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the temperature control of resistance furnace based on model predictive approach

The temperature control of resistance furnace based on model...

引用

IEEE international conference on Mechatronics, Robotics and Automation (ICMRA)

作者： Zhen Xu Weiyao Lan School of Aerospace Engineering Xiamen University Xiamen China

ISBN: (数字)9798350307535

ISBN: (纸本)9798350307542

Resistance furnace is widely used in chemical industry, which usually has the characteristics of large inertia, slow response speed and large overshoot, and the temperature cooling depends on natural cooling. Except the properties of the heating element, the temperature controller plays a vital role, and a good controller can save time and resources. At present, the controller of resistance furnace is mainly PID, which has the advantages of simple algorithm principle and is easy to use. To satisfy the increasing demand on the output response time and stability, we propose a novel temperature control method based on model predictive control (MPC). With identifying and processing the system model, the temperature controller is optimized by combining MPC and state observer. Experiments show that the output response performance of the resistance furnace is improved under the proposed controller.

关键词： Resistance Accuracy Cooling Furnaces Observers Predictive models Prediction algorithms Temperature control Time factors Predictive control

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Reinforcement Learning with Reward Shaping and Hybrid Exploration in Sparse Reward Scenes

Reinforcement Learning with Reward Shaping and Hybrid Explor...

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Industrial Cyber-Physical Systems (ICPS)

作者： Yulong Yang Weihua Cao Linwei Guo Chao Gan Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

High precision modeling in industrial systems is difficult and costly. Model-free intelligent control methods, represented by reinforcement learning, have been applied in industrial systems broadly. the hard evaluated of production states and the low value density of processing data causes sparse rewards, which lead to an insufficient performance of reinforcement learning. To overcome the difficulty of reinforcement learning in sparse reward scenes, a reinforcement learning method with reward shaping and hybrid exploration is proposed. By perfecting the rewards distribution in the state space of environment, the reward shaping can make the state-value estimation of reinforcement learning more accurate. By improving the rewards distribution in time dimension, the hybrid exploration can make the iteration of reinforcement learning more efficient and more stable. Finally, the effectiveness of the proposed method is verified by simulations.

关键词： Estimation Reinforcement learning Production Cyber-physical systems Stability analysis Intelligent control Convergence

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Short - Term Prediction of Coke Pushing Current Peak Based on Improved ARIMA Model

Short - Term Prediction of Coke Pushing Current Peak Based o...

引用

Industrial Cyber-Physical Systems (ICPS)

作者： Haiyang Wei Luefeng Chen Jie Hu Yi Ren Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Tokyo Institute of Technology Yokohama Japan

Coke pushing current is an indicator to evaluate the difficulty of coke pushing operation. the higher coke pushing current is, the greater coke pushing resistance is, and the more difficult coke is to push out. Accurate prediction of current peak during future coke pushing operation can provide more time for production personnel to adjust production status and avoid difficult coke pushing in carbonization chamber. In this paper, a combination prediction model based on VMD (Variational Mode Decomposition) and improved ARIMA (Autoregressive Integrated Moving Average) models is proposed. Firstly, VMD algorithm is used to decompose time series of coke pushing current peak, de-noising data, and extracting main information of time series. then, ARIMA model is used to predict mean change of linear elasticity and GARCH (Generalized Autore-gressive Conditional Heteroskedasticity) model is introduced to predict ARIMA model residual and improve heteroscedasticity of nonlinear part of time series, and then ARIMA-GARCH model is established. Finally, predicted value is obtained by the sum of each component prediction. the experimental results show that the proposed prediction model has a high prediction accuracy in the short-term prediction of coke pushing current peak. the scheme is applied to actual coking production to guide production of coke.

关键词： Resistance Ovens Time series analysis Noise reduction Production Predictive models Prediction algorithms

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Photovoltaic Data Cleaning Method Based on DBSCAN Clustering, Quartile Algorithm and Pearson Correlation Coefficient Interpolation Method

Photovoltaic Data Cleaning Method Based on DBSCAN Clustering...

引用

Energy, Electrical and Power engineering (CEEPE), international conference on

作者： Biyuan Wang Xingye Deng Taowei Chen Yidong Li Huizhou Power Supply Bureau Guangdong Power Grid Co Guangdong China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China

the proportion of photovoltaic power generation in the global energy structure is increasing year by year. Because of its volatility and periodicity, grid-connected photovoltaic challenges the safe and stable operation of power grid. One of the ways to solve this problem is data mining. Data mining needs high-quality data support, and data cleaning is one of the important means to improve data quality. Aiming at the problem of poor quality of original PV data, this paper analyzes two typical outliers of PV data and proposes a PV output data cleaning method based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm, quartile algorithm and Pearson correlation coefficient interpolation method. Comparison of DBSCAN clustering algorithm combined with the quartile method of outlier identification with the quartile method alone outlier identification, the effectiveness of the method proposed in this paper is verified. then using the method proposed in this paper for outlier identification based on Pearson correlation coefficient interpolation and cubic spline interpolation to verify the data filling effect, the results show that Pearson correlation coefficient interpolation is superior to cubic spline interpolation. Finally, two sets of data with different data cleaning methods are substituted into the Long short-term memory (LSTM) Model to verify accuracy of the cleaning method proposed in this paper.

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Emotion Recognition via Environmental Context and Human Body

Emotion Recognition via Environmental Context and Human Body

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Industrial Cyber-Physical Systems (ICPS)

作者： Cheng-Shan Jiang Zhen-Tao Liu School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan P. R. China

To promote the humanized interactive experience of the intelligent device and system, emotional intelligence has become a popular research field in the human-machine interaction. the previous research on emotion recognition based on computer vision has mostly been carried out by analysing facial expression or body posture, and psychological studies show that scene context also contributes some important information on emotion recognition. In addition, most context-aware emotion recognition studies focus on exploring the relevance analysis of environmental semantics, but the influence of feature encoder on semantic information embedding has not been fully discussed. In this paper, we proposed a Global Semantic Feature Enhancement-Dual Stream Densely Connected Network (GSFE-DSDCN) to enhance global semantic information learning from the perspectives of dimension and spatial. Densely connected pattern is introduced to concatenate the shallow and deep layers output, which fuses the semantic information of low-dimensional geometric features and high-dimensional abstract context features together. the Global Multi-Scale Feature Recalibration (GMSFR) module expands the receptive field in spatial, which effectively improves the global semantic features extraction capability of feature encoder. We evaluate the proposed method on the EMOTIC data set, and experimental results are shown to be competitive with the state-of-the-art algorithms.

关键词： Human computer interaction Emotion recognition Computer vision Fuses Biological system modeling Semantics Psychology

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advanced Persistent threats Detection based on Deep Learning Approach

Advanced Persistent Threats Detection based on Deep Learning...

引用

Industrial Cyber-Physical Systems (ICPS)

作者： Hope Nkiruka Eke Andrei Petrovski School of Computing Robert Gordon University Aberdeen UK School of Computing National Subsea Centre Aberdeen UK

advanced Persistent threats (APTs) have been a major challenge in securing both Information Technology (IT) and Operational Technology (OT) systems. APT is a sophisticated attack that masquerade their actions to navigates around defenses, breach networks, often, over multiple network hosts and evades detection. It also uses “low-and-slow” approach over a long period of time. Resource availability, integrity, and confidentiality of the operational cyber-physical systems (CPS) state and control is highly impacted by the safety and security measures in place. A framework multi-stage detection approach termed “ $APT_{DASAC}$ ” to detect different tactics, techniques, and procedures (TTPs) used during various APT steps is proposed. Implementation was carried out in three stages: (i) Data input and probing layer - this involves data gathering and pre-processing, (ii) Data analysis layer; applies the core process of “ $APT_{DASAC}$ ” to learn the behaviour of attack steps from the sequence data, correlate and link the related output and, (iii) Decision layer; the ensemble probability approach is utilized to integrate the output and make attack prediction. the framework was validated with three different datasets and three case studies. the proposed approach achieved a significant attacks detection capability of 86.36% with loss as 0.32%, demonstrating that attack detection techniques applied that performed well in one domain may not yield the same good result in another domain. this suggests that robustness and resilience of operational systems state to withstand attack and maintain system performance are regulated by the safety and security measures in place, which is specific to the system in question.

关键词： Navigation System performance Cyber-physical systems Predictive models Prediction algorithms control systems Robustness

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Study on Removal of PCDD/Fs from Flue Gas Processed by Pulse Corona Discharge Composite Technology

Study on Removal of PCDD/Fs from Flue Gas Processed by Pulse...

引用

China international Electrical and Energy conference (CIEEC)

作者： Lingang Weng Keji Qi Licheng Zheng Anfei Luo Yun Liu Jianjun Zhu Fangzhi Zhou Zhejiang Doway Advanced Technology Co. Ltd Hangzhou China Zhejiang Engineering Research Center of Odor Control Hangzhou China

In this study, the pulse corona discharge composite technology is used to treat flue gas. the pulse corona discharge technology is the main technology, and the bag filter and activated carbon adsorption are the auxiliary processes. the pulse corona discharge adopts the nanosecond narrow pulse power supply to achieve the intense pulse electric field, the electric field intensity is adjustable, and the multi-level discharge ionization superposition is used to remove the dioxins (PCDD/Fs) in the flue gas step by step. Under the given electric field intensity, PCDD/Fs in flue gas will undergo material dissociation and free radical oxidation reaction. With the increase of electric field intensity, the removal efficiency of PCDD/Fs will be increased gradually, and the maximum comprehensive removal efficiency of more than 97% can be achieved. this study shows that the efficient removal of PCDD/Fs in incineration flue gas without secondary pollution can be successfully achieved via the pulsed corona discharge composite technology proposed, and this technology can also be extended to the removal of PCDD/Fs in similar flue gas. this study provides effective theoretical basis for the large-scale promotion and application of this technology.

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Interpretable analysis of feature importance and implicit correlation based on sEMG grayscale. images

Interpretable analysis of feature importance and implicit co...

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Industrial Cyber-Physical Systems (ICPS)

作者： Xiaohu AO Feng Wang Juan Zhao Jinhua She School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

For patients requiring upper limb rehabilitation, the hand rehabilitation robot assists the patient in completing movements within a certain training trajectory to achieve therapeutic results. there have been studies based on deep learning to convert surface electromyography (sEMG) signals into sEMG images for motion intention analysis. Although good recognition accuracy has been achieved, the working principle of neural networks and the processing of image features by the networks are not well explained. the interpretability of deep neural networks determines human confidence in neural network decisions. In this paper, we design a method based on feature importance and implicit correlation for hand motion intention recognition, experimentally explored that convolutional neural networks have implicit definitions for sEMG grayscale images of the same hand gesture action, and verified the effectiveness of the designed method.

关键词： Deep learning Analytical models Correlation Image recognition Neural networks thumb Gray-scale

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Research on vehicle Path Tracking control based on MPC 22

Research on vehicle Path Tracking Control based on MPC

引用

Proceedings of the 2022 6th international conference on Electronic Information Technology and Computer engineering

作者： Yang Hou Yanyong Ren Shaojie Meng Hubei Key Laboratory of Advanced Technology for Automotive Component Wuhan University of Technology China

ISBN: (纸本)9781450397148

Aiming at the problem of path tracking of unmanned vehicles, this paper built a vehicle kinematics model and carried out discretization, designed a vehicle lateral motion controller based on model predict control (MPC) to track the vehicle's path on the basis of the existing path, and carried out controller simulation through MATLAB. Simulation results show that the controller has a certain path tracking accuracy.

关键词： Kinematic model Lateral control the MPC Path following

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Optimal Portfolio of Wind-solar-hydro Hybrid Renewable Generation based on Variable Speed Pumped Storage

Optimal Portfolio of Wind-solar-hydro Hybrid Renewable Gener...

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China international Electrical and Energy conference (CIEEC)

作者： Shizhe Xiang Da Xu Menglu Gao School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China DC Company of State Grid Hubei Electric Power Co. Ltd. Yichang China

Compared with separate solar or wind generation, the hybrid wind-solar-hydro renewable portfolio can achieve better generation characteristics due to their temporal-spatial complementarities. this paper proposes optimal portfolio of wind-solar-hydro hybrid renewable generation based on variable speed pumped storage (VSPS). In this portfolio, the complementarities of hydro-solar-wind hybrid renewable energy sources (RESs) are fully exploited via various conversion and storage devices for zero carbon 100% renewable supplies. Compared to traditional fixed speed pumped storage (FSPS), the VSPS has obvious advantages in power regulation and renewable accommodation, which is comprehensively modeled to capture its dynamic operational dispatchability and flexibility. the portfolio production, conversion, storage, and consumption are formulated via a two-stage framework, which takes the minimum system investment cost as the objective function. Case studies on southwest China are performed to show the effectiveness and superiority of the proposed methodology on cost-effective accommodation.

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