Chunhua Yang

Central South University | CSU

In response to the challenge of strongly nonlinear and multimode systems control, this paper introduces a weighted deep learning based adaptive predictive control method. This approach integrates LSTM networks for different operating modes using a set of weighting coefficients. These coefficients are dynamically updated during online control via an...

Tunable diode laser absorption spectroscopy (TDLAS) is widely used for gas concentration measurements due to its merits of rapid, noncontact, and high-precision detection. Precise control of laser emission can guarantee the accuracy of the absorption spectrum at a specific wavelength. However, in an unstable environment on a real-world pharmaceutic...

Multimode process monitoring plays a significant role in ensuring the stable operation of industrial processes under changing conditions. Due to the continuous emergence of new modes, some adaptive model updating methods are proposed. However, the updated model may forget important features learned in previous modes, thus reducing the monitoring pe...

In modern manufacturing, process monitoring is crucial in ensuring the stability and safety of production processes. However, the frequent changes in industrial conditions necessitate timely updates and retraining of the on-site deployment of data-driven process monitoring methods, which is a task unattainable with the limited computational resourc...

In cloud manufacturing of industrial processes, the accurate online prediction of product quality is the basis for realizing decision-making and control of the manufacturing process. However, frequent fluctuations in working conditions and data noise restrict the application of data-driven methods in industrial sites. In addition, the constrained r...

Weakly supervised semantic segmentation can significantly alleviate the annotation burden of the pixel-level collection used in full supervision. However, most existing works are based on simple images and only include a few tags, which are not applicable to free-space segmentation under complex driving scenes. In this study, we develop an effectiv...

This article presents a gas sensing method based on long-tune natural logarithmic wavelength modulation spectroscopy (long-tune ln-WMS) and explores means to improve its accuracy. The long-tune spectrum can detect multiple gases with high precision. In ln-WMS, due to the natural logarithm algorithm, the harmonic magnitude which is related to gas co...

In industrial sites, system operation conditions fluctuate due to changes in raw material and equipment status, making it critical to identify the operation conditions and obtain appropriate controllers accurately. Additionally, even for a specific operation condition, fixed control strategies may result in mismatches due to varying operational sta...

Time series in industrial processes often exhibits missing data caused by inevitable factors such as equipment failures and sensor errors. These missing data include vital information for the production process and directly impact subsequent modeling and analysis. Traditional imputation methods usually face challenges in capturing complex data dist...

With the rapid development of cloud computing, edge computing, and deep learning technologies, the implementation of soft sensor modeling within a cloud-edge collaboration architecture shows great potential and plays a pivotal role in achieving consistent and highly accuracy for industrial processes. However, traditional soft sensor models encounte...

Industrial predictive modeling, which provides valuable information for process monitoring and decision-making on process operation, plays a crucial role in the process industry. However, industrial processes commonly exhibit nonstationary characteristics caused by various process drifts, such as frequent variations in the properties of raw materia...

This article proposes a distributed capture strategy optimization method for the pursuit-evasion game involving multiple unmanned surface vehicles. Considering the limited perception range of each pursuer, a multiagent proximal policy optimization method combined with a novel velocity control mechanism is utilized to guide the pursuers in approachi...

Learning representations of two views of data such that the resulting representations are highly linearly correlated is appealing in machine learning. In this paper, we present a canonical correlation guided learning framework, which allows to be realized by deep neural networks (CCDNN), to learn such a correlated representation. It is also a novel...

Modern industrial processes often exhibit complex and uncertain operating state fluctuations due to the diversification of production materials, the complexity of production processes, and the harsh production environment. To address the real-time control challenge in multimode processes, this article proposes a learning framework for fuzzy neural...

Process industry indicator describes the production status and is crucial to the stable process operation. Its low sampling frequency makes it difficult to meet the indicator perception needs for real-time process control. Indicator estimation is a promising alternative to improve its obtaining frequency. However, the low sampling frequency of indi...

This paper presents novel modeling, analysis, and suppression methods for the demagnetization fault in maximum torque per ampere (MTPA)-driven permanent magnet motors (PMMs) with distributed windings. Firstly, a novel structural-mathematical technique is employed to formulate the analytical model (AM) for PMMs under a demagnetization fault. Unlike...

The control of industrial processes which can be generally described by nonlinear time-delay interconnected systems is very important. A decentralized control method based on adaptive dynamic programming (ADP) is proposed in this article, which can solve the control and stability problem for nonlinear time-delay interconnected systems using past st...

Partial least squares (PLS) model is the most typical data-driven method for quality-related industrial tasks like soft sensor. However, only linear relations are captured between the input and output data in the PLS. It is difficult to obtain the remaining nonlinear information in the residual subspaces, which may deteriorate the prediction perfor...

The intelligent goal of process manufacturing is to achieve high efficiency and greening of the entire production. Whereas the information system it used is functionally independent, resulting to knowledge gaps between each level. Decision-making still requires lots of knowledge workers making manually. The industrial metaverse is a necessary means...

Deep neural networks (DNNs) as one of the key enabling technologies have been widely used in Industrial Artificial Intelligence (IAI). However, recent research has revealed that they are quite vulnerable to adversarial attacks, arousing serious concerns about DNNs' robustness in many IAI-driven applications, such as industrial video analysis tasks....

This study addresses the antivibration issue of a full aircraft active landing gear system (LGS) under landing impact and runway excitations via a novel adaptive observer-based integral event-triggered control method. First, a full aircraft active LGS comprising a front gear and two synchronized main left and right gears with active suspensions is...

The roasting temperature is critical for enhancing product quality, reducing air pollution, and ensuring the long term operation of the zinc roasting process. However, optimizing the roasting temperature is challenging due to complex reaction mechanisms, feed composition fluctuations, and the coupling relationship with downstream processes. In this...

The real-time recognition of operating conditions is always critical to ensuring the efficient and stable operation of industrial flotation processes. Although the widespread use of smart devices enables the availability of multimodal data in flotation processes, recognizing operating conditions using cross-modal data information is still challengi...

Accurate and efficient roll mark detection on the strip steel surfaces is a fundamental but “hard” ultra-tiny target detection problem due to its small pixel occupation in low-contrast images. By fully exploiting the prior information of roll marks, this article proposed a Prior-Guided YOLOX network (PG-YOLOX). First, inspired by the prior that the...

This article concerns the investigation on the consensus problem for the joint state-uncertainty estimation of a class of parabolic partial differential equation (PDE) systems with parametric and nonparametric uncertainties. We propose a two-layer network consisting of informed and uninformed boundary observers where novel adaptation laws are devel...

Zinc rotary kiln is an important equipment in the nonferrous metallurgical industry. Due to unclear internal working conditions, its operation based on experience is random. Digital twin (DT) with virtual–real integration and synchronization ability is a necessary method to realize real-time and accurate monitoring for key variables, while its nowa...

In-process hot-rolled strip steel is suffering from some complicated yet unavoidable surface defects due to its harsh production environment. The automated visual inspection on defects consistently faces challenges of interclass similarity, intraclass difference, low contrast, and overlapping issue, which tend to trigger false or missed detections....

Blast furnace (BF) burden surface contains the most abundant, intuitive and credible smelting information and acquiring high-definition and high-brightness optical images of which is essential to realize precise material charging control, optimize gas flow distribution and improve ironmaking efficiency. It has been challengeable to obtain high-qual...

Soft sensors have been increasingly applied for quality prediction in complex industrial processes, which often have different scales of topology and highly coupled spatiotemporal features. However, the existing soft sensing models usually face difficulties in extracting the multiscale local spatiotemporal features in multicoupled complex process d...

This paper proposes a dynamic-projection-integrated particle-filtering-based identification strategy for the friction characteristic curve of a train wheelset under the slipping fault condition. This strategy aims to achieve the identification of the fault friction characteristic curve (FFCC) in the early slipping fault stage. First, a multi-dimens...

Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In ad...

In process industries, accurate prediction of critical quality variables is particularly important for process control and optimization. Usually, soft sensors have been developed to estimate the quality variables via process variables. However, there are often process variables that are far apart in topology but have high correlations, presenting c...

Accurate prediction of reaction temperature in rotary kiln is essential to realize its advanced process control and operational optimizations. However, the complexity of the physical and chemical reactions in the rotary kiln makes it difficult for the traditional mechanism model to characterize the dynamic kiln process. In this study, a deep learni...

During the operation of a distributed parameter system (DPS), its working conditions typically undergo dynamic changes. Although online learning methods can enable models to adapt to new working conditions to some extent, they often confront the “catastrophic forgetting” problem, where the updated model forgets historical working conditions. On the...

This paper presents a novel gas concentration detection approach based on wavelength modulation spectroscopy and linear convolution (LC-WMS). The linear convolution is first time utilized in the WMS measurement system to demodulate the absorption spectrum, and then to obtain the absolute harmonic signals related to the gas concentration. Theoretica...

Models are the key to model-based control strategies. However, due to the nonlinear and time-varying nature of industrial processes, plant-model mismatches are inevitable. Therefore, it is highly desirable to detect mismatches and update the model in a closed-loop system to avoid re-identifying the entire model. In this study, a mismatch detection...

Industrial systems often undergo dynamic changes during operation, which presents challenges for traditional identification and control methods. These challenges arise in two aspects: variations in model structure and parameters, and differences in control objectives across diverse operating conditions. Traditional static predictive control methods...

Visual surface defect detection is crucial for product quality control in the large-scale wood manufacturing industry. This study focuses on how to assist deep learning model in surviving in the challenges brought by complex texture backgrounds. A novel visual defect detection model, inter-layer information guidance feedback networks (I <sup xmlns:...

In the past decades, the removal of the semi-solid metal oxide slag has raised a significant concern in industrial manufacturing field. The presence of oxide slag may lead to a decrease in the quality of metal products, affecting their mechanical properties and visual appearance, which causes serious quality problems in manufacturing industry. Trad...

Precise control of roasting temperature is paramount for optimizing production efficiency in the zinc smelting process. However, existing research mainly focuses on average temperature control, and there is little research on temperature distribution control. To achieve this, a roasting temperature distribution model is first established based on t...

This article addresses the consensus problem of a class of unknown nonlinear multi-agent systems (MASs) under directed graphs via a novel model-free deep reinforcement learning (DRL) based fully distributed event-triggered control (ETC) method. First, the DRL-based feedback linearization approach is developed to learn an approximated linearized con...

Missing values are a common occurrence in industrial datasets, resulting from multiple sampling rates, sensor malfunctions, and transmission errors, whose presence can significantly affect the accuracy of data-driven models. An effective method to solve this problem is to impute the missing data in advance. This paper proposes a new position-encodi...

In this paper, the problem of state estimation for a class of Euler-Bernoulli beam systems is considered, where the state over the length of the beam is estimated using only measurements at the boundary points of the Euler-Bernoulli beam system. In particular, we consider the presence of unknown parameters and unstructured uncertainties that may ap...

Visual detection plays a vital role by enabling machines to learn from complex data and make informed decisions. In the context of "mixed line production - common line packaging" in the automobile wheel manufacturing industry, accurate wheel recognition is crucial for automated gripping, shelving, and sub-assembly, which also provides essential fee...

Accurate estimation of burden surface depth plays a crucial role in constructing the temperature field and optimizing reaction control in volatile kilns. However, most image-based depth estimation techniques require high-quality input images and achieve limited accuracy, which restrict their applications in actual harsh working conditions such as h...

Full-spectrum detection (FSD) is widely used in wastewater treatment processes (WWTPs) due to its higher resistance to interference compared to single-wavelength detection. However, acquiring full-spectrum typically involves precision and expensive equipment, which significantly raises detection costs. This article proposes a low-cost full-spectrum...

Camera decoration is an important part of smartphone. To achieve fully automated production, a dependable, efficient, and automatic method is required for camera decoration surface defect detection. This paper presents a detection scheme based on computer vision to improve the efficiency of screening defective products. Since there is no available...

The machine learning-based model is a promising paradigm for predicting invasive disease events (iDEs) in breast cancer. Feature selection (FS) is an essential preprocessing technique employed to identify the pertinent features for the prediction model. However, conventional FS methods often fail with imbalanced clinical data due to the bias toward...