随着光伏发电在全球能源体系中占比不断提升,超短期光伏发电量预测对电力系统调度与安全运行至关重要。然而,光伏发电量受多因素影响,具有显著随机性与波动性。为此,提出了一种基于TCN-BiLSTM-Attention模型的超短期光伏发电量预测方法...随着光伏发电在全球能源体系中占比不断提升,超短期光伏发电量预测对电力系统调度与安全运行至关重要。然而,光伏发电量受多因素影响,具有显著随机性与波动性。为此,提出了一种基于TCN-BiLSTM-Attention模型的超短期光伏发电量预测方法。首先通过皮尔逊相关分析筛选关键特征,并利用孤立森林算法检测异常值,结合线性插值法和标准化完成数据预处理。随后,通过时间卷积网络(Temporal Convolutional Network,TCN)提取时序特征,再利用双向长短期记忆网络(Bidirectional Long Short-Term Memory,BiLSTM)网络捕获前后向时间依赖关系,并在输出端引入注意力机制聚焦关键时间步特征。最后,在Desert Knowledge Australia Solar Centre(DKASC)数据集上的对比实验表明,与传统LSTM、BiLSTM模型相比,提出的TCN-BiLSTM-Attention模型在预测精度、稳定性等方面均表现出一定优势。展开更多
针对煤矿井下电控系统中DC-DC电源模块电容软故障类型多样、诊断精度不足的问题,提出了一种基于并行时序卷积网络(TCN)与图卷积网络(GCN)的融合模型。以150 W Boost型DC-DC电源为研究对象,采集电路中4个测点的电压信号。该模型通过TCN...针对煤矿井下电控系统中DC-DC电源模块电容软故障类型多样、诊断精度不足的问题,提出了一种基于并行时序卷积网络(TCN)与图卷积网络(GCN)的融合模型。以150 W Boost型DC-DC电源为研究对象,采集电路中4个测点的电压信号。该模型通过TCN捕获长时依赖特征,以GCN刻画测点拓扑关系;二者在特征层拼接,实现时间维与空间结构信息的互补融合。实验结果表明,该模型平均准确率达99.72%;在6 dB、4 dB、2 dB、0 dB信噪比条件下,准确率分别达到99.48%、98.54%、98.17%和93.78%,高于其他模型。该研究为煤矿井下电控设备中电容软故障的智能诊断提供了有效技术路径。展开更多
为更好地描述光伏出力不确定性,该文提出了一种基于时序卷积网络(temporal convolutional network,简称TCN)和双向长短期记忆(bidirectional long short term memory,简称BiLSTM)的光伏功率概率预测模型.首先,基于数值天气预报中的云量...为更好地描述光伏出力不确定性,该文提出了一种基于时序卷积网络(temporal convolutional network,简称TCN)和双向长短期记忆(bidirectional long short term memory,简称BiLSTM)的光伏功率概率预测模型.首先,基于数值天气预报中的云量和降雨量将历史数据集划分为晴天、多云天和阴雨天3种场景,生成具有相似天气类型的测试集和训练样本集:然后,应用TCN进行集成特征维度提取,利用BiLSTM神经网络建模进行输出功率和天气数据时间序列的双向拟合.针对传统区间预测分位数损失函数不可微的缺陷,引入Huber范数近似替代原损失函数,并应用梯度下降进行优化,构建改进的可微分位数回归(quantile regression,简称QR)模型,生成置信区间.最后,采用核密度估计(kerneldensity estimation,简称KDE)给出概率密度预测结果。以我国华东某地区分布式光伏电站作为研究对象,与现有概率预测方法相比,该文所提出的短期预测算法的功率区间各评价指标都有所改进,验证了所提方法的可靠性。展开更多
Digital twin technology is revolutionizing personalized healthcare by creating dynamic virtual replicas of individual patients.This paper presents a novel multi-modal architecture leveraging digital twins to enhance p...Digital twin technology is revolutionizing personalized healthcare by creating dynamic virtual replicas of individual patients.This paper presents a novel multi-modal architecture leveraging digital twins to enhance precision in predictive diagnostics and treatment planning of phoneme labeling.By integrating real-time images,electronic health records,and genomic information,the system enables personalized simulations for disease progression modeling,treatment response prediction,and preventive care strategies.In dysarthric speech,which is characterized by articulation imprecision,temporal misalignments,and phoneme distortions,existing models struggle to capture these irregularities.Traditional approaches,often relying solely on audio features,fail to address the full complexity of phoneme variations,leading to increased phoneme error rates(PER)and word error rates(WER).To overcome these challenges,we propose a novel multi-modal architecture that integrates both audio and articulatory data through a combination of Temporal Convolutional Networks(TCNs),Graph Convolutional Networks(GCNs),Transformer Encoders,and a cross-modal attention mechanism.The audio branch of the model utilizes TCNs and Transformer Encoders to capture both short-and long-term dependencies in the audio signal,while the articulatory branch leverages GCNs to model spatial relationships between articulators,such as the lips,jaw,and tongue,allowing the model to detect subtle articulatory imprecisions.A cross-modal attention mechanism fuses the encoded audio and articulatory features,enabling dynamic adjustment of the model’s focus depending on input quality,which significantly improves phoneme labeling accuracy.The proposed model consistently outperforms existing methods,achieving lower Phoneme Error Rates(PER),Word Error Rates(WER),and Articulatory Feature Misclassification Rates(AFMR).Specifically,across all datasets,the model achieves an average PER of 13.43%,an average WER of 21.67%,and an average AFMR of 12.73%.By capturing both the acoustic and articulatory intricacies of speech,this comprehensive approach not only improves phoneme labeling precision but also marks substantial progress in speech recognition technology for individuals with dysarthria.展开更多
文摘随着光伏发电在全球能源体系中占比不断提升,超短期光伏发电量预测对电力系统调度与安全运行至关重要。然而,光伏发电量受多因素影响,具有显著随机性与波动性。为此,提出了一种基于TCN-BiLSTM-Attention模型的超短期光伏发电量预测方法。首先通过皮尔逊相关分析筛选关键特征,并利用孤立森林算法检测异常值,结合线性插值法和标准化完成数据预处理。随后,通过时间卷积网络(Temporal Convolutional Network,TCN)提取时序特征,再利用双向长短期记忆网络(Bidirectional Long Short-Term Memory,BiLSTM)网络捕获前后向时间依赖关系,并在输出端引入注意力机制聚焦关键时间步特征。最后,在Desert Knowledge Australia Solar Centre(DKASC)数据集上的对比实验表明,与传统LSTM、BiLSTM模型相比,提出的TCN-BiLSTM-Attention模型在预测精度、稳定性等方面均表现出一定优势。
文摘为更好地描述光伏出力不确定性,该文提出了一种基于时序卷积网络(temporal convolutional network,简称TCN)和双向长短期记忆(bidirectional long short term memory,简称BiLSTM)的光伏功率概率预测模型.首先,基于数值天气预报中的云量和降雨量将历史数据集划分为晴天、多云天和阴雨天3种场景,生成具有相似天气类型的测试集和训练样本集:然后,应用TCN进行集成特征维度提取,利用BiLSTM神经网络建模进行输出功率和天气数据时间序列的双向拟合.针对传统区间预测分位数损失函数不可微的缺陷,引入Huber范数近似替代原损失函数,并应用梯度下降进行优化,构建改进的可微分位数回归(quantile regression,简称QR)模型,生成置信区间.最后,采用核密度估计(kerneldensity estimation,简称KDE)给出概率密度预测结果。以我国华东某地区分布式光伏电站作为研究对象,与现有概率预测方法相比,该文所提出的短期预测算法的功率区间各评价指标都有所改进,验证了所提方法的可靠性。
基金funded by the Ongoing Research Funding program(ORF-2025-867),King Saud University,Riyadh,Saudi Arabia.
文摘Digital twin technology is revolutionizing personalized healthcare by creating dynamic virtual replicas of individual patients.This paper presents a novel multi-modal architecture leveraging digital twins to enhance precision in predictive diagnostics and treatment planning of phoneme labeling.By integrating real-time images,electronic health records,and genomic information,the system enables personalized simulations for disease progression modeling,treatment response prediction,and preventive care strategies.In dysarthric speech,which is characterized by articulation imprecision,temporal misalignments,and phoneme distortions,existing models struggle to capture these irregularities.Traditional approaches,often relying solely on audio features,fail to address the full complexity of phoneme variations,leading to increased phoneme error rates(PER)and word error rates(WER).To overcome these challenges,we propose a novel multi-modal architecture that integrates both audio and articulatory data through a combination of Temporal Convolutional Networks(TCNs),Graph Convolutional Networks(GCNs),Transformer Encoders,and a cross-modal attention mechanism.The audio branch of the model utilizes TCNs and Transformer Encoders to capture both short-and long-term dependencies in the audio signal,while the articulatory branch leverages GCNs to model spatial relationships between articulators,such as the lips,jaw,and tongue,allowing the model to detect subtle articulatory imprecisions.A cross-modal attention mechanism fuses the encoded audio and articulatory features,enabling dynamic adjustment of the model’s focus depending on input quality,which significantly improves phoneme labeling accuracy.The proposed model consistently outperforms existing methods,achieving lower Phoneme Error Rates(PER),Word Error Rates(WER),and Articulatory Feature Misclassification Rates(AFMR).Specifically,across all datasets,the model achieves an average PER of 13.43%,an average WER of 21.67%,and an average AFMR of 12.73%.By capturing both the acoustic and articulatory intricacies of speech,this comprehensive approach not only improves phoneme labeling precision but also marks substantial progress in speech recognition technology for individuals with dysarthria.
