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Three-dimensional inversion of knot defects recognition in timber cutting

Journal of Forestry Research 2023年第4期34卷 1145-1152页

作者： Yizhuo Zhang Dapeng Jiang Zebing Zhang Jinhao Chen College of Computer Science and Technology Changzhou UniversityChangzhou 213164People’s Republic of China College of Mechanical and Electrical Engineering Northeast Forestry UniversityHarbin 150040People’s Republic of China

The comprehensive utilization of wood is the main goal of log cutting,but knot defects increase the diffi-culty of rationally optimizing *** to the lack of real shape data of knot defects in logs,it is diffi cult for detection methods to establish a correlation between signal and defect *** image-processing method is proposed for knot inversion based on distance regularized level set segmentation(DRLSE)and spatial vertex clustering,and with the inversion of the defects existing relative board position in the log,an inversion model of the knot defect is ***,the defect edges of the top and bottom images of the boards are extracted by DRLSE and ellipse fi tting,and the major axes of the ellipses made coplanar by angle correction;second,the coordinate points of the top and bottom ellipse edges are extracted to form a spatial straight line;third,to solve the intersection dispersion of spatial straight lines and the major axis plane,K-medoids clustering is used to locate the ***,with the vertex and the large ellipse,a 3D cone model is constructed which can be used to invert the shape of knots in the *** experiment was conducted on ten defective larch boards,and the experimental results showed that this method can accurately invert the shapes of defects in solid wood boards with the advantages of low cost and easy operation.

关键词： Timber knot inversion Distance regularized level set segmentation(DRLSE) Ellipse fi tting K-medoids cluster

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Trustworthy Vision for Autonomous Vehicles: A Robust Logic-Infused Deep Learning Approach 27

Trustworthy Vision for Autonomous Vehicles: A Robust Logic-I...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Chaghazardi, Zahra Fallah, Saber Tamaddoni-Nezhad, Alireza University of Surrey Dept. of Computer Science United Kingdom University of Surrey Mechanical Engineering Sciences United Kingdom

ISBN: (纸本)9798331505929

Deep learning constitutes a fundamental pillar in the field of image recognition within autonomous vehicles (AVs), facilitating precise predictions based on unprocessed data. However, unlike human cognition, deep learning models are susceptible to adversarial attacks. This paper proposes a novel approach, termed the Robust Logic-infused Deep Learning (RLDL) Approach, designed for traffic sign recognition. RLDL employs Inductive Logic Programming (ILP) to derive logical rules from a combination of positive and negative examples. These rules are subsequently transformed into a matrix of logical constraints, allowing for the assessment of logical consistency in predictions. Then, this logical consistency is incorporated into the neural network through the loss function. This study explores the impact of integrating logical constraints into deep learning models on the reliability of vision tasks in AVs. Our experiments demonstrate that the proposed method substantially enhances the accuracy of recognising traffic signs under adversarial attacks. © 2024 IEEE.

关键词： Contrastive Learning

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Multi-variable control to mitigate loads in CRISPRa networks 63

Multi-variable control to mitigate loads in CRISPRa networks

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Manoj, Krishna Grunberg, Theodore W. Vecchio, Domitilla Del Mit Department of Mechanical Engineering MA02139 United States Mit Department of Electrical Engineering and Computer Science MA02139 United States

ISBN: (纸本)9798350316339

The discovery of CRISPR-mediated gene activation (CRISPRa) has transformed the way in which we perform genetic screening, bioproduction and therapeutics through its ability to scale and multiplex. However, the emergence of loads on the key molecular resources constituting CRISPRa by the orthogonal short RNA that guide such resources to gene targets, couple theoretically independent CRISPRa modules. This coupling negates the ability of CRISPRa systems to concurrently regulate multiple genes independent of one another. In this paper, we propose to reduce this coupling by mitigating the loads on the molecular resources that constitute CRISPRa. In particular, we design a multi-variable controller that makes the concentration of these molecular resources robust to variations in the level of the short RNA loads. This work serves as a foundation to design and implement CRISPRa controllers for practical applications. © 2024 IEEE.

关键词： Molecular docking

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Quantitative phase imaging endoscopy with a metalens

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Light(science & Applications) 2024年第12期13卷 3196-3209页

作者： Aamod Shanker Johannes E.Fröch Saswata Mukherjee Maksym Zhelyeznyakov Steven L.Brunton Eric J.Seibel Arka Majumdar Department of Electrical and Computer Engineering University of WashingtonSeattleWA 98195USA Center for Vision Science University of RochesterNew YorkNY 14623USA Department of Mechanical Engineering University of WashingtonSeattleWA 98105USA Department of Physics University of WashingtonSeattle 98195USA

Quantitative phase imaging(QPI)recovers the exact wavefront of light from intensity *** and optical density maps of translucent microscopic bodies can be extracted from these quantified phase *** demonstrate quantitative phase imaging at the tip of a coherent fiber bundle using chromatic aberrations inherent in a silicon nitride hyperboloid *** method leverages spectral multiplexing to recover phase from multiple defocus planes in a single capture using a color *** 0.5mm aperture metalens shows robust quantitative phase imaging capability with a 28°field of view and 0.2πphase resolution(~0.1λin air)for experiments with an endoscopic fiber *** the spectral functionality is encoded directly in the imaging lens,the metalens acts both as a focusing element and a spectral *** use of a simple computational backend will enable real-time *** limitations in the adoption of phase imaging methods for endoscopy such as multiple acquisition,interferometric alignment or mechanical scanning are completely mitigated in the reported metalens based QPI.

关键词： phase bundle exact

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Graph Convolutional Networks for Lithium-Ion Battery Health Estimation 10

Graph Convolutional Networks for Lithium-Ion Battery Health ...

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10th IEEE International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia

作者： He, Yuxin Deng, Zhongwei Nie, Wenhao Jiang, Chunlin University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu611731 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China

ISBN: (纸本)9798350351330

Assessing the state of health (SOH) is essential for guaranteeing the safety and maintenance of lithium-ion batteries (LIBs). Nevertheless, due to the complex operating conditions, accurately estimating the capacity of LIBs is challenging. This study proposes a data-driven approach leveraging graph convolutional networks (GCN) to estimate the SOH of LIBs by utilizing multiple voltage segments. Then, the extracted voltage sequence is input into a graph generation layer(GGL), which transforms traditional sequential data into graph structured data. Finally, aggregating features from multiple nodes, and battery SOH estimation is achieved through a multi-layer perceptron. The efficacy of this approach in estimating battery SOH is validated using the MIT-Stanford dataset. Based on the results, the GCN method is able to achieve a high accuracy of 0.41% MAE and 0.63% RMSE, representing a substantial improvement over alternative methods. © 2024 IEEE.

关键词： Lithium-ion batteries

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Screening outstanding mechanical properties and low lattice thermal conductivity using global attention graph neural network

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Energy and AI 2023年第4期14卷 360-370页

作者： Joshua Ojih Alejandro Rodriguez Jianjun Hu Ming Hu Department of Mechanical Engineering University of South CarolinaSC 29208United States of America Department of Computer Science and Engineering University of South CarolinaSC 29208United States of America

mechanical and thermal properties of materials are extremely important for various engineering and scientific fields such as energy conversion and energy ***,the characterization of these properties via high throughput screening at the quantum level,although highly accurate,is inefficient and very time-and *** contrast,prediction at the classical level is highly efficient but less *** deploy scalable global attention graph neural network for accurate prediction of mechanical properties which bridge the gap between the accuracy at the quantum level and efficiency at the classical *** 10,158 elastic constants as training data,we trained the models on 5 mechanical properties,namely bulk modulus,shear modulus,Young’s modulus,Poisson’s ratio,and *** the trained model,we predicted 775,947 data in search of materials with ultrahigh *** further verify the recommended ultrahigh hardness materials by high precision first principles calculations,and we finally identify 20 structures with extreme hardness close to diamond,the hardest material in *** those,two super hard materials are completely new and have not been reported in literature so *** further recommend potential materials from bulk modulus prediction to search low lattice thermal conductivity,and we verify the thermal conductivity of 338 structures with first *** results demonstrate that one can find materials with extreme mechanical properties recommended by graph neural network and low thermal conductivity material from bulk modulus prediction with minimal first principles calculations of the structures(only 0.04%)in the large-scale materials pool.

关键词： Graph neural network Machine learning mechanical properties Ultrahigh hardness Lattice thermal conductivity DFT calculations Novel material discovery

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A Measurement Study of the Ethereum Underlying P2P Network

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computers, Materials & Continua 2024年第1期78卷 515-532页

作者： Mohammad ZMasoud Yousef Jaradat Ahmad Manasrah Mohammad Alia Khaled Suwais Sally Almanasra Electrical Engineering Department Al-Zaytoonah University of JordanAmman11733Jordan Mechanical Engineering Department Al-Zaytoonah University of JordanAmman11733Jordan Computer Science Department Al-Zaytoonah University of JordanAmman11733Jordan Faculty of Computer Studies Arab Open UniversityRiyadh11681Saudi Arabia

This work carried out a measurement study of the Ethereum Peer-to-Peer(P2P)network to gain a better understanding of the underlying *** was applied because it pioneered distributed applications,smart contracts,and ***,its application layer language“Solidity”is widely used in smart contracts across different public and private *** this end,we wrote a new Ethereum client based on Geth to collect Ethereum node ***,various web scrapers have been written to collect nodes’historical data fromthe Internet Archive and the Wayback Machine *** collected data has been compared with two other services that harvest the number of *** has collectedmore than 30% more than the other *** data trained a neural network model regarding time series to predict the number of online nodes in the *** findings show that there are less than 20% of the same nodes daily,indicating thatmost nodes in the network change *** poses a question of the stability of the ***,historical data shows that the top ten countries with Ethereum clients have not changed since *** popular operating system of the underlying nodes has shifted from Windows to Linux over time,increasing node *** results have also shown that the number of Middle East and North Africa(MENA)Ethereum nodes is neglected compared with nodes recorded from other *** opens the door for developing new mechanisms to encourage users from these regions to contribute to this ***,the model has been trained and demonstrated an accuracy of 92% in predicting the future number of nodes in the Ethereum network.

关键词： Ethereum measurement ethereum client neural network time series forecasting web-scarping wayback machine blockchain

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Reset-Free Reinforcement Learning via Multi-State Recovery and Failure Prevention for Autonomous Robots

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Tsinghua science and Technology 2024年第5期29卷 1481-1494页

作者： Xu Zhou Benlian Xu Zhengqiang Jiang Jun Li Brett Nener School of Mechanical Engineering Changshu Institute of TechnologyChangshu 215500China School of Automation Nanjing University of Science and TechnologyNanjing 210094China School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou 215009China Faculty of Medicine and Health The University of SydneySydney 2006Australia Department of Electrical Electronic and Computer EngineeringThe University of Western AustraliaPerth 6009Australia

Reinforcement learning holds promise in enabling robotic tasks as it can learn optimal policies via trial and ***,the practical deployment of reinforcement learning usually requires human intervention to provide episodic resets when a failure *** manual resets are generally unavailable in autonomous robots,we propose a reset-free reinforcement learning algorithm based on multi-state recovery and failure prevention to avoid failure-induced *** multi-state recovery provides robots with the capability of recovering from failures by self-correcting its behavior in the problematic state and,more importantly,deciding which previous state is the best to return to for efficient *** failure prevention reduces potential failures by predicting and excluding possible unsafe actions in specific *** simulations and real-world experiments are used to validate our algorithm with the results showing a significant reduction in the number of resets and failures during the learning.

关键词： reinforcement learning manual reset multi-state recovery failure prediction

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Electronic structure prediction of multimillion atom systems through uncertainty quantification enabled transfer learning

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npj Computational Materials 2024年第1期10卷 1423-1437页

作者： Shashank Pathrudkar Ponkrshnan Thiagarajan Shivang Agarwal Amartya S.Banerjee Susanta Ghosh Department of Mechanical Engineering-Engineering Mechanics Michigan Technological UniversityHoughtonMIUSA Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA90095USA Department of Materials Science and Engineering University of CaliforniaLos AngelesCA90095USA Faculty member of the Center for Data Sciences Michigan Technological UniversityHoughtonMIUSA

The ground state electron density—obtainable using Kohn-Sham Density Functional Theory(KSDFT)simulations—contains a wealth of material information,making its prediction via machine learning(ML)models ***,the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation,making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system ***,we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data,while comprehensively sampling systemconfigurations using *** ML models are less reliant on heuristics,and being based on Bayesian neural networks,enable uncertainty *** show that our models incur significantly lower data generation costs while allowing confident—and when verifiable,accurate—predictions for a wide variety of bulk systems well beyond training,including systems with defects,different alloy compositions,and at multi-million-atom ***,such predictions can be carried out using only modest computational resources.

关键词： alloy prediction transfer

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Implementation of Cross Layer Design with Localization Techniques in Wireless Sensor Networks Using Deep Learning 4

Implementation of Cross Layer Design with Localization Techn...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Sreethar, S. Srinivas, Regulavalasa Satya S Chanakyan, C. Prasath, S. Senthilkumar, G. Natrayan, L. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Tamil Nadu Chennai India MVJ College of Engineering Department of Mechanical Engineering Karnataka Bengaluru India Mohamed Sathak A J college of Engineering Department of Mechanical Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Poonamallee Chennai India Saveetha School of Engineering SIMATS Department of Mechanical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350385793

This study addresses the integration of wireless sensor networks, machine learning models, and go-layer architecture to embellish industrial processes in small-scale manufacturing. A strong monitoring infrastructure is produced by the incorporation of various sensors, proximity, temperature, pressure, vibration, UWB, and RFID. Cross-layer arrangement, along with localization methodologies, gives spatial context to sensor information, enhancing safety, predictive maintenance, and asset monitoring. K-Nearest Neighbors (KNN), Recurrent Neural Networks (RNN), Artificial Neural Networks (ANN), and Decision Trees (DT) act as predictive models, exhibiting outstanding overall performance across accuracy, precision, keep in mind, and F1 Score criteria. Practical deployment in a small-scale firm, displaying CNC machines and forging stations, illustrates the machine's versatility. Results suggest the models' success in forecasting replies relying completely on dynamic sensor data, allowing actual-time selection-making. The system's usefulness supports enhanced safety measures, simpler maintenance processes, and optimal asset management. In addition to offering practical insights to organisations navigating the challenges of Industry 4.0, this research adds to the theoretical framework of move-layer layout and machine learning integration. The suggested system emerges as a complete solution for intelligent operational improvement and performance in modern day industrial settings. © 2024 IEEE.

关键词： Predictive maintenance

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