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univariate imputation method for recovering missing data in wastewater treatment process

Chinese Journal of Chemical Engineering 2023年第1期53卷 201-210页

作者： Honggui Han Meiting Sun Huayun Han Xiaolong Wu Junfei Qiao Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China Engineering Research Center of Digital Community Ministry of EducationBeijing 100124China

High-quality data play a paramount role in monitoring,control,and prediction of wastewater treatment process(WWTP)and can effectively ensure the efficient and stable operation of system.Missing values seriously degrade the accuracy,reliability and completeness of the data quality due to network collapses,connection errors and data transformation failures.In these cases,it is infeasible to recover missing data depending on the correlation with other variables.To tackle this issue,a univariate imputation method(UIM)is proposed for WWTP integrating decomposition method and imputation algorithms.First,the seasonal-trend decomposition based on loess method is utilized to decompose the original time series into the seasonal,trend and remainder components to deal with the nonstationary characteristics of WWTP data.Second,the support vector regression is used to approximate the nonlinearity of the trend and remainder components respectively to provide estimates of its missing values.A self-similarity decomposition is conducted to fill the seasonal component based on its periodic pattern.Third,all the imputed results are merged to obtain the imputation result.Finally,six time series of WWTP are used to evaluate the imputation performance of the proposed UIM by comparing with existing seven methods based on two indicators.The experimental results illustrate that the proposed UIM is effective for WWTP time series under different missing ratios.Therefore,the proposed UIM is a promising method to impute WWTP time series.

关键词： univariate Self-similarity Waste water Algorithm Integration

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Identification of Gesture Based on Combination of Raw sEMG and sEMG Envelope Using Supervised Learning and univariate Feature Selection

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Journal of Bionic Engineering 2019年第4期16卷 647-662页

作者： Shili Liang Yansheng Wu Jianfei Chen Ling Zhang Peipei Chen Zongqian Chai Chunlei Cao School of Physics Northeast Normal UniversityChangchun 130022China College of Science Changchun University of Science and TechnologyChangchun 130022China

In this paper,we propose a novel study for gesture identification using surface electromyography(sEMG)signal,and the raw sEMG signal and the sEMG envelope signal are collected by the sensor at the same time.An efficient method of gesture identification based on the combination of two signals using supervised learning and univariate feature selection is implemented.In previous research techniques,researchers tend to use the raw sEMG signal and extract several constant features for classification,which inevitably causes a result of ignoring individual differences.Our experiment shows that both the optimal feature set and redundant feature set are not same for different subjects.In order to address this problem,we extract all the common features from two signals,up to 76 features,most of which has been established as the common EMG-based gesture index.In addition,extracting too many features in an application can reduce operational efficiency,so we apply for feature selection to get the optimal feature set and decrease the number of extracting feature.As a result,the combination of two signals is better than using a single signal.The feature selection can be used to select optimal feature set from all features to achieve the best classification performance for each subject.The experimental results demonstrate that the proposed method achieves the performance with the highest accuracy of 95%for identifying up to nine gestures only using two sensors.Finally,we develop a real-time intelligent sEMG-driven bionic hand system by using the proposed method.

关键词： gesture identification surface electromyography(sEMG) supervised learning univariate feature selection individual differences

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A frequency domain reliability analysis method for electromagnetic problems based on univariate dimension reduction method

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Science China(Technological Sciences) 2019年第5期62卷 787-798页

作者： PING MengHao HAN Xu JIANG Chao ZHONG JianFeng XIAO XiaoYa HUANG ZhiLiang WANG ZhongHua State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body College of Mechanical and Vehicle EngineeringHunan University Nanjing Research Institute of Electronics Technology

In this paper, a class of electromagnetic field frequency domain reliability problem is first defined. The frequency domain reliability refers to the probability that an electromagnetic performance indicator can meet the intended requirements within a specific frequency band, considering the uncertainty of structural parameters and frequency-variant electromagnetic parameters.And then a frequency domain reliability analysis method based on univariate dimension reduction method is proposed, which provides an effective calculation tool for electromagnetic frequency domain reliability. In electromagnetic problems, performance indicators usually vary with frequency. The method firstly discretizes the frequency-variant performance indicator function into a series of frequency points' functions, and then transforms the frequency domain reliability problem into a series system reliability problem of discrete frequency points' functions. Secondly, the univariate dimension reduction method is introduced to solve the probability distribution functions and correlation coefficients of discrete frequency points' functions in the system. Finally, according to the above calculation results, the series system reliability can be solved to obtain the frequency domain reliability, and the cumulative distribution function of the performance indicator can also be obtained. In this study,Monte Carlo simulation is adopted to demonstrate the validity of the frequency domain reliability analysis method. Three examples are investigated to demonstrate the accuracy and efficiency of the proposed method.

关键词： electromagnetic field frequency domain reliability system reliability random process discretization univariate dimension reduction method

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Robust topology optimization of multi-material lattice structures under material and load uncertainties

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Frontiers of Mechanical Engineering 2019年第2期14卷 141-152页

作者： Yu-Chin CHAN Kohei SHINTANI Wei CHEN Department of Mechanical Engineering Northwestern UniversityEvanstonIL 60208USA

Enabled by advancements in multi-material additive manufacturing,lightweight lattice structures consisting of networks of periodic unit cells have gained popularity due to their extraordinary performance and wide array of functions.This work proposes a density-based robust topology optimization method for meso-or macroscale multi-material lattice structures under any combination of material and load uncertainties.The method utilizes a new generalized material interpolation scheme for an arbitrary number of materials,and employs univariate dimension reduction and Gauss-type quadrature to quantify and propagate uncertainty.By formulating the objective function as a weighted sum of the mean and standard deviation of compliance,the tradeoff between optimality and robustness can be studied and controlled.Examples of a cantilever beam lattice structure under various material and load uncertainty cases exhibit the efficiency and flexibility of the approach.The accuracy of univariate dimension reduction is validated by comparing the results to the Monte Carlo approach.

关键词： robust topology optimization lattice structures multi-material material uncertainty load uncertainty univariate dimension reduction

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A solution to stochastic unit commitment problem for a wind-thermal system coordination

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Frontiers in Energy 2014年第2期8卷 192-200页

作者： B. SARAVANAN Shreya MISHRA Debrupa NAG School of Electrical Engineering VIT University Vellore 632014 India

Unit commitment （UC） problem is one of the most important decision making problems in power system. In this paper the UC problem is solved by considering it as a real time problem by adding stochasticity in the generation side because of wind-thermal co-ordination system as well as stochasticity in the load side by incorporating the randomness of the load. The most important issue that needs to be addressed is the achievement of an economic unit commitment solution after solving UC as a real time problem. This paper proposes a hybrid approach to solve the stochastic UC problem considering the volatile nature of wind and formulating the UC problem as a chance constrained problem in which the load is met with high probability over the entire time period.

关键词： unit commitment （UC） randomness wind generation univariate chance constrained

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Some issues on longitudinal data with nonignorable dropout, a discussion of“Statistical Inference for Nonignorable Missing-Data Problems: A SelectiveReview” by Niansheng Tang and Yuanyuan Ju

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Statistical Theory and Related Fields 2018年第2期2卷 137-139页

作者： Lei Wang School of Statistics and Data Science&LPMC Nankai UniversityTianjinPeople’s Republic of China

We thank Tang and Ju for their review on statistical inference for univariate response data with nonignorable missing.In this paper,we mainly discuss some issues on longitudinal data with nonignorable dropout.In research areas such as medicine,population health,economics,social sciences and sample surveys,data are often collected from every sampled subject at T time points,which are referred to as longitudinal data.Let Y=(y1,...,yT)be a T dimensional vector of the study variable with distribution denoted by p(Y),and X be a q-dimensional time-independent continuous covariate associated with Y.

关键词： nig thank univariate

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RGxE: An R Program for Genotype x Environment Interaction Analysis

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American Journal of Plant Sciences 2017年第7期8卷 1672-1698页

作者： Mahendra Dia Todd C. Wehner Consuelo Arellano Department of Horticultural Science North Carolina State University Raleigh USA Statistics Department North Carolina State University Raleigh USA

Genotype x environmental interaction (GxE) can lead to differences in performance of genotypes over environments. GxE analysis can be used to analyze the stability of genotypes and the value of test locations. We developed an Rlanguage program (RGxE) that computes univariate stability statistics, descriptive statistics, pooled ANOVA, genotype F ratio across location and environment, cluster analysis for location, and location correlation with average location performance. univariate stability statistics calculated are regression slope (bi), deviation from regression (S2d), Shukla’s variance (σi2), S square Wricke’s ecovalence (Wi), and Kang’s yield stability (YSi). RGxE is free and intended for use by scientists studying performance of polygenic or quantitative traits over multiple environments. In the present paper we provide the RGxE program and its components along with an example input data and outputs. Additionally, the RGxE program along with associated files is also available on GitHub at https://***/mahendra1/RGxE, http://***/todd-wehner/publications/software-sas-r-project/? and http://cuke.***.ncsu.edu/cucurbit/wehner/software.html.

关键词： Genotype x Environment Interaction R Programming Language RGxE univariate Multivariate

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Adsorption of Three Commercial Dyes onto Chitosan Beads Using Spectrophotometric Determination and a Multivariate Calibration Method

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Journal of Water Resource and Protection 2013年第4期5卷 446-457页

作者： Manuela Mincea Viorica Patrulea Ana Negrulescu Robert Szabo Vasile Ostafe Advanced Environmental Research Laboratories West University of Timisoara Timisoara Romania Faculty of Mathematics and Computer Science West University of Timisoara Timisoara Romania

A simple and rapid analytical method for the simultaneous quantification of three commercial azo dyes—Tartrazine (TAR), Congo Red (CR), and Amido Black (AB) in water is presented. The simultaneous assessment of the individual concentration of an organic dye in mixtures using a spectrophotometric method is a difficult procedure in analytical chemistry, due to spectral overlapping. This drawback can be overcome if a multivariate calibration method such as Partial Least Squares Regression (PLSR) is used. This study presents a calibration model based on absorption spectra in the 300 - 650 nm range for a set of 20 different mixtures of dyes, followed by the prediction of the concentrations of dyes in 6 validation mixtures, randomly selected, using the PLSR method. Estimated limits of detection (LOD) were 0.106, 0.047 and 0.079 mg/L for TAR, CR, and AB, respectively, and limits of quantification (LOQ) were 0.355, 0.157 and 0.265 mg/L for TAR, CR, and AB, respectively. Quantitative determination of the three azo dyes was performed following optimized adsorption experiments onto chitosan beads of mixtures of TAR, CR and AB. Adsorption isotherm and kinetic studies were carried out, proving that the proposed PLSR method is rapid, accurate and reliable.

关键词： univariate Calibration Multivariate Calibration Spectral Overlap Partial Least Squares Regression Commercial Dyes Adsorption

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Endocrinology

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China Medical Abstracts(Internal Medicine) 2000年第4期17卷 200-202页

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Neurological Disorder

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China Medical Abstracts(Internal Medicine) 2015年第1期32卷 58-64页

2015117 Clinical,electrophysiological and skin biopsy studies of peripheral neuropathy with small fibers involvement:a report of 34 cases.SUN Bo(孙博),et al.Dept Neurol,Chinese PLA General Hosp,Beijing 100853.Natl Med J China 2014;94(43):3397-3401.Objective To explore the relationship between clinical features,electrophysiology and intraepidermal nerve fiber density(IENFD)in peripheral neuropathy

关键词： neuropathy stenosis involvement Neurol intracranial ADHD biopsy hyperactivity univariate hippocampal

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