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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Gengshan Wang Zhijia Yang Tierui Gong State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Singapore University of Technology and Design Singapore

In this paper, we investigate the hybrid beamforming design for the full-duplex (FD) millimeter-wave (mmWave) multiple-input multiple-output (MIMO) system, aiming to maximize the overall spectral efficiency (SE) and cancel the self-interference (SI) simultaneously. The coupling transmit power constraints, unit-modulus constraints, and strong SI make the problem under study highly nonconvex. To solve the complicated problem, we first transform it into an equivalent yet more tractable weighted minimum mean square error minimization problem and then propose an effective two-stage hybrid beam-forming design. In the first stage, we propose to extend the known matrix approximation method to the FD scenario to obtain the initial hybrid beamforming design. In the second stage, we propose to use the block coordinate descent method to update the digital beamformers and combiners while retaining the obtained analog solutions, which sufficiently exploits the easily adjustable property of digital solutions to cancel the residual SI and compensate for the SE loss. We further analyze the computational complexity of the proposed design. Numerical results demonstrate that our proposed two-stage hybrid beamforming design is superior to conventional related methods.

关键词： Wireless communication Radio frequency Array signal processing Spectral efficiency Full-duplex system Transforms Minimization

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

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Annual Conference of Industrial Electronics Society

作者： Haibo Cheng Yunpeng He Peng Zeng Shichao Li Valeriy Vyatkin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction.

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Monocular Visual SLAM based on VGG Feature Point Extraction 39

Monocular Visual SLAM based on VGG Feature Point Extraction

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39th Chinese control Conference, CCC 2020

作者： Dai, Xu-Yang Meng, Qing-Hao Zheng, Wen-Jian Zhu, Shao-Kai Tianjin University Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin300072 China

ISBN: (纸本)9789881563903

Significant progress has been made in the field of visual SLAM (Simultaneous Localization and Mapping). However, the localization accuracy of visual SLAM can be significantly reduced in low-texture and illumination-changing environments. To solve these problems, an enhanced visual SLAM algorithm based on VGG (Visual Geometry Group) network was proposed in this paper. Firstly, the VGG network for feature point extraction was incorporated into the visual odometry (VO) to achieve robust camera pose estimation. Secondly, an automatic corner annotation method was adopted to set up the training database, which could reduce the workload of data annotation. Thirdly, to make the backend optimization more suitable for the VGG based VO, the BA (bundle adjustment) optimization process was improved. Experimental results showed that, the proposed visual SLAM method outperformed the mainstream ORB-SLAM (Oriented FAST and Rotated BRIEF SLAM) method, in terms of the number of effective feature points, the robustness of feature matching process to light changes, and the accuracy of robot pose estimation. © 2020 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Extraction

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Joint Routing and Charging Problem of Electric Vehicles with Incentive-Aware Customers Considering Spatio-Temporal Charging Prices

SSRN

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SSRN 2022年

作者： Yao, Canqi Chen, Shibo Salazar, Mauro Yang, Zaiyue School of Mechatronics Engineering Harbin Institute of Technology Harbin150000 China Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering The Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China Control Systems Technology Group Department of Mechanical Engineering Eindhoven University of Technology EindhovenMB 5600 Netherlands

This paper investigates the scheduling problem of a fleet of electric vehicles, providing mobility as a service to a set of time-specified customers, where the operator needs to solve the routing and charging problem jointly for each EV. Hereby we consider incentive-aware customers and propose that the operator offers monetary incentives to customers in exchange for time flexibility. In this way, the fleet operator can achieve a routing and charging schedule with lower costs, whilst the customers receive monetary compensation for their flexibility. Specifically, we first propose a bi-level optimization model whereby the fleet operator optimizes the routing and charging schedule accounting for the spatio-temporal varying charging price, jointly with a monetary incentive to reimburse the delivery time flexibility experienced by the customers. Concurrently the customers choose their own time flexibility by minimizing their own cost. Second, we cope with the computational burden coming from this nonlinear bi-level optimization model with an accurate reformulation approach consisting of the KKT optimality conditions, a Big-M-based linearization method, and the zero duality gap of convex optimization problems. This way, we convert the proposed problem into a single-level optimization problem, which can be solved by a strengthened generalized Benders decomposition method holding a faster convergence rate than the generalized Benders decomposition method. To evaluate the effectiveness of the proposed mathematical model, we carry out numerous simulation experiments by using the VRP-REP data of Belgium. The numerical results showcase that the proposed mathematical model can reduce the delivery fees for the customers together with the cost of operation incurred by the fleet operator. © 2022, The Authors. All rights reserved.

关键词： Sales

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Joint Routing and Charging Problem of Electric Vehicles with Incentive-aware Customers Considering Spatio-temporal Charging Prices

arXiv

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arXiv 2022年

关键词： Sales

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Research on Intrusion Detection of Industrial control System Based on FastICA-SVM Method 7th

Research on Intrusion Detection of Industrial Control System...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Chen, Haonan Liu, Xianda Wang, Tianyu Zhang, Xuejing College of Information Science and Engineering Northeastern University Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Beijing China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Beijing China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030786083

As an important part of guard system, intrusion detection systems have great significance to the security of industrial control systems. Cause the industrial control systems of the on-site environment is often very complicated, the process data extracted on-site have a wide variety of features, and they always have complex associations and are not independent. The intrusion detection algorithms for traditional industrial control systems often do not solve this problem, which results in a decrease in the accuracy, precision and high false negative rate (FNR), false positives rate (FPR). Aiming at solving the current problems, this paper proposes an algorithm combining the Fast Independent Principal Component Analysis (FastICA) and Support Vector Machine (SVM). Finally compared with the SVM and PCA-SVM algorithms, the experimental results show that the accuracy rate has been significantly improved and slight FPR. © 2021, Springer Nature Switzerland AG.

关键词： Intrusion detection

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A Shadowcasting-Based Next-Best-View Planner for Autonomous 3D Exploration

arXiv

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arXiv 2021年

作者： Batinovic, Ana Ivanovic, Antun Petrovic, Tamara Bogdan, Stjepan University of Zagreb Faculty of Electrical Engineering and Computing LARICS Laboratory for Robotics and Intelligent Control Systems Unska 3 Zagreb10000 Croatia

—In this paper, we address the problem of autonomous exploration of unknown environments with an aerial robot equipped with a sensory set that produces large point clouds, such as LiDARs. The main goal is to gradually explore an area while planning paths and calculating information gain in short computation time, suitable for implementation on an on-board computer. To this end, we present a planner that randomly samples viewpoints in the environment map. It relies on a novel and efficient gain calculation based on the Recursive Shadowcasting algorithm. To determine the Next-Best-View (NBV), our planner uses a cuboid-based evaluation method that results in an enviably short computation time. To reduce the overall exploration time, we also use a dead end resolving strategy that allows us to quickly recover from dead ends in a challenging environment. Comparative experiments in simulation have shown that our approach outperforms the current state-of-the-art in terms of computational efficiency and total exploration time. The video of our approach can be found at https://***/playlist?list=PLC0C6uwoEQ8ZDhny1V dmFXLeTQOSBibQl. © 2021, CC BY-SA.

关键词： Antennas

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Sim2Air - Synthetic Aerial Dataset for UAV Monitoring

arXiv

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arXiv 2021年

作者： Barisic, Antonella Petric, Frano Bogdan, Stjepan The University of Zagreb Faculty of Electrical Engineering and Computing LARICS Laboratory for Robotics and Intelligent Control Systems Unska 3 Zagreb10000 Croatia

In this paper we propose a novel approach to generate a synthetic aerial dataset for application in UAV monitoring. We propose to accentuate shape-based object representation by applying texture randomization. A diverse dataset with photo-realism in all parameters such as shape, pose, lighting, scale, viewpoint, etc. except for atypical textures is created in a 3D modelling software Blender. Our approach specifically targets two conditions in aerial images where texture of objects is difficult to detect, namely challenging illumination and objects occupying only a small portion of the image. Experimental evaluation of YOLO and Faster R-CNN detectors trained on synthetic data with randomized textures confirmed our approach by increasing the mAP value (17 and 3.7 percentage points for YOLO;20 and 1.1 percentage points for Faster R-CNN) on two test datasets of real images, both containing UAV-to-UAV images with motion blur. Testing on different domains, we conclude that the more the generalisation ability is put to the test, the more apparent are the advantages of the shape-based representation. © 2021, CC BY-NC-SA.

关键词： robotics

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Comparison Study of State Vector Estimation Methods for Moving Objects

Comparison Study of State Vector Estimation Methods for Movi...

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Ocean Studies (ICOS), International Conference on

作者： Irina Nemiro Anton Gubankov Laboratory of Marine Unmanned Aviation and Marine Aviation Systems Maritime State University Named after Admiral G.I. Nevelskoy Far Eastern Federal University Vladivostok Russia Robotics Laboratory Institute of Automation and Control Processes Maritime State University Named after Admiral G.I. Nevelskoy Far Eastern Federal University Vladivostok Russia

The paper considers the comparative study of different methods for state vector estimation of moving objects in conditions when data from sensors received with a low frequency, and the measurements themselves are very noisy. The Kalman filter is used for state vector estimation. Due to the low frequency of updating the measurement vector, it is proposed to increase the frequency of predicting the state of a moving object between these updates. The problem of identifying a moving object at noisy measurements is solved using three different methods: the nearest neighbor method, the probabilistic method of data association, and fuzzy logic. A comparative of the simulation results of the developed algorithm using the three considered methods is presented. When using fuzzy logic, significantly fewer false measurements are selected than with the other two methods, since the measurement of a moving object is selected based on several parameters of the object, but not only based on one parameter.

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Efficient Planar Pose Estimation via UWB Measurements

Efficient Planar Pose Estimation via UWB Measurements

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IEEE International Conference on robotics and Automation (ICRA)

作者： Haodong Jiang Wentao Wang Yuan Shen Xinghan Li Xiaoqiang Ren Biqiang Mu Junfeng Wu Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS) School of Data Science the Chinese University of HongKong Shenzhen P. R. China College of Control Science and Engineering and the State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou P. R. China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing P. R. China School of Mechatronic Engineering and Automation Shanghai University Shanghai P. R. China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing P. R. China

State estimation is an essential part of autonomous systems. Integrating the Ultra-Wideband (UWB) technique has been shown to correct the long-term estimation drift and bypass the complexity of loop closure detection. However, few works on robotics treat UWB as a stand-alone state estimation solution. The primary purpose of this work is to investigate planar pose estimation using only UWB range measurements. We prove the excellent property of a two-step scheme, which says we can refine a consistent estimator to be asymptotically efficient by one step of Gauss-Newton iteration. Grounded on this result, we design the GN-ULS estimator, which reduces the computation time significantly compared to previous methods and presents the possibility of using only UWB for real-time state estimation.

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