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A Survey on Perspective-n-Point Problem

A Survey on Perspective-n-Point Problem

引用

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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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 ...

引用

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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Data Room Management System Based on Target Detection Algorithm

Data Room Management System Based on Target Detection Algori...

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第40届中国控制会议

作者： Zhicheng Zhao Weihua Cao Kang Li Yitao 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

In recent years,streaming media services have been widely used in data room management *** order to record the user operations,host machine alarms and reminder in real time,target detection algorithms for streaming media have played an important *** target detection methods,limited by detection speed,are not suitable for such real-time ***,in this paper,we applies SSD（Single Shot MultiBox Detector） algorithm to data room management ***,we constructs a host operating image data set,and then perform data amplification by adding noise and ***,the SSD model is used to train a ***,after obtaining the target detection results,the NMS（Non-Maximum Suppression）algorithm is used to avoid the redundant detection *** the same time,some improvement measures are put forward to solve the problem of small target,leading to the difficulty of feature extraction and low detection *** experimental results demonstrate that the model can detect and track the target better in the video and meet the requirements of the real-time performance and accuracy of the system.

关键词： target detection data room management system streaming media SSD

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A pulse position coding-based mud pulse signal identification algorithm for Measurement While Drilling(MWD) system

A pulse position coding-based mud pulse signal identificatio...

引用

第32届中国过程控制会议(CPCC2021)

作者： Ziliang Yu Weihua Cao Chao Gan 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

Accurate identification of mud pulse signal is crucial for Measurement While Drilling(MWD) system due to its vital role in improving the drilling safety and *** this paper,a pulse position coding-based mud pulse signal identification algorithm is proposed for MWD system via two *** the signal preprocessing stage,wavelet filtering is introduced to reduce the noises in the raw mud pulse ***,a polynomial fitting-based detection method is used to remove the baseline drift in the *** the signal identification stage,a pulse signal position identification model is established to detect the pulse position,which does not need to set the detection threshold *** comparison results demonstrate that the proposed method has higher identification efficiency and accuracy than the conventional methods.

关键词： Mud pulse signal Wavelet analysis Polynomial fitting Pulse position identification model

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Anomaly Detection Based on Principle of Justifiable Granularity and Probability Density Estimation

Anomaly Detection Based on Principle of Justifiable Granular...

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Chinese Automation Congress (CAC)

作者： Sheng Du Xian Ma Xiang Li Min Wu Weihua Cao Witold Pedrycz 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 AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Sariyer/Istanbul Turkiye

Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes.

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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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Finite-Time State Estimation for Discrete-Time Nonlinear Singularly Perturbed complex Networks under New Event-Triggered Mechanism

Finite-Time State Estimation for Discrete-Time Nonlinear Sin...

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第40届中国控制会议

作者： Chao Yang Xiongbo Wan 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

This paper investigates the finite-time state estimation problem for a class of discrete-time nonlinear singularly perturbed complex networks under a new dynamic event-triggered mechanism(DETM). This new DETM is devised to adjust the date packet transmissions flexibly with hope to save network resources. By constructing a new Lyapunov function dependent on the information of the singular perturbation parameter(SPP) and DETM, a sufficient condition is derived which ensures that the error dynamics of state estimation is finite-time stable. The parameters of the state estimator are given by means of the solutions to several matrix inequalities and the upper bound of the SPP can be evaluated simultaneously. The effectiveness of the designed state estimator is demonstrated by a numerical example.

关键词： complex networks dynamic event-triggered mechanism singularly perturbed systems state estimation

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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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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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Adaptive Event-Triggered control of Virtual Player for Social Motor Coordination in Joint actions

Adaptive Event-Triggered Control of Virtual Player for Socia...

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第40届中国控制会议

作者： Bowen Guo Chao Zhai 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 this work,an adaptive event-triggered control approach is developed for a virtual player(VP) to generate the human-like trajectories in the mirror game,a simple yet effective paradigm for studying interpersonal *** taking into account individual motor signature,an online control algorithm is designed to produce joint improvised motions with a human player or another virtual player while exhibiting some desired kinematic *** the proposed control algorithm,the control actions can be adaptively switched according to the movement status of ***,stability analysis of the VP model driven by the feedback controller is ***,the proposed control approach is validated by matching the experimental data.

关键词： adaptive event-triggered control virtual player motor coordination joint action

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