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Carbon dioxide storage and cumulative oil production predictions in unconventional reservoirs applying optimized machine-learning models

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Petroleum Science 2025年第1期22卷 296-323页

作者： Shadfar Davoodi Hung Vo Thanh David A.Wood Mohammad Mehrad Sergey V.Muravyov Valeriy S.Rukavishnikov School of Earth Sciences&Engineering Tomsk Polytechnic UniversityLenin AvenueTomskRussia Laboratory for Computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityViet Nam Applied Science Research Center Applied Science Private UniversityAmmanJordan DWA Energy Limited LincolnUK Division for Automation&Robotics Tomsk Polytechnic UniversityLenin AvenueTomskRussia

To achieve carbon dioxide(CO_(2))storage through enhanced oil recovery,accurate forecasting of CO_(2) subsurface storage and cumulative oil production is *** study develops hybrid predictive models for the determination of CO_(2) storage mass and cumulative oil production in unconventional *** does so with two multi-layer perceptron neural networks(MLPNN)and a least-squares support vector machine(LSSVM),hybridized with grey wolf optimization(GWO)and/or particle swarm optimization(PSO).Large,simulated datasets were divided into training(70%)and testing(30%)groups,with normalization applied to both *** distance identifies/eliminates outliers in the training subset only.A non-dominated sorting genetic algorithm(NSGA-II)combined with LSSVM selected seven influential features from the nine available input parameters:reservoir depth,porosity,permeability,thickness,bottom-hole pressure,area,CO_(2) injection rate,residual oil saturation to gas flooding,and residual oil saturation to water *** models were developed and tested,with performance evaluated with an overfitting index(OFI),scoring analysis,and partial dependence plots(PDP),during training and independent testing to enhance model focus and *** LSSVM-GWO model generated the lowest root mean square error(RMSE)values(0.4052 MMT for CO_(2) storage and 9.7392 MMbbl for cumulative oil production)in the training *** trained model also exhibited excellent generalization and minimal overfitting when applied to the testing group(RMSE of 0.6224 MMT for CO_(2) storage and 12.5143 MMbbl for cumulative oil production).PDP analysis revealed that the input features“area”and“porosity”had the most influence on the LSSVM-GWO model's pre-diction *** paper presents a new hybrid modeling approach that achieves accurate forecasting of CO_(2) subsurface storage and cumulative oil *** also establishes a new standard for such forecasting,which can

关键词： Hybrid machine learning Least-squares support vector machine Grey wolf optimization Feature selection Carbon dioxide storage Enhanced oil recovery

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Rope-net: deep convolutional neural network via robust principal component analysis

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Machine Learning 2025年第7期114卷 1-26页

作者： Liu, Baichen Han, Zhi Chen, Xi’ai Wang, Yanmei Tang, Yandong State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China

Low-rank decomposition methods can compress parameters and accelerate training of deep convolutional neural networks (DCNNs) by mining the low-rankness in weights. However, for an image classification task, the test accuracy of DCNNs compressed by low-rank decomposition usually decreases. Our experiments show that the error term of low-rank decomposition is not sparse and not suitable for reutilizing to maintain accuracy. To overcome this problem, in this paper, we propose an effective compression approach of DCNNs by robust principal component analysis (RPCA) on the weights of convolutional and fully connected layers. Low-rank term of RPCA keeps advantages of low-rank decomposition. Sparse term of RPCA can be retrained via sparse learning to improve test accuracy because of its sparsity. We name this kind of DCNN as Rope-Net (DCNN via Robust Principal Component Analysis). Our Rope-ResNet110 (ResNet110 via RPCA) experiment on CIFAR10 demonstrates that Rope-ResNet110 can achieve: a) 3.3× parameter compression ratio and 0.33% accuracy improvement, b) 5× parameter compression ratio without accuracy drop. More remarkably, Rope-VGG16 achieves 8.5× parameter compression ratio and 5.48% accuracy improvement on randomly cutout CIFAR10 test set compared with VGG16, which validates strong robustness of Rope-Net. © The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2025.

关键词： Principal component analysis

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Semi-Autonomous Navigation Based on Local Semantic Map for Mobile Robot

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Journal of Shanghai Jiaotong university(Science) 2025年第1期30卷 27-33页

作者： ZHAO Yanfei XIAO Peng WANG Jingchuan GUO Rui Department of Automation Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghai200240China State Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240China State Grid Intelligence Technology Co. Ltd.Jinan250013China

Mobile robots represented by smart wheelchairs can assist elderly people with mobility *** paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,which can assist users to implement accurate navigation(e.g.,docking)in the environment without prior *** order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms,this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic *** last,comparative experiments were carried out in the real *** show that our method is superior in terms of safety,comfort and docking accuracy.

关键词： semi-autonomous navigation mobile robot semantic map voice interaction

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Acceleration-level coordinated control of a dual-arm free-floating space robot under hard joint constraints

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Advances in Space Research 2025年第11期75卷 8122-8139页

作者： Xue, Zhihui Liu, Jinguo Zhang, Xin Zhou, Hao Li, Yangmin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Kowloon999077 Hong Kong School of Electrical and Mechanical Engineering University of Portsmouth PortsmouthPO1 3DJ United Kingdom

Dual-arm space robots are becoming increasingly crucial in on-orbit servicing missions. The online motion control of a dual-arm redundant space robot with hard joint constraints presents significant challenges due to its free-floating base. In this article, we proposed an acceleration-level coordinated control framework to solve hard joint constraints and dynamic singularity. The proposed framework combines a task scheduling strategy to achieve saturation in the null space, so that different tasks can achieve trajectory tracking with minimal task scaling under given joint hard constraints. The framework can achieve efficient online control by sequentially abandoning the use of joints that exceed the joint constraint and reintroducing these commands at their saturation level by projecting them into the appropriate null space. Finally, we verify the tracking performance of the algorithm through numerical simulation and experimental research. By comparing the proposed algorithm with the quadratic programming and pseudo-inverse methods, the algorithm has shown excellent online motion control performance, with a computation time of approximately 1 ms within a single sampling period. © 2025 COSPAR

关键词： Quadratic programming

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Integrating Trajectory Optimization in Quality-Diversity for Kinodynamic Motion Planning

Integrating Trajectory Optimization in Quality-Diversity for...

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International Conference on Autonomous Robots and Agents, ICARA

作者： Konstantinos A. Asimakopoulos Konstantinos I. Chatzilygeroudis Department of Electrical & Computer Engineering Laboratory of Automation & Robotics (LAR) Patras Greece Department of Mathematics Computational Intelligence Laboratory (CILab) University of Patras Patras Greece

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Kinodynamic planning is a critical component of robotics, enabling robots to generate dynamically feasible trajectories while adhering to geometric constraints. Existing methods for kinodynamic planning primarily fall into two categories: (a) stochastic planners, such as Rapidly-Exploring Random Trees (RRT) and Probabilistic Roadmaps (PRM), which excel in state-space exploration due to their directed randomness, and (b) optimization-based approaches, which are well-suited for structured environments, producing smooth and optimal solutions. In this work, we present Hybrid Trajectory Exploration for Kinodynamic Planning (HyTraX), a hybrid framework that integrates the strengths of these two paradigms. Specifically, HyTraX enhances the effectiveness of the Go-Explore algorithm by incorporating trajectory optimization with random target selection as its core exploration strategy. We evaluate HyTraX on two challenging kinodynamic motion planning tasks: a car-like agent and a planar quadrotor navigating through maze environments. Our results demonstrate significant performance improvements with minimal task-specific tuning, highlighting the robustness and versatility of the proposed approach.

关键词： Legged locomotion Scalability Performance gain Probabilistic logic Robustness Planning Trajectory optimization Robots Tuning Quadrotors

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Impact of nano fillers on the mechanical and rheological behaviour of polydimethylsiloxane based nanocomposite elastomer

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Ceramics International 2025年第14期51卷 18852-18864页

作者： Mandal, Swaroop Kumar Kumar, Rahul Pradhan, Asisha Ranjan Kumar, Deepak Kumar, Satish Department of Automation and Robotics Dr. DY Patil Institute of Technology Pimpri Pune411018 India Advanced Composite and Smart Material Laboratory Department of Mechanical Engineering National Institute of Technology Jamshedpur 831014 India Department of Mechanical Engineering Parala Maharaja Engineering College Odisha 761003 India Rheology Lab Department of Mechanical Engineering National Institute of Technology Jamshedpur 831014 India

In this work, an advanced elastomer which is reinforced with nanocomposite of reduced graphene oxide and calcium carbonate (rGO/CaCO3) is fabricated. Its mechanical and rheological properties are investigated experimentally. The effect of rGO/CaCO3 nanocomposite was experimentally investigated using Dynamical Mechanical Analysis (DMA), Tensile test and Rheological test. The results indicate improved adhesion and physical strengthening effect between rGO/CaCO3 nanocomposite and the PDMS, that leads towards the decrement of extensional loss factor and hence increment in the extensional storage modulus. Also, the ultimate tensile stress and percentage elongation of the elastomers was found to be increased with increasing wt. % of rGO in CaCO3. It has been observed that 5 % rGO/CaCO3 nanocomposite in PDMS exhibits a higher tensile stress of 0.81 ± 0.040 MPa in comparison to pure PDMS (0.48 ± 0.024 MPa). The bare PDMS material has a maximum force of 12.94 ± 0.647 N, but the 5 % rGO/CaCO3/PDMS nanocomposite can tolerate a maximum force of 22.1 ± 1.105 N. Furthermore, the rheological properties of the material were altered, resulting in an increase in the viscosity of PDMS as the percentage (%) of rGO concentration in the rGO/CaCO3 nanocomposite increased. The presence of CaCO3 increases viscosity, however when 4 % (wt. %) rGO is added to CaCO3, the viscosity decreases compared to other filler concentrations in the elastomers. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Polydimethylsiloxane

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Towards Autonomous Cardiac Ultrasound Scanning: Combining Physician Expertise and Machine Intelligence

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical robotics and Bionics 2025年第2期7卷 782-792页

作者： Hao, Mingrui Zhang, Pengcheng Hou, Xilong Gu, Xiaolin Zhou, Xiao-Hu Hou, Zeng-Guang Chen, Chen Wang, Shuangyi Institute of Automation Chinese Academy of Sciences National Key Laboratory for Multi-Modal Artificial Intelligence Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100043 China Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Hong Kong Lingshu Medical Company Suzhou215123 China

Echocardiography serves as a prevalent modality for both heart disease diagnosis and procedural guidance in medical applications. Nevertheless, the conventional echocardiography examination heavily relies on the manual dexterity of the sonographer, leading to the suboptimal repeatability. Despite the extensive exploration of robot-assisted ultrasound systems, achieving a heightened level of automation in examinations and enhancing the practicality of these robotic platforms for primary utilization remain formidable challenges within the field. In this study, we introduce an innovative automatic acquisition method for cardiac views using a novel ultrasound robot. The method is designed to autonomously traverse and scan target positions and angular ranges to search and identify the target cardiac views. First, the target positions and angular ranges were derived from a professional sonographer's practice on 14 cases. Then, an automatic traversal scanning method is designed integrating visual guidance, human-machine collaboration, and path planning within the framework of a novel parallel mechanism-based ultrasound robot. Finally, we explore deep metric learning to search for target ultrasound images in the traversed ultrasound video. Experiments on the test set to evaluate the target ultrasound view searching algorithm achieved a mAP of 98.8% and a Rank-1 accuracy of 98.23%. Our method has been successfully validated by data from five subjects, achieving the acquisition of standard parasternal long-axis and short-axis cardiac views essential for diagnosis, demonstrating the effectiveness of the proposed method. © 2018 IEEE.

关键词： Motion planning

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A Collaborative Mapping System for A Brain-Inspired SLAM

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IEEE Internet of Things Journal 2025年

作者： Zhao, Hangpiao Li, Tao Zhang, Zhihui Tang, Fengzhen Shenyang Institute of Automation State Key Laboratory of Robotics Shenyang China University of Chinese Academy of Sciences Beijing China University of Science and technology of China Anhui China

Brain-inspired SLAM imitates the neural processing mechanism of the navigation system in the rodent brain. The low computational requirements and minimal storage demands make brain-inspired SLAM particularly well-suited for large-scale environment mapping. However, the increasing size of the mapping environment raises the likelihood of failures in map construction. As far as we know, there is currently no research on collaborative brain-inspired SLAM systems to improve mapping efficiency and error tolerance. In this paper, we propose a real-time collaborative mapping system to fuse local experience maps into the global map. In our method, an effective overlapping region detection based on sequence matching is designed to realize data association between local maps. Through these detected overlapped experience nodes, the relative poses are then obtained. Finally, a continuous map fusion graph relaxation (CMF-GR) algorithm is proposed to merge local maps and correct their drifts. In addition, we present a leader-follower collaborative mapping system to verify the proposed method. We validate the effectiveness and accuracy of our approach on publicly available datasets and real-world environments through comparison experiences and quantitative analysis. © 2014 IEEE.

关键词： Brain mapping

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Enhancing YOLOv5 for UAV target detection in complex aerial scenarios

Enhancing YOLOv5 for UAV target detection in complex aerial ...

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2024 International Conference on Mechatronic Engineering and Artificial Intelligence, MEAI 2024

作者： Jiao, Guangcan Zhang, Jinrui Yan, Zhiwen School of Automation and Electrical Engineering Shenyang Ligong University Liaoning Shenyang110159 China China Energy Construction Group Shenyang Electric Power Machinery Works Co. Ltd. Liaoning Shenyang113122 China China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110159 China

ISBN: (纸本)9781510689176

With the increasing use of UAV technology in military target tracking, traffic monitoring, and disaster observation, the demand for effective target detection algorithms has grown. However, challenges like occlusion, overlap, and scale variation in aerial images hinder existing methods. To address these, this paper improves the YOLOv5 model by introducing an SPD-conv module to reduce fine-grained information loss and inefficient feature learning, and a joint downsampling module in the neck network to prevent small target feature loss during fusion. Experimental results show significant improvements in accuracy and stability, offering a reliable solution for UAV-based target detection in military and agricultural fields. © 2025 SPIE.

关键词： Aircraft detection

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