supervised by ground-truth guidance,ensuring that only high-quality features propagate to subsequent stages.Inter-stage feature fusion modules are employed to aggregate complementary information,reinforcing reconstruction continuity and consistency.Extensive experiments on five benchmark datasets(Rain100H,Rain100L,RainKITTI2012,RainKITTI2015,and JRSRD)demonstrate that our method establishes new state-of-the-art results in both fidelity and perceptual quality,effectively removing rain streaks while preserving natural textures and structural integrity.展开更多
基于深度学习的网络攻击检测是对欧几里得数据进行建模,无法学习攻击数据中的结构特征。为此,提出一种基于改进图采样与聚合(graph sample and aggregate,GraphSAGE)的网络攻击检测算法。首先,将攻击数据从平面结构转换为图结构数据。其...基于深度学习的网络攻击检测是对欧几里得数据进行建模,无法学习攻击数据中的结构特征。为此,提出一种基于改进图采样与聚合(graph sample and aggregate,GraphSAGE)的网络攻击检测算法。首先,将攻击数据从平面结构转换为图结构数据。其次,对GraphSAGE算法进行了改进,包括在消息传递阶段融合节点和边的特征,同时在消息聚合过程中考虑不同源节点对目标节点的影响程度,并在边嵌入生成时引入残差学习机制。在两个公开网络攻击数据集上的实验结果表明,在二分类情况下,所提算法的总体性能优于E-GraphSAGE、LSTM、RNN、CNN算法;在多分类情况下,所提算法在大多数攻击类型上的F1值高于对比算法。展开更多
智能电网中,基于5G的挂轨巡检机器人可代替人力对电网设备进行智能高效地安全巡检,其在突破传统人工巡检限制的同时,也对室内定位算法的精度和稳定性提出了更高要求。针对传统室内定位方法定位精度低、稳定性差等问题,文章提出一种改进...智能电网中,基于5G的挂轨巡检机器人可代替人力对电网设备进行智能高效地安全巡检,其在突破传统人工巡检限制的同时,也对室内定位算法的精度和稳定性提出了更高要求。针对传统室内定位方法定位精度低、稳定性差等问题,文章提出一种改进的图采样与聚合(graph sample and aggregate,GraphSAGE)神经网络的定位方法。首先将多种射频信号的指纹数据转换为异构图形数据,输入至GraphSAGE神经网络中得到初始定位结果,再利用加权K近邻算法进行定位结果优化。实验结果证明,提出的改进的GraphSAGE神经网络定位算法有效提高了定位精度,且具有较高的系统稳定性。展开更多
基金The Key R&D Project of Jilin Province,Grant/Award Number:20230201067GX。
文摘Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational scenarios,considering the large amount of historical operational snapshot data.Specifically,DTSAs analyse the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios.A Gramian angular summation field-based operational scenario image encoder was designed to convert operational scenario sequences into highdimensional spaces.This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models.The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots.Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional feature-screening methods.In addition,experiments with different new energy access ratios were conducted to verify the robustness of the proposed method.DTSAs enable dispatchers to master the operation experience of the power system in advance,and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy.
文摘In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes. We introduce features of the states of the original problem, and we formulate a smaller "aggregate" Markov decision problem, whose states relate to the features. We discuss properties and possible implementations of this type of aggregation, including a new approach to approximate policy iteration. In this approach the policy improvement operation combines feature-based aggregation with feature construction using deep neural networks or other calculations. We argue that the cost function of a policy may be approximated much more accurately by the nonlinear function of the features provided by aggregation, than by the linear function of the features provided by neural networkbased reinforcement learning, thereby potentially leading to more effective policy improvement.
基金supported by Key Scientific and Technological Research Program of Henan Province(Grant No.252102211111).
文摘Rain streaks introduced by atmospheric precipitation significantly degrade image quality and impair the reliability of high-level vision tasks.We present a novel image deraining framework built on a three-stage dual-residual architecture that progressively restores rain-degraded content while preserving fine structural details.Each stage begins with a multi-scale feature extractor and a channel attention module that adaptively emphasizes informative representations for rain removal.The core restoration is achieved via enhanced dual-residual blocks,which stabilize training and mitigate feature degradation across layers.To further refine representations,we integrate crossdimensional spatial attention supervised by ground-truth guidance,ensuring that only high-quality features propagate to subsequent stages.Inter-stage feature fusion modules are employed to aggregate complementary information,reinforcing reconstruction continuity and consistency.Extensive experiments on five benchmark datasets(Rain100H,Rain100L,RainKITTI2012,RainKITTI2015,and JRSRD)demonstrate that our method establishes new state-of-the-art results in both fidelity and perceptual quality,effectively removing rain streaks while preserving natural textures and structural integrity.
文摘基于深度学习的网络攻击检测是对欧几里得数据进行建模,无法学习攻击数据中的结构特征。为此,提出一种基于改进图采样与聚合(graph sample and aggregate,GraphSAGE)的网络攻击检测算法。首先,将攻击数据从平面结构转换为图结构数据。其次,对GraphSAGE算法进行了改进,包括在消息传递阶段融合节点和边的特征,同时在消息聚合过程中考虑不同源节点对目标节点的影响程度,并在边嵌入生成时引入残差学习机制。在两个公开网络攻击数据集上的实验结果表明,在二分类情况下,所提算法的总体性能优于E-GraphSAGE、LSTM、RNN、CNN算法;在多分类情况下,所提算法在大多数攻击类型上的F1值高于对比算法。
文摘智能电网中,基于5G的挂轨巡检机器人可代替人力对电网设备进行智能高效地安全巡检,其在突破传统人工巡检限制的同时,也对室内定位算法的精度和稳定性提出了更高要求。针对传统室内定位方法定位精度低、稳定性差等问题,文章提出一种改进的图采样与聚合(graph sample and aggregate,GraphSAGE)神经网络的定位方法。首先将多种射频信号的指纹数据转换为异构图形数据,输入至GraphSAGE神经网络中得到初始定位结果,再利用加权K近邻算法进行定位结果优化。实验结果证明,提出的改进的GraphSAGE神经网络定位算法有效提高了定位精度,且具有较高的系统稳定性。
