Shihua Luo

Jiangxi University of Finance and Economics

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Jiusun Zeng

Hangzhou Normal University

Chuanhou Gao

Zhejiang University

Lei Xie

Zhejiang University

Liu Xiang-guan

Zhejiang University

Ling Jian

China University of Petroleum, East China

Hongye Su

Zhejiang University

Uwe Krüger

Rensselaer Polytechnic Institute

Yi Liu

Zhejiang University

Qiansheng Zhang

East China Normal University

Jinhui Cai

China Jiliang University

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College Art Association

124

Chinese Academy of Sciences

Jiangxi University of Finance and Economics

Zhejiang University

University of Chinese Academy of Sciences

Shanghai Jiao Tong University

Tsinghua University

Changsha University of Science and Technology

Huazhong University of Science and Technology

China Jiliang University

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Separation of soy isoflavones from soy sauce residue by MIL-100(Fe)

Journal of Chromatography B (2022)

Yunhui Cheng Shihua Luo Fuqi Huang Xiangyi Yang Mao‐Long Chen

Residue (chemistry)

10.1016/j.jchromb.2022.123431

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Citations (4)

Prediction of Silicon Content in Hot Metal Based on Genetic Algorithms

Min Zhao Liu Xiang-guan Shihua Luo

In blast furnace (BF) ironmaking process, hot metal silicon content is an important index. Not only is silicon content a significant quality variable, it also reflects the thermal state of BF. A novel genetic algorithm was proposed. Time lag of each pivotal variable was calculated respectively. Based on the time lag analysis, the genetic algorithm is used to approach the fittest equation that describes the behavior between [Si] and the variables. With the calculated equation, the forecasting accuracy is up to 88%. Data, used in this paper, were collected from No.1 BF at Laiwu Iron and Steel Group Co

Content (measure theory)

State variable

10.1109/wcica.2006.1713481

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Citations (3)

Stationarity and normality testing for silicon content in molten iron of blast furnace

Shihua Luo Bin Zhou Jian Huang

As [Si] time series of No. 7 BF at Handan Iron and Steel Group Co., No. 1 BF at Laiwu Iron and Steel Group Co. and No.6 BF at Linfen Iron and Steel Group Co. to be sample space. In a significant level of α = 0.05 , temperature fluctuations of three BF reject stability hypothesis based on runs test. In a significant level of α = 0.01, such fluctuations reject the normal distribution hypothesis and show a significant skewed to the right, peak and fat tail features based on D test, skewness test and kurtosis test. The result is helpful for modeling intelligent control of BF.

Kurtosis

Normality test

10.1109/wcica.2010.5554966

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Citations (1)

Some results on the computing of Tukey’s halfspace median

Statistical Papers (2017)

Xiaohui Liu Shihua Luo Yijun Zuo

Tukey's range test

Position (finance)

Value (mathematics)

Sample (material)

10.1007/s00362-017-0941-5

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Citations (15)

A novel spatial-temporal fusion deep neural network for soft sensing of industrial processes

Hang Ouyang Jiusun Zeng Yifan Li Shihua Luo

This paper proposes a novel spatial-temporal fusion deep neural network for soft sensing of industrial processes. Different from previous deep learning based methods, the proposed model considers a new structure by combining the gated recurrent unit(GRU) and deep convolutional neural network(DCNN). The temporal correlation is captured by the GRU framework and the spatial correlation/input-output relationship is revealed using the DCNN structure. The resultant features are then fused and fed into a dense layer to produce the final estimations. A backpropagating algorithm is used to optimize the parameters of the model. The developed soft sensor considers both the spatial and temporal information and hence are more accurate in soft sensing of important quality variables that are difficult to measure in industrial processes. The effectiveness and feasibility of the soft sensor is verified by an application to the sulfur recovery unit(SRU).

Soft sensor

Sensor Fusion

10.1109/cac51589.2020.9326650

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Citations (2)

Identification of LPV system using locally weighted technique

Applied mathematics/Applied Mathematics. A Journal of Chinese Universities/Gao-xiao yingyong shuxue xuebao (2010)

Jiusun Zeng Chuanhou Gao Shihua Luo

Identification

10.1007/s11766-010-2334-6

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Citations (1)

The limit of finite sample breakdown point of Tukey's halfspace median for general data

arXiv (Cornell University) (2016)

Xiaohui Liu Shihua Luo Yijun Zuo

Under special conditions on data set and underlying distribution, the limit of finite sample breakdown point of Tukey's halfspace median ($\frac{1} {3}$) has been obtained in literature. In this paper, we establish the result under \emph{weaker assumption} imposed on underlying distribution (halfspace symmetry) and on data set (not necessary in general position). The representation of Tukey's sample depth regions for data set \emph{not necessary in general position} is also obtained, as a by-product of our derivation.

Position (finance)

Sample (material)

Representation

10.48550/arxiv.1604.07104

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Improved PCA-based Fault Isolation using Sparse Group Lasso

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) (2020)

Wei Chen Jiusun Zeng Yifan Li Shihua Luo

In industrial process control, data-driven fault detection and isolation methods have developed rapidly due to the easy availability of large amount of data. In fault isolation, principal component analysis(PCA) based contribution plot is a standard tool. The problem of PCA based contribution plot is that they are affected by the so called smearing effect. In fact, industrial process variables can be classified into groups according to their correlation or process structure, hence it is straightforward to consider the group-wise fault isolation problem. This paper introduces the sparse group Lasso as a regularization method to improve the fault isolation ability of PCA based contribution plot. The sparse group Lasso term considers both group-wise sparsity and within-group sparsity. Hence more accurate diagnosis can be obtained. In order to solve the optimization problem of sparse group Lasso, an efficient algorithm based on ADMM(Alternating Direction of Method of Multipliers) is proposed. Application study to the Tennessee Eastman(TE) process shows that the proposed method can better isolate faulty variables than competitive methods.

Lasso

Regularization

Isolation

10.1109/ddcls49620.2020.9275117

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Citations (1)

Subcellular localization of DNA nanodevices and their applications

Chemical Communications (2023)

Xia Liu Shuting Cao Yue Gao Shihua Luo Ying Zhu

Schematic illustration of the subcellular localization of DNA nanodevices and their applications.

10.1039/d2cc06017e

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Citations (5)

Mixture Probabilistic Linear Discriminant Analyzer for Process Fault Classification

2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS) (2019)

Yi Liu Jiusun Zeng Lei Xie Xun Lang Shihua Luo

Fault misclassification results in misleading locations of root faults, which brings economic loss and safety concerns. In order to enhance the performance of fault classification, this paper proposes a novel mixture fault classifier based on probabilistic linear discriminant analyzer (PLDA). Unlike the probabilistic model with a single latent variable, more useful information can be extracted by PLDA using within- and between-class latent variables. With the introduced state and mixture variables, the proposed mixture PLDA (MPLDA) classifier assigns a test sample to different components for each class in probability. For the class label decision, a robust state inference strategy is developed, which includes investigating the effect of the shared between-class variable on conditional probability and adopting the voting scheme to collect evidences from training samples to identify the class source of the test sample. After classifier construction, the model parameters are obtained by the expectation-maximization (EM) algorithm. The validity of MPLDA classifier in fault classification is then illustrated by the application of the Tennessee Eastman (TE) process.

Probabilistic classification

10.1109/safeprocess45799.2019.9213320

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