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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Cheng, Haibo Li, Shichao Zeng, Peng Vyatkin, Valeriy 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 Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350399714

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry. © 2023 IEEE.

关键词： Long short-term memory

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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Automatic speaker identification system based on MLP network and deep learning in the presence of severe interference

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Multimedia Tools and Applications 2025年 1-42页

作者： Shafik, Amira Sedik, Ahmed El-Shafai, Walid Khalaf, Ashraf A. M. El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafr Elsheikh University Kafr El‑Shaikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Automatic Speaker Identification (ASI) is so crucial for security. Current ASI systems perform well in quiet and clean surroundings. However, in noisy situations, the robustness of an ASI system against additive noise and interference is a crucial factor. An investigation of the impact of interference on ASI system performance is presented in this paper, which introduces algorithms for achieving high ASI system performance. The objective is to resist the interference of various forms. This paper presents two models for the ASI task in the presence of interference. The first one depends on Normalized Pitch Frequency (NPF) and Mel-Frequency Cepstral Coefficients (MFCCs) as extracted features and Multi-Layer Perceptron (MLP) as a classifier. In this model, we investigate the utilization of a Discrete Transform (DT), such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST), to increase the robustness of extracted features against different types of degradation through exploiting the sub-band decomposition characteristics of DWT and the energy compaction property of DCT and DST. This is achieved by extracting features directly from contaminated speech signals in addition to features extracted from discrete transformed signals to create hybrid feature vectors. The enhancement techniques, such as Spectral Subtraction (SS), Winer Filter, and adaptive Wiener filter, are used in a preprocessing stage to eliminate the effect of the interference on the ASI system. In the second model, we investigate the utilization of Deep Learning (DL) based on a Convolutional Neural Network (CNN) with speech signal spectrograms and their Radon transforms to increase the robustness of the ASI system against interference effects. One of this paper goals is to introduce a comparison between the two models and build a more robust ASI system against severe interference. The experimental results indicate that the two proposed models lead to satisfa

关键词： Additive noise

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Experimental Validation of an Interface for a Human-Robot Interaction Within a Collaborative Task 1

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Proceedings of the 8th International Conference on Interactive Collaborative robotics, ICR 2023

作者： Mustafin, Maksim Chebotareva, Elvira Li, Hongbing Magid, Evgeni Intelligent Robotics Department Institute of Information Technology and Intelligent Systems Kazan Federal University 35 Kremlyovskaya st. Kazan420111 Russia Department of Instrument Science and Engineering Shanghai Jiao Tong University Minhang Shanghai200240 China Tikhonov Moscow Institute of Electronics and Mathematics HSE-University 34 Tallinn st. Moscow123458 Russia

ISBN: (数字)9783031431111

ISBN: (纸本)9783031431104

This paper presents a prototype of a non-contact UR robot based Virtual Control (UR-VC) system for collaborative robots of the UR family, which is based on computer vision techniques and a virtual interaction interface. A control method involved specific hand movements within a field of view of a web camera, which was connected to a laptop with the running UR-VC system. We present the UR-VC system and the results of an experimental validation. To inquire if the UR-VC system is comfortable and user-friendly for an interaction with collaborative robots and to study opportunities for a further development and expansion directions of the system, we designed a test case that simulates a joint product assembly in a collaborative workspace. The constructed collaborative workspace included the UR3e robot, the laptop with the running UR-VC system and assembly parts for a collaborative task. 24 participants were involved in the experiments. First, the participants learned how to control the robot using the UR-VC system. After the training, all participants successfully controlled the robot using the proposed interface for performing the collaborative task. Participants’ experience of operating the robot was analyzed via surveys, their unconstrained comments and video recordings of the experiments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Virtual reality

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Enhancing Mobile Robot Navigation: A Graph Decomposition Submodule for TEA*

Enhancing Mobile Robot Navigation: A Graph Decomposition Sub...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Filipe Cardoso Diogo Miguel Matos Marina Brilhante Pedro Costat Héber Sobreira Carlos Silva CRIIS - Centre of Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto Porto Portugal Flowbotic Mobile Systems SA Mangualde Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Rising industrial complexity demands efficient mobile robots to drive automation and productivity. Effective navigation relies on perception, localization, mapping, path planning, and motion control, with path planning being key. The Time Enhanced A * (TEA *) algorithm extends A * by adding time as a dimension to resolve temporal conflicts in multi-robot coordination. However, inconsistencies in edge lengths within the graph can hinder optimal path calculation. To address this, a Graph Decomposition submodule was developed to standardize edge lengths and temporal costs. Integrated into a ROS-based fleet coordination system, this approach significantly reduces execution time and improves coordination capacity.

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PL-TD3: A Dynamic Path Planning Algorithm of Mobile Robot

PL-TD3: A Dynamic Path Planning Algorithm of Mobile Robot

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Tan, Yijian Lin, Yunhan Liu, Tong Min, Huasong Wuhan University of Science and Technology College of Computer Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan430081 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

In this paper, Prioritized Experience Replay (PER) strategy and Long Short Term Memory (LSTM) neural network are introduced to the path planning process of mobile robots, which solves the problems of slow convergence and inaccurate perception of dynamic obstacles with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. We dubbed this new method as PL-TD3. Firstly, we improve the convergence speed of the algorithm by introducing PER strategy. Secondly, we use LSTM neural network to achieve the improvement of the algorithm for dynamic obstacle perception. In order to verify the method of this paper, we design static environment, dynamic environment and adaptability to dynamic experiments to compare and analyze the methods before and after improvement. The experimental results show that PL-TD3 outperforms TD3 in terms of execution time and execution path length in all environments. © 2022 IEEE.

关键词： Motion planning

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Assessment the feasibility of an AI model predicting lower extremity joint moments during walking in patients with cerebral palsy

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Gait & Posture 2024年 113卷 199-200页

作者： Firooz Salami Mustafa Erkam Ozates Yunus Ziya Arslan Sebastian Immanuel Wolf Universitätsklinikum Heidelberg Orthopedics and Trauma Surgery Heidelberg Germany Institute of Graduate Studies in Science and Engineering- Turkish-German University Department of Robotics and Intelligent Systems Istanbul Turkey

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Generating Manipulation Sequences using Reinforcement Learning and Behavior Trees for Peg-In-Hole Task

Generating Manipulation Sequences using Reinforcement Learni...

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Xu, Jiahua Lin, Yunhan Zhou, Haotian Min, Huasong Wuhan University of Science and Technology College of Computer Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan430081 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Reinforcement Learning (RL), a method of learning skills through trial-and-error, has been successfully used in many robotics applications in recent years. We combine manipulation primitives (MPs), behavior trees (BTs), and reinforcement learning to propose an algorithm for peg-in-hole tasks, which speeds up the convergence of the RL model and enhance the adaptability of the dynamic environment. Manipulation primitives are used as actions for RL, which can reduce the gap between control instruction and robotic actions and speed up the convergence of the RL model. Behavior trees are used as robot behavior control, which makes robots can actively adapt to the changes in the environment. In experiments, RL-BT, from the combination of RL and BT, is designed for the peg-in-hole task in the Gazebo simulation environment by using UR5 as the actuator. The experiments are conducted on a simple peg and a complex multi-hole peg by three aspects, which include convergence speed verification experiment, adaptability of dynamic environment experiment, and algorithm robustness experiment. The experiment result proves that our RL-BT can speed up the convergence and adapt to the changes in the environment. © 2022 IEEE.

关键词： Reinforcement learning

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Learning Embeddings with Centroid Triplet Loss for Object Identification in Robotic Grasping

Learning Embeddings with Centroid Triplet Loss for Object Id...

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IEEE International Conference on Automation science and Engineering (CASE)

作者： Anas Gouda Max Schwarz Christopher Reining Sven Behnke Alice Kirchheim TU Dortmund Lamarr Institute for Machine Learning and Artificial Intelligence Autonomous Intelligent Systems - Computer Science VI & Center for Robotics University of Bonn Germany Fraunhofer IML

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Foundation models are a strong trend in deep learning and computer vision. These models serve as a base for applications as they require minor or no further fine-tuning by developers to integrate into their applications. Foundation models for zero-shot object segmentation such as Segment Anything (SAM) output segmentation masks from images without any further object information. When they are followed in a pipeline by an object identification model, they can perform object detection without training. Here, we focus on training such an object identification model. A crucial practical aspect for an object identification model is to be flexible in input size (number of input images). As object identification is an image retrieval problem, a suitable method should handle multi-query multigallery situations without constraining the number of input images (e.g. by having fixed-size aggregation layers). The key solution to train such a model is the centroid triplet loss (CTL), which aggregates image features to their centroids. CTL yields high accuracy, avoids misleading training signals and keeps the model input size flexible. In our experiments, we establish a new state of the art on the ArmBench object identification task, which shows general applicability of our model. We furthermore demonstrate an integrated unseen object detection pipeline on the challenging HOPE dataset, which requires finegrained detection. There, our pipeline matches and surpasses related methods which have been trained on dataset-specific data. Code and pretrained models are available. 5

关键词： Training Location awareness Image segmentation Accuracy Pipelines Object detection Object segmentation Market research Object recognition Proposals

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Automated analysis of fundus images for the diagnosis of retinal diseases: a review

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Research on Biomedical Engineering 2024年第1期40卷 225-251页

作者： Berto, Alexa Scarpa, Fabio Tsiknakis, Nikos Manikis, Georgios Fotiadis, Dimitrios I. Marias, Kostas Scarpa, Alberto D-Eye s.r.l. Padua Italy Department of Information Engineering University of Padova Padua35131 Italy Heraklion70013 Greece Department of Biomedical Research Institute of Molecular Biology and Biotechnology FORTH Ioannina45115 Greece Department of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina45110 Greece Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion71004 Greece

Purpose: Colour fundus images are widely used in diagnosis treatment decision of several retinal diseases such as diabetic retinopathy (DR), glaucoma and age-related macular degeneration (AMD). These very common conditions must be detected early and monitored to prevent progression and avoid permanent damage. Fundus images revealed to be useful also for the diagnosis of cataracts. In recent years, various automatic diagnostic support methods have been proposed in the literature, with the aim of facilitating widespread screening and obtaining quantitative, objective, and reproducible information. Methods: In this review paper, an overview of traditional, machine learning and modern deep learning techniques for ophthalmic disease diagnosis (i.e. glaucoma, DR, AMD, and cataract) using retinal fundus images is presented. In addition, various publicly available image datasets used for such purposes are described. Results: The current main challenges and findings are identified, as well as common aspects and discrepancies between the various methods developed for the various diseases. Conclusion: The overview of what has been done about all pathologies, rather than only on a specific one, could favour a migration of the best solutions and, hopefully, the development of a more precise and clinically useful automatic analysis of all pathologies. Important critical insights and research trends are also discussed. Graphical abstract: (Figure presented.) © The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering 2023.

关键词： Pathology

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