In order to identify continuous B-cell epitopes effectively and to increase the success rate of experimental identification, the modified Back Propagation artificial neural network (BP neural network) was used to pred...In order to identify continuous B-cell epitopes effectively and to increase the success rate of experimental identification, the modified Back Propagation artificial neural network (BP neural network) was used to predict the continuous B-cell epitopes, and finally the predictive model for the B-cells epitopes was established. Comparing with the other predictive models, the prediction performance of this model is more excellent (AUC = 0.723). For the purpose of verifying the performance of the model, the prediction to the SWISS PROT NUMBER: P08677 was carried on, and the satisfying results were obtained.展开更多
The intersection of the Industrial Internet of Things(IIoT)and artificial intelligence(AI)has garnered ever-increasing attention and research interest.Nevertheless,the dilemma between the strict resource-constrained n...The intersection of the Industrial Internet of Things(IIoT)and artificial intelligence(AI)has garnered ever-increasing attention and research interest.Nevertheless,the dilemma between the strict resource-constrained nature of IIoT devices and the extensive resource demands of AI has not yet been fully addressed with a comprehensive solution.Taking advantage of the lightweight constructive neural network(LightGCNet)in developing fast learner models for IIoT,a convex geometric constructive neural network with a low-complexity control strategy,namely,ConGCNet,is proposed in this article via convex optimization and matrix theory,which enhances the convergence rate and reduces the computational consumption in comparison with LightGCNet.Firstly,a low-complexity control strategy is proposed to reduce the computational consumption during the hidden parameters training process.Secondly,a novel output weights evaluated method based on convex optimization is proposed to guarantee the convergence rate.Finally,the universal approximation property of ConGCNet is proved by the low-complexity control strategy and convex output weights evaluated method.Simulation results,including four benchmark datasets and the real-world ore grinding process,demonstrate that ConGCNet effectively reduces computational consumption in the modelling process and improves the model’s convergence rate.展开更多
提出了一种根据接收正交频分复用(orthogonal frequency division multiplexing,OFDM)信号估计发射机IQ不平衡与非线性,并以此作为发射机指纹进行通信设备身份认证的方法.首先根据共轭对称导频估计多径信道脉冲响应,接着根据信道脉冲响...提出了一种根据接收正交频分复用(orthogonal frequency division multiplexing,OFDM)信号估计发射机IQ不平衡与非线性,并以此作为发射机指纹进行通信设备身份认证的方法.首先根据共轭对称导频估计多径信道脉冲响应,接着根据信道脉冲响应估计、共轭反对称导频与非线性功放的线性近似放大倍数估计发射机的IQ不平衡参数组合,然后进行发射机非线性的B-Spline神经网络模型系数估计,最后从非线性模型系数估计中提取相似因子,与IQ不平衡参数组合估计构成发射机指纹的特征矢量后进行通信设备身份的识别或确认.理论推导与数值仿真显示,该方法可用于OFDM通信设备的物理层高强度认证与防假冒等.展开更多
文摘In order to identify continuous B-cell epitopes effectively and to increase the success rate of experimental identification, the modified Back Propagation artificial neural network (BP neural network) was used to predict the continuous B-cell epitopes, and finally the predictive model for the B-cells epitopes was established. Comparing with the other predictive models, the prediction performance of this model is more excellent (AUC = 0.723). For the purpose of verifying the performance of the model, the prediction to the SWISS PROT NUMBER: P08677 was carried on, and the satisfying results were obtained.
文摘The intersection of the Industrial Internet of Things(IIoT)and artificial intelligence(AI)has garnered ever-increasing attention and research interest.Nevertheless,the dilemma between the strict resource-constrained nature of IIoT devices and the extensive resource demands of AI has not yet been fully addressed with a comprehensive solution.Taking advantage of the lightweight constructive neural network(LightGCNet)in developing fast learner models for IIoT,a convex geometric constructive neural network with a low-complexity control strategy,namely,ConGCNet,is proposed in this article via convex optimization and matrix theory,which enhances the convergence rate and reduces the computational consumption in comparison with LightGCNet.Firstly,a low-complexity control strategy is proposed to reduce the computational consumption during the hidden parameters training process.Secondly,a novel output weights evaluated method based on convex optimization is proposed to guarantee the convergence rate.Finally,the universal approximation property of ConGCNet is proved by the low-complexity control strategy and convex output weights evaluated method.Simulation results,including four benchmark datasets and the real-world ore grinding process,demonstrate that ConGCNet effectively reduces computational consumption in the modelling process and improves the model’s convergence rate.
文摘提出了一种根据接收正交频分复用(orthogonal frequency division multiplexing,OFDM)信号估计发射机IQ不平衡与非线性,并以此作为发射机指纹进行通信设备身份认证的方法.首先根据共轭对称导频估计多径信道脉冲响应,接着根据信道脉冲响应估计、共轭反对称导频与非线性功放的线性近似放大倍数估计发射机的IQ不平衡参数组合,然后进行发射机非线性的B-Spline神经网络模型系数估计,最后从非线性模型系数估计中提取相似因子,与IQ不平衡参数组合估计构成发射机指纹的特征矢量后进行通信设备身份的识别或确认.理论推导与数值仿真显示,该方法可用于OFDM通信设备的物理层高强度认证与防假冒等.
