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Autonomous inverse encoding guides 4D nanoprinting for highly programmable shape morphing

International Journal of Extreme Manufacturing 2025年第3期7卷 467-482页

作者： Shuaiqi Ren Zhiang Zhang Ruokun He Jiahao Fan Guangming Wang Hesheng Wang Bing Han Yong-Lai Zhang Zhuo-Chen Ma Department of Automation Shanghai Jiao Tong UniversityShanghai 200240People’s Republic of China Institute of Medical Robotics School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai 200240People’s Republic of China State Key Laboratory of Integrated Optoelectronics College of Electronic Science and EngineeringJilin UniversityChangchun 130012People’s Republic of China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240People’s Republic of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240People’s Republic of China Department of Engineering University of CambridgeCambridge CB21PZUnited Kingdom

Highly programmable shape morphing of 4D-printed micro/nanostructures is urgently desired for applications in robotics and intelligent ***,due to the lack of autonomous holistic strategies throughout the target shape input,optimal material distribution generation,and fabrication program output,4D nanoprinting that permits arbitrary shape morphing remains a challenging task for manual *** this study,we report an autonomous inverse encoding strategy to decipher the genetic code for material property distributions that can guide the encoded modeling toward arbitrarily pre-programmed 4D shape *** tuning the laser power of each voxel at the nanoscale,the genetic code can be spatially programmed and controllable shape morphing can be realized through the inverse encoding *** this strategy,the 4D-printed structures can be designed and accurately shift to the target morphing of arbitrarily hand-drawn lines under ***,as a proof-of-concept,a flexible fiber micromanipulator that can approach the target region through pre-programmed shape morphing is autonomously inversely encoded according to the localized spatial *** strategy may contribute to the modeling and arbitrary shape morphing of micro/nanostructures fabricated via 4D nanoprinting,leading to cutting-edge applications in microfluidics,micro-robotics,minimally invasive robotic surgery,and tissue engineering.

关键词： femtosecond laser fabrication 4D printing two-photon polymerization autonomous inverse encoding stimuli-responsive materials

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NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and Denoising

arXiv

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arXiv 2024年

作者： Deng, Tianchen Wang, Yanbo Xie, Hongle Wang, Hesheng Wang, Jingchuan Wang, Danwei Chen, Weidong Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

In recent years, there have been significant advancements in 3D reconstruction and dense RGB-D SLAM systems. One notable development is the application of Neural Radiance Fields (NeRF) in these systems, which utilizes implicit neural representation to encode 3D scenes. This extension of NeRF to SLAM has shown promising results. However, the depth images obtained from consumer-grade RGB-D sensors are often sparse and noisy, which poses significant challenges for 3D reconstruction and affects the accuracy of the representation of the scene geometry. Moreover, the original hierarchical feature grid with occupancy value is inaccurate for scene geometry representation. Furthermore, the existing methods select random pixels for camera tracking, which leads to inaccurate localization and is not robust in real-world indoor environments. To this end, we present NeSLAM, an advanced framework that achieves accurate and dense depth estimation, robust camera tracking, and realistic synthesis of novel views. First, a depth completion and denoising network is designed to provide dense geometry prior and guide the neural implicit representation optimization. Second, the occupancy scene representation is replaced with Signed Distance Field (SDF) hierarchical scene representation for high-quality reconstruction and view synthesis. Furthermore, we also propose a NeRF-based self-supervised feature tracking algorithm for robust real-time tracking. Experiments on various indoor datasets demonstrate the effectiveness and accuracy of the system in reconstruction, tracking quality, and novel view synthesis. © 2024, CC BY-NC-SA.

关键词： Geometry

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A Multimodal Fault Diagnosis Model Utilizing GAF Transformation for controllable Pitch Propeller Hydraulic system

A Multimodal Fault Diagnosis Model Utilizing GAF Transformat...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Huihuang Cai Wei Chen Huanglong Fu Hua Wang Jingcheng Wang Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Shanghai Marine Equipment Research Institute Shanghai China Department of Automation the Key Laboratory of System Control and Information Processing Ministry of Education of China and the Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University Xi'an China

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The hydraulic system of the controllable pitch propeller (CPP) serves as the power source for adjusting the blade pitch. Due to its structural complexity and harsh operating conditions, any malfunction of this system could significantly impair the vessel's navigation. Apart from the pitch command and the pitch feedback of CPP itself, the number and types of sensors added for fault diagnosis are not fixed. Therefore, this study establishes a multimodal fault diagnosis model based on Gramian angular field(GAF). By considering the deviation values of pitch feedback and pitch command as key variables, this model transforms them into a two-dimensional image using GAF, thereby obtaining more effective feature information. Image features are extracted using ResNet, while the temporal features of additional sensors are extracted using GRU. Finally, a multi-head attention mechanism is employed for feature fusion. This method achieves a classification accuracy of 100% in the classification of four types of hydraulic system faults simulated on the experimental platform.

关键词： Fault diagnosis Propellers Navigation Hydraulic systems Transforms Sensor phenomena and characterization Feature extraction Sensor systems Marine vehicles Load modeling

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Designing a Secure Smart Remote Patient Monitoring and Warning system using Big Data and Internet of Things Ecosystems 10

Designing a Secure Smart Remote Patient Monitoring and Warni...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Ramesh, S.P. Abdulwahid, Ali Hadi Anjum, Afsana Venkatesh, N. Singh, Rajesh Chakravarthi, M. Kalyan Amet University Department of Computer Science and Engineering Tamil Nadu Kanathur India Southern Technical University Engineering Technical College Department of Control and Automation Basra Iraq Jazan University Department of Information Technology and Security Jazan Saudi Arabia School of Computer Science and Artificial Intelligence Sr University Telangana Warangal India Uttaranchal Institute of Technology Uttaranchal University India School of Electronics Engineering VIT-AP University Amaravathi India

ISBN: (纸本)9789380544472

The demand on healthcare is growing as a result of the expanding global population, rising demands for successful treatment, and an underlying improvement in life quality. Consequently, healthcare remains one of the most significant social and economic problems in the world, necessitating the development of new and more sophisticated scientific and technological treatments.E-health services are provided via personalised healthcare systems to meet the medical and support needs of the ageing population. An important development in the Big Data age is the Internet of Things, which facilitates several real-time engineering solutions through improved services. Healthcare systems are now using analytics over IoT data streams as a stream of user data to find new information, forecast rapid recognition, and make decisions on life-threatening situations. In this study, researchers have carried out a thorough review of the hottest recent revolutionary technologies in bespoke healthcare systems, with a focus on hypervisor, cloud infrastructure, big data analytic, the Web of Things, and smartphone apps. In order to make early disease detection and diagnosis possible, it is important to develop a better healthcare system. We have examined these problems and suggested potential solutions while offering secure e-health services. The purpose of this study is to design a smart and secure remote monitoring and warning system using the latest emerging technologies in 21stcentury such as big data analytics and Internet of Things (IoT) ecosystems. In this study the researchers discussed a detailed research methodology to meet the outcomes of the reerach. All the relevant findings and analysis are explained in descriptive manner. Moreover, this study highlights the constantly expanding requirements for better medical systems in the present and offers potential directions for future research. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Internet of things

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Approximate state estimation with large-scale sensor networks under event-based sensor scheduling strategy

Approximate state estimation with large-scale sensor network...

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IEEE Conference on Decision and control

作者： Xinhui Liu Meiqi Cheng Dawei Shi Ling Shi MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing Institute of Technology Beijing China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we investigate the issues of real-time sensor scheduling and state estimator design within large-scale sensor network systems. Specifically, data redundancy sometimes occurs in large-scale sensor arrays due to the excessive proximity of sensing units in the spatial domain, leading to high similarity in their measurement data. Currently, it is difficult to account for such redundancy in sensor scheduling algorithms found in existing literature, where the optimal subset of sensors is generally selected by optimizing objective functions formulated from certain performance criteria. To tackle this problem, we introduce an event-based sensor scheduling strategy, the triggering condition of which is designed founded on the similarity of sensor data, so as to identify the most informative subset of sensors for state estimation. To evaluate the impact of the sensor scheduling protocol on system observability, we propose a new notion of $\mathscr{E}(\varepsilon)$-observability, based on which an observability criterion is derived. In addition, we have designed a set-valued state estimation algorithm, which takes into account the intricate measurement information structure inherent within the sensor selection mechanism. The performance enhancement of the proposed estimator is also investigated. Finally, numerical experiments are conducted to validate the effectiveness of the proposed estimation algorithm and to verify the performance improvement.

关键词： Protocols Event detection Scheduling algorithms Redundancy Approximation algorithms Real-time systems Sensors State estimation Observability Sensor arrays

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Incremental Joint Learning of Depth, Pose and Implicit Scene Representation on Monocular Camera in Large-scale Scenes

arXiv

引用

arXiv 2024年

作者： Deng, Tianchen Wang, Nailin Wang, Chongdi Yuan, Shenghai Wang, Jingchuan Wang, Danwei Chen, Weidong The Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Dense scene reconstruction for photo-realistic view synthesis has various applications, such as VR/AR, autonomous vehicles. However, most existing methods have difficulties in large-scale scenes due to three core challenges: (a) inaccurate depth input. Accurate depth input is impossible to get in real-world large-scale scenes. (b) inaccurate pose estimation. Existing methods are not robust enough with the growth of cumulative errors in large scenes and long sequence. (c) insufficient scene representation capability. A single global radiance field lacks the capacity to effectively scale to large-scale scenes. To this end, we propose an incremental joint learning framework, which can achieve accurate depth, pose estimation, and large-scale scene reconstruction. A vision transformer-based network is adopted as the backbone to enhance performance in scale information estimation. For pose estimation, a feature-metric bundle adjustment (FBA) method is designed for accurate and robust camera tracking in large-scale scenes. In terms of implicit scene representation, we propose an incremental scene representation method to construct the entire large-scale scene as multiple local radiance fields to enhance the scalability of 3D scene representation. Extended experiments have been conducted to demonstrate the effectiveness and accuracy of our method in depth estimation, pose estimation, and large-scale scene reconstruction. The code will be open-sourced upon paper acceptance. © 2024, CC BY-NC-SA.

关键词： Depth perception

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Torque Monitoring system at Rolling Mill Stand Spindles

Torque Monitoring System at Rolling Mill Stand Spindles

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International Conference on Industrial engineering, Applications and Manufacturing (ICIEAM)

作者： O. A. Gasiyarova R. A. Lisovsky M. A. Zinchenko Department of Automation and Control Moscow Polytechnic University Moscow Russia Power Engineering and Automated Systems Institute Nosov Magnitogorsk State Technical University Magnitogorsk Russia

ISBN: (数字)9798331512194

ISBN: (纸本)9798331512200

Today on-line monitoring of the technical state and assessment of the service life of electromechanical systems at rolling mills is an urgent problem. To resolve it, it is necessary to substantiate the method for calculation of the remaining life under dynamic loads and to develop technical means for the method implementation. The authors have studied the oscillograms of speeds and torques of the motors of the top and bottom rolls main drives (TMD and BMD) for the plate mill 5000. A considerable difference between static (and consequently, dynamic) torques of these motors has been confirmed. When the loaded electrical drive reaches the limit torque values, it causes the loss of the motor control and increased amplitudes of elastic torque at the spindle when the rolls pick up metal. This confirms that it is important to study the dependence between elastic torque amplitude and thickness reduction and to develop the method to calculate mechanical equipment life at intermittent dynamic loads. An information system for on-line monitoring of elastic torques at spindles has been developed. Elastic torque recovery is conducted by the observer that additionally calculates the extent of equipment wear. The authors have substantiated the calculation of fatigue loading based on the Palmgren-Miner linear damage rule. The article also considers analytical expressions used to obtain dependences between wear and torque degree during metal pickup. The authors have calculated the spindle wear for different time periods at rolling sheets for actual plant stock. As a result, the difference in fatigue life of TMD and BMD spindles was confirmed. The authors give recommendations on the commercial adoption of the solutions and applying BigData technologies for dataset processing.

关键词： Motor drives Torque Loading Metals Observers Motors Fatigue Torque measurement Electromechanical systems Monitoring

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SoftNeRF: A Self-Modeling Soft Robot Plugin for Various Tasks

SoftNeRF: A Self-Modeling Soft Robot Plugin for Various Task...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Jiwei Shan Yirui Li Qiyu Feng Ditao Li Lijun Han Hesheng Wang Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Building a self-model for robots, enabling them to simulate their physical selves and predict future states without direct interaction with the physical world, is crucial for robot motion planning and control. Existing self-modeling methods primarily focus on rigid robots and typically require significant time, effort, and resources to gather training data. In this study, we introduce SoftNeRF, a self-supervised visual self-model designed for soft robots. We use a hybrid neural shape representation based on the Signed Distance Function (SDF) to capture both the geometry and complex nonlinear motion of soft robots. By leveraging differentiable rendering, our method learns a self-model from readily available RGB images, similar to how humans understand their physical state through reflection. To improve training efficiency and model accuracy, we propose an error-guided adaptive sampling strategy. SoftNeRF can serve as a plug-in for various downstream tasks, even when trained with data unrelated to those tasks. We demonstrate SoftNeRF’s ability to support shape prediction and motion planning for robots in both simulated and real-world environments. Furthermore, SoftNeRF excels in detecting and recovering from damage, thereby enhancing machine resilience. Code is available at: https://***/irmvlab/soft-nerf.

关键词： Training Visualization Adaptation models Accuracy Shape Training data Soft robotics Rendering (computer graphics) Sampling methods Planning

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Design and Implementation of a Wide-Swing CMOS Multiplier for AC Source Signal Tracking and Modulation

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Circuits, systems, and Signal Processing 2024年第9期43卷 5377-5400页

作者： Wang, Wei Xu, Sendren Sheng-Dong Chen, Chi-Lin Chiu, Huang-Jen Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei 106335 Taiwan Graduate Institute of Automation and Control National Taiwan University of Science and Technology Taipei 106335 Taiwan Advanced Manufacturing Research Center National Taiwan University of Science and Technology Taipei 106335 Taiwan Himax Technologies Inc. Taipei 100507 Taiwan

The multiplier is a pervasive critical component today. However, most of them are difficult to be integrated into an integrated circuit. In this study, we proposed a novel analog multiplier for AC power application, using CMOS process to realize the analog voltage multiplication function of double-ended input and single-ended output. Under the working voltage of 3.3 V, the multiplier can operate with a wide input voltage range of 0–1.6 V (about 48.8% of the working voltage) and a wide output voltage range of 0–2.56 V (about 77.5% of the working voltage). The multiplier is implemented through the current difference between two NMOSs operating in the linear region and the saturation region, respectively. Compared with the existing technique, the proposed analog multiplier has a wider input and output range, and better linearity. It is suitable for AC power signal tracking and modulation. Finally, the United Microelectronics Corporation 0.18 μm 1P4M CMOS standard process was used for physical implementation. Moreover, there is not any BJT or special device used. It was measured that the analog multiplier circuit consumes about 0.56 mW at a working voltage of 3.3 V. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： AC power signal modulation AC source signal tracking Analog integrated circuit (IC) design Analog multipliers Circuits and systems

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A Transfer Gated Recurrent Unit Model for Battery Health Monitoring Under Cross-Domain Conditions

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Tian, Jilun Xie, Jiale Zhang, Jiusi Huang, Congsheng Jiang, Yuchen Luo, Hao Chow, Mo-Yuen Yin, Shen Harbin Institute of Technology School of Astronautics Department of Control Science and Engineering Harbin150001 China North China Electric Power University Department of Automation Baoding071003 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China National Taipei University of Technology Department of Electrical Engineering Taipei10608 Taiwan University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China Norwegian University of Science and Technology Faculty of Engineering Department of Mechanical and Industrial Engineering Trondheim7034 Norway

Battery health monitoring is crucial for ensuring maintenance reliability and safety, with broad applications in various industrial systems. Data-driven state-of-health (SOH) estimation methods can perform excellently using massive historical data without prior physical knowledge. In the battery aging process, this article extracts the essential health indicators to construct the model and adopts a suitable data preprocessing method to reduce the influence of noise. There is still a limitation for data-driven methods regarding data quality, and the domain shift caused by different operating conditions will damage estimation performance. To tackle the issue, this article proposes a transfer feature transformation (TFT) based on mean maximum discrepancy (MMD) to get a new feature space to get domain-invariant features with sufficient information. We propose a multilayer gated recurrent unit (GRU) neural network with fully connection (FC) layers to uncover hidden relationships in degraded data. Fusing TFT and GRU-FCs methods can realize an end-to-end SOH estimation under cross-domain conditions. We apply real-world public Center of Advanced Life Cycle engineering (CALCE) and NASA battery datasets and lab testing-based accelerated aging data to validate the performance of algorithms. By comparison, our proposed TFT-GRU-FCs method can get the best performance of all algorithms, and we have also given sufficient results as support. © 1963-2012 IEEE.

关键词： Electronic health record

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