Stochastic Binary Neural Networks for Qualitatively Robust Predictive Model Mapping

We consider qualitatively robust predictive mappings of stochastic environmental models, where protection against outlier data is incorporated. We utilize digital representations of the models and deploy stochastic binary neural networks that are pre-trained to produce such mappings. The pre-training is implemented by a back propagating supervised learning algorithm which converges almost surely to the probabilities induced by the environment, under general ergodicity conditions.

基于改进BNN-LSTM的风电功率概率预测

针对确定性的风电功率预测难以提供预测结果的波动区间和支撑风险决策的问题,以贝叶斯网络为基础,通过将先验分布置于LSTM网络层权重参数之上,构建了贝叶斯LSTM神经网络(BNN-LSTM)。以时间卷积神经网络(TCNN)处理风电功率预测的历史时序数据,提取时序数据的关联特征。使用互信息熵方法分析了风电功率的气象数据集,剔除关联性小的变量,对气象数据集进行降维处理。并采用嵌入(embedding)结构学习风电功率时间分类特征。随后将TCNN处理后的时序数据、降维后的气象数据以及时间分类特征数据一起送入BNN-LSTM预测模型,通过在某风电数据集不同算法的概率预测指标pinball损失和Winkler评分的对比验证,可知,本文所提方法能从可对风电功率波动做出较为准确的响应,预测效果更好。

Faster-than- Nyquist rate communication via convolutional neural networks- based demodulators

A demodulator based on convolutional neural networks（ CNNs） is proposed to demodulate bipolar extended binary phase shifting keying（ EBPSK） signals transmitted at a faster-thanNyquist（ FTN） rate, solving the problem of severe inter symbol interference（ ISI） caused by FTN rate signals. With the characteristics of local connectivity, pooling and weight sharing,a six-layer CNNs structure is used to demodulate and eliminate ISI. The results showthat with the symbol rate of 1. 07 k Bd, the bandwidth of the band-pass filter（ BPF） in a transmitter of 1 k Hz and the changing number of carrier cycles in a symbol K = 5,10,15,28, the overall bit error ratio（ BER） performance of CNNs with single-symbol decision is superior to that with a doublesymbol united-decision. In addition, the BER performance of single-symbol decision is approximately 0. 5 d B better than that of the coherent demodulator while K equals the total number of carrier circles in a symbol, i. e., K = N = 28. With the symbol rate of 1. 07 k Bd, the bandwidth of BPF in a transmitter of 500 Hz and K = 5,10,15,28, the overall BER performance of CNNs with double-symbol united-decision is superior to those with single-symbol decision. Moreover, the double-symbol uniteddecision method is approximately 0. 5 to 1. 5 d B better than that of the coherent demodulator while K = N = 28. The demodulators based on CNNs successfully solve the serious ISI problems generated during the transmission of FTN rate bipolar EBPSK signals, which is beneficial for the improvement of spectrum efficiency.

Human and Machine Vision Based Indian Race Classification Using Modified-Convolutional Neural Network

The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue,we have carried out empirical research on classifying Indian people to their geographical regions.This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India,referring to human vision.We have created an Automated Human Intelligence System(AHIS)to evaluate human visual capabilities.Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features.We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy.The proposed model achieved mean F1 and Matthew Correlation Coefficient(MCC)of 0.92 and 0.84,respectively,on the validation set,outperforming the traditional Convolutional Neural Network(CNN).The CNN-Contoured Face(CNN-FC)model is developed to train contoured face images to investigate the influence of face shape.Finally,to cross-validate the accuracy of these models,the traditional CNN model is trained on the same dataset.With an accuracy of 92.98%,the Modified-CNN(M-CNN)model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems.A novel Indian regional face dataset is created for supporting this supervised classification work,and it will be available to the research community.

Non-Intrusive Load Identification Model Based on 3D Spatial Feature and Convolutional Neural Network

Load identification method is one of the major technical difficulties of non-intrusive composite monitoring. Binary V-I trajectory image can reflect the original V-I trajectory characteristics to a large extent, so it is widely used in load identification. However, using single binary V-I trajectory feature for load identification has certain limitations. In order to improve the accuracy of load identification, the power feature is added on the basis of the binary V-I trajectory feature in this paper. We change the initial binary V-I trajectory into a new 3D feature by mapping the power feature to the third dimension. In order to reduce the impact of imbalance samples on load identification, the SVM SMOTE algorithm is used to balance the samples. Based on the deep learning method, the convolutional neural network model is used to extract the newly produced 3D feature to achieve load identification in this paper. The results indicate the new 3D feature has better observability and the proposed model has higher identification performance compared with other classification models on the public data set PLAID.

GAAF:Searching Activation Functions for Binary Neural Networks Through Genetic Algorithm

Binary neural networks(BNNs)show promising utilization in cost and power-restricted domains such as edge devices and mobile systems.This is due to its significantly less computation and storage demand,but at the cost of degraded performance.To close the accuracy gap,in this paper we propose to add a complementary activation function(AF)ahead of the sign based binarization,and rely on the genetic algorithm(GA)to automatically search for the ideal AFs.These AFs can help extract extra information from the input data in the forward pass,while allowing improved gradient approximation in the backward pass.Fifteen novel AFs are identified through our GA-based search,while most of them show improved performance(up to 2.54%on ImageNet)when testing on different datasets and network models.Interestingly,periodic functions are identified as a key component for most of the discovered AFs,which rarely exist in human designed AFs.Our method offers a novel approach for designing general and application-specific BNN architecture.GAAF will be released on GitHub.

MOLTEN SALT PHASE DIAGRAMS CALCULATION USING ARTIFICIAL NEURAL NETWORK OR PATTERN RECOGNITION-BOND PARAMETERS

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Integrated Evolving Spiking Neural Network and Feature Extraction Methods for Scoliosis Classification

Adolescent Idiopathic Scoliosis(AIS)is a deformity of the spine that affects teenagers.The current method for detecting AIS is based on radiographic images which may increase the risk of cancer growth due to radiation.Photogrammetry is another alternative used to identify AIS by distinguishing the curves of the spine from the surface of a human’s back.Currently,detecting the curve of the spine is manually performed,making it a time-consuming task.To overcome this issue,it is crucial to develop a better model that automatically detects the curve of the spine and classify the types of AIS.This research proposes a new integration of ESNN and Feature Extraction(FE)methods and explores the architecture of ESNN for the AIS classification model.This research identifies the optimal Feature Extraction(FE)methods to reduce computational complexity.The ability of ESNN to provide a fast result with a simplicity and performance capability makes this model suitable to be implemented in a clinical setting where a quick result is crucial.A comparison between the conventional classifier(Support Vector Machine(SVM),Multi-layer Perceptron(MLP)and Random Forest(RF))with the proposed AIS model also be performed on a dataset collected by an orthopedic expert from Hospital Universiti Kebangsaan Malaysia(HUKM).This dataset consists of various photogrammetry images of the human back with different types ofMalaysian AIS patients to solve the scoliosis problem.The process begins by pre-processing the images which includes resizing and converting the captured pictures to gray-scale images.This is then followed by feature extraction,normalization,and classification.The experimental results indicate that the integration of LBP and ESNN achieves higher accuracy compared to the performance of multiple baseline state-of-the-art Machine Learning for AIS classification.This demonstrates the capability of ESNN in classifying the types of AIS based on photogrammetry images.

Application of BP Neural Network in Classification and Prediction of Blended Learning Achievements

Analyzing and predicting the learning behavior data of students in blended teaching can provide reference basis for teaching.Aiming at weak generalization ability of existing algorithm models in performance prediction,a BP neural network is introduced to classify and predict the grades of students in the blended teaching.L2 regularization term is added to construct the BP neural network model in order to reduce the risk of overfitting.Combined with Pearson coefficient,effective feature data are selected as the validation dataset of the model by mining the data of Chao-Xing platform.The performance of common machine learning algorithms and the BP neural network are compared on the dataset.Experiments show that BP neural network model has stronger generalizability than common machine learning models.The BP neural network with L2 regularization has better fitting ability than the original BP neural network model.It achieves better performance with improved accuracy.

Supervised Learning for Gene Regulatory Network Based on Flexible Neural Tree Model

Gene regulatory network (GRN) inference from gene expression data remains a big challenge in system biology. In this paper, flexible neural tree (FNT) model is proposed as a binary classifier for inference of gene regulatory network. A novel tree-based evolutionary algorithm and firefly algorithm (FA) are used to optimize the structure and parameters of FNT model, respectively.The two E.coli networks are used to test FNT model and the results reveal that FNT model performs better than state-of-the-art unsupervised and supervised learning methods.

Classification of Gastric Lesions Using Gabor Block Local Binary Patterns

The identification of cancer tissues in Gastroenterology imaging poses novel challenges to the computer vision community in designing generic decision support systems.This generic nature demands the image descriptors to be invariant to illumination gradients,scaling,homogeneous illumination,and rotation.In this article,we devise a novel feature extraction methodology,which explores the effectiveness of Gabor filters coupled with Block Local Binary Patterns in designing such descriptors.We effectively exploit the illumination invariance properties of Block Local Binary Patterns and the inherent capability of convolutional neural networks to construct novel rotation,scale and illumination invariant features.The invariance characteristics of the proposed Gabor Block Local Binary Patterns(GBLBP)are demonstrated using a publicly available texture dataset.We use the proposed feature extraction methodology to extract texture features from Chromoendoscopy(CH)images for the classification of cancer lesions.The proposed feature set is later used in conjuncture with convolutional neural networks to classify the CH images.The proposed convolutional neural network is a shallow network comprising of fewer parameters in contrast to other state-of-the-art networks exhibiting millions of parameters required for effective training.The obtained results reveal that the proposed GBLBP performs favorably to several other state-of-the-art methods including both hand crafted and convolutional neural networks-based features.

基于改进二进制粒子群算法优化DBN的轴承故障诊断

针对滚动轴承故障振动信号非平稳性的特点,对二进制粒子群优化算法(binary particles swarm optimization,BPSO)和深度信念网络(deep belief network,DBN)进行研究,提出一种基于局部均值分解(local mean decomposition,LMD)和IBPSO-DBN的轴承故障诊断方法。提出用加权惯性权重改进BPSO迭代过程中的固定权重,再用改进BPSO优化DBN的隐含层神经元个数和学习率。该方法先对信号进行LMD,提取出各PF分量的散布熵和时域指标,并构建特征矩阵,然后把特征矩阵输入改进BPSO-DBN模型中训练,实现滚动轴承故障诊断和分类。采用试验轴承数据做验证并与其他诊断方法对比,结果表明,基于LMD和BPSO-DBN的滚动轴承故障诊断方法具有较好的故障识别率。

水库年径流改进EEMD-BNN神经网络耦合预测模型研究

为提高年径流预测预报精度,促进水库防汛抗旱、优化调度和水资源管理与保护工作顺利开展,引入三次样条插值对EEMD经验模态分解进行优化,并与BNN神经网络相融合构建EEMD-BNN水库径流预测耦合模型。三次样条插值能改进EEMD对上、下包络线的光滑拟合,便于模型准确提取径流特性的IMF模态分量和趋势项。基于变分推理的贝叶斯神经网络对IMF分量进行学习训练后,经聚合重构获得能真实反映径流时间序列特征的预测数据。结果表明,改进EEMD-BNN模型对水库径流具有很好的预测适用性和有效性,相比传统EEMD模型和EEMD-BP模型,收敛性好、精度高且具备全局寻优稳定性,可为水库中长期径流预测提供一种新的参考方法。

基于多频特征和纹理增强的轻量化图像超分辨率重建

现有基于卷积神经网络主要关注图像重构的精度,忽略了过度参数化、特征提取不充分以及计算资源浪费等问题。针对上述问题,提出了一种轻量级多频率特征提取网络(MFEN),设计了轻量化晶格信息交互结构,利用通道分割和多模式卷积组合减少参数量;通过分离图像的低频、中频以及高频率信息后进行特征异构提取,提高网络的表达能力和特征区分性,使其更注重纹理细节特征的复原,并合理分配计算资源。此外,在网络内部融合局部二值模式(LBP)算法用于增强网络对纹理感知的敏感度,旨在进一步提高网络对细节的提取能力。经验证,该方法在复杂度和性能之间取得了良好的权衡,即实现轻量有效提取图像特征的同时重建出高分辨率图像。在Set5数据集上的2倍放大实验结果最终表明,相比较于基于卷积神经网络的图像超分辨率经典算法(SRCNN)和较新算法(MADNet),所提方法的峰值信噪比(PSNR)分别提升了1.31 dB和0.12 dB,参数量相比MADNet减少了55%。

深度学习在糖尿病视网膜病变分类领域的研究进展

糖尿病视网膜病变是导致糖尿病患者视力受损的主要原因之一,早期的分类诊断对于病情的治疗与控制具有重要意义。深度学习方法能够自动提取视网膜病变的特征并进行分类,因此成为糖尿病视网膜病变分类的重要工具。介绍常用的糖尿病视网膜病变数据集及评价指标,总结了深度学习在糖尿病视网膜病变二分类中的应用;综述了不同的经典深度学习模型在糖尿病视网膜病变严重程度分类中的应用,重点阐述卷积神经网络的分类诊断方法,并对不同方法进行综合对比分析;最后讨论该领域面临的挑战,并对未来发展方向进行了展望。

基于深度学习的Linux系统DKOM攻击检测

直接内核对象操纵(DKOM)攻击通过直接访问和修改内核对象来隐藏内核对象,是主流操作系统长期存在的关键安全问题。对DKOM攻击进行基于行为的在线扫描适用的恶意程序类型有限且检测过程本身易受DKOM攻击影响。近年来,针对潜在受DKOM攻击的系统进行基于内存取证的静态分析成为一种有效和安全的检测方法。现有方法已能够针对Windows内核对象采用图神经网络模型进行内核对象识别,但不适用于Linux系统内核对象,且对于缺少指针字段的小内核对象的识别有效性有限。针对以上问题,设计并实现了一种基于深度学习的Linux系统DKOM攻击检测方案。首先提出了一种扩展内存图结构刻画内核对象的指针指向关系和常量字段特征,利用关系图卷积网络对扩展内存图的拓扑结构进行学习以实现内存图节点分类,使用基于投票的对象推测算法得出内核对象地址,并通过与现有分析框架Volatility的识别结果对比实现对Linux系统DKOM攻击的检测。提出的扩展内存图结构相比现有的内存图结构能更好地表示缺乏指针但具有常量字段的小内核数据结构的特征,实现更高的内核对象检测有效性。与现有基于行为的在线扫描工具chkrootkit相比,针对5种现实世界Rootkit的DKOM行为,所提方案实现了更高的检测有效性,精确度提高20.1%,召回率提高32.4%。

功能磁共振在中医药治疗缺血性脑卒中领域的研究热点与前沿

背景:探讨功能磁共振在中医药治疗缺血性脑卒领域的研究现状与前沿热点,把握未来研究趋势,以期为后续该领域相关研究提供参考依据。目的:采用CiteSpace知识图谱结合二元Logistic回归方程可视化解析基于功能磁共振中医药治疗缺血性脑卒中领域中的研究热点与前沿,把握未来研究趋势,并进一步探索与脑卒中后功能障碍类型相关的神经活动异常脑区分布。方法:检索中国知网、万方、维普、中国生物医学文献数据库及Web of Science核心集数据库的相关文献,采用CiteSpace软件绘制关键词共现、关键词聚类时间线、关键词突显性检测、共被引文献图谱可视化分析该领域研究热点与前沿热点;采用二元Logistic回归分析拟合与缺血性脑卒中后不同功能障碍相关的神经活动异常的脑区分布。结果与结论:①共纳入354篇文献进行Citespace知识图谱分析,国内外年度发文量显示,该领域研究热度自2000-2022年总体呈上升趋势,发展前景良好,但核心力量主要集中在国内。②关键词共现与聚类时间线分析结果显示,失语、偏瘫和认知障碍为热点卒中后功能障碍类型;电针、针刺、头针为热点干预措施;功能连接为热点分析方法,静息态功能磁共振为热点扫描技术;各研究热点时间跨度均较长,提示具有一定的研究价值且研究逐步深入,推动了该领域研究进展;但目前干预措施以针刺为主,缺少对中药、中成药、针药并用等其他中医药疗法的研究。③关键词突显性检测结果显示,功能连接、图论、度中心性、默认网络、随机对照试验的影响力大,爆发力强,为该领域当下及今后的前沿热点词,提示该领域未来研究应加强对全脑网络信息整合的研究,同时加强对临床试验设计的科学性与严谨性。④共被引文献分析结果显示,缺血性脑卒中的流行病学调查、针刺治疗脑卒中的安全性及有效性、不同任务下大脑激活模式、脑卒中后脑网络功能异常的神经病理机制为该领域研究的理论基础;探索中医药靶向脑区与神经网络,揭示中医药促进脑卒中后神经重构的脑效应机制为该领域的研究方向。⑤共纳入255篇文献进行二元Logistic回归分析结果显示,感觉运动皮质、运动前区的神经功能异常与脑卒中后运动功能障碍的发生呈正相关;海马、小脑后叶、楔前叶、颞下回、前扣带回的神经功能异常与脑卒中后认知障碍的发生呈正相关;楔叶、角回、前额叶的神经功能异常与脑卒中后情感障碍的发生呈正相关;前扣带回、小脑后叶的神经功能异常与脑卒中后吞咽障碍的发生呈正相关。⑥上述脑区是感觉运动网络、默认网络及奖赏环路的核心脑区,提示为缺血性脑卒中的危险因素,其中与功能障碍相关的脑网络内或网络间功能异常可能是中医药干预的潜在靶区,但神经活动激活或抑制的具体变化仍有待未来研究补充完善。

基于机器学习的电弧行为识别与特征分析

电弧熔丝增材制造过程中电弧行为是影响零件成形精度及质量的关键因素之一,针对电弧熔丝增材制造过程中电弧无振荡、摇摆振荡以及圆周振荡3种电弧状态的监测图像,提出一种基于局部二值模式(local binary pattern,LBP)与GoogLeNet神经网络结合识别电弧模式的新方法.结果表明,通过局部二值模式获取电弧形态图像中的纹理特征,然后建立GoogLeNet神经网络模型,相比于直接对原始图像进行神经网络的训练,该方法可有效识别电弧长度、宽度以及左右最大倾角随堆积层数的变化规律,从而精准判别电弧所属状态.针对常规存在熔池、熔滴以及复杂背景等因素干扰的电弧形态图像,该方法处理后可获得更清晰的电弧边缘轮廓,更有利于将熔池、熔滴和电弧的形态边界进行划分,最终的状态识别准确率可达99.50%,为电弧熔丝增材制造过程中的电弧状态监测提供理论参考.

超高强钢U形件热冲压的NSGA-Ⅱ多目标优化方法

为了减小超高强钢U形件热冲压成形的回弹角和生产周期,提出了基于二元耦合选择NSGA-Ⅱ算法的多目标优化方法。介绍了超高强钢温度和微观组织随热冲压成形过程的变化情况,以减小成形回弹角和冲压周期为目标建立了多目标优化模型,选择了坯料初始温度、冲压速度、保压压强作为优化的试验因素。在优化空间中随机抽取了50组采样点,根据试验得到了试验指标参数值。使用单个自适应神经元网络对试验指标和试验因素间的模型进行了回归,提出了二元耦合选择NSGA-Ⅱ算法进行优化模型求解。对优化后的参数组合进行生产验证,优化后回弹角均值比厂家产品减小了27.59%,单件的生产周期减小了3.52%,且回弹角和生产周期的标准差略有减小,说明优化后的生产质量、生产效率、生产稳定性均有所提高。

一阶逻辑中基于treelet图神经网络的前提选择

前提选择是解决自动定理证明器面对大规模问题时性能降低的有效方法。当前面向一阶逻辑中前提选择的主流图神经网络忽略了逻辑公式图内部的节点顺序信息。针对此问题,将一种面向高阶逻辑公式的保序方法拓展到一阶逻辑中,并提出了一种基于treelet的图神经网络模型。该模型在信息聚合时一部分聚合中心节点的父、子节点信息,另一部分聚合节点顺序信息。实验分析表明:基于treelet的图神经网络模型在前提选择任务中比最优的主流图神经网络模型的分类准确率提高了约2%。