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检索条件"任意字段=4th International Conference on Robotics and Control Engineering, RobCE 2024"

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Cross-Scale Point Cloud Completion Method for Space Non-cooperative Targets Based on Probability Distribution Generation

Cross-Scale Point Cloud Completion Method for Space Non-coop...

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Computer Communication and Artificial Intelligence (CCAI), international conference on

作者： Yifei Zhi Jie Li Weifeng Liu Shengchao Dai College of Electrical and Control Engineering Shaanxi University of Science & Technology Xi’an China

ISBN: (数字)9798350362763

ISBN: (纸本)9798350362770

In space debris capture missions, the missing or incomplete point cloud data caused by sensor characteristics and occlusion factors increase the difficulty of subsequent recognition tasks. Point cloud completion can enhance its three-dimensional structural information to improve the accuracy of recognition tasks. However, the structural scale differences of space non- cooperative targets are significant, and the edge loss of point clouds collected in a rolling state is severe. Existing point cloud completion methods overlook the impact of scale differences on point clouds, making it challenging to obtain diverse information on local geometric details under cross-scale conditions. To address these issues, we propose a novel two-stage dual-channel completion network, PSPC, to generate complete point clouds. By combining skeleton point features with probability distribution features to capture the topological relationships between neighboring regions and distant points in sparse and dense point clouds, we transform these relationships into feature representations that conform to actual distributions to predict missing points with the same probability distribution as the original point cloud. Particularly, we introduce a cross-scale generation module, using the fused features as guidance to adaptively generate rough point clouds of different scales, enabling the completion of targets with different scale point clouds. We conducted extensive experiments on standard and scale- compressed datasets. Qualitative and quantitative experimental results demonstrate that our proposed network outperforms current mainstream methods in cross-scale completion.

关键词： Point cloud compression Satellites Space debris Graphics processing units Transforms Probability distribution Skeleton

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A DAOs-Based Publishing System for Advancing Open Access

A DAOs-Based Publishing System for Advancing Open Access

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Digital Twins and Parallel Intelligence (DTPI), IEEE international conference on

作者： Siji Ma Juanjuan Li Sangtian Guan Qinghua Ni Fei Lin Tengchao Zhang Jun Huang Fei-Yue Wang Faculty of Innovation Engineering Macau University of Science and Technology Macau China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

the development of the scientific publishing system has remarkably enhanced global accessibility to research findings and substantially increased the visibility and dissemination of academic publications. However, significant challenges still exist in effectively safeguarding the intellectual property rights of contributors, such as the unauthorized usage of materials, the complexity of enforcing intellectual property rights across various legal jurisdictions, and high instances of plagiarism and content misuse. Additionally, financial barriers related to open access may restrict the participation of economically disadvantaged researchers, potentially biasing scientific records towards more affluent research initiatives. To address these issues, a novel decentralized framework is formulated to ensure truly open access. this framework leverages blockchain for immutable record-keeping and clear attribution of authorship, to prevent unauthorized usage and plagiarism. Besides, it also utilizes a copyright-sharing model based on decentralized autonomous organizations and operations (DAOs), where smart contracts automatically enforce copyright and access policies to ensure fair compensation for authors and researchers. Furthermore, the copyright sharing model based on non-fungible tokens (NFT) and gradual ownership optimization (GOO) mechanism is proposed to ensure fair and accurate recognition and compensation for scholarly contributions.

关键词： Open Access Law Plagiarism Smart contracts Intellectual property Research initiatives Scientific publishing Nonfungible tokens Remuneration Optimization

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A Study on Pupils' Motivation to Pursue a STEM Career 2nd

A Study on Pupils' Motivation to Pursue a STEM Career

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22nd international conference on Interactive Collaborative Learning (ICL) / 48th IGIP international conference on engineering Pedagogy - Impact of the 4th Industrial Revolution on engineering Education

作者： Jaeggle, Georg Merdan, Munir Koppensteiner, Gottfried Lepuschitz, Wilfried Posekany, Alexandra Vincze, Markus Vienna Univ Technol ACIN Inst Automat & Control Vienna Austria PRIA Pract Robot Inst Vienna Austria Vienna Inst Technol TGM Vienna Austria

ISBN: (纸本)9783030402747;9783030402730

the industry is facing with the increasing lack of STEM graduates every year. It is of vital importance to motivate pupils for technical schools or schools in the STEM fields. this paper analyzes data from 693 pupils about their interest in a STEM career as well as their attitude towards STEM in general. Within this study, we also analyze the influence of the participation in robotics workshops. the method is a quantitative survey with questionnaires. Results show what mostly influenced the pupils to pursue a STEM career as well as the influence of robotics on them. Finally, the paper presents three STEM-related projects, which we also analyzed in regard to their impact on the pupils.

关键词： STEM careers Out-of-school-activities Educational robotics Technical high school Interest in STEM

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Random Coupled Neural Network with Sand Cat Swarm Optimization for Automatic Object Detection in Aerial Images

Random Coupled Neural Network with Sand Cat Swarm Optimizati...

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： Seeniappan Kaliappan Bhawna Sinha M. Ramya Srinivas Aluvala Nitheesh S R Maranan Department of Mechanical Engineering KCG College of Technology Chennai Tamil Nadu India Department of Computer Applications (MCA) Patna Women's College Patna Division of Research and Development Lovely Professional University Phagwara Punjab India School of Computer Science & Artificial Intelligence SR University Warangal Telangana India Department of Mechatronics Engineering KCG College of Technology Chennai Tamil Nadu India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Object detection in aerial images plays a crucial role in parking space management, traffic monitoring, and surveillance systems. Every day, vast amounts of aerial and satellite imagery are being collected, and analyzing this extensive data can greatly benefit numerous practical applications. However, traditional Automatic Object detection in aerial image methods struggles with challenges posed by complex backgrounds and detecting small objects in large-scale scenes. To address the previously mentioned challenges, a Random Coupled Neural Network with Sand Cat Swarm Optimization (RCNNet+SCSO) is proposed for automatic object detection in aerial images, ensuring high accuracy. In this approach, images from the UC Merced dataset are used, and to initially reduce noise in the input data, Shape-Aware Mesh Normal Filtering (SAMNF) is introduced. Next, the pre-processed images are subjected to object detection using Improved YOLO-V4. Following that, classification is performed with the Random Coupled Neural Network (RCNNet), which is optimized by Sand Cat Swarm Optimization (SCSO), resulting in automatic object detection in aerial images with enhanced efficiency and accuracy. the performance of the proposed RCNNet+SCSO is evaluated using the UC Merced dataset, achieving 99.2% accuracy and demonstrating superior results compared to existing methods. the outcomes of the proposed technique showed automatic object detection in aerial images that enhance the efficiency of various applications, including urban planning, traffic management, and surveillance. By leveraging advanced algorithms, it accurately identifies vehicles, buildings, and other objects, even in complex environments. this technology offers improved scalability and precision compared to traditional methods.

关键词： Accuracy Filtering Surveillance Heuristic algorithms Neural networks Urban planning Object detection Traffic control Particle swarm optimization Vehicle dynamics

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One-dimensional and three-dimensional simulation of aircraft equipment cabin combined with spray cooling 24

One-dimensional and three-dimensional simulation of aircraft...

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Proceedings of the 2024 4th international conference on Computational Modeling, Simulation and Data Analysis

作者： Ying Zhao Qing Cao Yuanxun Zhu Xiaobin Shen Peng Liu Shuai Su Yu Sun Guiping Lin AVIC Shenyang Aircraft Design and Research Institute Shenyang Liaoning China Hangzhou International Innovation Institute Beihang University Hangzhou Zhejiang China School of Aeronautic Science and Engineering Beihang University Beijing China Hangzhou International Innovation Institute Beihang University Hangzhou Zhejiang China

ISBN: (纸本)9798400711831

Aircraft, especially hypersonic aircraft, are subjected to harsh thermal environments during flight, which makes thermal safety problems particularly prominent. the coupling action of aerodynamic heat from the outside and heat generated by internal equipment poses a severe challenge to the design of structural thermal protection systems and in-flight thermal control systems. the increasing power density of various electronic devices in the aircraft equipment cabin requires certain means to ensure that the thermal environment in the equipment compartment meets the operating conditions of electronic equipment. After conducting research, this study selected liquid nitrogen as a low-temperature consumable working fluid for the spray cooling of the equipment cabin. A one-dimensional simulation network model was built to calculate the liquid nitrogen flow required for aircraft equipment cabin spray cooling. the three-dimensional flow field simulation was used to verify the accuracy of the one-dimensional calculation. At the same time, the accuracy of the Fluent software simulation was verified using existing data. the results of the calculation were visualized and compared for typical flight conditions, and the flow field and temperature field in the equipment cabin, as well as the temperature of the equipment wall, were obtained. One-dimensional and three-dimensional iterations were achieved, and the feasibility and rationality of the working fluid evaporation cooling in the equipment cabin of the aircraft were determined.

关键词： Aircraft equipment cabin

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Clustering and Equivalent Modeling Method for Direct-Drive Wind Farms Based on Particle Swarm Neural Network

Clustering and Equivalent Modeling Method for Direct-Drive W...

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Energy, Power and Electrical engineering (EPEE), international conference on

作者： Liang Tu Libin Huang Haiping Guo Binjiang Hu Jingyue Zhang Hui Luo China Southern Power Grid State Key Laboratory of HVDC Electric Power Research Institute Guangzhou China National Energy Power Grid Technology R&D Centre China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System China Southern Power Grid Guangzhou China China Southern Power Grid CSG Key Laboratory for Power System Simulation Electric Power Research Institute Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

In the realm of large-scale integration of wind farms or clusters into power grids, the fault transient characteristics of these grids have experienced significant transformations. Unfortunately, the prevalent single-machine equivalent models are inadequate in accurately depicting the fault transient behavior of wind farms or clusters. Moreover, their simulation computations are exceedingly intensive, resulting in suboptimal simulation efficiency. To address these challenges, this paper introduces a clustering and equivalent modeling approach tailored for direct-drive wind farms, leveraging the capabilities of a particle swarm neural network. Initially, a transient model for direct-drive wind turbines is formulated, and fault characteristic influence factors are established through theoretical analysis. these factors encompass the distance between the wind turbine and the point of common coupling (PCC), the implementation of DC-side current limiting measures, wind speed, and reactive power at the outlet. Following this, the Euclidean distance of the influence factor set is computed, and the initial cluster centers of wind turbines are derived using an enhanced max-min distance method. Ultimately, an equivalent clustering model is formulated by utilizing the influence factors and initial cluster centers as the training set. Simulation outcomes reveal that, in comparison to the single-machine equivalent model, this method enhances simulation efficiency by a range of ${6 2 \%}$ to ${6 7 \%}$ and reduces the steady-state error in fault current simulation by ${0 . 8 \%}$ to ${1 . 9 \%}$.

关键词： Training Computational modeling Wind speed Current measurement Wind farms Data models Wind turbines Steady-state Transient analysis Particle swarm optimization

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Smart Helmet with Pollution Reduction System, Accident Detection and Alerting System

Smart Helmet with Pollution Reduction System, Accident Detec...

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Electronics, Computing, Communication and control Technology (ICECCC), international conference on

作者： Janardhan B V Naveen kumar G N Bhavana Lakshman H Meenakshi R Patil Nikhil Vinayagamurthy Tejas B N Adithya M G Dept of Electronics and Communiation Engineering CMR instuitute of technology Bengaluru Karnataka India Embedded system engineer Panacea Medical Technologies Pvt. Ltd 4th phase Malur KIADB Industrial Area Malur Karnataka Trainee Engineer BEL Attikatte post Kadaba Hobli Gubbi Tq Tumkur dist Karnataka India Systems engineer Infosys Ashok Nagar Mangalore

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

In light of the escalating issues arising from the increasing presence of 2-wheel motor vehicles in densely populated cities despite regulations on engine standards and maintenance, the streets often witness vehicles emitting noxious, malodorous dark smoke. Additionally, the use of leaded gasoline-diesel blends in four-wheeler engines poses health hazards. While ISO-certified full-face helmets are recommended for safety, they can be physically exhausting and uncomfortable for long rides, potentially exposing riders to polluted air. Furthermore, a rising trend in motorcycle accidents is frequently attributable to factors like helmet non-compliance, delayed accident reporting, late hospital admissions, and intoxication. the statistics are stark, with hundreds of thousands of young individuals visiting emergency rooms each year and tens of thousands requiring extended hospital stays. In 2021, the National Crime Records Bureau of India reported a staggering 1,55,622 road accident fatalities, with two-wheelers involved in 44.5 percent of these incidents, particularly concentrated in Tamil Nadu and Uttar Pradesh. To address these complex challenges comprehensively, the proposal advocates for the development of a smart helmet integrating a pollution reduction system, accident detection and alerting system. this aims to significantly alleviate the adverse effects of air pollution and reduce the incidence and severity of motorcycle accidents, ultimately enhancing the safety and well-being of daily commuters in congested urban landscapes as well as long rides.

关键词： Head Road accidents Hospitals Roads Urban areas Motorcycles Safety

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Anticipating Evolutionary Trajectories in Sheet Metal Cutting Using Machine Learning

Anticipating Evolutionary Trajectories in Sheet Metal Cuttin...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Taifa Ayoub Mir Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

In the abstract, Sheet metal fabrication is an important component of numerous sectors, necessitating strong quality control methods to assure the integrity of all manufactured products. the current study investigates the use of advanced machine learning algorithms, notably Convolutional Neural Networks (also known as CNNs) or Random Forest, to detect sheet metal faults. the investigation assesses the model's performance in ten distinct fault categories, including wrinkles, oil preserving, die marks, edge cracking, laminated material, buckling, corrosion, fishy eyes, scratching, or tool marks. the evaluation metrics include precision, recall, or F1-score, which provide an in-depth evaluation of the algorithm's ability to properly identify and categorize faults. the results indicate remarkable precision values of over 94% across most classes, showing a low probability of false positives. Furthermore, high recall values, which exceed 95% in certain classes, demonstrate the model's efficacy in collecting a considerable fraction of true positives. the F1 scores regularly exceed 94%, indicating a balanced performance in recall and accuracy for each fault class. Support values give information about the distribution of cases across various categories, including higher support on prevalent issues like Wrinkling or Petroleum Canning. Support proportion numbers provide insight into the relative incidence of each fault in the dataset, which gives a more nuanced view of a model's effectiveness. Macro Average, Weighted Ordinary, or Micro Average measures provide a global view of the model's efficacy across every category, with accuracy frequently above 99%. the findings support the classification model's robustness and reliability in detecting sheet metal defects. the results of this research have important implications regarding enhancing procedures for quality assurance in sheet metal production, paving the way for improved effectiveness or automated fault detection systems.

关键词： Weight measurement Accuracy Oils Computational modeling Metals Quality control Production Fish Canning Petroleum

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On the Simulation of a Neuromorphic Robotic Arm with Dynamic control for Rapid Trajectories Evaluation 16th

On the Simulation of a Neuromorphic Robotic Arm with Dyn...

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16th EAI international conference on Simulation Tools and Techniques, SIMUTools 2024

作者： Trejos-Hernández, Diógenes Cerezuela-Escudero, Elena Linares-Barranco, Alejandro Robotics and Technology of Computers Lab ETSII-EPS I3US SCORE Universidad de Sevilla Seville Spain

ISBN: (纸本)9783031873447

the development of software simulators for robotic applications involves many advantages such as faster trajectory planning in virtual worlds, savings in energy consumption, reduction in the purchase of real robots, feasibility in collecting datasets for AI model training, etc. Commercial, free-access, open-source simulators for any type of robot can be found on the Internet and in the literature. Some of them allow the implementation of specific or new control models. For a behavior closer to the real world, the following must be taken into account: kinematics and dynamics, gravity, masses, etc. this paper proposes a new simulator for a neuromorphic robotic arm (modified Scorbot ER-VII) whose hardware controllers are based on Field Programmable Logic Arrays (FPGA) to allow the implementation of neuro-inspired motor controllers, such as Spiking Proportional-Integral-Derivative (SPID), to manage each joint of the robot. this simulator is developed in Matlab and Simulink and is available to the public. the simulator allows for the evaluation of different trajectories with two controllers, an Independent Joint control (IJC) and a Computerized Torque control (CTC). It has been tested for square and lemniscate trajectories with a small number of internally interpolated points to create reference trajectories with more points. RSME improvements of more than 10x have been measured for CTC over IJC, requiring less than 13 ms per point evaluated for a 3.6 GHz i7 computer and 32 GB RAM. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Robotic arms

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Study on Topology Design and Selection of Low-carbon Micro Grid for Highway Traffic

Study on Topology Design and Selection of Low-carbon Micro G...

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Energy engineering and Power Systems (EEPS), international conference on

作者： Xin Li Biao Wang Ji Ke Zhuangzhuang Liu College of Electronic and Control Engineering Chang'an University Xi'an Shaanxi Province College of Energy and Electrical Engineering Chang'an University Xi'an Shaanxi Province College of Highway Chang'an University Xi'an Shaanxi Province

ISBN: (数字)9798350366914

ISBN: (纸本)9798350366921

Pursuing low-carbon transport has led to exploring of new highway energy solutions, including developing intelligent and electrified highway energy systems. the low-carbon highway micro grid topology design based on AC-DC hybrid micro grids is viable for integrating distributed power generation units and high-power loads in highway areas. Traditional highway distribution networks were analyzed to design the topologies, and a distribution network selection and design index system were constructed. this system considered factors such as investment cost, power supply reliability, energy efficiency, emission reduction, and the self-consistency rate of new energy sources. Based on analyzing power demand across different scenarios, such as motorway toll stations, service areas, and tunnels, AC and DC micro grid topology design schemes were proposed. Radial and complementary ring-type structures were preferred for toll stations and service areas. For tunnel micro grids, the topology design was categorized into conventional, longer, and ultra-long/tunnel cluster types based on tunnel length. Simple radial structures were adopted for conventional tunnel micro grids, while longer and ultra-long/tunnel cluster types utilized a ring-type structure to ensure a dual-bus power supply for critical loads. Using the proposed evaluation system, the micro grid topology of each scenario underwent equivalent and simplified analysis. Subsequently, its power supply reliability was calculated based on the series-parallel relationship and reliability of each part of the branch where the loads were located. the results consistently met the system requirements. Economic and social indicators exhibited a strong positive correlation with the capacity of distributed generation units, thus validating the feasibility of the designed micro grid topology. the design scheme and evaluation index system laid the groundwork for future low-carbon highway micro grid topology designs.

关键词： Road transportation Power demand Network topology Distribution networks Topology Power system reliability Distributed power generation Reliability Indexes Investment

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