Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.

Lightweight and highly robust memristor-based hybrid neural networks for electroencephalogram signal processing

Memristor-based neuromorphic computing shows great potential for high-speed and high-throughput signal processing applications,such as electroencephalogram(EEG)signal processing.Nonetheless,the size of one-transistor one-resistor(1T1R)memristor arrays is limited by the non-ideality of the devices,which prevents the hardware implementation of large and complex networks.In this work,we propose the depthwise separable convolution and bidirectional gate recurrent unit(DSC-BiGRU)network,a lightweight and highly robust hybrid neural network based on 1T1R arrays that enables efficient processing of EEG signals in the temporal,frequency and spatial domains by hybridizing DSC and BiGRU blocks.The network size is reduced and the network robustness is improved while ensuring the network classification accuracy.In the simulation,the measured non-idealities of the 1T1R array are brought into the network through statistical analysis.Compared with traditional convolutional networks,the network parameters are reduced by 95%and the network classification accuracy is improved by 21%at a 95%array yield rate and 5%tolerable error.This work demonstrates that lightweight and highly robust networks based on memristor arrays hold great promise for applications that rely on low consumption and high efficiency.

APPROXIMATION ANALYSES FOR FUZZY VALUED FUNCTIONS IN L_1(μ)-NORM BY REGULAR FUZZY NEURAL NETWORKS

By defining fuzzy valued simple functions and giving L1(μ) approximations of fuzzy valued integrably bounded functions by such simple functions, the paper analyses by L1(μ)-norm the approximation capability of four-layer feedforward regular fuzzy neural networks to the fuzzy valued integrably bounded function F : Rn → FcO(R). That is, if the transfer functionσ: R→R is non-polynomial and integrable function on each finite interval, F may be innorm approximated by fuzzy valued functions defined as to anydegree of accuracy. Finally some real examples demonstrate the conclusions.

Bearings Intelligent Fault Diagnosis by 1-D Adder Neural Networks

Integrated with sensors,processors,and radio frequency(RF)communication modules,intelligent bearing could achieve the autonomous perception and autonomous decision-making,guarantying the safety and reliability during their use.However,because of the resource limitations of the end device,processors in the intelligent bearing are unable to carry the computational load of deep learning models like convolutional neural network(CNN),which involves a great amount of multiplicative operations.To minimize the computation cost of the conventional CNN,based on the idea of AdderNet,a 1-D adder neural network with a wide first-layer kernel(WAddNN)suitable for bearing fault diagnosis is proposed in this paper.The proposed method uses the l1-norm distance between filters and input features as the output response,thus making the whole network almost free of multiplicative operations.The whole model takes the original signal as the input,uses a wide kernel in the first adder layer to extract features and suppress the high frequency noise,and then uses two layers of small kernels for nonlinear mapping.Through experimental comparison with CNN models of the same structure,WAddNN is able to achieve a similar accuracy as CNN models with significantly reduced computational cost.The proposed model provides a new fault diagnosis method for intelligent bearings with limited resources.

基于BP神经网络模型优化Fe_(1-x)O基氨合成催化剂

运用BP神经网络建立了助催化剂含量与催化剂活性之间的预测模型,对Fe_(1-x)O基氨合成催化剂的助催化剂进行优化。首先将前期实验数据整理归纳为含有3、4、5、6和7个助催化剂等5类催化剂,以助催化剂含量(体积分数)为输入变量,以425℃反应器出口氨浓度(活性)为输出变量,对助催化剂进行优化。结果表明,BP神经网络预测模型拟合值均方误差最高为0.2784,预测值均方误差最高为0.1592,构建的BP神经网络模型准确度较高。在该模型的基础上,运用多种群遗传算法进行极值寻优,求解最优的催化剂配方,并进行实验验证。结果表明,根据优化结果制备5个样品的实验测定值与预测值的相对误差最高为2.88%,优化结果较为准确;含有7个助催化剂的催化剂活性最高为18.83%,比原样本的统计平均活性值(17.52%)高1.31%,相对提高7.48%,助催化剂含量优化取得满意的结果。

Chaotic Neural Network Technique for "0-1" Programming Problems

0-1 programming is a special case of the integer programming, which is commonly encountered in many optimization problems. Neural network and its general energy function are presented for 0-1 optimization problem. Then, the 0-1 optimization problems are solved by a neural network model with transient chaotic dynamics (TCNN). Numerical simulations of two typical 0-1 optimization problems show that TCNN can overcome HNN's main drawbacks that it suffers from the local minimum and can search for the global optimal solutions in to solveing 0-1 optimization problems.

(3+1)维Hirota双线性方程的lump解

非线性发展方程是现代数学的一重要分支,其精确解的计算一直都是非线性科学领域的主流与焦点问题.lump解是精确解析解的一种特殊形式,以(3+1)维Hirota双线性方程为例对此展开研究.首先,利用Hirota双线性方法研究其经典lump解.其次,以双线性神经网络方法为基础,借助符号计算方法,得到方程的高阶lump解,主要是4阶lump解的计算.最后,通过对参数赋予一些特殊值,借助Maple软件,绘制出相关的三维图、密度图、相图以及传播图等,得到一些新的现象,同时展示了所求出的解的动力学行为.

Author Gender Prediction in an Email Stream Using Neural Networks

With the rapid growth of the Internet in recent years, the ability to analyze and identify its users has become increasingly important. Authorship analysis provides a means to glean information about the author of a document originating from the internet or elsewhere, including but not limited to the author’s gender. There are well-known linguistic differences between the writing of men and women, and these differences can be effectively used to predict the gender of a document’s author. Capitalizing on these linguistic nuances, this study uses a set of stylometric features and a set of word count features to facilitate automatic gender discrimination on emails from the popular Enron email dataset. These features are used in conjunction with the Modified Balanced Winnow Neural Network proposed by Carvalho and Cohen, an improvement on the original Balanced Winnow created by Littlestone. Experiments with the Modified Balanced Winnow show that it is effectively able to discriminate gender using both stylometric and word count features, with the word count features providing superior results.

基于1DCNN-GRU的启闭机液压系统故障诊断

由于启闭机液压系统内部结构复杂,故障信号不易采集,使用AMESim软件搭建启闭机液压系统仿真模型,构建6种典型故障数据集。基于这些数据集,提出一维卷积神经网络(1 Dimensional Convolutional Neural Network,1DCNN)与门控循环单元(Gated Recurrent Unit,GRU)相结合的故障诊断方法,利用1DCNN提取信号数据的空间特征和GRU提取信号数据的时间特征,实现对信号数据空间及时间特征的融合,并对融合特征进行分类识别。

基于LSTM与1DCNN的导弹轨迹预测方法

针对弹道导弹等超远程攻击目标的轨迹难以预测的问题,提出一种基于长短期记忆(long short-term memory,LSTM)网络与一维卷积神经网络(1-dimensional convolutional neural network,1DCNN)的目标轨迹预测方法。首先,建立三自由度导弹运动模型,依据再入类型设计3种目标轨迹数据,构建机动数据库,解决轨迹数据的来源问题。其次,采用重复分割与滑动窗口的方法对轨迹数据进行预处理。然后,基于LSTM与1DCNN设计了一种目标类型分类网络,对目标进行初步分类。最后,基于1DCNN设计轨迹预测网络,对目标轨迹进行预测。仿真结果表明,提出的轨迹预测网络能够完成轨迹预测任务,预测误差在合理范围内。

Application of Neural Network in Fault Location of Optical Transport Network

Due to the increasing variety of information and services carried by optical networks, the survivability of network becomes an important problem in current research. The fault location of OTN is of great significance for studying the survivability of optical networks. Firstly, a three-channel network model is established and analyzing common alarm data, the fault monitoring points and common fault points are carried out. The artificial neural network is introduced into the fault location field of OTN and it is used to judge whether the possible fault point exists or not. But one of the obvious limitations of general neural networks is that they receive a fixedsize vector as input and produce a fixed-size vector as the output. Not only that, these models is even fixed for mapping operations (for example, the number of layers in the model). The difference between the recurrent neural network and general neural networks is that it can operate on the sequence. In spite of the fact that the gradient disappears and the gradient explodes still exist in the neural network, the method of gradient shearing or weight regularization is adopted to solve this problem, and choose the LSTM (long-short term memory networks) to locate the fault. The output uses the concept of membership degree of fuzzy theory to express the possible fault point with the probability from 0 to 1. Priority is given to the treatment of fault points with high probability. The concept of F-Measure is also introduced, and the positioning effect is measured by using location time, MSE and F-Measure. The experiment shows that both LSTM and BP neural network can locate the fault of optical transport network well, but the overall effect of LSTM is better. The localization time of LSTM is shorter than that of BP neural network, and the F1-score of LSTM can reach 0.961566888396156 after 45 iterations, which meets the accuracy and real-time requirements of fault location. Therefore, it has good application prospect and practical value to introduce neural network into the fault location field of optical transport network.

Imprinted Zac1 in neural stem cells

Neural stem cells(NSCs) and imprinted genes play an important role in brain development. On historical grounds, these two determinants have been largely studied independently of each other. Recent evidence suggests, however, that NSCs can reset select genomic imprints to prevent precocious depletion of the stem cell reservoir. Moreover, imprinted genes like the transcriptional regulator Zac1 can fine tune neuronal vs astroglial differentiation of NSCs. Zac1 binds in a sequence-specific manner to pro-neuronal and imprinted genes to confer transcriptional regulation and furthermore coregulates members of the p53-family in NSCs. At the genome scale, Zac1 is a central hub of an imprinted gene network comprising genes with animportant role for NSC quiescence, proliferation and differentiation. Overall, transcriptional, epigenomic, and genomic mechanisms seem to coordinate the functional relationships of NSCs and imprinted genes from development to maturation, and possibly aging.

Remaining Useful Life Prediction of Aeroengine Based on Principal Component Analysis and One-Dimensional Convolutional Neural Network

In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.

PREDICTION OF FLOW STRESS OF HIGH-SPEED STEEL DURING HOT DEFORMATION BY USING BP ARTIFICIAL NEURAL NETWORK

The hot deformation behavior of TI (18W-4Cr-1V) high-speed steel was investigated by means of continuous compression tests performed on Gleeble 1500 thermomechan- ical simulator in a wide range of tempemtures (950℃-1150℃) with strain rotes of 0.001s-1-10s-1 and true strains of 0-0. 7. The flow stress at the above hot defor- mation conditions is predicted by using BP artificial neural network. The architecture of network includes there are three input parameters:strain rate,temperature T and true strain , and just one output parameter, the flow stress ,2 hidden layers are adopted, the first hidden layer includes 9 neurons and second 10 negroes. It has been verified that BP artificial neural network with 3-9-10-1 architecture can predict flow stress of high-speed steel during hot deformation very well. Compared with the prediction method of flow stress by using Zaped-Holloman parumeter and hyperbolic sine stress function, the prediction method by using BP artificial neurul network has higher efficiency and accuracy.

A unified C-band and Ku-band geophysical model function determined by neural network approach

The geophysical model function （GMF） describes the relationship between backscattering and sea surface wind, so that wind vec- tors can be retrieved from backscattering measurement. The GMF plays an important role in ocean wind vector retrievals, its performance will directly influence the accuracy of the retrieved wind vector. Neural network （NN） approach is used to develop a unified GMF for C-band and Ku-band （NN-GMF）. Empirical GMF CMOIM and QSCAT-1 are used to generate the simulated training data-set, and Gaussian noise at a signal noise ratio of 30 dB is added to the data-set to simulate the noise in the backscat- tering measurement. The NN-GMF employs radio frequency as an additional parameter, so it can be applied for both C-band and Ku-band. Analyses show that the %predicted by the NN-GMF is comparable with the σpredicted by CMOIM and QSCAT-1. Also the wind vectors retrieved from the NN-GMF and empirical GMF CMOIM and QSCAT-1 are comparable, indicating that the NN-GMF is as effective as the empirical GMF, and has the advantages of the universal form.

AB009.Learning dynamics in a neural network model of the primary visual cortex

Background:The primary visual cortex(V1)is a key component of the visual system that builds some of the first levels of coherent visual representations from sparse visual inputs.While the study of its dynamics has been the focus of many computational models for the past years,there is still relatively few research works that put an emphasis on both synaptic plasticity in V1 and biorealism in the context of learning visual inputs.Here,we present a recurrent spiking neural network that is capable of spike timing dependent plasticity(STDP)and we demonstrate its capacity to discriminate spatio-temporal orientation patterns in noisy natural images.Methods:A two stage model was developed.First,natural images flux(be it videos/gratings/camera)were converted into spikes,using a difference of gaussians(DOG)approach.This transformation approximates the retina-lateral geniculate nucleus(LGN)organization.Secondly,a spiking neural network was build using PyNN simulator,mimicking cortical neurons dynamics and plasticity,as well as V1 topology.This network was then fed with spikes generated by the first model and its ability to build visual representations was assessed using control gratings inputs.Results:The neural network exhibited several interesting properties.After a short period of learning,it was capable of learning multiples orientations and reducing noise in such learned feature,compared to the inputs.These learned features were stable even after increasing the noise in inputs and were found to not only encoding the spatial properties of the input,but also its temporal aspects(i.e.,the time of each grating presentation Conclusions:Our work shows that topological structuring of the cortical neural networks,combined with simple plasticity rules,are sufficient to drive strong learning dynamics of natural images properties.This computational model fits many properties found in the literature and provides some theoritical explanations for the shape of tuning curve of certain layers of V1.Further investigations are now conducted to validate its properties against the neuronal responses of rodents,using identical visual stimuli.

基于1-D CNN的二阶段OFDM系统定时同步方法

针对存在多径干扰的正交频分复用系统的定时同步准确性低的问题,提出基于一维卷积神经网络(1-D CNN)的二阶段OFDM系统定时同步方法。在第一阶段,利用经典互相关方法实现路径特征初始抽取,捕获可分辨路径上的定时辅助同步点;基于定时辅助同步点构建1-D CNN网络学习第二阶段中的定时偏移;最后,结合两阶段处理,获得系统最终的定时同步偏移估计。相比于基于压缩感知的定时同步方法和基于极限学习机的定时同步方法,所研究的二阶段OFDM系统定时同步方法提高了定时同步准确性,并有效地降低计算复杂度与处理延迟。

A New Searching Strategy for the Lost Plane Based on RBF Neural Network Model and Global Optimization Model

In this paper, we construct two models for the searching task for a lost plane. Model 1 determines the searching area. We predict the trajectory of floats generated after the disintegration of the plane by using RBF neural network model, and then determine the searching area according to the trajectory. With the pass of time, the searching area will also be constantly moving along the trajectory. Model 2 develops a maritime search plan to achieve the purpose of completing the search in the shortest time. We optimize the searching time and transform the problem into the 0-1 knapsack problem. Solving this problem by improved genetic algorithm, we can get the shortest searching time and the best choice for the search power.

AR-MED共振特征增强的风电齿轮箱故障诊断

针对风电齿轮箱故障时脉冲成分往往淹没在其他频率分量中,早期故障特征难以有效提取的问题,提出一种自回归最小熵解卷积(AR-MED)共振特征增强的风电齿轮箱故障诊断方法,并结合一维卷积神经网络(1DCNN),实现齿轮箱高精度故障诊断。首先,使用共振稀疏分解算法(RSSD)将振动信号分解成含有噪声和谐波成分的高共振分量和含有故障冲击成分的低共振分量;其次,对低共振分量使用自回归最小熵解卷积运算,增强低共振分量中微弱的周期性冲击成分;最后,构建自回归最小熵解卷积共振特征增强的1DCNN模型,将分解得到的谐波分量和周期性冲击分量进行特征融合以及有针对的训练和分类。实验结果表明,与现有故障诊断模型相比,所提方法在提取风电齿轮箱的故障特征信息以及提高故障诊断精度方面具有有效性和优越性。

RBF神经网络与NSGA-II混合算法用于±1 100kV穿墙套管3维电场模拟及内屏蔽结构优化

±1 100 kV特高压穿墙套管是直流输电系统中的重要设备,但目前对其3维静电场模拟和内屏蔽层结构智能优化鲜有报道。鉴于此,以特高压穿墙套管及其内屏蔽结构为研究对象,建立了3维有限元模型进行电场模拟。提出了应用径向基函数(RBF)神经网络与NSGA-II混合算法对套管内屏蔽结构进行多目标优化,并运用经典显示函数验证了该算法的有效性。在此基础上,建立了穿墙套管内屏蔽结构的多目标优化数学模型,结合RBF神经网络与NSGA-II混合算法对内屏蔽结构进行了优化设计,使套管内屏蔽各关键位置处电场强度(简称场强)均满足控制要求。研究表明:与自由网格划分相比,体旋转扫掠网格划分可使有限元模型生成的节点数量降低58.2%;墙体和均压环对套管复合外套有较好的屏蔽作用,且高场强区主要集中在内屏蔽表面,优化后最高场强降低14.5%。3维电场模拟结果可为穿墙套管的设计、制造和运行提供数据和理论依据,且所提算法能较好地解决大场域、多介质复杂模型结构优化耗时较多的问题。