Alpha helix is a common type of secondary structure in the protein structure that consists of repeating helical turns. Patterns in the protein sequences that cause this repetitive pattern in the structure have long be...Alpha helix is a common type of secondary structure in the protein structure that consists of repeating helical turns. Patterns in the protein sequences that cause this repetitive pattern in the structure have long been sought. We used the discrete Fourier transform (DFT) to detect the periodicity signals correlated to the helical structure. We studied the distribution of multiple properties along the protein sequence, and found a property that showed strong periodicity correlated with the helical structure. Using a short-time Fourier transform (STFT) method, we investigated the amplitude of the periodical signals at each amino acid position. The results show that residues in the helix structure tend to display higher amplitudes than residues outside of the helices. This tendency is dramatically strengthen when sequence profiles obtained from multiple alignment were used to detect the periodicity. A simple method that predicted helices based on the amplitude yielded overall true positive rate (TPR) of 63%, 49% sensitivity, 72% specificity, and 0.22 Matthews Correlation Coefficient (MCC). The performance seemed to depend on the length of helices that the proteins had.展开更多
通过旋转薄膜烘箱(RTFOT)对SBS改性沥青分别进行1.5、3和4.5 h的老化,并对老化后的试样分别添加西卡(XK)再生剂和改性信拓-3(XT-3)再生剂及不同掺量(4%、8%、12%)的再生剂制得再生沥青,然后对原样、不同老化时间及再生SBS改性沥青进行...通过旋转薄膜烘箱(RTFOT)对SBS改性沥青分别进行1.5、3和4.5 h的老化,并对老化后的试样分别添加西卡(XK)再生剂和改性信拓-3(XT-3)再生剂及不同掺量(4%、8%、12%)的再生剂制得再生沥青,然后对原样、不同老化时间及再生SBS改性沥青进行红外光谱(FTIR)分析。结果表明:SBS改性沥青在老化过程中亚砜基(S=O)官能团指数增大,丁二烯(CH 2=CH 2)基指数减小,老化时间延长,基质沥青老化加深,SBS改性剂不断降解;添加再生剂后亚砜基特征峰、官能团指数减小,普通再生剂的减小程度大于改性再生剂;XK再生剂在制备再生SBS改性沥青过程中再生剂的老化及SBS改性剂的降解程度大于XT-3;再生剂掺量为8%时,对1.5 h短期老化SBS改性沥青改善效果明显;再生剂对4.5 h RTFOT老化SBS改性沥青亚砜基指数恢复最大,3 h RTFOT老化SBS改性沥青恢复最小,恢复后的亚砜基指数几乎相等。展开更多
为了解决冲击噪声下长短时记忆(long short term memory,LSTM)神经网络调制信号识别方法抗冲击噪声能力弱和超参数难以确定的问题,本文提出了一种演化长短时记忆神经网络的调制识别方法。利用基于短时傅里叶变换的卷积神经网络(convolut...为了解决冲击噪声下长短时记忆(long short term memory,LSTM)神经网络调制信号识别方法抗冲击噪声能力弱和超参数难以确定的问题,本文提出了一种演化长短时记忆神经网络的调制识别方法。利用基于短时傅里叶变换的卷积神经网络(convolution neural network,CNN)去噪模型对数据集去噪;结合量子计算机制和旗鱼优化器(sailfish optimizer,SFO)设计了量子旗鱼算法(quantum sailfish algorithm,QSFA)去演化LSTM神经网络以获得最优的超参数;使用演化长短时记忆神经网络作为分类器进行自动调制信号识别。仿真结果表明,采用所设计的CNN去噪和演化长短时记忆神经网络模型,识别准确率有了大幅度的提高。量子旗鱼算法演化LSTM神经网络模型降低了传统LSTM神经网络容易陷于局部极小值或者过拟合的概率,当混合信噪比为0 dB,所提方法对11种调制信号的平均识别准确率达到90%以上。展开更多
文摘Alpha helix is a common type of secondary structure in the protein structure that consists of repeating helical turns. Patterns in the protein sequences that cause this repetitive pattern in the structure have long been sought. We used the discrete Fourier transform (DFT) to detect the periodicity signals correlated to the helical structure. We studied the distribution of multiple properties along the protein sequence, and found a property that showed strong periodicity correlated with the helical structure. Using a short-time Fourier transform (STFT) method, we investigated the amplitude of the periodical signals at each amino acid position. The results show that residues in the helix structure tend to display higher amplitudes than residues outside of the helices. This tendency is dramatically strengthen when sequence profiles obtained from multiple alignment were used to detect the periodicity. A simple method that predicted helices based on the amplitude yielded overall true positive rate (TPR) of 63%, 49% sensitivity, 72% specificity, and 0.22 Matthews Correlation Coefficient (MCC). The performance seemed to depend on the length of helices that the proteins had.
文摘通过旋转薄膜烘箱(RTFOT)对SBS改性沥青分别进行1.5、3和4.5 h的老化,并对老化后的试样分别添加西卡(XK)再生剂和改性信拓-3(XT-3)再生剂及不同掺量(4%、8%、12%)的再生剂制得再生沥青,然后对原样、不同老化时间及再生SBS改性沥青进行红外光谱(FTIR)分析。结果表明:SBS改性沥青在老化过程中亚砜基(S=O)官能团指数增大,丁二烯(CH 2=CH 2)基指数减小,老化时间延长,基质沥青老化加深,SBS改性剂不断降解;添加再生剂后亚砜基特征峰、官能团指数减小,普通再生剂的减小程度大于改性再生剂;XK再生剂在制备再生SBS改性沥青过程中再生剂的老化及SBS改性剂的降解程度大于XT-3;再生剂掺量为8%时,对1.5 h短期老化SBS改性沥青改善效果明显;再生剂对4.5 h RTFOT老化SBS改性沥青亚砜基指数恢复最大,3 h RTFOT老化SBS改性沥青恢复最小,恢复后的亚砜基指数几乎相等。
文摘为了解决冲击噪声下长短时记忆(long short term memory,LSTM)神经网络调制信号识别方法抗冲击噪声能力弱和超参数难以确定的问题,本文提出了一种演化长短时记忆神经网络的调制识别方法。利用基于短时傅里叶变换的卷积神经网络(convolution neural network,CNN)去噪模型对数据集去噪;结合量子计算机制和旗鱼优化器(sailfish optimizer,SFO)设计了量子旗鱼算法(quantum sailfish algorithm,QSFA)去演化LSTM神经网络以获得最优的超参数;使用演化长短时记忆神经网络作为分类器进行自动调制信号识别。仿真结果表明,采用所设计的CNN去噪和演化长短时记忆神经网络模型,识别准确率有了大幅度的提高。量子旗鱼算法演化LSTM神经网络模型降低了传统LSTM神经网络容易陷于局部极小值或者过拟合的概率,当混合信噪比为0 dB,所提方法对11种调制信号的平均识别准确率达到90%以上。
