A comparison in a back-bead prediction of gas metal arc welding using multiple regression analysis and artificial neural network

This research was done on the basis of prediction that there is a relationship between welding parameters and geometry of the back-bead in arc welding which is a gap. Multiple regression analysis and artificial neural network were used as methods for predicting the geometry of the back-bead. The multiple regression analysis and the artificial neural network were formed, and the analysis data or verification data which were used in the formation process of the multiple regression, and the training data or test data which were used in the formation process of the artificial neural network, were used to perform the prediction of the back-bead. Through this research, it was found that the error rate predicted by the artificial neural network was smaller than that predicted by the multiple regression analysis, in terms of the width and depth of the back-bead. It was also found that between the two predictions, the prediction of the width of the back-bead was superior to the prediction of the depth in both methods.     

色貌模型的人工神经网络方法的研究

色貌模型(CAM)主要解决不同观察条件、不同背景和不同环境下的颜色真实再现问题。采用人工神经网络(ANN)的方法来实现目前最新的色貌模型CIECAM02的预测,包括正向预测(从色度参数到色貌属性参数)和逆向预测(从色貌属性参数到色度参数),应用自然色系统(NCS)中的部分色样作为神经网络的训练和测试样本。由于正向输出色貌属性参数空间不是均匀的,对于网络预测精度用特殊方法评估,而对于逆向模型则可直接利用LAB色差公式评价。测试的结果表明:用神经网络对CIECAM02模型的预测达到了较高的精度。     

Using Short Wave Visible–Near Infrared Reflectance Spectroscopy to Predict Soil Properties and Content

In this study, short wave visible–near infrared reflectance spectroscopy was evaluated for prediction of diverse soil properties related to four different soil series of several regions in Jiangxi, China. A total of 240 soil samples were collected for the calibration (n = 168) and prediction (n = 72) sets. The used wavelength range of short wave visible–near infrared reflectance spectroscopy is 325–1075 nm. Partial least squares regression and back propagation neural network were used to develop models for soil properties such as organic matter and extractable forms of calcium, magnesium, and potassium. Performance of these models was also compared and analyzed. The input of back propagation neural network was the first six principal components resulted from the principal component analysis and the optimal number of latent variables obtained from partial least squares regression. The overall results showed that the performance of partial least squares regression model was inferior to all back propagation neural network models. The best prediction was obtained with latent variables as input of back propagation neural network model for organic matter (determination coefficient = 0.84 and relative predictive determinant = 2.38), which was classified as very good model predictions. The prediction of calcium, magnesium, and potassium was classified as fair (determination coefficient = 0.56–0.68 and relative predictive determinant = 1.51–1.61), where quantitative predictions were considered possible. It is recommended to adopt latent variables as input for back propagation neural network model predicting soil properties with short wave visible–near infrared reflectance spectroscopy. In conclusion, short wave visible–near infrared reflectance spectroscopy was variably successful in estimating soil properties and showed potential for substituting laboratory analyses.     

Early-warning signals for an outbreak of the influenza pandemic

Over the course of human history, influenza pandemics have been seen as major disasters, so studies on the influenza virus have become an important issue for many experts and scholars. Comprehensive research has been performed over the years on the biological properties, chemical characteristics, external environmental factors and other aspects of the virus, and some results have been achieved. Based on the chaos game representation walk model, this paper uses the time series analysis method to study the DNA sequences of the influenza virus from 1913 to 2010, and works out the early-warning signals indicator value for the outbreak of an influenza pandemic. The variances in the CGR walk sequences for the pandemic years (or + -1 to 2 years) are significantly higher than those for the adjacent years, while those in the non-pandemic years are usually smaller. In this way we can provide an influenza early-warning mechanism so that people can take precautions and be well prepared prior to a pandemic.     

模糊神经网络在电子器件微波易损性评估中的应用

 应用模糊神经网络预测了电子器件失效阈值随高功率微波参数的变化关系。结合电子器件实验数据较少的情况，提出用可能性理论估计器件失效可能性分布的评估方法;结合模糊神经网络的学习预测能力，得到电子器件失效的可能性分布;并将可能性分布和用信息扩散估计方法得到的概率分布进行了比较，前者能够更好地利用实验数据估计器件失效的可能性。     

模糊神经网络在电子器件微波易损性评估中的应用

应用模糊神经网络预测了电子器件失效阈值随高功率微波参数的变化关系。结合电子器件实验数据较少的情况,提出用可能性理论估计器件失效可能性分布的评估方法;结合模糊神经网络的学习预测能力,得到电子器件失效的可能性分布;并将可能性分布和用信息扩散估计方法得到的概率分布进行了比较,前者能够更好地利用实验数据估计器件失效的可能性。     

基于XRF-CNN土壤重金属Zn元素含量预测模型研究

结合X射线荧光光谱法,针对土壤中重金属元素Zn含量的预测问题,提出基于深度卷积神经网络回归预测模型.对原始土壤进行相关预处理,用粉末压片法制作土壤压片,采用X射线荧光光谱法(X-Ray-fluorescence,XRF)获取土壤光谱,相比于传统检测方式,XRF法具有检测速度快、精度高、操作简单、不破坏样品属性并且可实现...     

互连导线串扰问题的人工神经网络预测

提出应用人工神经网络对互连导线间串扰问题进行预测的方法.选择对互连导线串扰响应有影响的相关参数作为输入预测因子,用基于误差反向传播的BP网络构造输入预测因子与串扰响应输出之间的映射关系,并用MTL和FDTD法计算获得的训练样本集对构造好的BP网络进行训练,建立基于BP网络的导线串扰的预测模型.最后,将串扰的BP预测结果和和测试样本进行比较,表明该方法有效.     

URLLC occasional large time delay prediction based on unbalanced regression and LSTM

Occasional large time delay is one of the bottlenecks for 5G applied to the industrial field. Accurate measurement, analysis, and prediction of communication network latency are of great significance. Due to the strong randomness and small number of samples in ultra-reliable and low latency communication (URLLC), the prediction task of occasional large time delay is very challenging. This paper proposes an URLLC occasional large time delay prediction method based on unbalanced regression algorithms and long–short term memory (LSTM). We first decompose the original sequence by using the variational mode decomposition (VMD) to obtain relatively stable component sequences. The parameters of the VMD are automatically optimized by grasshopper optimization algorithm (GOA). In order to improve the prediction effect of URLLC occasional large time delay, we introduce unbalanced regression algorithms. Before the VMD decomposition of data samples, the SMOGN is introduced to balance the number of large-time delay samples and common-time delay samples. Next we calculate the weight of each sample based on the rarity of each sample point and our proposed LDSWeight. Finally, LSTM is used to complete cost-sensitive learning. The experimental results show that the URLLC occasional large time delay prediction method proposed in this paper has better prediction accuracy than other methods.     

基于机载高光谱成像的柑橘产量预测模型研究

果树的隔年结果现象严重影响果园的果实产量和经济效益。选择受隔年结果现象影响较为严重的柑橘作为研究对象,运用机载高光谱成像仪在较早生长季节(2003年4、5、6月)获取柑橘果树的高光谱图像,利用偏最小二乘回归(PLS)确定基于高光谱图像数据的模型预测变量,建立柑橘产量的多元线性回归(MLR)和人工神经网络(ANN)预测模型。研究结果表明,利用5月份获得的高光谱图像建立的模型具有最优的产量预测效果, 而且PLS-MLR模型比PLS-ANN模型具有更好的稳定性和一致性。该研究结果为今后研制和开发基于高光谱成像技术的柑橘产量预测方法提供了重要的理论和技术基础。     

基于人工神经网络的滴膜共存冷凝传热模型的研究

为了研究影响滴膜共存冷凝传热特性的因素,如滴膜区间面积比、滴膜相对位置、表面分割方式,表面过冷度等对冷凝传热的特性共同作用,本文应用人工神经网络技术,建立表面分割数、滴膜区面积比、凝液环数、表面过冷度与强化传热比之间的综合评价预测模型。结果表明,基于Matlab语言的人工神经网络模型具有较高的预测准确率及泛化能力,能够很好的评价和预测不同条件下的冷凝传热特性。     

Prediction of aqueous solubility of compounds based on neural network

Neural network algorithms are gradually applied to the field of chemical informatics. In this paper, the neural network models are used to predict the properties of compounds based on Molecular Information. We predict the aqueous solubility of compounds, and evaluate the prediction results of the Neural Networks including CNN, RNN, DNN, SNN. The performance of the models in predicting the solubility is able to meet or exceed the predicted effect of the method based on the molecular structure (ESOL). DNN model performance is of more accuracy, and RNN performance is of better stability. This method can directly avoid complex molecular structure characterisation, and provide a convenient and flexible way to predict properties of compounds.     

Prediction of Time Series Gene Expression and Structural Analysis of Gene Regulatory Networks Using Recurrent Neural Networks

Methods for time series prediction and classification of gene regulatory networks (GRNs) from gene expression data have been treated separately so far. The recent emergence of attention-based recurrent neural network (RNN) models boosted the interpretability of RNN parameters, making them appealing for the understanding of gene interactions. In this work, we generated synthetic time series gene expression data from a range of archetypal GRNs and we relied on a dual attention RNN to predict the gene temporal dynamics. We show that the prediction is extremely accurate for GRNs with different architectures. Next, we focused on the attention mechanism of the RNN and, using tools from graph theory, we found that its graph properties allow one to hierarchically distinguish different architectures of the GRN. We show that the GRN responded differently to the addition of noise in the prediction by the RNN and we related the noise response to the analysis of the attention mechanism. In conclusion, this work provides a way to understand and exploit the attention mechanism of RNNs and it paves the way to RNN-based methods for time series prediction and inference of GRNs from gene expression data.     

灰色极限学习机在滚动轴承故障预测中的应用

针对较强噪声环境下的滚动轴承故障预测问题,为提高轴承故障预测的精度,提出并研究了一种新的滚动轴承预测技术。采用将灰色模型和极限学习机（ELM）相结合的方法,针对轴承运行状态值的非线性特点,先将样本数据进行灰色处理,解决数据的随机性和波动性问题,然后代入学习速度快,泛化精度高的ELM神经网络进行训练。在训练完毕后,对未来的轴承运行状态数据进行分析,将其与轴承设备的理论诊断标准相比较以达到故障预测的目的。     

Quantum neural networks: Current status and prospects for development

The idea of quantum artificial neural networks, first formulated in [34], unites the artificial neural network concept with the quantum computation paradigm. Quantum artificial neural networks were first systematically considered in the PhD thesis by T. Menneer (1998). Based on the works of Menneer and Narayanan [42, 43], Kouda, Matsui, and Nishimura [35, 36], Altaisky [2, 68], Zhou [67], and others, quantum-inspired learning algorithms for neural networks were developed, and are now used in various training programs and computer games [29, 30]. The first practically realizable scaled hardware-implemented model of the quantum artificial neural network is obtained by D-Wave Systems, Inc. [33]. It is a quantum Hopfield network implemented on the basis of superconducting quantum interference devices (SQUIDs). In this work we analyze possibilities and underlying principles of an alternative way to implement quantum neural networks on the basis of quantum dots. A possibility of using quantum neural network algorithms in automated control systems, associative memory devices, and in modeling biological and social networks is examined.     

Analysis of Glaucoma Diagnosis with Automated Classifiers using Stratus Optical Coherence Tomography

The study compared the performances of two classification methods including logistic regression analysis and artificial neural network (ANN) in terms of the area under the receiver operating characteristic curves for differentiating glaucomatous from normal eyes in Taiwan Chinese population based solely on the quantitative assessment of summary data reports from the Stratus optical coherence tomography (OCT). The logistic regression analysis and ANNs showed promise for increasing diagnostic accuracy of glaucoma using summary data from Stratus OCT. The results can be used as the basis for further improving the diagnostic accuracy of glaucoma     

基于循环神经网络的缝洞型油藏油井产量预测

利用神经网络的强大非线性映射和拟合能力，构建神经网络产量预测模型，并针对油田生产数据的高误差、易缺省等特性和曲线拟合预测不易收敛的情况，提出了训练数据集扩充方法和改良的均方误差损失函数.在拟合油井产量方面取得了显著的效果.     

A Probabilistic Re-Intepretation of Confidence Scores in Multi-Exit Models

In this paper, we propose a new approach to train a deep neural network with multiple intermediate auxiliary classifiers, branching from it. These ‘multi-exits’ models can be used to reduce the inference time by performing early exit on the intermediate branches, if the confidence of the prediction is higher than a threshold. They rely on the assumption that not all the samples require the same amount of processing to yield a good prediction. In this paper, we propose a way to train jointly all the branches of a multi-exit model without hyper-parameters, by weighting the predictions from each branch with a trained confidence score. Each confidence score is an approximation of the real one produced by the branch, and it is calculated and regularized while training the rest of the model. We evaluate our proposal on a set of image classification benchmarks, using different neural models and early-exit stopping criteria.