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一种新的光谱数据自动阈值选择标准

中科院半导体研究所,集成光电子学国家重点实验室

北京人民大学,外国语学院

Highlights

  • Threshold selection is crucial to noise elimination in spectroscopy data processing.

  • A nonparametric and unsupervised method of automatic threshold selection is proposed.

  • The new approach is effective when the distribution of noises is not preconditioned or the gap between signals and noises is not obvious.

  • The method is efficient, easy to implement and widely applicable when compared with previous ones.

Abstract

A nonparametric and unsupervised method of automatic threshold selection to eliminate noise for spectroscopy data processing is described in this paper.

A detecting scheme, named bi-trapezoid criteria, is devised where the threshold is selected according to the turning corner present on the uprising intensity trace. This selecting procedure is very simple, utilizing only basic summation on the sorted intensity sequence to optimize threshold for distinguishing between noises and signals.

This approach is effective in selecting appropriate value to filter noise when the distribution of noises is not preconditioned or the gap between signals and noises is not obvious. Testing on both artificial and authentic data under specific quantitative evaluation condition shows that this new method performs better than previous ones.

基于最大相关熵标准多元校正回归

湖北大学,湖北省应用数学重点实验室

北京铀矿地质研究所

中科院生态环境科学研究中心

中科院自动化研究所

中科院上海生物科学研究所,脑科学与智能技术卓越中心

Highlights

  • A maximum correntropy criterion based regression model is proposed.

  • A nonlinear correntropy-based metric is used to replace the traditional least-squares metric.

  • A half-quadratic optimization technique is developed to solve the correntropy-based model.

  • The nonlinear Gaussian function in MCC leads to an accurate estimation of the regression relation.

  • It outperforms some modified PLS algorithms and robust regression methods.

Abstract

The least-squares criterion is widely used in the multivariate calibration models. Rather than using the conventional linear least-squares metric, we employ a nonlinear correntropy-based metric to describe the spectra-concentrate relations and propose a maximum correntropy criterion based regression (MCCR) model.

To solve the correntropy-based model, a half-quadratic optimization technique is developed to convert a non-convex and nonlinear optimization problem into an iteratively re-weighted least-squares problem. Finally, MCCR can provide an accurate estimation of the regression relation by alternatively updating an auxiliary vector represented as a nonlinear Gaussian function of fitted residuals and a weight computed by a regularized weighted least-squares model.

The proposed method is compared to some modified PLS algorithms and robust regression methods on four real near-infrared (NIR) spectra data sets. Experimental results demonstrate the efficacy and effectiveness of the proposed method.

动态加权分散PCA方法在动态过程故障检测与诊断中的应用

宁波大学,电气工程与计算机科学学院

Highlights

  • A weighted dynamic decentralized PCA method is proposed for dynamic process monitoring.

  • The dynamic feature is characterized for each measured variable through weighting strategy.

  • Case studies demonstrate the priority and the promise of the proposed WDDPCA model.

Abstract

Based on an argument that some process variables can influence other process variables with time-delays, dynamic decentralized principal component analysis (DDPCA) was recently proposed for modeling and monitoring dynamic processes, and it has achieved superior monitoring performance than its counterparts, such as dynamic PCA and dynamic latent variables (DLV).

Although experimental results have demonstrated the promise of selecting dynamic feature (i.e., auto-correlated and cross-correlated variables with time-delays) for each measured variable in handling dynamic process data, it can be easily verified that the dynamic feature selection suffers from a proper determination of a cutoff parameter.

To tackle this issue, an alternative formulation of DDPCA through using variable-weighted method is proposed. The dynamic feature is characterized individually by assigning different weights to different variables with time-delays. The weighted variables are then used to form a block corresponding to each variable, fault detection and diagnosis are thus implemented based on these block PCA models.

The superiority of the proposed weighted DDPCA (WDDPCA) method over dynamic PCA, DLV, and DDPCA are explored by two industrial processes. The comparisons apparently illustrate the salient monitoring performance that can be achieved by WDDPCA.

个人信用的极限学习机对复杂样品的光谱定量分析预测稳定性的改善

天津工业大学,分离膜与膜工艺国家重点实验室

天津工业大学,环境与化学工程学院

Highlights

  • A novel ensemble method named as subagging ELM was proposed.

  • Subagging strategy was introduced to improve the stability of ELM.

  • The performance of the method was tested with fuel oil and blood samples.

  • The proposed method can achieve much better stability and higher accuracy than ELM.

Abstract

Extreme learning machine (ELM) has been attracted increasing attentions for its fast learning speed and excellent generalization performance. However, the prediction result of a single ELM regression model is usually unstable due to the randomly generating of the input weights and hidden layer bias.

To overcome this drawback, an ensemble form of ELM, termed as subagging ELM, was proposed and used for spectral quantitative analysis of complex samples. In the approach, a series of ELM sub-models was built by randomly selecting a certain number of samples from the original training set without replacement, and then the predictions of these sub-models were combined by a simple averaging way to give the final ensemble prediction. The performance of the method was tested with fuel oil and blood samples.

Compared to a single ELM model, the results confirm that subagging ELM can achieve much better stability and higher accuracy than ELM.

故障检测和使用增强KECA化工过程诊断

内蒙古工业大学,电力学院

北京工业大学,电子信息与控制工程学院

数字社区教育部工程研究中心

Highlights

  • A new MSPCA-KECA-based method is developed for fault detection and diagnosis.

  • MSPCA is proposed to extract fault-symptom features for multi-scale problem.

  • Each KECA classifier is dedicated to a specific fault.

  • The Cauchy-Schwarz (CS) divergence is a measure of the similarity between two probability density functions.

  • Results show that the proposed method outperforms KPCA, KICA and KECA.

Abstract

As the main concerns of abnormal event management in process engineering, fault detection and diagnosis have attracted more and more attention recently. A new monitoring method based on kernel entropy component analysis(KECA) is proposed for nonlinear chemical process. Then, an angle-based statistic is designed to express the distinct angular structure that KECA reveals, which is able to measure the similarity between probability density functions.

Likewise, each KECA classifier is dedicated to a specific fault, which provides an expendable framework for incorporating new faults identified in the process. As to the fault features are submerged because of multi-scale property of process data, an enhanced KECA method for fault detection and diagnosis is developed, by adding multi-scale principal component analysis(MSPCA) for features extraction to improve the classification effect of KECA.

The effectiveness of the proposed approach is demonstrated by applying to Tennessee Eastman process. The MSPCA based method essentially captures the fault-symptom correlation, whereas KECA can be an effective method for process fault diagnosis.

基于数据密度估计的增强型实时软测量方法

中国石油大学自动化系

Highlights

  • Soft sensor calibration method by Just-in-time strategy based on estimation of data density.

  • We make precise sampling possible during the deployment of Just-in-time method.

  • We proposed a mechanism to partition the history data base into some differently dense zones.

Abstract

Soft sensor is an efficacious solution to predict the hard-to-measure target variable by using the process variables. In practical application scenarios, however, the target feedback cycle is usually larger than that of process variables which causes a lack of sufficient prediction errors during the period of a target feedback cycle.

Consequently soft sensor cannot make calibration timely and performance deteriorates. We proposed an enhanced just-in-time (JIT) soft sensor calibration method using data density estimation. The enhanced JIT method as the core is basically implemented by the estimate of data density of the history database. First the database is divided into a plenty of data blocks. The center of each block is calculated in pair of the process and target variables respectively.

For each center we designed a criterion to preliminarily work out the corresponding optimized sampling number to indirectly represent the data density of each block and further use pooling strategy to partition the database into some differently dense zones. Ultimately we obtain the data density of the database making precise sampling feasible to improve the performance of the JIT-based method. The proposed calibration method is tested through comparative experiments on a pH neutralization facility in our laboratory and is verified feasible and effective.

基于相似度的鲁棒概率隐变量回归模型及其核扩展

浙江科技大学,自动化与电气工程学院

台湾中原大学,化学工程系

浙江大学,工业控制技术国家重点实验室

Highlights

  • A robust probability latent variable regression (RPLVR) is proposed.

  • A robust probability kernel latent variable regression (RPKLVR) is proposed.

  • RPLVR is extended to its nonlinear RKPLVR model.

  • Statistics of RKPLVR for fault detection is derived utilizing the kernel trick.

  • RPLVR and RKPLVR based monitoring methods for fault detection are applied.

Abstract

In most industries, process and quality measurements with outliers are often collected. The outliers would have negative influences on data-based modelling and process monitoring. In our previous work on probability latent variable regression (PLVR), the model is constructed under the assumption that the data quality of the process characteristics is good and the operation processes are linear. In this article, a robust PLVR (RPLVR) model is developed.

Then it is extended to its nonlinear form, called robust probability kernel latent variable regression (RPKLVR). Both models can reduce the effects of outliers. RPLVR and RPKLVR are the weighted probability models. The similarity of each sample among all the collected data would be chosen as the weighting factor for each sample. Thus, the outliers for modelling are weakened.

With the weighted training data, an expectation-maximization algorithm of training RPLVR and RPKLVR are derived. The corresponding statistics are also systematically constructed for the fault detection. Two case studies are presented to illustrate the effectiveness of the proposed methods.

故障诊断和预后的废水过程数据不完整的自联想神经网络和ARMA模型

佛山大学,自动化学院

华南理工大学,自动化科学与工程学院

新加坡国立大学,生物医学工程系

东京农工大学,化学工程系

Highlights

  • Development of shallow and deep ANNs for fault diagnosis.

  • KDE-based fault diagnosis control limit to reduce the false alarms and faulty declaration.

  • A minimization strategy was proposed to deal with the missing value estimation.

  • An ARMA model to make multi-step-ahead prediction for SPE.

  • This methodology was validated through highly and lowly instrumented WWTPs, respectively.

Abstract

The use of large number of on-line sensors in control and automation for optimized operation of WWTPs is increasing popular, which makes manual expert-based evaluation impossible. Auto-associative Neural Networks (ANN) with shallow and deep structure are proposed for fault diagnosis in this paper.

The proposed methodology not only provides a recursive minimization strategy to deal with missing values but also offers Kernel Density Estimation (KDE) to alleviate the Gaussian assumption of derived data. The resulted fault diagnosis statistic, the sum of squared residuals (SPE) can be predicted over a long horizon by performing a multi-step ARMA model (Auto-Regressive and Moving Average Model).

The proposed fault diagnosis framework has been validated by process data collected from two WWTPs with different dynamic characteristics. The results showed that the proposed methodology is capable of detecting sensor faults and process faults with good accuracy under different scenarios (highly and lowly instrumented WWTP).

一种新的非线性功能扩展基于PLS(fepls)及其软测量中的应用

北京化工大学,信息科学与技术学院

教育部智能系统工程技术研究中心

北京化工大学,经济与管理科学学院

Highlights

  • An effective nonlinear FEPLS model is proposed.

  • Function expansion is adopted to effectively expand the input space to high nonlinear space.

  • A good model is found between the expanded inputs and the outputs by using PLS.

  • FEPLS model is easy to construct and is applied to modeling complex chemical processes.

  • FEPLS could achieve good prediction performance.

Abstract

A novel robust nonlinear partial least square model is proposed to handle the nonlinearity and collinearity problems of process data. The proposed model integrates a nonlinear functional link artificial neural network (FLANN) with a traditional partial least square (PLS). There are two parts in the proposed model: a nonlinear mapping part and a linear regression part. In the nonlinear mapping part, the input space is effectively extended to nonlinear space through the functional expansion block of FLANN.

The PLS regression (PLSR) is adopted in the linear part. Thus, a novel robust nonlinear PLS integrated with functional expansion (FEPLS) is built. The proposed FEPLS model is very easy to construct. First, a traditional FLANN is selected. Second, the input space is expanded to nonlinear space using the functional expansion block.

Third, the collinearity among the expanded variables and the expected outputs is eliminated by extracting input latent variables and output latent variables through PLS projection, respectively. Finally, an optimal regression model between the expanded variables and the expected outputs is established by using PLSR.

To evaluate the performance of the proposed model, case studies of modeling two complex chemical processes are provided. Four more models of FLANN, extreme learning machine based PLS (ELM-PLS), kernel PLS (KPLS), and PLSR are also developed for comparisons. Simulation results illustrated that the proposed FEPLS model could improve the prediction performance.

芝麻油掺假多元检测

中国农业科学院油料作物研究所

农业部油料作物生物学与遗传改良重点实验室

农业部真菌毒素检测重点实验室

农业部油料产品质量安全风险评价实验室

农业部油料产品质量检验检测中心

西藏省农业科学院畜牧科学研究院,农业质量标准与检验研究所

湖北生物资源绿色转化合作创新中心

Highlights

  • Multivariate adulteration detection for sesame oil by one-class support vector machine.

  • Lowest adulteration levels of the OC-SVM model were calculated.

  • One-class model is promising tool to identify authenticity of edible oil and food.

Abstract

Multivariate and untargeted adulterations are real cases of oil adulteration in practice. In this study, one-class support vector machine (OC-SVM) was used to build the model for detecting multivariate and untargeted adulterations of sesame oil. The predictive model was subsequently validated by an independent test set. The results indicated that the OC-SVM model could completely detect the adulterated oils. Moreover, oils adulterated with different levels of mixed edible oils were simulated by Monte Carlo method and employed to determine the lowest adulteration level of the predictive model. Compared with earlier studies, the OC-SVM model proposed for sesame oil in this study is more robust to detect untargeted and multivariate adulteration.

智能实验室系统设计视频

【设计周】智能家具系统—麻省理工学院媒体实验室设计项目

拓展阅读

家用智能照明系统的设计.doc:https://jz.docin.com/p-2121956716.html

智能办公体验馆智能化系统规划设计:http://baijiahao.baidu.com/s?id=1598489908863182217&wfr=spider&for=pc

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PLC的智能交通灯控制系统设计说明书:https://wenku.baidu.com/view/d0089c4253ea551810a6f524ccbff121dd36c532.html

什么是系统架构设计?:https://zhidao.baidu.com/question/486009431.html

基于Cygnal单片机的智能电源管理系统的设计:https://wenku.baidu.com/view/b53a28a6b0717fd5360cdcc9.html

智能产品系统设计案例_相关论文(共226574篇)_百度学术:http://xueshu.baidu.com/s?wd=%E6%99%BA%E8%83%BD%E4%BA%A7%E5%93%81%E7%B3%BB%E7%BB%9F%E8%AE%BE%E8%AE%A1%E6%A1%88%E4%BE%8B&tn=SE_baiduxueshu_c1gjeupa&ie=utf-8&sc_hit=1

(论文)高速公路智能控制柜升级系统设计:http://www.doc88.com/p-7867716938059.html

智能型电子系统设计:https://wenku.baidu.com/view/4e1c0e30b94ae45c3b3567ec102de2bd9605de3a.html

相关问答

问:大连大学先进设计与智能计算教育部重点实验室怎么样

答:导师包括辽宁省院士后备人选(大连理工大学博导)、国家杰出青年科学基金获得者(大连理工大学兼职博导)以及辽宁特聘教授、辽宁省高等学校杰出青年学者计划入选者等一批优秀的教师。实验室还在大连理工大学、东北大学具有博士生培养招生权。


问:人工智能领域有哪些重要的学术会议和顶级实验室

答:人工智能是对人的意识、思维的信息过程的模拟。人工智能不是人的智能,但能像人那样思考、也可能超过人的智能。
人工智能的定义可以分为两部分,即“人工”和“智能”。“人工”比较好理解,争议性也不大。有时我们会要考虑什么是人力所能及制造的,或者人自身的智能程度有没有高到可以创造人工智能的地步,等等。但总的来说,“人工系统”就是通常意义下的人工系统。
人工智能在计算机领域内,得到了愈加广泛的重视。并在机器人,经济政治决策,控制系统,仿真系统中得到应用。
用来研究人工智能的主要物质基础以及能够实现人工智能技术平台的机器就是计算机,人工智能的发展历史是和计算机科学技术的发展史联系在一起的。


问:关于通信读研,西电智能信息处理实验室焦李成组和北邮电子院宋梅实验室哪个更好?求解答,急等。

答:读硕的话,必然是北邮的。


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答:现在市面上大致有两种托福 TPO 模考,一种是模考软件,另一种是在线模考,TPO 模考软件 Bug 和题目错误较多,建议谨慎选择,而在线模考经过仔细校对,有详细的笔记和题目解析,做完生成报告,方便查看,备考托福效率更高,题主可以加“托福急救站”企鹅裙自取。


问:朗播英语智能学习实验室为什么会如此火爆?

答:因为你脑子有病


问:上海宙汉智能科技有限公司怎么样?

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