An accurate landslide displacement prediction is an important part of landslide warning system. Aiming at the dynamic characteristics of landslide evolution and the shortcomings of traditional static prediction models...An accurate landslide displacement prediction is an important part of landslide warning system. Aiming at the dynamic characteristics of landslide evolution and the shortcomings of traditional static prediction models, this paper proposes a dynamic prediction model of landslide displacement based on singular spectrum analysis(SSA) and stack long short-term memory(SLSTM) network. The SSA is used to decompose the landslide accumulated displacement time series data into trend term and periodic term displacement subsequences. A cubic polynomial function is used to predict the trend term displacement subsequence, and the SLSTM neural network is used to predict the periodic term displacement subsequence. At the same time, the Bayesian optimization algorithm is used to determine that the SLSTM network input sequence length is 12 and the number of hidden layer nodes is 18. The SLSTM network is updated by adding predicted values to the training set to achieve dynamic displacement prediction. Finally, the accumulated landslide displacement is obtained by superimposing the predicted value of each displacement subsequence. The proposed model was verified on the Xintan landslide in Hubei Province, China. The results show that when predicting the displacement of the periodic term, the SLSTM network has higher prediction accuracy than the support vector machine(SVM) and auto regressive integrated moving average(ARIMA). The mean relative error(MRE) is reduced by 4.099% and 3.548% respectively, while the root mean square error(RMSE) is reduced by 5.830 mm and 3.854 mm respectively. It is concluded that the SLSTM network model can better simulate the dynamic characteristics of landslides.展开更多
Verbal short-term memory (vSTM) has been shown to be associated with language development in typical and atypical populations. In this study, we investigated cognitive and language skills in 33 school-aged children wi...Verbal short-term memory (vSTM) has been shown to be associated with language development in typical and atypical populations. In this study, we investigated cognitive and language skills in 33 school-aged children with ASD (6 - 12 years old) with both typical and low levels of intelligence (18 with typical non-verbal IQ [>80 in Raven] and 15 with low non-verbal IQ [p p < 0.05). Regression analysis showed that expressive vocabulary was predicted by non-verbal IQ and vSTM, syntactic production was predicted by vSTM and picture comprehension was predicted by non-verbal IQ. Conversely, expressive vocabulary could predict non-verbal IQ, vSTM, immediate visual memory, delayed visual memory, and visual information recall. It seems that vSTM is a strong predictor of language skills for children with ASD, just like it is for other typical and atypical populations. Finally, dissociations exist in individual performances between non-verbal IQ and memory on the one hand and language skills (expressive vocabulary, syntactic production) on the other hand. We discuss the significance of these findings in terms of previous results reported in ASD literature as well as in terms of clinical implications and intervention in ASD individuals.展开更多
A solar radio spectrometer records solar radio radiation in the radio waveband.Such solar radio radiation spanning multiple frequency channels and over a short time period could provide a solar radio spectrum which is...A solar radio spectrometer records solar radio radiation in the radio waveband.Such solar radio radiation spanning multiple frequency channels and over a short time period could provide a solar radio spectrum which is a two dimensional image.The vertical axis of a spectrum represents frequency channel and the horizontal axis signifies time.Intrinsically,time dependence exists between neighboring columns of a spectrum since solar radio radiation varies continuously over time.Thus,a spectrum can be treated as a time series consisting of all columns of a spectrum,while treating it as a general image would lose its time series property.A recurrent neural network(RNN)is designed for time series analysis.It can explore the correlation and interaction between neighboring inputs of a time series by augmenting a loop in a network.This paper makes the first attempt to utilize an RNN,specifically long short-term memory(LSTM),for solar radio spectrum classification.LSTM can mine well the context of a time series to acquire more information beyond a non-time series model.As such,as demonstrated by our experimental results,LSTM can learn a better representation of a spectrum,and thus contribute better classification.展开更多
Spectrum prediction is a promising technology to infer future spectrum state by exploiting inherent patterns of historical spectrum data.In practice,for a given spectrum band of interest,when facing relatively scarce ...Spectrum prediction is a promising technology to infer future spectrum state by exploiting inherent patterns of historical spectrum data.In practice,for a given spectrum band of interest,when facing relatively scarce historical data,spectrum prediction based on traditional learning methods does not work well.Thus,this paper proposes a cross-band spectrum prediction model based on transfer learning.Firstly,by analysing service activities and computing the distances between various frequency points based on Dynamic Time Warping,the similarity between spectrum bands has been verified.Next,the features,which mainly affect the performance of transfer learning in the crossband spectrum prediction,are explored by leveraging transfer component analysis.Then,the effectiveness of transfer learning for the cross-band spectrum prediction has been demonstrated.Further,experimental results with real-world spectrum data demonstrate that the performance of the proposed model is better than the state-of-theart models when the historical spectrum data is limited.展开更多
针对时频谱模型估计语音不准确的问题,文中提出采用模型变换的方式来获得噪声和语音的对数概率密度函数,同时借助带噪语音、干净语音和噪声之间的对数关系并结合最小均方误差(Minimum Mean Square Error,MMSE)估计理论推导出估计语音对...针对时频谱模型估计语音不准确的问题,文中提出采用模型变换的方式来获得噪声和语音的对数概率密度函数,同时借助带噪语音、干净语音和噪声之间的对数关系并结合最小均方误差(Minimum Mean Square Error,MMSE)估计理论推导出估计语音对数谱的时频掩模。基于语音和噪声的对数概率分布推导出了一种软掩模,该软掩模可对带噪语音的对数子带进行加权以降低噪声,提高语音估计的准确性。仿真结果表明,与未处理的含噪语音相比,所提方法在噪声抑制方面具有3 dB以上的提升,基于最小均方误差的时频掩模和软掩模在听觉感知方面的平均提升量分别为27.7%和29.4%,在可懂度方面的平均提升量分别为12.7%和14.3%。展开更多
基金supported by the Natural Science Foundation of Shaanxi Province under Grant 2019JQ206in part by the Science and Technology Department of Shaanxi Province under Grant 2020CGXNG-009in part by the Education Department of Shaanxi Province under Grant 17JK0346。
文摘An accurate landslide displacement prediction is an important part of landslide warning system. Aiming at the dynamic characteristics of landslide evolution and the shortcomings of traditional static prediction models, this paper proposes a dynamic prediction model of landslide displacement based on singular spectrum analysis(SSA) and stack long short-term memory(SLSTM) network. The SSA is used to decompose the landslide accumulated displacement time series data into trend term and periodic term displacement subsequences. A cubic polynomial function is used to predict the trend term displacement subsequence, and the SLSTM neural network is used to predict the periodic term displacement subsequence. At the same time, the Bayesian optimization algorithm is used to determine that the SLSTM network input sequence length is 12 and the number of hidden layer nodes is 18. The SLSTM network is updated by adding predicted values to the training set to achieve dynamic displacement prediction. Finally, the accumulated landslide displacement is obtained by superimposing the predicted value of each displacement subsequence. The proposed model was verified on the Xintan landslide in Hubei Province, China. The results show that when predicting the displacement of the periodic term, the SLSTM network has higher prediction accuracy than the support vector machine(SVM) and auto regressive integrated moving average(ARIMA). The mean relative error(MRE) is reduced by 4.099% and 3.548% respectively, while the root mean square error(RMSE) is reduced by 5.830 mm and 3.854 mm respectively. It is concluded that the SLSTM network model can better simulate the dynamic characteristics of landslides.
文摘Verbal short-term memory (vSTM) has been shown to be associated with language development in typical and atypical populations. In this study, we investigated cognitive and language skills in 33 school-aged children with ASD (6 - 12 years old) with both typical and low levels of intelligence (18 with typical non-verbal IQ [>80 in Raven] and 15 with low non-verbal IQ [p p < 0.05). Regression analysis showed that expressive vocabulary was predicted by non-verbal IQ and vSTM, syntactic production was predicted by vSTM and picture comprehension was predicted by non-verbal IQ. Conversely, expressive vocabulary could predict non-verbal IQ, vSTM, immediate visual memory, delayed visual memory, and visual information recall. It seems that vSTM is a strong predictor of language skills for children with ASD, just like it is for other typical and atypical populations. Finally, dissociations exist in individual performances between non-verbal IQ and memory on the one hand and language skills (expressive vocabulary, syntactic production) on the other hand. We discuss the significance of these findings in terms of previous results reported in ASD literature as well as in terms of clinical implications and intervention in ASD individuals.
基金supported by the National Natural Science Foundation of China(Grant Nos.61572461,11790305,61811530282,61872429,61661146005 and U1611461)CAS 100-Talents(Dr.Xu Long)
文摘A solar radio spectrometer records solar radio radiation in the radio waveband.Such solar radio radiation spanning multiple frequency channels and over a short time period could provide a solar radio spectrum which is a two dimensional image.The vertical axis of a spectrum represents frequency channel and the horizontal axis signifies time.Intrinsically,time dependence exists between neighboring columns of a spectrum since solar radio radiation varies continuously over time.Thus,a spectrum can be treated as a time series consisting of all columns of a spectrum,while treating it as a general image would lose its time series property.A recurrent neural network(RNN)is designed for time series analysis.It can explore the correlation and interaction between neighboring inputs of a time series by augmenting a loop in a network.This paper makes the first attempt to utilize an RNN,specifically long short-term memory(LSTM),for solar radio spectrum classification.LSTM can mine well the context of a time series to acquire more information beyond a non-time series model.As such,as demonstrated by our experimental results,LSTM can learn a better representation of a spectrum,and thus contribute better classification.
基金supported by the National Key R&D Program of China under Grant 2018AAA0102303 and Grant 2018YFB1801103the National Natural Science Foundation of China (No. 61871398 and No. 61931011)+1 种基金the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province (No. BK20190030)the Equipment Advanced Research Field Foundation (No. 61403120304)
文摘Spectrum prediction is a promising technology to infer future spectrum state by exploiting inherent patterns of historical spectrum data.In practice,for a given spectrum band of interest,when facing relatively scarce historical data,spectrum prediction based on traditional learning methods does not work well.Thus,this paper proposes a cross-band spectrum prediction model based on transfer learning.Firstly,by analysing service activities and computing the distances between various frequency points based on Dynamic Time Warping,the similarity between spectrum bands has been verified.Next,the features,which mainly affect the performance of transfer learning in the crossband spectrum prediction,are explored by leveraging transfer component analysis.Then,the effectiveness of transfer learning for the cross-band spectrum prediction has been demonstrated.Further,experimental results with real-world spectrum data demonstrate that the performance of the proposed model is better than the state-of-theart models when the historical spectrum data is limited.
文摘针对时频谱模型估计语音不准确的问题,文中提出采用模型变换的方式来获得噪声和语音的对数概率密度函数,同时借助带噪语音、干净语音和噪声之间的对数关系并结合最小均方误差(Minimum Mean Square Error,MMSE)估计理论推导出估计语音对数谱的时频掩模。基于语音和噪声的对数概率分布推导出了一种软掩模,该软掩模可对带噪语音的对数子带进行加权以降低噪声,提高语音估计的准确性。仿真结果表明,与未处理的含噪语音相比,所提方法在噪声抑制方面具有3 dB以上的提升,基于最小均方误差的时频掩模和软掩模在听觉感知方面的平均提升量分别为27.7%和29.4%,在可懂度方面的平均提升量分别为12.7%和14.3%。
