Emotion mismatch between training and testing is one of the important factors causing the performance degradation of speaker recognition system. In our previous work, a bi-model emotion speaker recognition (BESR) meth...Emotion mismatch between training and testing is one of the important factors causing the performance degradation of speaker recognition system. In our previous work, a bi-model emotion speaker recognition (BESR) method based on virtual HD (High Different from neutral, with large pitch offset) speech synthesizing was proposed to deal with this problem. It enhanced the system performance under mismatch emotion states in MASC, while still suffering the system risk introduced by fusing the scores from the unreliable VHD model and the neutral model with equal weight. In this paper, we propose a new BESR method based on score reliability fusion. Two strategies, by utilizing identification rate and scores average relative loss difference, are presented to estimate the weights for the two group scores. The results on both MASC and EPST shows that by using the weights generated by the two strategies, the BESR method achieve a better performance than that by using the equal weight, and the better one even achieves a result comparable to that by using the best weights selected by exhaustive strategy.展开更多
在遥感海浪数据质量控制研究中,由于数据的复杂与不规则性,传统质量控制方法对海浪数据单点异常值的检测具有一定局限性。深度学习具有强大的特征学习能力,在解决非线性复杂问题方面具有一定优势,将其应用在数据质量控制领域可以提高异...在遥感海浪数据质量控制研究中,由于数据的复杂与不规则性,传统质量控制方法对海浪数据单点异常值的检测具有一定局限性。深度学习具有强大的特征学习能力,在解决非线性复杂问题方面具有一定优势,将其应用在数据质量控制领域可以提高异常值检测能力。本研究采用遥感海浪有效波高数据,构建双向长短期记忆网络(bi-directional long short term memory,Bi-LSTM)模型对有效波高进行预测,结合阈值方法进行异常检测,与3σ准则法、孤立森林模型法、 LSTM模型法以及VAE-LSTM模型法进行异常检测精度比较,探究基于Bi-LSTM的质量控制模型在遥感海浪数据异常值检测方面的能力。试验结果表明,Bi-LSTM质量控制模型具有良好的异常值检测效果,其精准率、召回率、 F1分数和运行时间分别为91%、 93%、 92和3.35 s,综合评价效果最佳,可有效对遥感海浪数据进行质量控制。展开更多
【目的】锂离子电池作为电动汽车和储能系统的关键组件,其健康状态(state of health,SOH)的准确预测对于确保系统可靠性、延长电池寿命以及优化能源管理具有重要意义。然而,电池在实际运行时因受多因素影响而导致性能衰减。为此,提出一...【目的】锂离子电池作为电动汽车和储能系统的关键组件,其健康状态(state of health,SOH)的准确预测对于确保系统可靠性、延长电池寿命以及优化能源管理具有重要意义。然而,电池在实际运行时因受多因素影响而导致性能衰减。为此,提出一种基于电化学阻抗谱(electrochemical impedance spectroscopy,EIS)数据的高精度SOH预测方法。【方法】采用EIS数据进行实验,并利用线性Kramers-Kronig算法对EIS数据进行预处理。采用卷积神经网络(convolutional neural network,CNN)、双向长短时记忆(bi-directional long short-term memory,BiLSTM)网络、注意力(Attention)组合模型作为预测模型,将EIS数据的阻抗实部、虚部和模值作为输入,利用CNN从中提取出重要特征,并结合BiLSTM网络模型来预测SOH。此外,通过加入Attention机制和Dropout算法来优化模型。【结果】通过不同温度下锂离子电池SOH预测及与CNN-BiLSTM、BiLSTM模型的对比表明,CNNBiLSTM-Attention模型在25、35、45℃时SOH预测效果更优,精度较2个模型分别提升了46.1%和77.9%。【结论】基于EIS数据的SOH预测是可行的,CNN-BiLSTMAttention组合模型能够实现锂离子电池SOH的精准、高效预测,具有较强的实用性。展开更多
文摘Emotion mismatch between training and testing is one of the important factors causing the performance degradation of speaker recognition system. In our previous work, a bi-model emotion speaker recognition (BESR) method based on virtual HD (High Different from neutral, with large pitch offset) speech synthesizing was proposed to deal with this problem. It enhanced the system performance under mismatch emotion states in MASC, while still suffering the system risk introduced by fusing the scores from the unreliable VHD model and the neutral model with equal weight. In this paper, we propose a new BESR method based on score reliability fusion. Two strategies, by utilizing identification rate and scores average relative loss difference, are presented to estimate the weights for the two group scores. The results on both MASC and EPST shows that by using the weights generated by the two strategies, the BESR method achieve a better performance than that by using the equal weight, and the better one even achieves a result comparable to that by using the best weights selected by exhaustive strategy.
文摘在遥感海浪数据质量控制研究中,由于数据的复杂与不规则性,传统质量控制方法对海浪数据单点异常值的检测具有一定局限性。深度学习具有强大的特征学习能力,在解决非线性复杂问题方面具有一定优势,将其应用在数据质量控制领域可以提高异常值检测能力。本研究采用遥感海浪有效波高数据,构建双向长短期记忆网络(bi-directional long short term memory,Bi-LSTM)模型对有效波高进行预测,结合阈值方法进行异常检测,与3σ准则法、孤立森林模型法、 LSTM模型法以及VAE-LSTM模型法进行异常检测精度比较,探究基于Bi-LSTM的质量控制模型在遥感海浪数据异常值检测方面的能力。试验结果表明,Bi-LSTM质量控制模型具有良好的异常值检测效果,其精准率、召回率、 F1分数和运行时间分别为91%、 93%、 92和3.35 s,综合评价效果最佳,可有效对遥感海浪数据进行质量控制。
文摘【目的】锂离子电池作为电动汽车和储能系统的关键组件,其健康状态(state of health,SOH)的准确预测对于确保系统可靠性、延长电池寿命以及优化能源管理具有重要意义。然而,电池在实际运行时因受多因素影响而导致性能衰减。为此,提出一种基于电化学阻抗谱(electrochemical impedance spectroscopy,EIS)数据的高精度SOH预测方法。【方法】采用EIS数据进行实验,并利用线性Kramers-Kronig算法对EIS数据进行预处理。采用卷积神经网络(convolutional neural network,CNN)、双向长短时记忆(bi-directional long short-term memory,BiLSTM)网络、注意力(Attention)组合模型作为预测模型,将EIS数据的阻抗实部、虚部和模值作为输入,利用CNN从中提取出重要特征,并结合BiLSTM网络模型来预测SOH。此外,通过加入Attention机制和Dropout算法来优化模型。【结果】通过不同温度下锂离子电池SOH预测及与CNN-BiLSTM、BiLSTM模型的对比表明,CNNBiLSTM-Attention模型在25、35、45℃时SOH预测效果更优,精度较2个模型分别提升了46.1%和77.9%。【结论】基于EIS数据的SOH预测是可行的,CNN-BiLSTMAttention组合模型能够实现锂离子电池SOH的精准、高效预测,具有较强的实用性。
