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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： Rajanish Kumar Kaushal V. Velmurugan S. Mahendran Arul Prakash M Balvender Singh Joshuva Arockia Dhanraj Department of Electrical Engineering Chandigarh University Mohali Punjab India Department of Electronics and Communication Engineering Vel Tech Rangarajan and Dr. Sagunthala R and D Institute of Science and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Er. Perumal Manimekalai College of Engineering Hosur India Department of Mechanical Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Electrical Engineering Government Women Engineering College Ajmer Rajasthan India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Chennai Tamil Nadu India

The heat from solar energy can be used to heat the air and use that heat for drying. Solar trackers are used to effectively track the solar collector to heat the air through a solar thermal collector. The drying hot air can be arranged as layer type trackers as per our requirement.A geo thermal heat pump uses geothermal energy to heat your home in winter and cool it in summer in a cost-effective and renewable way. A mini-split heat pump is the second best way to heat or cool your home or commercial space. In this paper an innovative machine learning model was proposed to enhance the tracking of solar panels. The main objective is to obtain the maximum energy of the solar thermal collector. A tracker uses many times more energy than a washing machine. So the efficiency of both can be improved by researching and selecting more energy efficient machines.A portable solar powered cooling unit uses a solar panel and inverter to provide relief from overheating by converting the DC power provided by the panel into 3-phase AC power. In a saturation point the proposed model achieved 87.20% of integrated tracking, 91.59% of distributed tracking, 90.20% of combined tracking, 89.11% of heat based tracking and 93.77% of high power solar tracking. The proposed model achieved higher level of energy optimization. This will possible because the proposed model monitor and generate the high level of energy because of the machine monitoring model.

关键词： Integrated optics Heat pumps Cogeneration Optical saturation Adaptive optics Solar panels Solar heating

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Addressing Intermediate Verification Latency in Online Learning Through Immediate Pseudo-labeling and Oriented Synthetic Correction

Addressing Intermediate Verification Latency in Online Learn...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zixin Zhong Liyan Song Fengzhen Tang Bo Yuan Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Faculty of Computing Harbin Institute of Technology Harbin China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In non-stationary data streams, the challenges of concept drift are further compounded by the issue of Intermediate Verification Latency (IVL), which can impede timely model adaptation. IVL refers to the finite delay between the arrival of data features and their corresponding labels. This delay could pose a significant challenge in adapting models to new concepts, ultimately hindering predictive performance. However, existing IVL approaches exhibit certain limitations. Some approaches passively wait for delayed labels, thereby overlooking temporarily unlabeled data. Other approaches employ pseudo-labeling for immediate model updates, but may risk losing valuable information when reverting model states to rectify previous pseudo-labeling mistakes. To overcome these limitations, we propose a novel approach called Micro-cluster based Immediate Pseudo-Labeling with Oriented Synthetic Correction (MIPLOSC). MIPLOSC leverages micro-cluster systems to effectively capture data distributions, thus facilitating its two core components: immediate pseudo-labeling and oriented synthetic correction. The immediate pseudo-labeling mechanism facilitates immediate utilization of temporarily unlabeled data, and the oriented synthetic correction mechanism enables finergrained rectification from previous erroneous pseudo-labels and concept drift, minimizing the loss of learned information. Experimental studies validated the effectiveness of MIPLOSC in addressing IVL, demonstrating its superiority over competing methods in both space consumption and predictive performance across varying degrees of label delay.

关键词： Adaptation models Costs Concept drift Noise Neural networks Predictive models Robustness

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Acquiring Large Field-of-View Optical Super-Resolution Images Using Adaptive As-Natural-As-Possible (AANAP) Image Stitching Method

Acquiring Large Field-of-View Optical Super-Resolution Image...

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IEEE International Conference on Cyber Technology in automation, Control, and Intelligent Systems

作者： Hui Sun Meng Chen Cong Wu Hao Luo Qingjiu Chen Boliang Jia Guanglie Zhang Wen Jung Li Department of Mechanical Engineering City University of Hong Kong Hong Kong SAR China Hong Kong Centre for Cerebro-cardiovascular Health Engineering (COCHE) State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China City University of Hong Kong Shenzhen Research Institute (CityUSRI) Shenzhen China

Optical super resolution (OSR) is a technique that enhances the spatial resolution of images beyond the diffraction limit of the imaging system. OSR images can provide more details and information compared with conventional optical microscopes that are limited by the diffraction limit of light and thus have a best spatial resolution of about 200 nm. Moreover, the trade-off between magnification and field of view (FOV) hinders capturing large-scale structures and fine details simultaneously. Therefore, novel microscopy techniques that can overcome these limitations and provide high-resolution and large-FOV images of biological samples need to be developed. In this paper, a new method combining microsphere-based optical microscopic super-resolution imaging system and the image stitching method are proposed to improve the resolution and FOV conventional optical microscopy. First, the technique captures multiple images through the microsphere at different positions by moving the samples. Then, the technique stitches together the images using a registration algorithm to form a high-resolution, wide-field image. This technique can overcome the diffraction limit of optical microscopy and achieve sub wavelength resolution with a large FOV. It can be used in various fields of science and engineering, such as cell imaging, tissue imaging, and disease diagnosis.

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Design and development of soft robotic gripper for food packaging

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AIP Conference Proceedings 2024年第1期3035卷

作者： Mohan Arumugam Naga Dhatshana Ramalingarishi Prabukarthi Arumugam Suresh Mayilswamy Department of Mechanical Engineering PSG College of Technology Coimbatore India Department of Robotics and Automation Engineering PSG College of Technology Coimbatore India

Grasping and handling delicate objects is an active research area that is evolving fast throughout recent years. Soft grippers are identified as an effective solution since they have the capacity to handle workpieces of various shapes and interacts with disordered environments efficiently. Soft robotic grippers are generally built with the aid of soft, flexible, and compliant materials, which are able to adapt themselves to the shape of the part so that the soft component that is being handled is not being damaged. In the present article, a four-finger soft robotic gripper is designed and developed using elastic material and pneumatic actuators. Simulation of deformation for different finger designs was performed using finite element analysis and the optimal design was fabricated by means of the curing method. The mold is 3D printed and the silicone material is poured for curing to form the finger shape. The fabricated soft robotic gripper is utilized for handling deformable objects like – tomatoes, apples, and mangoes. It can work in the manufacturing environment from injection molding to assembling drinking cups to packaging bakery items.

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Obstacles Avoidance Path Planning for Mobile Robot Based on Cellular Automata

Obstacles Avoidance Path Planning for Mobile Robot Based on ...

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robotics, Intelligent Control and Artificial Intelligence (RICAI), International Conference on

作者： Lili Li Wenwei Li Jinglun Huang School of Automation Science and Engineering South China University of Technology China Institute of Intelligent Manufacturing Shunde Polytechnic Foshan China Department of Technical Research South China Robotics Innovation Research Institute Foshan China Foshan Zhiyouren Technology Co. Ltd. Foshan China School of Electronics and Information Engineering Wuyi University Jiangmen China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

In order to quickly plan the shortest path for mobile robot in two-dimensional space, a two-stage obstacles avoidance path planning algorithm based on cellular automata was presented in this paper. For the first stage, the distance between each free cell to the ending cell was gradually generated by the evolution of cellular automata, and the algorithm’s cellular space, cellular states, Moore type neighbors and evolutionary rules were described, respectively. For the second stage, by using the idea of the negative gradient, a method of generating the shortest path based on the cells’ state values was proposed. Accordingly, the corresponding flow chart and Matlab programme of the algorithm were designed. The effectiveness of the algorithm was verified by the simulation and the physical experiment. The results showed that the shortest path in complex environment can be efficiently planned by using our cellular automata algorithm.

关键词： Technological innovation Extraterrestrial phenomena Automata Path planning Filling Mobile robots Time complexity MATLAB Artificial intelligence Intelligent control

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Reconfigurable Magnetic Microswarm for Thrombolysis under Ultrasound Imaging

Reconfigurable Magnetic Microswarm for Thrombolysis under Ul...

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2020 IEEE International Conference on robotics and automation, ICRA 2020

作者： Wang, Qianqian Wang, Ben Yu, Jiangfan Schweizer, Kathrin Nelson, Bradley J. Zhang, Li Department of Mechanical and Automation Engineering Shatin NT Hong Kong Eth Zurich Institute of Robotics and Intelligent Systems Zurich Switzerland

ISBN: (纸本)9781728173955

We propose thrombolysis using a magnetic nanoparticle microswarm with tissue plasminogen activator (tPA) under ultrasound imaging. The microswarm is generated in blood using an oscillating magnetic field and can be navigated with locomotion along both the long and short axis. By modulating the input field, the aspect ratio of the microswarm can be reversibly tuned, showing the ability to adapt to different confined environments. Simulation results indicate that both in-plane and out-of-plane fluid convection are induced around the microswarm, which can be further enhanced by tuning the aspect ratio of the microswarm. Under ultrasound imaging, the microswarm is navigated in a microchannel towards a blood clot and deformed to obtain optimal lysis. Experimental results show that the lysis rate reaches -0.1725 ± 0.0612 mm3/min in the 37C blood environment under the influence of the microswarm-induced fluid convection and tPA. The lysis rate is enhanced 2.5-fold compared to that without the microswarm (-0.0681 ± 0.0263 mm3/min). Our method provides a new strategy to increase the efficiency of thrombolysis by applying microswarm-induced fluid convection, indicating that swarming micro/nanorobots have the potential to act as effective tools towards targeted therapy. © 2020 IEEE.

关键词： Aspect ratio

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6DoF-ICTC: A Deep Image Feature Extraction Network Combining Transformers and CNNs for 6DoF Pose Estimation 12

6DoF-ICTC: A Deep Image Feature Extraction Network Combining...

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12th International Conference on CYBER Technology in automation, Control, and Intelligent Systems, CYBER 2022

作者： Zhang, Liming Zhou, Xin Zhu, Baixian Wang, Can Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China University of Science and Technology of China China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

In this work, we propose a new image feature extraction network for 6D pose estimation tasks. The majority of existing 6D pose estimation networks are based on the RGBD input, which is the fusion of images and point clouds. They are mainly working on keypoint extraction, point cloud feature extraction, and other tasks, however, image features, on the other hand, mainly rely on CNN networks that are currently advanced. With the development of Vision Transformer in the field of computer vision, the results on ImageNet demonstrate its benefit of extracting global features, and we see new directions that the Transformers can bring to the area of image feature extraction. ICTC is a Transformer paired with a CNN network that fuses the retrieved global features into each pixel and then does local feature extraction. This provides greater robustness to 6D pose estimation In complex situations such as shifting lighting conditions and object occlusion. Our method achieves 96% accuracy using the average ADD metric on the LineMOD dataset, demonstrating our method's effectiveness. © 2022 IEEE.

关键词： Feature extraction

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Drone-Based Predictive Structure Analysis: A Review

Drone-Based Predictive Structure Analysis: A Review

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Sustainable Emerging Innovations in engineering and Technology (ICSEIET), International Conference on

作者： Trushant Patil Durgesh Nandan Kanhaiya Sharma Deepak Parashar Department of Robotics and Automation SIT Symbiosis International (Deemed University) Pune India Department of Electronics and Telecommunication Engg. SIT Symbiosis International (Deemed University) Pune India Department of Computer Science & Engineering SIT Symbiosis International (Deemed University) Pune India Faculty of Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India

Drone-based predictive structure analysis is a rapidly growing field that has the potential to revolutionize infrastructure and building inspection. In this review of the research, we give a general overview of how drones could increase the effectiveness and precision of structural analysis, particularly for keeping an eye on difficult-to-reach locations. The usage of drones for 3D mapping, inspection, and monitoring of diverse constructions, such as concrete buildings, bridges, tunnels, and dams. The investigations show that drone-based inspection can produce 3D models and high-resolution photos, allowing for remote monitoring and the early identification of possible problems. The findings show that drones may greatly boost infrastructure management's safety and effectiveness, making them an important tool for predictive structural research.

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Capacitor Minimization in Analog Synapses for Efficient and Compact Binarized SNNs

IEEE Transactions on Circuits and Systems for Artificial Int...

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IEEE Transactions on Circuits and Systems for Artificial Intelligence 2025年第2期2卷 114-125页

作者： Mikail Yayla Ming-Liang Wei Simon Thomann Albi Mema Chia-Lin Yang Jian-Jia Chen Hussam Amrouch Design Automation for Embedded Systems Group TU Dortmund University Dortmund Germany Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany Department of the Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Technical University of Munich (TUM) Munich Germany TUM School of Computation Information and Technology Chair of AI Processor Design Munich Institute of Robotics and Machine Intelligence Munich Germany Department of Computer Science and Information Engineering Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Department of Computer Science TU Dortmund University Dortmund Germany Lamarr Institute Dortmund Germany

Accelerating the immense workloads of Neural Networks (NNs) is a critical challenge, and analog computing-based approaches present a promising solution. Among these, the Integrate-and-Fire (IF) Spiking Neural Network (SNN) stands out for its potential in efficient neural computation. However, achieving high inference accuracy through precise multiply-accumulate (MAC) operations necessitates a large membrane capacitor—224 to 547 times larger than the synapse array at the 14nm technology node—resulting in prohibitive area costs. Additionally, the large capacitor size introduces higher energy consumption and longer access times. Thus, a key research challenge is maintaining accuracy while mitigating the costs associated with the large membrane capacitor. In this work, we propose a HW/SW Codesign method, called CapMin, for capacitor size minimization in analog computing IF-SNNs. CapMin minimizes the capacitor size by reducing the number of spike times needed for accurate operation of the HW, based on the absolute frequency of MAC level occurrences in the SW. To increase the operation of IF-SNNs to current variation, we propose the method CapMin-V, which trades capacitor size for protection based on the reduced capacitor size found in CapMin. In our experiments, CapMin achieves more than a 14 $\times$ reduction in capacitor size along with a 34% reduction in energy consumption over the state of the art, while CapMin-V achieves increased variation tolerance in the IF-SNN operation, requiring only a small increase in capacitor size.

关键词： Capacitors Circuits Accuracy Capacitance Micromechanical devices Costs Neurons Integrated circuit modeling Spiking neural networks Hardware

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A strictly predefined-time convergent and anti-noise fractional-order zeroing neural network for solving time-variant quadratic programming in kinematic robot control

arXiv

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arXiv 2025年

作者： Yang, Yi Li, Xiao Wang, Xuchen Liu, Mei Yin, Junwei Li, Weibing Voyles, Richard M. Ma, Xin Multi-Scale Medical Robotics Center The Chinese University of Hong Kong 999077 Hong Kong Department of Mechanical and Automation Engineering The Chinese University of Hong Kong 999077 Hong Kong Qingdao266580 China School of Mechanical Engineering Dalian Jiaotong University Dalian116028 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China School of Engineering Technology Purdue University West LafayetteIN47907 United States

This paper proposes a strictly predefined-time convergent and anti-noise fractional-order zeroing neural network (SPTC-AN-FOZNN) model, meticulously designed for addressing time-variant quadratic programming (TVQP) problems. This model marks the first variable-gain ZNN to collectively manifest strictly predefined-time convergence and noise resilience, specifically tailored for kinematic motion control of robots. The SPTC-AN-FOZNN advances traditional ZNNs by incorporating a conformable fractional derivative in accordance with the Leibniz rule, a compliance not commonly achieved by other fractional derivative definitions. It also features a novel activation function designed to ensure favorable convergence independent of the model’s order. When compared to five recently published recurrent neural networks (RNNs), the SPTC-AN-FOZNN, configured with 0 © 2025, CC BY.

关键词： Quadratic programming

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