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共 292 条记录，以下是271-280 订阅

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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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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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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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advanced Persistent threats Detection based on Deep Learning Approach

Advanced Persistent Threats Detection based on Deep Learning...

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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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Design and Development of Big Data System for Blast Furnace Ironmaking

Design and Development of Big Data System for Blast Furnace ...

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Industrial Cyber-Physical Systems (ICPS)

作者： Qifu Chen Jinhua She Min W Zhuofu Zhang Zhenhong Liao School of Future Technology 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 Hunan Valin Lianyuan Iron and Steel Co. Ltd. Loudi China School of Engineering Tokyo University of Technology Tokyo Japan School of Automation. China University of Geosciences Wuhan China

the process of blast furnace ironmaking is com-posed of several sub-processes. the process of blast furnace smelting is complicated and the data of each sub-process are scattered and not concentrated. the traditional working mode of subjective judgment based on manual experience is not conducive to the stable production of the blast furnace. therefore, this paper develops a big data system for the production process of blast furnace, which is of great significance to realize the digital production and intelligent production of blast furnace. According to the system architecture of blast furnace ironmaking, this paper mainly designs and develops the data acquisition layer, data storage layer, data analysis layer and data application layer of the big data system.

关键词： Software design Data acquisition Smelting Memory Systems architecture Production User interfaces

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Modeling and control of Fuel Cell/Supercapacitor Fed Open-End-Winding Induction Motor in Electric Vehicles

Modeling and Control of Fuel Cell/Supercapacitor Fed Open-En...

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international conference on Renewable Energies for Developing Countries (REDEC)

作者： Khaled Safsouf Jean Sawma Hadi Y. Kanaan Faculty of Engineering - ESIB Saint-Joseph University of Beirut Beirut Lebanon Dept. Electrical Engineering Ecole de Technologie Supérieure Montreal Quebec Canada

this paper describes the model of hybrid clean energy feeding an open-end winding induction motor. Mainly, the objective of this paper is to regulate the distribution of power between different power sources using inner/outer loop algorithms. the inner loop governs the motor current and power allocation among the sources, whereas the outer loop is responsible for regulating the voltage of the storage element and ensuring that the primary source is operating at its optimal capacity. the hybrid system utilizes a fuel cell as the primary energy source and a supercapacitor as the secondary energy source. the primary energy source is responsible for providing the required energy to the electric vehicle as well as to the storage source in a specific case, while the secondary energy source is utilized to supplement power during acceleration and to capture and store excess energy during the braking process. As well as the fast-acting and dynamic nature of the supercapacitor can aid the fuel cell in its initiation process. the presence of a supercapacitor in fuel cell-powered vehicles is highly promising as it has the potential to substantially decrease hydrogen consumption and enhance vehicle efficiency. the hybrid DC sources are linked to the terminals of the stator of the induction motor (IM) through a dual inverter. the OEWIM configuration is defined by the removal of several passive elements, resulting in a system structure that is not bulky or complex, with improved reliability, efficiency, redundancy, and power quality compared to the traditional structure. A field-oriented torque method (FOC) is used in the induction motor control.

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the temperature control of resistance furnace based on model predictive approach

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

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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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Research on vehicle Path Tracking control based on MPC 22

Research on vehicle Path Tracking Control based on MPC

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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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Spatial Attention Enhanced Pipeline Inner Surface Defect Detection and Digital Twin Modelling 23

Spatial Attention Enhanced Pipeline Inner Surface Defect Det...

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Proceedings of the 2023 6th international conference on algorithms, Computing and Artificial Intelligence

作者： Guangheng Xin Yuan Dong Yi Zhao Chuanshui Liu Shuzhi Zhao School of Electronic and Control Engineering Chang?an University China Huayou Steel Pipe Company China

ISBN: (纸本)9798400709203

Defect detection of inner defects of pipelines holds significant importance. Our investigation on previous works has indicated that most vision based defect detection methods can not deliver satisfactory results due to complexity of the problem. In this work, a new method termed Sim-RTMDet. Our proposal is empowered to better capture detailed features of the defect regions, thereby improving detection performance. Additionally, for better visualization of the detection results, we created a digital twin virtual model of the inner surface inspection of the pipeline. the experimental results demonstrate significant improvements in both detection performance and digital-twin accuracy, thereby validating the effectiveness of our proposed approach.

关键词： Digital twin

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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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