Recognizing human interactions in RGB videos is a critical task in computer vision,with applications in video surveillance.Existing deep learning-based architectures have achieved strong results,but are computationall...Recognizing human interactions in RGB videos is a critical task in computer vision,with applications in video surveillance.Existing deep learning-based architectures have achieved strong results,but are computationally intensive,sensitive to video resolution changes and often fail in crowded scenes.We propose a novel hybrid system that is computationally efficient,robust to degraded video quality and able to filter out irrelevant individuals,making it suitable for real-life use.The system leverages multi-modal handcrafted features for interaction representation and a deep learning classifier for capturing complex dependencies.Using Mask R-CNN and YOLO11-Pose,we extract grayscale silhouettes and keypoint coordinates of interacting individuals,while filtering out irrelevant individuals using a proposed algorithm.From these,we extract silhouette-based features(local ternary pattern and histogram of optical flow)and keypoint-based features(distances,angles and velocities)that capture distinct spatial and temporal information.A Bidirectional Long Short-Term Memory network(BiLSTM)then classifies the interactions.Extensive experiments on the UT Interaction,SBU Kinect Interaction and the ISR-UOL 3D social activity datasets demonstrate that our system achieves competitive accuracy.They also validate the effectiveness of the chosen features and classifier,along with the proposed system’s computational efficiency and robustness to occlusion.展开更多
光伏发电功率的准确预测对于优化能源管理和电网规划及优化调度具有重要的意义。针对以往光伏发电功率预测方法预测精度不高,传统混合网络模型存在参数选择不确定性和收敛速度较慢的问题,基于历史气象数据和光伏发电数据,提出一种结合...光伏发电功率的准确预测对于优化能源管理和电网规划及优化调度具有重要的意义。针对以往光伏发电功率预测方法预测精度不高,传统混合网络模型存在参数选择不确定性和收敛速度较慢的问题,基于历史气象数据和光伏发电数据,提出一种结合向量加权平均(weighted mean of vectors,INFO)算法、卷积网络(convolutional neural network,CNN)和双向长短期记忆网络(bi-directional long short-term memory,BiLSTM)的光伏发电功率预测方法。首先,选取与光伏发电功率预测相关的多种气象因素,含太阳辐射、温度、湿度、风速、气压等气象参数,并分析它们与光伏发电功率之间的关系,然后使用INFO算法对CNNBiLSTM混合网络预测模型的隐藏层节点数、初始学习率和L2正则化系数进行优化,INFO算法通过自适应调整这些参数,缩短了手动调制参数的时间,提高了超参数设置的精度和效率。实验结果表明,通过INFO算法优化的CNN-BiLSTM混合网络相比传统CNN-BiLSTM混合网络具有更高的预测精度。展开更多
超短期电力负荷预测作为电力系统的基本组成,能为生产调度计划的制定提供重要依据。然而,电力负荷具有非线性、时变性和不确定性,充分挖掘其潜在特征并分别预测,是提升预测准确性的关键。提出一种基于自适应局部迭代滤波(adaptive local...超短期电力负荷预测作为电力系统的基本组成,能为生产调度计划的制定提供重要依据。然而,电力负荷具有非线性、时变性和不确定性,充分挖掘其潜在特征并分别预测,是提升预测准确性的关键。提出一种基于自适应局部迭代滤波(adaptive local iterative filtering,ALIF)的BiGRU-Attention-XGBoost电力负荷组合预测模型。该模型基于ALIF-SE实现将历史负荷序列分解重组为周期序列、波动序列和趋势序列;通过Attention机制对BiGRU模型进行改进,并结合XGBoost模型构建基于时变权重组合的电力负荷预测模型。实验分析表明,输入模型数据经过ALIF-SE处理后预测精度有明显提升;所提组合模型在工作日和节假日均具有较好的预测效果,预测误差大部分在5%以下;通过在不同负荷数据集下进行实验对比,验证了所提预测方法的可迁移性。实验结果证明,所提模型具有有效性、准确性和可行性。展开更多
This paper proposes Flex-QUIC,an AIempowered quick UDP Internet connections(QUIC)enhancement framework that addresses the challenge of degraded transmission efficiency caused by the static parameterization of acknowle...This paper proposes Flex-QUIC,an AIempowered quick UDP Internet connections(QUIC)enhancement framework that addresses the challenge of degraded transmission efficiency caused by the static parameterization of acknowledgment(ACK)mechanisms,loss detection,and forward error correction(FEC)in dynamic wireless networks.Unlike the standard QUIC protocol,Flex-QUIC systematically integrates machine learning across three critical modules to achieve high-efficiency operation.First,a contextual multi-armed bandit-based ACK adaptation mechanism optimizes the ACK ratio to reduce wireless channel contention.Second,the adaptive loss detection module utilizes a long short-term memory(LSTM)model to predict the reordering displacement for optimizing the packet reordering tolerance.Third,the FEC transmission scheme jointly adjusts the redundancy level based on the LSTM-predicted loss rate and congestion window state.Extensive evaluations across Wi-Fi,5G,and satellite network scenarios demonstrate that Flex-QUIC significantly improves throughput and latency reduction compared to the standard QUIC and other enhanced QUIC variants,highlighting its adaptability to diverse and dynamic network conditions.Finally,we further discuss open issues in deploying AI-native transport protocols.展开更多
基金supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Recognizing human interactions in RGB videos is a critical task in computer vision,with applications in video surveillance.Existing deep learning-based architectures have achieved strong results,but are computationally intensive,sensitive to video resolution changes and often fail in crowded scenes.We propose a novel hybrid system that is computationally efficient,robust to degraded video quality and able to filter out irrelevant individuals,making it suitable for real-life use.The system leverages multi-modal handcrafted features for interaction representation and a deep learning classifier for capturing complex dependencies.Using Mask R-CNN and YOLO11-Pose,we extract grayscale silhouettes and keypoint coordinates of interacting individuals,while filtering out irrelevant individuals using a proposed algorithm.From these,we extract silhouette-based features(local ternary pattern and histogram of optical flow)and keypoint-based features(distances,angles and velocities)that capture distinct spatial and temporal information.A Bidirectional Long Short-Term Memory network(BiLSTM)then classifies the interactions.Extensive experiments on the UT Interaction,SBU Kinect Interaction and the ISR-UOL 3D social activity datasets demonstrate that our system achieves competitive accuracy.They also validate the effectiveness of the chosen features and classifier,along with the proposed system’s computational efficiency and robustness to occlusion.
文摘光伏发电功率的准确预测对于优化能源管理和电网规划及优化调度具有重要的意义。针对以往光伏发电功率预测方法预测精度不高,传统混合网络模型存在参数选择不确定性和收敛速度较慢的问题,基于历史气象数据和光伏发电数据,提出一种结合向量加权平均(weighted mean of vectors,INFO)算法、卷积网络(convolutional neural network,CNN)和双向长短期记忆网络(bi-directional long short-term memory,BiLSTM)的光伏发电功率预测方法。首先,选取与光伏发电功率预测相关的多种气象因素,含太阳辐射、温度、湿度、风速、气压等气象参数,并分析它们与光伏发电功率之间的关系,然后使用INFO算法对CNNBiLSTM混合网络预测模型的隐藏层节点数、初始学习率和L2正则化系数进行优化,INFO算法通过自适应调整这些参数,缩短了手动调制参数的时间,提高了超参数设置的精度和效率。实验结果表明,通过INFO算法优化的CNN-BiLSTM混合网络相比传统CNN-BiLSTM混合网络具有更高的预测精度。
文摘超短期电力负荷预测作为电力系统的基本组成,能为生产调度计划的制定提供重要依据。然而,电力负荷具有非线性、时变性和不确定性,充分挖掘其潜在特征并分别预测,是提升预测准确性的关键。提出一种基于自适应局部迭代滤波(adaptive local iterative filtering,ALIF)的BiGRU-Attention-XGBoost电力负荷组合预测模型。该模型基于ALIF-SE实现将历史负荷序列分解重组为周期序列、波动序列和趋势序列;通过Attention机制对BiGRU模型进行改进,并结合XGBoost模型构建基于时变权重组合的电力负荷预测模型。实验分析表明,输入模型数据经过ALIF-SE处理后预测精度有明显提升;所提组合模型在工作日和节假日均具有较好的预测效果,预测误差大部分在5%以下;通过在不同负荷数据集下进行实验对比,验证了所提预测方法的可迁移性。实验结果证明,所提模型具有有效性、准确性和可行性。
基金supported in part by the National Key R&D Program of China with Grant number 2019YFB1803400.
文摘This paper proposes Flex-QUIC,an AIempowered quick UDP Internet connections(QUIC)enhancement framework that addresses the challenge of degraded transmission efficiency caused by the static parameterization of acknowledgment(ACK)mechanisms,loss detection,and forward error correction(FEC)in dynamic wireless networks.Unlike the standard QUIC protocol,Flex-QUIC systematically integrates machine learning across three critical modules to achieve high-efficiency operation.First,a contextual multi-armed bandit-based ACK adaptation mechanism optimizes the ACK ratio to reduce wireless channel contention.Second,the adaptive loss detection module utilizes a long short-term memory(LSTM)model to predict the reordering displacement for optimizing the packet reordering tolerance.Third,the FEC transmission scheme jointly adjusts the redundancy level based on the LSTM-predicted loss rate and congestion window state.Extensive evaluations across Wi-Fi,5G,and satellite network scenarios demonstrate that Flex-QUIC significantly improves throughput and latency reduction compared to the standard QUIC and other enhanced QUIC variants,highlighting its adaptability to diverse and dynamic network conditions.Finally,we further discuss open issues in deploying AI-native transport protocols.
