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Acoustic vector sensor underwater communications in the Makai Experiment

Proceedings of Meetings on Acoustics 2021年第1期44卷

作者： Fabricio de Abreu Bozzi Sérgio Manuel Machado Jesus 1Laboratory for Robotics and Engineering Systems (LARSys) University of Algarve Faro Algarve 8005-139 PORTUGAL fabriciobozzi@*** sjesus@ualg.pt

The performance of a vector sensor array (VSA) receiving communication signals in the Makai experiment is quantified. The VSA composed of 4 vector sensors (VS) was tied to a drifting research vessel in shallow water. A bottom-moored source was used to transmit signals from 8k to 14kHz. In the present study, pressure and particle velocity channels are weighted-combined leading to a directional gain (VS beam steering). The communication chain for coherent modulation is composed of noise normalization, VS beam steering, synchronization, Doppler tracking, and a single Decision Feedback *** noise normalization step (denoising) is used since the noise power is not uniformly distributed among the pressure and velocity channels. This normalization emphasizes the vertical particle velocity, which benefit communication performance. Bit error rate(BER) performance is estimated for the pressure-only array, a single VS, and the VSA. It is shown that a single VS may provide similar communication performance to four pressure sensors. The BER for the shorter range (230m) varies from 0 to 5%, depending on the number of sensors used. It is noticed that in a multipath environment, steering to the Direction of Arrival (DoA) elevation may not lead to the lowest error for communications.

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Application of Graph-Curvature Features in Auxiliary Diagnosis for Histopathological Image Identification of Gastric Cancer

SSRN

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SSRN 2023年

作者： He, Ruilin Li, Chen Yang, Xinyi Yang, Jinzhu Jiang, Tao Grzegorzek, Marcin Sun, Hongzan Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang China School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information Technology Chengdu China Institute for Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland Shengjing Hospital of China Medical University Shenyang China School of Computer Science and Engineering

Background: Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors, but it has some drawbacks. This paper explores a computer-aided diagnostic method that can identify benign and malignant gastric cancer with histopathology images. Method: This article obtains the most suitable process through multiple experiments, compared multiple methods and features for classification. Firstly, the U-net is applied to segment the image. Next, the nucleus is extracted from the segmented image and the Minimum Spanning Tree (MST) diagram structure is drawn. The third step is to extract the graph-curvature features of histopathology image according to the MST image. Finally, by inputting graph-curvature features into the classifier, the recognition results for benign or malignant can be obtained. Result: During the experiment, we use various methods for comparison. In the image segmentation stage, U-net, watershed algorithm and Otsu threshold segmentation methods are used respectively. Combined with multiple indicators, we find that the U-net method is the most suitable for segmentation of histopathology images. In the feature extraction stage, in addition to extracting graph-edge feature and graph-curvature feature, several basic image features are also extracted, including Red, Green, Blue feature, Gray-Level Co-occurrence Matrix feature, Histogram of Oriented Gradient feature, and Local Binary Pattern feature. In the classifier design stage, we experimented with various methods, such as Support vector machine (SVM), Random forest, Artificial Neural Network, K Nearest Neighbors, VGG-16 and Inception-V3. Through the comparison and analysis, the classification results with an accuracy of 98.57% can be obtained by inputting the graph-curvature feature into SVM classifier. Conclusion: This paper has created a unique feature, graph-curvature feature based on MST to represent and analyze histopathology images. This graph-based feature can be used

关键词： Local binary pattern

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Bacteria Flagella-Mimicking Polymer Multilayer Magnetic Microrobots

SSRN

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SSRN 2024年

作者： Wu, Zhiguang Lu, Liang Bai, Shuang Shi, Jiaqi Zhang, Zhuotao Pan, Yunlu Wang, Wei Wu, Wenfeng Sun, Shoubin Li, Xu Hou, Gang Huang, Tianyun Jia, Yuxin Zuo, Kaiwen Granovsky, Alexander Wu, He Nikolai, Perov School of Medicine and Health Harbin Institute of Technology Harbin China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China The First Affiliated Hospital of Harbin Medical University Harbin China National Center for Respiratory Medicine State Key Laboratory of Respiratory Health and Multimorbidity National Clinical Research Center for Respiratory Diseases Institute of Respiratory Medicine Chinese Academy of Medical Sciences Department of Pulmonary and Critical Care Medicine Center of Respiratory Medicine China-Japan Friendship Hospital Beijing China College of Science Sichuan Agricultural University Chengdu China The Fourth Affiliated Hospital of Harbin Medical University Harbin China Department of Ophthalmology The Second Hospital Jilin University Changchun China Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China Department of Mathematics Imperial College London United Kingdom Department of Computer Science University of Warwick Coventry United Kingdom Magnetism Department Faculty of Physics Lomonosov Moscow State University Moscow Russia Harbin Medical University China

Learning from natural bacteria flagellum, we demonstrate a magnetic polymer multilayer conical microrobot that bestow the controllable propulsion upon external rotating magnetic field with uniform intensity. The magnetic microrobots were constructed by template-assisted Layer-by-Layer technique technique and subsequent functionalization of magnetic particle onto the big opening of the microrobots. Geometric variables of the polymer microrobots, such as the diameter and wall thickness, can be controlled by selection of porous template and layers of assembly. The microrobots perform controllable propulsion through the manipulation of magnetic field. The comparative analysis of the movement behavior reveals that the deformation of microrobots may attributed to the propulsion upon rotating magnetic field, which is similar as that of natural bacteria. The influence of actuation frequencies and frequencies on the velocity of the microrobots is studied. Such use of polymer polymer multilayer tubular micro/nanoarchitectures results in significant savings in the processing costs and provides a simple and cost-effective fabrication route for the mass production of magnetic polymer microrobots. © 2024, The Authors. All rights reserved.

关键词： Bacteria

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Temporal memory relation network for workflow recognition from surgical video

arXiv

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arXiv 2021年

作者： Jin, Yueming Long, Yonghao Chen, Cheng Zhao, Zixu Dou, Qi Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering T Stone Robotics Institute Chinese University of Hong Kong Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Automatic surgical workflow recognition is a key component for developing context-aware computer-assisted systems in the operating theatre. Previous works either jointly modeled the spatial features with short fixed-range temporal information, or separately learned visual and long temporal cues. In this paper, we propose a novel end-to-end temporal memory relation network (TMRNet) for relating long-range and multi-scale temporal patterns to augment the present features. We establish a long-range memory bank to serve as a memory cell storing the rich supportive information. Through our designed temporal variation layer, the supportive cues are further enhanced by multi-scale temporal-only convolutions. To effectively incorporate the two types of cues without disturbing the joint learning of spatio-temporal features, we introduce a non-local bank operator to attentively relate the past to the present. In this regard, our TMRNet enables the current feature to view the long-range temporal dependency, as well as tolerate complex temporal extents. We have extensively validated our approach on two benchmark surgical video datasets, M2CAI challenge dataset and Cholec80 dataset. Experimental results demonstrate the outstanding performance of our method, consistently exceeding the state-of-the-art methods by a large margin (e.g., 67.0% v.s. 78.9% Jaccard on Cholec80 dataset). Copyright © 2021, The Authors. All rights reserved.

关键词： Surgery

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Robust Stabilizing Control for Oscillatory Base Manipulators by Implicit Lyapunov Method

Research Square

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Research Square 2021年

作者： Guo, Yufei Hou, Baolin Xu, Shengyue Mei, Ruilin Wang, Zhigang Huynh, Van Thanh Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education School of Machinery and Automation Wuhan University of Science and Technology Wuhan430081 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan430081 China Shool of Mechanical Engineering Nanjing University of Science and Technology Nanjing210094 China Shool of Engineering Deakin University GeelongVIC3217 Australia

Oscillatory base manipulators (OBMs) are a kind of mechanical systems suffering from unexpected base oscillations. The oscillations affect tremendously system stability. Various control methods have been explored, but most of them require measurement or prediction of the oscillations. This study is concerned with a novel OBM–the autoloader, which are used in modern, autonomous main battle tanks. The base oscillation of the autoloader is hard to be obtained in practice. Furthermore, control synthesis for autoloaders is complicated with intrinsic payload uncertainty and actuator saturation. To address these issues, a novel robust control scheme is proposed in this work relying on the implicit Lyapunov method. Moreover, a novel two-Degree-of-Freedom manipulator operating on a vibrating base is constructed to realize the proposed control. To the best of the authors’ knowledge, this is the first study considering both control and hardware implementation for the OBM-like autoloaders. Experimental results demonstrate that, although without prior information of the base oscillation, the proposed controller exhibits good robustness against the base oscillation and payload uncertainty. © 2021, CC BY.

关键词： Manipulators

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Classification of sEMG Biomedical Signals for Upper-Limb and Hand Rehabilitation Using a Hybrid CNN-SVM Architecture

Classification of sEMG Biomedical Signals for Upper-Limb and...

引用

Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Sami Briouza Hassène Gritli Nahla Khraief Safya Belghith Dilbag Singh Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Le Belvédère Tunis Tunisia Higher Institute of Information and Communication Technologies University of Carthage Borj Cedria Tunis Tunisia RISC Lab (LR16ES07) National Engineering School of Tunis University of Tunis El Manar Le Belvédère Tunis Tunisia School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju South Korea

ISBN: (纸本)9781665490597

Electromyography (EMG) classification has been an important step to achieve the rehabilitation goal for lower/upper limbs and hands using robotic devices. To perform this step effectively, many researchers have adopted machine learning and deep learning algorithms. In this study, a hybrid CNN-SVM architecture was developed for the classification of surface EMG (sEMG) signals. The CNN part of the proposed architecture is used to extract relevant features from the data and the SVM part would use the extracted features for the classification task. This can be helpful as it will reduce human input and make results more consistent. For this work, we use the Ninapro DB2’s dataset, which contains 3 different Exercises B, C, and D. Thus, we obtained the following accuracy results: an accuracy of 78.56% for Exercise B, an accuracy of 72.84% for Exercise C, and an accuracy of 88.24% for Exercise D.

关键词： Support vector machines Performance evaluation Biological system modeling Feature extraction Electromyography Classification algorithms Data mining

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Research on PMSM Vector Control System Based on Fuzzy PI Parameter Self-Tuning 10

Research on PMSM Vector Control System Based on Fuzzy PI Par...

引用

10th International Conference on Power and Energy systems, ICPES 2020

作者： Ding, Jianhua Zhang, Tao Tao, Ran Wang, Anna Northeastern University College of Information Science and Engineering Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Electrical Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9781665404945

Based on the analysis of the permanent magnet synchronous motor (PMSM) mathematical model, a PMSM rotor field-oriented double closed-loop vector control system is constructed. Aiming at the defects of slow speed response, large overshoot, severe torque ripple, and poor anti-disturbance ability in the traditional PI controller used in the system speed loop, a fuzzy PI parameter self-tuning controller is proposed. The controller can set the two parameters of proportional and integral online according to fuzzy control rules. The system simulation model is built based on Matlab/Simulink. The simulation results show that the fuzzy PI parameter self-tuning controller has the advantages of fast dynamic response, high steady-state precision and strong anti-disturbance capability. © 2020 IEEE.

关键词： Permanent magnets

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Image-guided breast biopsy of MRI-visible lesions with a hand-mounted motorised needle steering tool

arXiv

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arXiv 2021年

作者： Lagomarsino, Marta Groenhuis, Vincent Casadio, Maura Welleweerd, Marcel K. Siepel, Françoise J. Stramigioli, Stefano Laboratory Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Drienerlolaan 5 Enschede7522NB Netherlands Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Via Opera Pia 13 Genoa16145 Italy Department of Advanced Robotics Istituto Italiano di Tecnologia Genoa Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy

A biopsy is the only diagnostic procedure for accurate histological confirmation of breast cancer. When sonographic placement is not feasible, a Magnetic Resonance Imaging(MRI)-guided biopsy is often preferred. The lack of real-time imaging information and the deformations of the breast make it challenging to bring the needle precisely towards the tumour detected in pre-interventional Magnetic Resonance (MR) images. The current manual MRI-guided biopsy workflow is inaccurate and would benefit from a technique that allows real-time tracking and localisation of the tumour lesion during needle insertion. This paper proposes a robotic setup and software architecture to assist the radiologist in targeting MR-detected suspicious tumours. The approach benefits from image fusion of preoperative images with intraoperative optical tracking of markers attached to the patient's skin. A hand-mounted biopsy device has been constructed with an actuated needle base to drive the tip toward the desired direction. The steering commands may be provided both by user input and by computer guidance. The workflow is validated through phantom experiments. On average, the suspicious breast lesion is targeted with a radius down to 2.3 mm. The results suggest that robotic systems taking into account breast deformations have the potentials to tackle this clinical challenge. Copyright © 2021, The Authors. All rights reserved.

关键词： Biopsy

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Short-Term Photovoltaic Power Prediction Modeling Based on AdaBoost Algorithm and Elman 10

Short-Term Photovoltaic Power Prediction Modeling Based on A...

引用

10th International Conference on Power and Energy systems, ICPES 2020

作者： Sun, Wenxuan Zhang, Tao Tao, Ran Wang, Anna Northeastern University College of Information Science and Engineering Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Electrical Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9781665404945

In recent years, with the rapid expansion of the installed capacity of renewable energy systems, the availability, stability and quality of smart grids have become increasingly important[1]. The application of renewable energy production forecasting has also been rapidly developed, especially in the field of solar photovoltaic (PV)[2][3]. In the example of solar PV output prediction, machine learning and hybrid technologies have been implemented for many applications. In this paper, a high-precision PV system output power prediction model based on improved AdaBoost and Elman is proposed. Multiple model using integrated AdaBoost algorithm fusion with the bat algorithm for the parameters of optimized combination of weak Elman neural network predictor to become a higher prediction precision, the method of strong predictor of the model can according to the weather information, such as temperature, solar radiation and the history of the output of the PV system data, the probability of photovoltaic power generation for 12 hours and deterministic prediction. The prediction accuracy of the model is determined by Root Mean Squared Error (RMSE). Experimental results show that the prediction accuracy of this algorithm is better than that of other benchmark models, and the algorithm can effectively predict the volatility and irregularity of complex time series. © 2020 IEEE.

关键词： Adaptive boosting

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Anisotropic tensile properties and high-cycle fatigue behavior of Ti6Al4V fabricated by single and dual laser-powder bed fusion

引用

Materials science and engineering: A 2025年 941卷

作者： Li, Guotai Hu, Peichen Fan, Yuzhan Wu, Ming Chen, Mingjun Yu, Tianyu State Key Laboratory of Robotics and System Harbin Institute of Technology Heilongjiang Harbin150001 China Key Laboratory of Micro-systems and Micro-structures Manufacturing Ministry of Education Heilongjiang Harbin150001 China Department of Computer Science KU Leuven Leuven3001 Belgium Department of Mechanical Engineering KU Leuven Leuven3001 Belgium

The development of multi laser-powder bed fusion (PBF) technology has significantly reduced the production time for large engine components. However, the impact of thermal effects and microstructural characteristics in the laser beam overlap region on the anisotropic mechanical behavior of metallic materials remains unclear. This study investigated the anisotropic tensile properties and high-cycle fatigue behavior of Ti6Al4V produced by both single laser-PBF (SL-PBF) and dual laser-PBF (DL-PBF). As-built DL-PBF samples exhibited high strength (1404 MPa) but low ductility (3.6 %). After heat treatment, the samples achieved a strength-ductility balance, with an ultimate tensile strength of 1089 MPa, and an elongation of 17.1 %. The mechanical properties of Ti6Al4V produced by DL-PBF were influenced by the α lath thickness, dislocation density and texture. The precipitation of small-angle grain boundaries during DL-PBF process was found to contribute to the strong anisotropy. Due to the influence of internal pore forming, the fatigue strength of the DL-PBF samples was lower than that of the SL-PBF samples. Pores in the dual-laser beam overlap region accelerated the fatigue crack propagation, resulting in a venation-like pattern. In contrast, cracks in the SL-PBF samples propagated in a circular pattern around internal pore. A high-speed camera was employed to capture the dual-laser beams dynamics during pore formation. This study provides a comprehensive analysis of the mechanical properties and pore formation mechanisms in the DL-PBF process. © 2025 Elsevier B.V.

关键词： Tensile strength

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