Novel method for the evaluation of data quality based on fuzzy control

One of the goals of data collection is preparing for decision-making, so high quality requirement must be satisfied. Rational evaluation of data quality is an effective way to identify data problem in time, and the quality of data after this evaluation is satisfactory with the requirement of decision maker. A fuzzy neural network based research method of data quality evaluation is proposed. First, the criteria for the evaluation of data quality are selected to construct the fuzzy sets of evaluating grades, and then by using the learning ability of NN, the objective evaluation of membership is carried out, which can be used for the effective evaluation of data quality. This research has been used in the platform of ＇data report of national compulsory education outlay guarantee＇ from the Chinese Ministry of Education. This method can be used for the effective evaluation of data quality worldwide, and the data quality situation can be found out more completely, objectively, and in better time by using the method.

Multi-component quantitative and feed-forward neural network for pattern classification of raw and wine-processed Corni Fructus

Background:To promote the quality evaluation,clarify the processing mechanism and distinguish origins of Corni Fructus(cornus)from different regions.Methods:This study developed a high performance liquid chromatography method for simultaneous determination of 5-hydroxymethylfurfural,2 phenolic acids and 4 iridoid glycosides and the reference fingerprint of cornus from different regions.In addition,the feedforward neural network model provided a pattern classification of sample regions.Results:The content of morroniside and loganin were the highest in all raw cornus samples ranging from 9.45μg/mg to 16.3μg/mg and 6.64μg/mg to 13.7μg/mg,respectively.The level of sweroside in raw cornus from Henan(0.83μg/mg^(-1).39μg/mg)and Zhejiang(0.64μg/mg^(-1).17μg/mg)were greater than other origins.After wine-processing,the glucose or fructose were dehydrated to increase the levels of 5-hydroxymethylfurfural.The C-4 position of-COOCH3 of hot-sensitive iridoid glycosides was hydrolyzed to generate-COOH as stable components.Polyphenol derivatives may be degraded to increase the content of phenolic acid.Subsequently,an excellent feedforward neural network model for identification of raw cornus and wine-prepared cornus was established which could distinguish the sample origins.Conclusion:This work provided a trustworthy method to evaluate the quality and distinguish the sources of cornus.Meanwhile,the clear processing mechanism provided a scientific foundation for controlling the cornus quality during wine-processing.

On-line evaluating on quality of mild steel joints in resistance spot welding

A method was developed to realize quality evaluation on every weld-spot in resistance spot welding based on information processing of artificial intelligent. Firstly, the signals of welding current and welding voltage, as information source, were synchronously collected. Input power and dynamic resistance were selected as monitoring waveforms. Eight characteristic parameters relating to weld quality were extracted from the monitoring waveforms. Secondly, tensile-shear strength of the spot-welded joint was employed as evaluating target of weld quality. Through correlation analysis between every two parameters of characteristic vector, five characteristic parameters were reasonably selected to found a mapping model of weld quality estimation. At last, the model was realized by means of the algorithms of Radial Basic Function neural network and sample matrixes. The results showed validations by a satisfaction in evaluating weld quality of mild steel joint on-line in spot welding process.

基于SOFM神经网络的口语教学质量评价模型研究

为了对高校英语教师的口语课堂教学质量进行评价,文中提出了利用SOFM神经网络进行口语课堂教学质量评价的方法,建立了基于SOFM神经网络的教学质量评价模型,并对模型进行训练和泛化能力测试。仿真结果表明:建立的评价模型能够根据获胜神经元在竞争层的位置对教师的教学质量进行分类,收敛速度快,分类准确率高,泛化能力强,验证了模型的有效性。

FANN-Based Surface Water Quality Evaluation Model and Its Application in the Shaoguan Area

A fuzzy neural network model is proposed to evaluate water quality. The model contains two parts： first, fuzzy mathematics theory is used to standardize the samples; second, the RBF neural network and the BP neural network are used to train the standardized samples. The proposed model was applied to assess the water quality of 16 sections in 9 rivers in the Shaoguan area in 2005. The evaluation result was compared with that of the RBF neural network method and the reported results in the Shaoguan area in 2005. It indicated that the performance of the proposed fuzzy neural network model is practically feasible in the application of water quality assessment and its operation is simple.

GA-BP Air Quality Evaluation Method Based on Fuzzy Theory

With the rapid development of China’s economy,the scale of the city has been continuously expanding,industrial enterprises have been increasing,the discharge of multiple pollutants has reached the top of the world,and the environmental problems become more and more serious.The air pollution problem is particularly prominent.Air quality has become a daily concern for people.In order to control air pollution,it is necessary to grasp the air quality situation in an all-round way.It is necessary to evaluate air quality.Accurate results of air quality evaluation can help people know more about air quality.In this paper,refers to previous research results and different evaluation methods,combined with artificial neural network,fuzzy theory,genetic algorithm,GA-BP hybrid algorithm based on fuzzy theory is proposed to evaluate air quality.At the same time,for the problem that the two-grade standard of air quality annual evaluation is not suitable for practical application,the four-grade standard for annual air quality evaluation has been proposed,and its practicality has been verified through experiments.By setting contrast experiments and comparing the air quality evaluation model based on standard BP algorithm,it is proved that the fuzzy GA-BP evaluation model is better than the standard BP model,both in efficiency and accuracy.

Predicting gas-bearing distribution using DNN based on multi-component seismic data: Quality evaluation using structural and fracture factors

The tight-fractured gas reservoir of the Upper Triassic Xujiahe Formation in the Western Sichuan Depression has low porosity and permeability. This study presents a DNN-based method for identifying gas-bearing strata in tight sandstone. First, multi-component composite seismic attributes are obtained.The strong nonlinear relationships between multi-component composite attributes and gas-bearing reservoirs can be constrained through a DNN. Therefore, we identify and predict the gas-bearing strata using a DNN. Then, sample data are fed into the DNN for training and testing. After optimized network parameters are determined by the performance curves and empirical formulas, the best deep learning gas-bearing prediction model is determined. The composite seismic attributes can then be fed into the model to extrapolate the hydrocarbon-bearing characteristics from known drilling areas to the entire region for predicting the gas reservoir distribution. Finally, we assess the proposed method in terms of the structure and fracture characteristics and predict favorable exploration areas for identifying gas reservoirs.

Comparative Study of Transfer Learning Models for Retinal Disease Diagnosis from Fundus Images

While the usage of digital ocular fundus image has been widespread in ophthalmology practice,the interpretation of the image has been still on the hands of the ophthalmologists which are quite costly.We explored a robust deep learning system that detects three major ocular diseases:diabetic retinopathy(DR),glaucoma(GLC),and age-related macular degeneration(AMD).The proposed method is composed of two steps.First,an initial quality evaluation in the classification system is proposed to filter out poorquality images to enhance its performance,a technique that has not been explored previously.Second,the transfer learning technique is used with various convolutional neural networks(CNN)models that automatically learn a thousand features in the digital retinal image,and are based on those features for diagnosing eye diseases.Comparison performance of many models is conducted to find the optimal model which fits with fundus classification.Among the different CNN models,DenseNet-201 outperforms others with an area under the receiver operating characteristic curve of 0.99.Furthermore,the corresponding specificities for healthy,DR,GLC,andAMDpatients are found to be 89.52%,96.69%,89.58%,and 100%,respectively.These results demonstrate that the proposed method can reduce the time-consumption by automatically diagnosing multiple eye diseases using computer-aided assistance tools.

Indoor Environment Quality Monitoring and Evaluation System Based on LoRa Communication

Indoor environmental quality has always been the focus of people’s long-term attention. How to monitor the indoor environmental level conveniently and accurately is a problem that people pay attention to now. After research, an indoor environment level monitoring system based on LoRa communication is designed. The system is mainly divided into two parts, the detection node, and the monitoring terminal. Temperature, humidity, light intensity, noise, formal-dehyde, and carbon dioxide are detected through the node with STM32F103ZET6 microcontroller as the controller;the data is sent to the monitoring terminal for display through LoRa communication. At the same time, the T-S fuzzy neural network (TSFNN) is improved by the particle swarm optimization (PSO) algorithm to classify the indoor environment quality level. Experimental test: the total error of the improved TSFNN model test set is reduced by 8.6007. The system can monitor the indoor environment level objectively and reliably, and has high practical value.

Image aesthetic quality evaluation using convolution neural network embedded learning

A way of embedded learning convolution neural network(ELCNN) based on the image content is proposed to evaluate the image aesthetic quality in this paper. Our approach can not only solve the problem of small-scale data but also score the image aesthetic quality. First, we chose Alexnet and VGG_S to compare for confirming which is more suitable for this image aesthetic quality evaluation task. Second, to further boost the image aesthetic quality classification performance, we employ the image content to train aesthetic quality classification models. But the training samples become smaller and only using once fine-tuning cannot make full use of the small-scale data set. Third, to solve the problem in second step, a way of using twice fine-tuning continually based on the aesthetic quality label and content label respective is proposed, the classification probability of the trained CNN models is used to evaluate the image aesthetic quality. The experiments are carried on the small-scale data set of Photo Quality. The experiment results show that the classification accuracy rates of our approach are higher than the existing image aesthetic quality evaluation approaches.

基于BP神经网络的高校教师精准教学能力评价模型构建

通过精准教学以促进学生个性化成长是教育理想和国家政策的不懈追求.高校教师是实施精准教学的“基”,现有关于其教学能力的评价体系中普遍存在概念不清和多采用主观构建评价指标的问题.为此,开展了基于BP神经网络的高校教师精准教学能力评价模型研究.首先,以理论研究为基础,对精准教学能力进行等级划分并构建评价指标框架,运用层级分析法建立指标权重;其次,利用BP神经网络智能学习的特性,以不同数据类型的指标值为输入,对应能力综合值为输出,检验精准教学能力分级及指标权重的合理性,进而生成较为客观的评价模型;最后,利用开发的评价系统和调查问卷进行样本数据采集和模型检验,从神经网络对数据的分类、拟合及仿真结果来看,模型能够对高校教师的精准教学能力进行客观评价,教师对模型测量结果的准确性也具有较高认可度.

基于CNN-SVM和集成学习的固井质量评价方法

为解决固井质量评价问题,提出一种基于CNN-SVM和集成学习的固井质量评价方法.首先,针对DenseNet模型采取缩减网络层数、增加多尺度卷积层、嵌入卷积注意力模块等改进措施,以提高模型的训练速度和评价准确率;其次,利用InceptionV1模块和扩张卷积构建一个模型复杂度相对较小且评价准确率相对较高的Inception-DCNN模型;再次,优选3个经典的卷积神经网络模型(ResNet50,MobileNetV3-Small, GhostNet),利用卷积神经网络强大的特征提取能力及支持向量机的结构风险最小化能力,将上述模型分别与支持向量机组合成新的CNN-SVM模型,以提升模型的泛化能力;最后,采用Bagging方式将5个新的CNN-SVM模型集成为一个强学习器,从而提升评价结果的准确度,增强模型的抗干扰能力.实验结果表明,该方法对测试集中的3类评价样本的准确率为97.69%,与单个模型和其他方法相比提升了1~9个百分点,验证了采用基于CNN-SVM和集成学习的方法进行固井质量评价是切实可行的.

面向人眼宽视场视觉成像质量的评价方法

为考虑边缘视觉的影响,实现对人眼宽视场条件下视觉成像质量的量化,提出一种基于孪生神经网络的多视域成像质量评价方法。构建个性化眼模型,根据波前像差值获得不同视场处的成像图;利用色彩差异分割成像图中的不同区域,将其作为子图像以样本对的形式输入到孪生神经网络中,提取图像的多维特征;模拟人眼对色彩的差异化感知,对区域图像质量评价值进行加权,得到对整幅图像的质量评价。为验证算法的有效性,在TID2013、LIVE和CSIQ这3个图像数据库上进行实验,其结果表明,该方法对多视场处成像质量的量化评估有良好的性能。

基于改进FNN-BP网络的304不锈钢薄板焊接质量推断模型

针对目前激光焊接领域的激光焊接参数智能设定的发展方向,智能焊接系统的焊接参数推定模块成为了热点研究对象。在分析了焊接工艺参数对焊接质量的影响之后,搭建了一种基于改进模糊专家系统和BP神经网络的激光焊接质量推断模型,该模型包括两部分内容,即基于焊接速度、焊接功率和离焦量的焊接质量模糊推断和基于预测值、板材厚度、峰值功率和占空比的BP修正神经网络。焊接质量模糊推断,首先基于已有人工经验进行焊接参数模糊化和焊接规则库建立,然后通过分析确定模糊推断类型,最后进行模糊推断输出焊接质量预测值;BP神经网络修正,基于板材厚度等参数对不同板材厚度下焊缝图像质量评分和平面度差值进行预测值修正,以获得更加准确的推断值。通过实验证明,该不锈钢薄板智能激光焊接系统具有一定的可行性和重要的工程意义。

基于智能进化算法的可见水印对抗攻击

随着公民版权意识的提高,越来越多含有水印的图像出现在生活中。然而,现有的研究表明,含有水印的图像会导致神经网络分类错误,这对神经网络的普及和应用构成了巨大的威胁。对抗训练是解决这类问题的防御方法之一,但是需要使用大量的水印对抗样本作为训练数据。为此,提出了一种基于智能进化算法的可见水印对抗攻击方法来生成高强度的水印对抗样本。该方法不仅能快速生成水印对抗样本,而且还能使其最大程度地攻击神经网络。此外,该方法还加入了图像质量评价指标来约束图像的视觉损失,从而使水印对抗样本更加美观。实验结果表明,所提方法相比于基准水印攻击方法时间复杂度更低,相比于基准黑盒攻击对神经网络攻击成功率更高。

基于MLP-CNN的固井质量智能评价方法

固井质量的好坏关系到油气井的产量和寿命,目前最常用的方法是使用声幅—变密度测井进行评估,但是解释过程复杂,且与重大风险相关的决策需要根据固井解释结果做出。因此,固井质量评价必须由经验丰富的专家进行解释,耗时耗力。为了提高固井解释的效率,本文基于VGG、ResNet等卷积神经网络对固井质量进行自动解释,但是准确率不足。于是,本文提出一种多层感知机和卷积神经网络并联的方法(MLP-CNN),声幅数据输入到多层感知机中,变密度图输入卷积神经网络中;针对变密度图存在不同尺度信息的特征(条纹的粗细、明暗、形状),本文修改了卷积神经网络的结构,设置了大小不同的卷积核,提取不同尺度信息。本文使用了塔里木油田富源区块的9000个数据进行训练和验证,结果表明,相较于传统的VGG、ResNet等卷积网络,MLP和CNN并联网络有效提高了固井质量识别的准确率,评价精度为90%,并且相较于单一尺度卷积核,多个大小不同卷积核的卷积神经网络算法更适合于固井变密度图像特征的提取,本文修改了卷积神经网络部分结构,建立的带有3个尺寸不同卷积核的MLP-CNN神经网络比单一卷积核的MLP-CNN模型提高了5%的准确率;同时,本文对比了7种网络的时间复杂度和空间复杂度,结果表明,MLP-CNN并联网络能有效避免大量的无效卷积,节省了模型计算成本,提高模型的计算效率。最后,为了测试模型的迁移性,本文使用塔里木油田满深和跃满区块的6万条数据进行了测试,评价准确率达89.16%,迁移效果良好,模型具有较强的鲁棒性。

高光谱结合人工神经网络对丹参药材的质量评价

采用高光谱结合人工神经网络,建立了不同来源的丹参药材的鉴别方法。采集了8类不同来源丹参药材样品的高光谱数据,用Savitzky-Golay平滑滤波方法对高光谱数据进行预处理,再采用反向传播-人工神经网络方法建立分类模型。结果显示,经Savitzky-Golay平滑滤波光谱预处理后,建立的模型对测试集的分类准确率达到98.75%。研究表明,高光谱结合人工神经网络方法是一种很有前景的丹参药材的质量评价方法。

基于BP神经网络的飞行员着舰训练品质评估

舰载战斗机在深海作战中发挥着重要作用,飞行员着舰训练品质的高低直接影响着舰载机的战斗力,以往对飞行员着舰训练品质的评估采用人工方式,很少尝试神经网络。针对这方面的不足,提出了基于反向传播(BP)神经网络的飞行员着舰训练品质评估方法。利用着舰飞行参数、舰载机尾钩挂锁情况以及专家组评分,构建数据集;对网络进行训练和测试,确定网络参数,训练着舰评估网络模型;通过验证集对网络进行仿真验证,验证模型的可靠性。验证结果表明,该网络能较为准确地评估着舰分数和尾钩挂锁情况,可为飞行员着舰训练提供参考。

基于卷积神经网络的汽车行人警示音评价系统设计

为了使电动汽车的行人警示音符合人耳主观感受及汽车品牌定位,设计了一个基于卷积神经网络的声品质评价系统,实现了对行人警示音频的客观评价。采用等级评分对设计好的音频文件进行主观评价,并获得主观评分。基于ISO 532-1:2014标准计算音频文件的响度、粗糙度、抖动度、烦扰度、尖锐度等声品质客观参数,并将其作为卷积神经网络模型的特征输入。评价模型的输出设定为豪华,舒适,科技三个指标。经过数据训练,模型可以有效输出给定指标的评价分数,并与主观评价分数吻合良好。所提出的模型可以实现端到端的声品质客观评价,评价结果能够有效反映人耳主观感受,从而为行人警示音的快速评价提供新的方法。