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Measurement Method of the Least Mean Square Adaptive Filtering Parameters for Gas-solid Flow

Measurement Method of the Least Mean Square Adaptive Filteri...

引用

Chinese Automation Congress (CAC)

作者： A. Ruidong Xie B. Yingmin Yi C. Haichuan Yang Key Lab of Manufacturing Equipment of Shaanxi Province Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

With regard to the unknown prior knowledge of signal noise in the parameter measurement of gas-solid flow, the present paper puts forward the AC signal processing method based on the least mean square (LMS) adaptive filtering. In addition, the floating processor DSP-TMS320F28335 is employed to collect AC signal of electrostatic sensors and the acquired signals are processed by the aid of LMS adaptive filtering algorithm which adjusts the filter weights according to the criterion of the least mean square error to achieve the optimal filtering. Drawing on the experimental platform, the solid-phase velocity is measured online in real time for the gas-solid flow. The results show that the method can improve the accuracy and stability for the velocity measurement of gas-solid flow.

关键词： Filtering Velocity measurement Filtering algorithms Adaptive filters Solids Atmospheric measurements Particle measurements

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Improved Kalman Filtering Algorithm Based Multi-Sensor Integration for Silicon Single Crystal Growth Process

Improved Kalman Filtering Algorithm Based Multi-Sensor Integ...

引用

International Symposium on Autonomous systems (ISAS)

作者： Ruidong Xie Pei Zhu Wenle Ma Zheqi Zhang Yingmin Yi Zheng Guo Key Lab of Manufacturing Equipment of Shaanxi Province Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

The study primarily delves into addressing the data filtering challenge inherent in the silicon single crystal growth process by proposing a multi-sensor integrated detection approach. This approach is designed to alleviate the diminished filtering accuracy resulting from the reliance on a single sensor and the conventional Kalman filtering algorithm. By utilizing an enhanced adaptive Kalman filtering algorithm and the maximum likelihood estimation criterion to incorporate the innovation variance directly into the gain calculation of the Kalman filter, dynamic adjustment of the estimation model is achieved while interference from system noise and measurement noise is reduced. Finally, simulation-based comparative experiments between the adaptive and conventional Kalman filtering algorithms are performed. The results show that the performance of the adaptive Kalman filtering algorithm is superior to that of the conventional Kalman filtering algorithm in terms of accuracy and stability when detecting signals from multiple sensors. The research findings furnish dependable data support for refining the decision-making process in crystal growth technology, augmenting the precision and scientific integrity of crystal safety production scheduling and decision-making endeavors.

关键词： Crystal growth Filtering Noise Decision making Crystals Production Filtering algorithms

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Modeling and Simulation Design of Single Crystal Silicon Growth by Czochralski Method

Modeling and Simulation Design of Single Crystal Silicon Gro...

引用

Mechatronics Technology and intelligent Manufacturing (ICMTIM), International Conference on

作者： Ruidong Xie Wenle Ma Zheqi Zhang Pei Zhu Yingmin Yi Key Lab of Manufacturing Equipment of Shaanxi Province Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

ISBN: (数字)9798350363265

ISBN: (纸本)9798350363272

Due to difficulties in observing crystal state and the high cost of crystal preparation, the teaching of the Czochralski single crystal silicon production is complex. To solve the above problem, a digital twins-inspired virtual simulation system for the growth of single crystal silicon was developed. The 3D models of the single crystal furnace, single crystal silicon rods, and other related objects were created using Blender. The nonlinear model of the single crystal silicon growth process was established based on the heat conduction during crystal growth and geometric deformation. The 3D models were then imported into the web page using ***. The PID control algorithm was used to control the temperature and pulling speed, and finally, the B/S architecture was adopted to design and implement the virtual simulation experiment system for the growth of Czochralski single crystal silicon. The virtual simulation method will help to overcome the shortcomings of on-site experiment teaching and enhance students’ understanding of the entire single crystal silicon preparation process.

关键词： Crystal growth Heating systems Solid modeling Three-dimensional displays PI control Furnaces Education

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The Laser Point Detecting Method Based on Faster R-CNN

The Laser Point Detecting Method Based on Faster R-CNN

引用

Chinese Automation Congress (CAC)

作者： Ruidong Xie Haichuan Yang Yingmin Yi Qimeng Shi keying Wang Chaoyang Zheng Key Lab of Manufacturing Equipment of Shaanxi Province Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Aiming at issues of low accuracy and poor anti-interference ability in laser point detecting, a target detection method based on Faster R-CNN was proposed. The laser point position detecting method is realized by calibrating the laser point data set, initializing the weight coefficient and the training network model. Experiments illustrate that the proposed method is more accurate in cases of strong noise such as the light changing, the background changing and the smoke filling the air. The accuracy of the method is 98.1%, as a better detection effect and generalization ability.

关键词： Feature extraction Laser modes Training Proposals Laser noise Convolution Three-dimensional printing

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control-oriented fault detection of solid oxide fuel cell system unknown input on fuel supply

Control-oriented fault detection of solid oxide fuel cell sy...

引用

作者： Wu, Xiao-long Xu, Yuan-wu Xue, Tao Shuai, Junchao Jiang, Jianhua Deng, Zhonghua Fu, Xiaowei Li, Xi School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science & Technology Wuhan China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Wuhan Industrial Economic Development Research Center Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China

As the solid oxide fuel cell (SOFC) system work environment is a high-temperature environment for a long time, it is difficult to obtain the SOFC stack internal state change directly. When the fault occurs, it is difficult to determine where the fault occurs. Moreover, the existing literature ignores the impact of faults, which creates many problems for SOFC system control. Therefore, a state observer-based fault detection method, which is used to detect the input flow sensor fault and the fuel input fault, is proposed. Their advantage is that they do not need data processing. To realize the fault detection, the observer is used to track the changes of SOFC stack chamber temperature. To obtain the observer estimation parameter, an approach from the actual stack structure parameters is employed to approximate the observer parameters. The results show the proposed fault detect method can judge fuel input fault type quickly and shield the disturbances signals from the sensor effectively. The proposed method also can be used to other operating points or air input fault. © 2018 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd

关键词： Fault detection

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A method of laser point temperature detection based on CNN

A method of laser point temperature detection based on CNN

引用

Chinese Automation Congress (CAC)

作者： Ruidong Xie Haichuan Yang Yingmin Yi Qimeng Shi keying Wang Jiabin Huang Key Lab of Manufacturing Equipment of Shaanxi Province Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

There has been a direct relationship between the temperature of the laser point and the quality of the casting in the process of the 3D printing. In the paper, a method based on convolutional neural network (CNN) was proposed to estimate the temperature of the laser point. The collected temperature data used were trained by the deep-learning method. A new structure of the model was proposed on the part of the CNN model, which improved from original LeNet. The process of the prediction for the testing set was carried out through the new model. The unknown temperature in the testing set can be estimated. The experimental result illustrates that the proposed method is satisfactory.

关键词： Laser modes Temperature measurement Training Temperature distribution Surface emitting lasers Testing Measurement by laser beam

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A UAV Path Planning Algorithm Based on an Improved D* Lite Algorithm for Forest Firefighting

A UAV Path Planning Algorithm Based on an Improved D* Lite A...

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Chinese Automation Congress (CAC)

作者： Zhaohui Luo Youmin Zhang Lingxia Mu Jing Huang Jing Xin Han Liu Shangbin Jiao Guo Xie Yingmin Yi Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China Concordia University Montreal Canada

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

This paper proposes a new type of forest firefighting method. When one or more suspected fire spots are found in the forest, the firefighting Unmanned Aerial Vehicles (UAVs) can safely and quickly reach the desired location, conduct a thorough investigation of the suspected fire spots. If a fire occurs, it is necessary to drop firefighting materials to put out the fire. At the same time, considering the characteristics of limited endurance and low load capability have always limited the applications of UAVs, Unmanned Ground Vehicles (UGVs) are considered in this paper as an additional and comprehensive problem solution to provide energy and supplement materials for UAVs. In order to enable firefighting UAV to plan a safe and effective path in real time in the complex forest environment, a new improved D*Lite algorithm is proposed for UAVs in the mission team. Even when the 3D workspace is partially known or completely unknown, the algorithm can plan an effective path so that the firefighting UAV can move to the fire spots and UGV in real time.

关键词： Path planning Heuristic algorithms Unmanned aerial vehicles Forestry Smoothing methods Real-time systems Planning

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Adaptive Generalized Projective Synchronization of a New Fractional Chaotic system with uncertain parameters

Adaptive Generalized Projective Synchronization of a New Fra...

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第30届中国控制与决策会议

作者： Jiaoru Huang Kun Zhong Fucai Qian The International Joint Research Center of Autonomous Systems and Intelligent Control Xi'an Technological University Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

This paper investigates the generalized projective synchronization scheme of a new fractional chaotic system, which can display a four-wing chaotic attractor when the order of system is in the range of 0.82 and ***, considering the situation with unknown parameters, an adaptive nonlinear controller is obtained for synchronization and parameters identification based on fractional-order Lyapunov stability theorem. The fractional-order adaptation law, which can update parameters estimation errors online, is derived to ensure the stability of synchronization error system. Numerical simulations are used to verify the results.

关键词： Four-wing chaotic attractors uncertain parameter generalized projective synchronization parameter identification

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MFGB: Molecular Properties Prediction leveraging Self-supervised Morgan Fingerprint Representation Learning

MFGB: Molecular Properties Prediction leveraging Self-superv...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Fang, Shuyan Liu, Yu Liu, Song Wuhan University of Science and Technology School of Computer Science and Technology Wuhan China Wuhan University of Science and Technology Hubei Prov. Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Wuhan University of Science and Technology Hubei Provincial Key Laboratory of Occupational Hazard Identification and Control Hubei Province Wuhan China

ISBN: (纸本)9798350337488

Simple molecular graphs or molecular line notations are insufficient for molecular representation learning models that automatically learn molecule representations to acquire deep semantic features about chemistry. Therefore, we proposed the Morgan Fingerprint Graph, a novel molecular graph model constructed with the nodes of the Morgan Fingerprint Identifier, whose nodes incorporate various properties of the atom neighborhood. Based on our Morgan Fingerprint Graph, we trained a BERT-based molecular representation model MFGB which employs a local rather than global attention module to adapt the model to the Morgan Fingerprint Graph. The attention module of MFGB regulates the exchange of information in the attention layer based on the connection information of the atoms in the molecules. To verify the effectiveness of MFGB, we selected several mainstream datasets for evaluation. The results demonstrated that our method improve the performance of molecular properties prediction. In addition, we analyzed MFGB from both the over-smoothing and the attention perspectives. The analysis indicates that the node vector representations learned from the Morgan Fingerprint Graph are generally less smoothing, and the attention distributions of MFGB match the partial charge density distribution of the molecules. © 2023 IEEE.

关键词： Molecular property prediction Molecular representation learning Morgan Fingerprint Self-supervised learning

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Liu, Yajun Ding, Yulian Li, Aimin Fei, Rong Guo, Xie Wu, Fangxiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China University of Saskatchewan Division of Biomedical Engineering Saskatoon Canada Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9781665468190

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs' functions. © 2022 IEEE.

关键词： Embeddings

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