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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： An, Di Difrieri, Michael Chen, YangQuan University of California Electrical Engineering & Computer Science Merced United States University of California Department of Mechanical Engineering Merced United States

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

The foundation of soil carbon management is the measurement of soil carbon content, which potentially enables many carbon-negative or carbon-neutral technologies for fighting climate change and improving soil health for greater crop yield. Several researchers used a non-intrusive method to quantify soil organic carbon content using ground penetrating radar (GPR) with a fixed sensor configuration. The sensor we used in this study, however, is compactly comprised of an array of 18 radar transmitter (TX) and receiver (RX) pairs. It is necessary to propose an assessment of sensing performance which can avoid possible failure in identifying the correct soil carbon spatial-temporal changes. In this paper, we provide a comprehensive assessment of the evaluation of non-intrusive methods for sensing soil carbon content when a radar array is used. Specifically, our proposed evaluation score utilizes explicit physical knowledge as a data-driven metric to find the optimal antenna pair combination for our radar array sensor under different sensing tasks and environments. We evaluated our soil carbon sensing score (SCSS) using the data collected from real-world soil sample experiments. The results show that the optimal antenna pair has the greatest sensing ability to measure soil carbon content in a variety of sensing environments and sensing distances, with a 36% increase in classification accuracy. © 2022 IEEE.

关键词： Radar antennas

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Electronic System Integration Technology for Intelligent Missile Weapons

Electronic System Integration Technology for Intelligent Mis...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Xu Wang Dashan Shi Kerong Cai Shanghai Institute of Mechanical and Electrical Engineering China Aerospace Science and Technology Corporation Shanghai China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Aiming at the development trend of intelligent missile weapons, this paper analyzes and summarizes the four intelligent characteristics of missile weapons, proposes an integrated design method of electronic system for intelligent missile weapons, and discusses the hardware architecture, software architecture and communication architecture of the system. The functions of the intelligent integrated information processing system are abstracted and mapped to several functional modules. The hardware architecture adopts the mode of passive backplane plus functional modules based on VNX standard, the software architecture adopts the mode of open multi-level operating system plus application software, and the communication architecture adopts the low, medium and high-speed hybrid bus structure. An intelligent, integrated and modular platform architecture that can realize missile autonomous decision-making, information fusion, intelligent killing and cooperative operation is built. Finally, the key technologies required for the integrated architecture of intelligent missile weapon electronic system are discussed and its feasibility is analyzed.

关键词： Missiles Software architecture Operating systems Backplanes Systems architecture computer architecture Information processing Hardware Application software Standards

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Automatically Classifying Vestibular Gait Using Time-series Data from Wearable IMUs 46

Automatically Classifying Vestibular Gait Using Time-series ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Jabri, Safa Spicher, Lucy Carender, Wendy Wiens, Jenna Sienko, Kathleen H. University of Michigan Department of Mechanical Engineering Ann ArborMI United States Michigan Medicine Department of Otolaryngology Ann ArborMI United States University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI United States

ISBN: (纸本)9798350371499

Disorders affecting the vestibular system affect the ability to maintain balance and increase the risk of falls. Identifying individuals with vestibular deficits is an important step to inform referral to specialized testing, vestibular rehabilitation, and fall-prevention interventions. Instrumented gait assessments using wearable inertial measurement units (IMUs) and machine learning (ML) algorithms could support the accurate and automated identification of individuals with vestibular deficits. While prior work has demonstrated the feasibility of the automatic classification of vestibular gait, it relied on manual feature-engineering whereby discriminative features are identified and calculated prior to model training. The goal of this study was to develop and validate ML models that automatically learn from minimally pre-processed IMU data to classify gait kinematics from individuals with vestibular deficits and age-matched controls. Thirty study participants (15 with vestibular deficits and 15 age-matched controls) walked with their eyes closed on a 6-meter walkway with an IMU placed on the left arm. Two Bi-directional LSTM (BiLSTM) models were trained on the minimally pre-processed timeseries data alone as well as fusing the timeseries data with engineered features used in prior work. Classification performance was reported and compared to performance from feature-based approaches in terms of area under the receiver operating characteristic curve (AUROC) scores. Results showed that the BiLSTM models trained on minimally pre-processed time-series data achieved excellent classification performance (AUROC = 0.86), and their performance was comparable (p-value > 0.05) to previously published Random Forest models trained on engineered gait features extracted from the same dataset (AUROC = 0.89). These findings highlight that BiLSTM models were able to learn discriminative patterns from the minimally pre-processed IMU data in vestibular gait classification tasks. © 20

关键词： BiLSTM gait IMU machine learning timeseries vestibular wearable

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Swarm Intelligence for Multi-Robot Coordination in Industrial Environments 9

Swarm Intelligence for Multi-Robot Coordination in Industria...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Prakash, Kode Jaya Muthukumaran, M. Sandhiya, R. Rajpradeesh, T. Deshmukh, Mrunalini A. Thanikodi, Sathish Vnr Vignana Jyothi Institute of Engineering & Technology Department of Mechanical Engineering Telangana Hyderabad500090 India Karpagam College of Engineering Department of Electrical And Electronics Engineering Tamil Nadu Coimbatore641032 India Rmk College of Engineering And Technology Department of Computer Science Engineering Tamil Nadu Puduvoyal601206 India Kalasalingam Academy of Research And Education Department of Mechanical Engineering Tamil Nadu Srivilliputhur626126 India Shree L. R. Tiwari College of Engineering Department of Civil Engineering Maharashtra Thane401107 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350365092

The operation of mass intelligence in order to improve multi-robot collaboration within artificial environments. With the ever-increasing demand for robotization in the field of diligence, the efficient collaboration of several robots is becoming increasingly necessary for optimising operations such as manufacturing, examination, and shipping. The collaborative gesture of social nonentity colonies served as the inspiration for swarm intelligence, which provides a decentralised and tone-organizing solution to the coordination of a platoon of robots. The technique that has been offered investigates mass algorithms, such as Ant Colony Optimisation and flyspeck mass Optimisation, with the purpose of facilitating efficient work allocation, path planning, and resource optimisation among the robots. The purpose of this is to evaluate the performance of mass intelligence in terms of refining collaboration, reducing the amount of time required to complete tasks, and improving the overall effectiveness of multi-robot systems in complex artificial settings. This evaluation is carried out through extensive simulations and experiments conducted in the actual world. The findings bring significant perceptivity to the field of robotics and robotization, emphasising the possibility of mass intelligence for addressing the issues of scalability and rigidity in artificial settings. © 2024 IEEE.

关键词： Swarm intelligence

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Thermal management challenges and approaches for liquid hydrogen-fueled aircraft

Thermal management challenges and approaches for liquid hydr...

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AIAA Aviation Forum and ASCEND, 2024

作者： Ordonez, Juan C. Sailabada, Chaianan Guo, Wei Cooley, Lance Cheetham, Peter Kim, Chul Zheng, Jim P. Gladin, Jonathan Cattafesta, Louis FAMU-FSU College of Engineering Department of Mechanical Engineering Center for Advanced Power Systems and Energy and Sustainability Center Florida State University TallahasseeFL32310 United States Electrical and Computer Engineering FAMU-FSU College of Engineering and Center for Advanced Power Systems Florida State University TallahasseeFL32310 United States Center for Advanced Power Systems FAMU-FSU College of Engineering and Center for Advanced Power Systems Florida State University TallahasseeFL32310 United States Department of Electrical Engineering The State University of New York at Buffalo BuffaloNY14260 United States School of Aerospace Engineering Aerospace Systems Design Laboratory Georgia Institute of Technology AtlantaGA30332 United States Department of Mechanical Materials and Aerospace Engineering Illinois Institute of Technology ChicagoIL60616 United States

ISBN: (数字)9781624107160

ISBN: (纸本)9781624107160

This paper presents an overview of thermal management challenges associated with aircraft that rely on liquid hydrogen as the sole fuel source. The paper discusses strategies that can enhance the overall cooling capacity and enable a larger contribution of fuel cells to the overall power. Design considerations for heat exchangers to integrate cryogenic hydrogen with secondary thermal management loops, outer mold line cooling, and additional thermal management strategies are presented. Cooling requirement estimates for a blended-wing aircraft (112 passengers, 2400 nautical miles) are reported. A finite element simulation using RANS suggests a cooling capacity rate of the order of 10 kW/m2 is available through the Outer Mold Line (OML). The paper also reports on the cooling capacity available by using hydrogen as a heat sink to cool the power train. For the IZEA concept, the hydrogen stream to the fuel cell can handle close to 6% of the fuel cell heat generation. © 2024, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Hydrogen fuels

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Graph Convolutional Networks for Predicting mechanical Characteristics of 3D Lattice Structures 13th

Graph Convolutional Networks for Predicting Mechanical Chara...

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13th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2024

作者： Oleka, Valentine Zahedi, Seyyed Mohsen Taherkhani, Aboozar Baserinia, Reza Zahedi, S. Abolfazl Yang, Shengxiang School of Computer Science and Informatics De Montfort University Leicester United Kingdom School of Engineering and Sustainable Development De Montfort University Leicester United Kingdom Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough University Loughborough United Kingdom

ISBN: (纸本)9783031579189

Recent advancements in deep learning methods encouraged researchers to apply them to process 3D objects. Initially, convolutional neural networks which have shown their ability in the processing of 2D images were used for 3D object processing. These methods need a complex process to convert 3D objects to 2D images. This conversion leads to increased computation cost and possible information loss during the transformation. This research introduces a Graph Convolutional Network approach for predicting mechanical properties of custom-designed 3D lattice structures for tissue engineering applications. Seventeen scaffold geometrics were generated for training while eight were used for testing. Unlike traditional preprocessing into images, this methodology reduces preprocessing by leveraging GCNs to directly process 3D geometrics in graph form. The experimental results show the efficiency of our proposed method in predicting 3D lattice structures. © IFIP International Federation for Information Processing 2024.

关键词： Convolution

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OceanPlan: Hierarchical Planning and Replanning for Natural Language AUV Piloting in Large-scale Unexplored Ocean Environments

arXiv

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arXiv 2024年

作者： Yang, Ruochu Zhang, Fumin Hou, Mengxue School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States Department of Electrical and Computer Engineering Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Hong Kong School of Electrical Engineering University of Notre Dame Notre Dame United States

We develop a hierarchical LLM-task-motion planning and replanning framework to efficiently ground an abstracted human command into tangible Autonomous Underwater Vehicle (AUV) control through enhanced representations of the world. We also incorporate a holistic replanner to provide real-world feedback with all planners for robust AUV operation. While there has been extensive research in bridging the gap between LLMs and robotic missions, they are unable to guarantee success of AUV applications in the vast and unknown ocean environment. To tackle specific challenges in marine robotics, we design a hierarchical planner to compose executable motion plans, which achieves planning efficiency and solution quality by decomposing long-horizon missions into sub-tasks. At the same time, real-time data stream is obtained by a replanner to address environmental uncertainties during plan execution. Experiments validate that our proposed framework delivers successful AUV performance of long-duration missions through natural language piloting. © 2024, CC BY.

关键词： Autonomous underwater vehicles

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Courteous MPC for Autonomous Driving with CBF-Inspired Risk Assessment 27

Courteous MPC for Autonomous Driving with CBF-Inspired Risk ...

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

作者： Zhang, Yanze Lyu, Yiwei Demir, Sude E. Zhou, Xingyu Yang, Yupeng Wang, Junmin Luo, Wenhao University of North Carolina at Charlotte Department of Computer Science CharlotteNC28223 United States Carnegie Mellon University Department of Electrical and Computer Engineering PittsburghPA15213 United States University of Texas at Austin Walker Department of Mechanical Engineering AustinTX78712 United States

ISBN: (纸本)9798331505929

With more autonomous vehicles (AVs) sharing roadways with human-driven vehicles (HVs), ensuring safe and courteous maneuvers that respect HVs' behavior becomes increasingly important. To promote both safety and courtesy in AV's behavior, an extension of Control Barrier Functions (CBFs)-inspired risk evaluation framework is proposed in this paper by considering both noisy observed positions and velocities of surrounding vehicles. The perceived risk by the ego vehicle can be visualized as a risk map that reflects the understanding of the surrounding environment and thus shows the potential for facilitating safe and courteous driving. By incorporating the risk evaluation framework into the Model Predictive Control (MPC) scheme, we propose a Courteous MPC for ego AV to generate courteous behaviors that 1) reduce the overall risk imposed on other vehicles and 2) respect the hard safety constraints and the original objective for efficiency. We demonstrate the performance of the proposed Courteous MPC via theoretical analysis and simulation experiments. © 2024 IEEE.

关键词： Autonomous vehicles

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Smart Water Management in Agriculture: Enhancing Crop Productivity with Restricted Boltzmann Machine 1

Smart Water Management in Agriculture: Enhancing Crop Produc...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Gore, K.B. Chidambararaj, N. Kartheesan, L. Ghamande, Manasi Vyankatesh Sarode, Ghanasham C. Kalra, Gourav SNJB's Ls K. B. Jain College of Engineering Department of Mechanical Engineering Nashik India St. Joseph's College of Engineering Department of Electrical and Electronics Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Technology Department of Engineering Science and Humanities Pune India Dr. D.Y. Patil Institute of Technology Department of Civil Engineering Pune India Department of Mechanical Engineering Ambala India

ISBN: (纸本)9798350352931

Effective water management in agriculture is crucial for sustainable food production in the face of water scarcity and climate change. The focus of this literature is on new methods and technology that aim to maximize water usage in agricultural activities and improve irrigation efficiency. Efficient water management practices are critical for increasing agricultural yields while lowering water wastage. By implementing water-saving measures and utilizing efficient irrigation systems, farmers can enhance yields while reducing pressure on scarce freshwater resources. Preprocessing, feature selection, and model training are all part of the suggested method. During the preprocessing phase, an Adaptive Inverse Normalization Layer is employed. The RFE methodology relies on a ranking algorithm that sorts the dataset from best to worst according to the attributes that are considered most important. Throughout training, the proposed approach utilized the RBM model without interruption. With a 93.21% average accuracy, this cutting-edge approach surpasses both DBN and KNN. © 2024 IEEE.

关键词： Irrigation

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2D-LLFE-NVC: 2D Laser Line Feature Extraction Using Normal Vector Clustering 30

2D-LLFE-NVC: 2D Laser Line Feature Extraction Using Normal V...

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30th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2024

作者： Ou, Jinshun Ren, Jiying Li, Wenguang Huang, Panling Zhou, Jun Zhou, Jiehan Zhang, Tao Shandong University School of Mechanical Engineering Jinan China Shandong University of Science and Technology School of Computer Science and Technology Qingdao China University of Oulu Faculty of Information Technology and Electrical Engineering Oulu Finland Qingdao Hanhe Cable Co. Ltd. Qingdao China

ISBN: (纸本)9798350391916

This paper presents a novel 2D laser data line feature extraction algorithm based on normal vector clustering(2D-LLFE-NVC). The algorithm consists of normal vector calculation, normal normalization based on multi-scale operator normal difference and median filter, and line feature extraction based on normal vector clustering. Unlike existing methods, 2D-LLFE-NVC leverages the continuity of laser point clouds and detects breakpoints using multi-scale operators based on normal differences. The extracted line features are more accurate and correct on a global scale. To validate its performance, we conducted SLAM experiments in a 5000-square-meter indoor environment using both the proposed algorithm and existing methods. By comparing the line feature points extracted by these different approaches with manually annotated line feature points, and demonstrating the superior performance of the proposed method in terms of correctness and completeness. © 2024 IEEE.

关键词： Median filters

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