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Investigation into phantom downlinks and development of a prevention algorithm in rotary steerable system

Petroleum 2021年第3期7卷 349-355页

作者： Fei Li Shaanxi Key Laboratory of Measurement and Control Technology for Oil and Gas Wells Xi’an Shiyou UniversityXi’an710065China School of Electronic Engineering Xi’an Shiyou UniversityXi’an710065China

This paper investigated the failure cause of phantom downlinks of a flow rate detection mechanism in a rotary steerable system and developed a prevention *** is the process of controlling a drilling tool from the surface by sending commands to *** drillers send downlinks to the rotary steerable system to adjust steering parameters to achieve the desired well *** downlink demodulation of downhole tools is achieved through the measurement of flow rate and correlation of waveform with predefined command models;downlink acceptance is based on the correlation *** case study revealed that phantom downlinks were due to the variation of turbine rotation speed,which was the result of alternator load changes.A prevention algorithm of conducting a signal energy check was proposed and *** algorithm can successfully prevent phantom downlinks that are usually of low-level energy and only accept authentic *** work improved the downlinking reliability of the rotary steerable *** methodology can be further applied to other downlink demodulation mechanisms using collar speed or pressure measurement.

关键词： Downlink Demodulation Drilling Rotary steerable system Energy check

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Rydberg-Atom Terahertz Heterodyne Receiver with Ultrahigh Spectral Resolution

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Chinese Physics Letters 2024年第8期41卷 17-21页

作者： Zhenyue She Xiaojie Zhu Yayi Lin Xianzhe Li Xiaolin Yang Yanfei Shang Yuqin Teng Haitao Tu Kaiyu Liao Caixia Zhang Xiaohong Liu Jiehua Chen Wei Huang Key Laboratory of Atomic and Subatomic Structure and Quantum Control(Ministry of Education) School of PhysicsSouth China Normal UniversityGuangzhou 510006China Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials South China Normal UniversityGuangzhou 510006China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Frontier Research Institute for PhysicsSouth China Normal UniversityGuangzhou 510006China GPETR Center for Quantum Precision Measurement South China Normal UniversityGuangzhou 510006China State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics Innovation Academy for Precision Measurement Science and TechnologyChinese Academy of SciencesWuhan 430071China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Atomic Frequency Standards Innovation Academy for Precision Measurement Science and TechnologyChinese Academy of SciencesWuhan 430071China Wuhan Institute of Quantum Technology Wuhan 430206China

Terahertz heterodyne receivers with high sensitivity and spectral resolution are crucial for various ***,we present a room-temperature atomic terahertz heterodyne receiver that achieves ultrahigh sensitivity and frequency *** a signal frequency of 338.7 GHz,we obtain a sensitivity of 2.88±0.09V·cm^(−1)·Hz^(−1/2) for electric field *** calibrated linear dynamical range spans approximately 89 dB,ranging from−110 dBV/cm to−21 dBV/*** demodulate a 400 symbol stream encoded in 4-state phase-shift keying,demonstrating excellent phase detection *** scanning the frequency of the local oscillator,we realize a terahertz spectrometer with Hz level frequency *** resolution is more than two orders of magnitude higher than that of existing terahertz *** demonstrated terahertz heterodyne receiver holds promising potential for working across the entire terahertz spectrum,significantly advancing its practical applications.

关键词： magnitude holds resolution

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Lightweight Vision Transformer for Small Data Sets 38

Lightweight Vision Transformer for Small Data Sets

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Fang, Wenxing Su, Yanxu Institutes of Physical Science and Information Technology Anhui University Hefei China School of Artificial Intelligence Anhui University Hefei230601 China Southeast University Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9798350303636

With the rapid development of Transformers in the field of computer vision, models based on Transformers have become highly competitive architectures in the area. Although variants of Transformer models have achieved increasing accuracy on image classification tasks, the size of the training set and the number of parameters required by the models have increased dramatically. When dealing with small datasets, such models face problems such as overfitting and undergeneralization, leading to poor accuracy on the test set. We propose a new lightweight vision transformer (LVT) to address these issues. We reconstructed the backbone network, which learns the relationship between pixels through local window self-attention and global self-attention computation. We also use the attention pooling approach to fuse the token sequences generated by the backbone network more meticulously. We trained on the CIFAR-10 and CIFAR-100 datasets from scratch and compared them with a modern convolutional neural network. The experimental results show that LVT outperforms the modern convolutional neural network in terms of accuracy and efficiency. On the test set of CIFAR-10, we have obtained an accuracy of 96.83%, which indicates that our model can effectively solve the problems facing the training of small datasets and has a wide range of application prospects. © 2023 IEEE.

关键词： lightweight pooling self-attention windows

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Hierarchical control Scheme for VSC-MTDC System with multiple Renewable Energy Sites Based on Optimal Power Flow and Droop Scheme 6

Hierarchical Control Scheme for VSC-MTDC System with multipl...

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6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022

作者： Raza, Asif Huang, Qi Li, Jian Bamisile, Olusola Dongsheng, Cai Raza, Ghalib University of Electronic Science & Technology of China Sichuan Provincial Key Lab of Power System Wide -Area Measurement and Control School of Mechanical and Electrical Engineering China Chengdu University of Technology College of Nuclear and Automation Engineering Sichuan Provincial Key Lab of Power System Wide-Area Measurement and Control School of Mechanical and Electrical Engineering University of Electronic Science & Technology of China China University of Electronic Science & Technology of China China Chengdu University of Technology College of Nuclear and Automation Engineering Sichuan China Benazir Bhutto Shaheed University of Technology & Skill Development Khairpur Pakistan

ISBN: (纸本)9798350347159

This paper designs a hierarchical control system to improve control performance in a multi-terminal high voltage direct current based on the voltage source converter (VSC-MTDC) transmission system. In this control scheme, the primary control layer employs droop control, while the secondary control layer has developed optimal power flow control (OPF) for maintaining the variation of DC voltage and regulating the active power, and simultaneously minimizing power loss. In the event of a power disturbance or a change in power generation or consumption in the DC system, based on OPF results a new stable droop operating mode is established to stabilize DC voltage variation and regulate active power in the network. A ±400 kV four-terminal VSC-MTDC test network has been developed by using PSCAD and MATLAB simulation tools to assess the proposed control scheme. The primary controller is designed in PSCAD, and the secondary optimum control technique is applied in MATLAB. The results of steady-state and transient operating modes demonstrate that the planned control technique can achieve DC voltage regulation and keep active power, and minimize the power losses in the MTDC system. © 2022 IEEE.

关键词： MATLAB

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Fault Diagnosis Method of Rolling Bearings Under Different Working Conditions Based on Federated Feature Transfer Learning

Fault Diagnosis Method of Rolling Bearings Under Different W...

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Sensing, measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Shouqiang Kang Jiawei Yang Yulin Sun Yujing Wang Qingyan Wang V.I. Mikulovich School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China Belarusian State University Minsk Belarus

A rolling bearing fault diagnosis method based on the federated feature transfer learning is proposed for the low accuracy of the diagnosis model in the presence of large differences in data distribution under different working conditions, difficulty in obtaining labeled data and non-sharing of data among different users. This method performs wavelet transformation on the time domain vibration data of rolling bearings to obtain a time-frequency diagram. The priori labeled public data and the multi-user island private data are regarded as the source domain and the target domain. The multi-representation feature extraction structure is introduced to improve the original residual network. Based on an improved residual network and multi-representation features in the source domain and the target domain, every local model and a federated global model are constructed. Through verification of bearing data, the proposed method can establish an effective fault diagnosis model with high fault diagnosis accuracy. It can integrate the knowledge of isolated island data without sharing data among multiple users.

关键词： Fault diagnosis Employee welfare Wavelet transforms Adaptation models Wavelet domain Transfer learning Rolling bearings

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Correction to: An advanced reinforcement learning control method for quadruped robots in typical urban terrains (International Journal of Machine Learning and Cybernetics, (2024), 10.1007/s13042-024-02478-9)

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International Journal of Machine Learning and Cybernetics 2025年 1-2页

作者： Yan, Chi Wang, Ning Gao, Hongbo Wang, Xinmiao Tang, Chao Zhou, Lin Li, Yuehua Wang, Yue School of Information Science and Technology University of Science and Technology of China No. 96 Jinzhai Road Anhui Hefei230026 China School of Information and Security Chongqing College of Mobile Communication No. 36 Dengying Avenue Qijiang District Sichuan Chongqing401520 China Institute of Advanced Technology University of Science and Technology of China No. 5089 Wangjiang West Road Anhui Hefei230088 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Nanyang639798 Singapore Zhejiang Lab Kechuang Avenue Zhongtai Sub-District Zhejiang Hangzhou311121 China College of Control Science and Engineering Zhejiang University Zhejiang Hangzhou310027 China

In this article, the affiliation details for Authors "Ning Wang2, Hongbo Gao1,3,4, Yuehua Li5, Yue Wang6" were interchanged and it is corrected now. Incorrect affiliation: 2Institute of Advanced technology, University of Science and technology of China, No. 5089 Wangjiang West Road, Hefei 230088, Anhui, China 3school of Electrical and Electronic engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798 Nanyang, Singapore 4Zhejiang Lab, Kechuang Avenue, Zhongtai Sub-District, Hangzhou 311121, Zhejiang, China 5College of control Science and engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China 6school of Information and Security, Chongqing College of Mobile communication, No. 36 Dengying Avenue, Qijiang District, Chongqing 401520, Sichuan, China Correct affiliation: 2school of Information and Security, Chongqing College of Mobile communication, No. 36 Dengying Avenue, Qijiang District, Chongqing 401520, Sichuan, China 3Institute of Advanced technology, University of Science and technology of China, No. 5089 Wangjiang West Road, Hefei 230088, Anhui, China 4school of Electrical and Electronic engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798 Nanyang, Singapore 5Zhejiang Lab, Kechuang Avenue, Zhongtai Sub-District, Hangzhou 311121, Zhejiang, China 6College of control Science and engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

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An Obstacle Avoidance Path Planning Algorithm for Manipulator in Deep Crate Grasping

An Obstacle Avoidance Path Planning Algorithm for Manipulato...

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2023 China Automation Congress, CAC 2023

作者： Yao, Zhuobin Gan, Yahui Han, Zao Gao, Huan Li, Ang School of Automation Southeast University Key Laboratory of Measurement and Control of Systems of Engineering Ministry of Education NanJing China Intel Joint Research Institute on Intelligent Edge Computing Nanjing YingQi Intelligent Technology Co. Ltd. NanJing China

ISBN: (纸本)9798350303759

The deep crate grasping problem is a major challenge for manipulator in industrial applications. In order to solve the issue of deep crate grasping and ensure the path quality of the algorithm, this paper proposes a new hybrid obstacle avoidance planning algorithm based on goal-biased rapidly exploring random tree and improved artificial potential field (RRT-APF). Initially, the algorithm strategizes an efficient trajectory for the working element within the operational area. Subsequently, it dissects this trajectory through sampling, creating a sequence of sub-target points. These points serve as the basis for obstacle avoidance planning within the manipulator's configuration space, mitigating planning complexities. Moreover, the RRT algorithm is employed to address the challenge of local optima within the artificial potential field method. The stochastic nature of the RRT algorithm plays a crucial role in enabling the APF algorithm to break free from local optima. Ultimately, the efficacy of the algorithm is demonstrated through the planning of a path for obstacle avoidance on the DOBOT CR5 manipulator using MATLAB. © 2023 IEEE.

关键词： Trees (mathematics)

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Boosting Unsupervised Monocular Depth Estimation with Auxiliary Semantic Information

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China communications 2021年第6期18卷 228-243页

作者： Hui Ren Nan Gao Jia Li State Key Laboratory of Media Convergence and Communication、Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism(Communication University of China)、Beijing Key Laboratory of Modern Entertainment Technology(Communication University of China)、School of Information and Communication EngineeringCommunication University of China.No.1Dingfuzhuang StreetChaoyang DistrictBeijing100024China

Learning-based multi-task models have been widely used in various scene understanding tasks,and complement each other,i.e.,they allow us to consider prior semantic information to better infer *** boost the unsupervised monocular depth estimation using semantic segmentation as an auxiliary *** address the lack of cross-domain datasets and catastrophic forgetting problems encountered in multi-task training,we utilize existing methodology to obtain redundant segmentation maps to build our cross-domain dataset,which not only provides a new way to conduct multi-task training,but also helps us to evaluate results compared with those of other *** addition,in order to comprehensively use the extracted features of the two tasks in the early perception stage,we use a strategy of sharing weights in the network to fuse cross-domain features,and introduce a novel multi-task loss function to further smooth the depth *** experiments on KITTI and Cityscapes datasets show that our method has achieved state-of-the-art performance in the depth estimation task,as well improved semantic segmentation.

关键词： unsupervised monocular depth estimation semantic segmentation multi-task model

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A Spectrum Sensing Method Based on CNN-LSTM Deep Neural Network 7

A Spectrum Sensing Method Based on CNN-LSTM Deep Neural Netw...

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7th IEEE International Conference on Network Intelligence and Digital Content, IC-NIDC 2021

作者： Zhang, Shujian Xu, Zhan Tian, Lu Yang, Xiaolong Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Beijing100101 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China

ISBN: (纸本)9781665405829

Spectrum sensing can effectively improve the low utilization of spectrum resources and is one of the crucial components of cognitive radio networks. This paper proposes a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network cascaded spectrum sensing model. The model uses CNN to analyze the Short-Time Fourier transform spectrogram of the blind signal. Then the generated feature vector or feature map is passed to the LSTM according to the timestamp. Finally, it detects a signal in a specific spectrum and classifies the signal type to identify multiple signals accurately. The neural network model improves the detection probability by simultaneously acquiring the spatial and temporal characteristics of the blind signal. The experimental results show that the method in this paper can detect a variety of signals with higher detection probability within a wide range of SNR, especially under the condition of low SNR. © 2021 IEEE.

关键词： Cognitive radio

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Combined calibration method for large field of view multi-camera system

Combined calibration method for large field of view multi-ca...

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2022 Chinese Automation Congress, CAC 2022

作者： Sun, Wei Huo, Ju Li, You Xue, Muyao Zhou, JianBao Harbin Institute of Technology School of Electrical Engineering and Automation Harbin China Shanghai Space Propulsion Technology Research Institute Shanghai China Harbin Nuoxin University of Technology Measurement and Control Technology Co. Ltd. Harbin China

ISBN: (纸本)9781665465335

In the calibration process of multi-camera system with large field of vie23, aiming at the problem of self occlusion in the plane calibration method and the problem of low calibration accuracy of one-dimensional calibration methods, a calibration method of multi-camera system based on the combination of plane target and one-dimensional target is proposed. Firstly, a mathematical model of multi-camera calibration is established with pin-hole model and rigid body transformation theory;the internal parameters and distortion coefficients of each camera are calibrated by two-dimensional plane target;on this basis, analyze the uncertainty of imaging error of circular feature points, cost function of random sample consensus algorithm(RANSAC) is improved. Using the camera imaging model, the essential matrix between cameras is estimated, and the initial value of camera external parameters is decomposed and determined;lastly, multiple nonlinear optimization algorithms are used to refine and obtain the camera matrix, The simulation and actual experimental results show that the multi camera system combination calibration method proposed in this paper can realize the parameter calibration of four camera measurement system, and the measurement space is 2000mm×2000mm×2000mm, the 3D reconstruction error is less than 0.15mm. Compared with the traditional calibration method, it has more accurate calibration accuracy. © 2022 IEEE.

关键词： Calibration

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