Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management systems.Deep learning-based methods have been shown to be effective in predicting RUL by leveraging batter...Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management systems.Deep learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series data.However,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be improved.To address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL prediction.The MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level features.Additionally,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL predictions.The experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation process.Our code and dataset are available at the website of github.展开更多
大语言模型(large language model,LLM)通过处理和理解自然语言数据,实现高质量的信息检索、知识提取等功能,为中医药研究提供了新机遇。基于中医药大模型发展现状,梳理了LLM开发过程中的数据存储与处理方法,概述了检索增强生成、混合...大语言模型(large language model,LLM)通过处理和理解自然语言数据,实现高质量的信息检索、知识提取等功能,为中医药研究提供了新机遇。基于中医药大模型发展现状,梳理了LLM开发过程中的数据存储与处理方法,概述了检索增强生成、混合专家模型、人类反馈强化学习、知识蒸馏等人工智能方法,归纳了LLM训练微调与性能评价方法。针对中医药数据的特点,从高质量数据集构建、多领域专家系统融合、信息快速提取、训练与调优等方面入手,提出了中医药LLM的构建策略,并分析了LLM在中医药领域的具体应用场景,为中医药领域LLM的构建和应用提供参考,推动中医药现代化和智能化发展。展开更多
深度学习模型已被广泛应用于超短期风电功率预测。对黑盒深度学习模型预测过程的决策逻辑进行解释和分析,有利于提升预测模型和预测结果的可信度,减小不确定性造成的电力系统运行风险。对此,文章针对经典的长短期记忆网络(long short-te...深度学习模型已被广泛应用于超短期风电功率预测。对黑盒深度学习模型预测过程的决策逻辑进行解释和分析,有利于提升预测模型和预测结果的可信度,减小不确定性造成的电力系统运行风险。对此,文章针对经典的长短期记忆网络(long short-term memory,LSTM)超短期风电功率预测模型,提出了一种基于决策树混合专家模型(decision tree mixture of experts,DTMOE)的模块化代理解释方法。将LSTM模型内部的预测过程分解为两个相对独立的模块,采用DTMOE分别对两个模块的输入输出进行拟合,通过分析DTMOE的拟合结果对LSTM模型的预测过程和逻辑进行映射解析。算例分析表明,DTMOE模型对原始黑盒模型有较高的拟合精度与可解释性能力;DTMOE模型的可视化结果可以解析和展现LSTM模型预测过程的决策路径以及关键影响特征。展开更多
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.62102191,61872114,and 61871020).
文摘Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management systems.Deep learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series data.However,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be improved.To address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL prediction.The MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level features.Additionally,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL predictions.The experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation process.Our code and dataset are available at the website of github.
文摘大语言模型(large language model,LLM)通过处理和理解自然语言数据,实现高质量的信息检索、知识提取等功能,为中医药研究提供了新机遇。基于中医药大模型发展现状,梳理了LLM开发过程中的数据存储与处理方法,概述了检索增强生成、混合专家模型、人类反馈强化学习、知识蒸馏等人工智能方法,归纳了LLM训练微调与性能评价方法。针对中医药数据的特点,从高质量数据集构建、多领域专家系统融合、信息快速提取、训练与调优等方面入手,提出了中医药LLM的构建策略,并分析了LLM在中医药领域的具体应用场景,为中医药领域LLM的构建和应用提供参考,推动中医药现代化和智能化发展。
文摘深度学习模型已被广泛应用于超短期风电功率预测。对黑盒深度学习模型预测过程的决策逻辑进行解释和分析,有利于提升预测模型和预测结果的可信度,减小不确定性造成的电力系统运行风险。对此,文章针对经典的长短期记忆网络(long short-term memory,LSTM)超短期风电功率预测模型,提出了一种基于决策树混合专家模型(decision tree mixture of experts,DTMOE)的模块化代理解释方法。将LSTM模型内部的预测过程分解为两个相对独立的模块,采用DTMOE分别对两个模块的输入输出进行拟合,通过分析DTMOE的拟合结果对LSTM模型的预测过程和逻辑进行映射解析。算例分析表明,DTMOE模型对原始黑盒模型有较高的拟合精度与可解释性能力;DTMOE模型的可视化结果可以解析和展现LSTM模型预测过程的决策路径以及关键影响特征。
