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第32届中国过程控制会议(CPCC2021)

作者： Zhuoxuan Jiang Jianqi An Zhilin Tang Xin Chen Min Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

During the construction of the tunnel excavation with the method of drilling and blasting,overbreak and underbreak occur ***,overbreak and underbreak affect the cost,efficiency,and safety of tunnel *** paper presents a detection method for overbreak and underbreak of tunnels based on three-dimensional laser point ***,this paper obtains point cloud data of a tunnel by a 3 D laser scanner,preprocesses the point cloud data based on Gaussian filter,and extracts midlines of the tunnel based on random sampling consistency(RANSAC) to obtain attitude and trend information of the ***,cross-sections of the tunnel are extracted according to the midline of the ***,the position and value of the overbreak and underbreak are got according to comparing the projections of the cross-sections of the tunnel with a planned extent of the ***,this method was applied to an evaluation of overbreak and underbreak of a tunnel,and the results show that the method in this paper detects overbreak and underbreak easily,quickly,and accurately.

关键词： Overbreak and underbreak three-dimensional laser point cloud

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Electroencephalogram Emotion Recognition Using Variational Modal Decomposition Based Dispersion Entropy Feature Extraction

Electroencephalogram Emotion Recognition Using Variational M...

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第40届中国控制会议

作者： Si-Jun Hu Zhen-Tao Liu Xue-Wen Ding School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

Electroencephalogram（EEG） emotion recognition has gained considerable attention due to its ability to reflect people’s inner emotional states objectively and *** extraction is a critical step in EEG emotion recognition because of non-stationarity and irregularity of EEG signals.A feature extraction method using Variational Modal Decomposition（VMD）to extract Dispersion Entropy（DispEn） is proposed in this *** EEG signal is decomposed into several components,and DispEn of each component is extracted in eight emotion-related *** method was tested on DEAP dataset in which the EEG emotional states are accessed in Valence-Arousal emotional *** emotional states（i.e.,HVHA,HVLA,LVHA,LVLA） are classified by Support Vector Machine（SVM）.The experimental results show that the accuracy of emotion recognition is 77.87%,which demonstrates its effectiveness.

关键词： EEG Emotion recognition VMD Dispersion Entropy Feature Extraction SVM

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A dulcimer keys positioning method based on improved Roberts operator

A dulcimer keys positioning method based on improved Roberts...

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2021 China Automation Congress, CAC 2021

作者： Tang, De Zhang, Ziying Chen, Xin Xiao, Zhe Qin, Mengxi School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan430074 China School of Arts and Communication China University of Geosciences Wuhan430074 China

ISBN: (纸本)9781665426473

It is important for the dulcimer robot to obtain the spatial coordinates of the dulcimer phonemes. However, the traditional manual positioning method is both inefficient and does not meet the intelligence requirements of modern robot. A visual positioning method based on improved Roberts operator is proposed in this paper on account of solving these problems, which can obtain the 3D coordinates of the dulcimer keys in a short time. First, an improved Roberts edge detection operator is designed, which fully detects the edge information of the target by transforming the scale. Further, this paper proposes a dulcimer keys positioning method by fusing RGB and depth image information. This method has a better positioning effect for dulcimer keys with complex background and small size. The experimental results show that the visual positioning method proposed in this paper can effectively identify and locate the dulcimer keys, and it has better robustness to the lighting environment. © 2021 IEEE

关键词： Binocular vision

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Design of a prediction and optimization system for plate shape in roller quenching process

Design of a prediction and optimization system for plate sha...

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第40届中国控制会议

作者： Wen Zhang Min Wu Cong Chen Sheng Du Luefeng Chen School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

Plate shape is one of the key quality indices of steel plates after *** is of great significance to realize the prediction and optimization of plate shape for obtaining high quality steel *** paper designs a prediction and optimization system for plate shape in roller quenching ***,the roller quenching process is described in detail,the design objectives are analyzed,and the architecture of the system is ***,the system is designed from four parts:the prediction model of plate shape,the comprehensive evaluation model of plate shape,the intelligent optimization model of operating parameters and the case ***,the prediction and optimization system is applied to the industrial *** results of preliminary tests show that the system improves the quality of plate shape.

关键词： Roller quenching process Plate shape prediction intelligent optimization Case matching

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Multi-condition recognition method based on LSTM＿GRU for heating processes

Multi-condition recognition method based on LSTM＿GRU for he...

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第40届中国控制会议

作者： Qingyi Zhu Weihua Cao Wenshuo Song School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

Aiming at the multi-condition problem of Continuous Annealing Processes(CAP), this paper proposes a new method based on Long Short-Term Memory(LSTM) and Gated Recurrent Unit(GRU) models to identify multiple conditions in ***, this work analyzes the parameters in CAP, selects the key variables that affect the working conditions, and then selects a piece of data in the CAP work process as the training data set to train the constructed LSTMRU neural network. This method realizes the recognition of different working conditions in CAP, which saves training time, simplifies internal *** with the traditional method, this method avoids the recognition error caused by personal experience factors, and the model accuracy has greatly improved.

关键词： Working condition recognition LSTMGRU Metallurgy Process industry CAP

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Learning deep multimodal feature representation with asymmetric multi-layer fusion

arXiv

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arXiv 2021年

作者： Wang, Yikai Sun, Fuchun Lu, Ming Yao, Anbang State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Cognitive Computing Laboratory Intel Labs China

We propose a compact and effective framework to fuse multimodal features at multiple layers in a single network. The framework consists of two innovative fusion schemes. Firstly, unlike existing multimodal methods that necessitate individual encoders for different modalities, we verify that multimodal features can be learnt within a shared single network by merely maintaining modality-specific batch normalization layers in the encoder, which also enables implicit fusion via joint feature representation learning. Secondly, we propose a bidirectional multi-layer fusion scheme, where multimodal features can be exploited progressively. To take advantage of such scheme, we introduce two asymmetric fusion operations including channel shuffle and pixel shift, which learn different fused features with respect to different fusion directions. These two operations are parameter-free and strengthen the multimodal feature interactions across channels as well as enhance the spatial feature discrimination within channels. We conduct extensive experiments on semantic segmentation and image translation tasks, based on three publicly available datasets covering diverse modalities. Results indicate that our proposed framework is general, compact and is superior to state-of-the-art fusion frameworks. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

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A Survey on Perspective-n-Point Problem

A Survey on Perspective-n-Point Problem

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第40届中国控制会议

作者： Shiye Pan Xinmei Wang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

Determining the position and orientation of the camera is a crucial problem with the rapidly developing technology in visual Simultaneous Localization and Mapping(SLAM),augmented reality and 3 D *** the Pn P(perspective-n-point) problem is an effective method to calculate the pose of the camera and is also the most widely used method in many *** this paper,the methods for Pn P problem,including special Pn P problem and general Pn P problem are summarized ***,due to importance of performing Pn P methods in practical applications,ability to handle outliers for Pn P methods is ***,the main problems of the current researches on Pn P problem are presented.

关键词： Perspective-n-point Problem Camera Pose Estimation Robustness to Outliers

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Detection of Sequential Alarm Patterns in complex Industrial Facilities Using ClaSP and Top-K Algorithms

Detection of Sequential Alarm Patterns in Complex Industrial...

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第40届中国控制会议

作者： Zhuang Wang Wenkai Hu Weihua Cao Min Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

In modern complex industrial processes,due to poor rationalization of alarm systems and the complexity of process interconnections,alarm floods are commonly *** floods are also identified as the main causes of many industrial *** valid approach to deal with alarm floods is to mine meaningful alarm sequential patterns from alarm *** identified patterns can help to analyze root causes or to configure dynamic alarming *** this paper,a method based on the combination of ClaSP and Top-K is proposed to mine interesting alarm sequential patterns from historical alarm *** contributions of this study are twofold:1) A pattern mining approach is adapted to mine interesting patterns from alarm flood sequences;2) A pattern compression strategy is proposed to reduce pattern redundancy.A case study is presented to demonstrate the effectiveness of the proposed method.

关键词： Alarm system alarm flood sequential pattern mining process monitoring

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A novel drilling rate of penetration(ROP) prediction method using data pre-processing techniques and T-S fuzzy inference

A novel drilling rate of penetration(ROP) prediction method ...

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第40届中国控制会议

作者： Xiang Wang Chao Gan Weihua Cao School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

Rate of penetration(ROP) prediction is crucial for the drilling optimization and cost-savings. In this paper, a novel drilling ROP prediction method is proposed and the prediction model can be divided into two stages(data pre-processing and T-S fuzzy inference modeling). In the first stage, four data pre-processing techniques(Reduction, re-sampling, wavelet filtering, and normalization) are used step by step to improve the quality of drilling data. In the second stage, T-S fuzzy inference method is introduced to establish the ROP prediction model. The experiment is executed by using the data from actual drilling process and the results demonstrate the effectiveness of proposed method in prediction accuracy compared with two conventional methods(response surface method and support vector regression).

关键词： Rate of penetration Drilling optimization Data pre-processing T-S fuzzy inference Drilling process

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Deep learning-based spatial modeling method for 3D formation drillability field

Deep learning-based spatial modeling method for 3D formation...

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第40届中国控制会议

作者： Lei Peng Chao Gan Weihua Cao School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

3D formation drillability field is crucial for drilling optimization and control due to its vital role in describing the spatial formation environment. Conventional geostatistical and machine learning methods are introduced to establish the ***, the modeling accuracy should be further improved to meet the high-level requirement of drilling engineering. In this paper, a novel deep learning-based spatial modeling method is proposed for 3D formation drillability field. First of all, the drilling process and its characteristics are described and analyzed. After that, long short-term memory（LSTM）, a deep learning method is proposed to establish the 3D formation drillability field model. The inputs of the model are the ground and depth coordinates and the output of the model is the formation drillability. Finally, 3D modeling and final test experiments are executed and the drilling data are from Xujiawei area, Northeast China. The results show the effectiveness of proposed method in modeling accuracy compared with four conventional methods（Random forest, Support vector regression, Scattered Interpolation, and Kriging）.

关键词： Spatial modeling Formation drillability LSTM 3D field Drilling process

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