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共 15865 条记录，以下是201-210 订阅

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Aircraft Control Level Evaluation: Environmental Factor analysis and Quantitative Evaluation System Construction 7

Aircraft Control Level Evaluation: Environmental Factor Anal...

引用

7th international conference on pattern Recognition and Artificial intelligence, PRAI 2024

作者： Zhao, Zihan College of Mathematics and Statistics Sichuan University of Science & Engineering Sichuan Zigong643000 China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350890

In the globalized aviation industry, maintaining safety and efficiency presents significant challenges. This study evaluates aircraft control proficiency using real flight data, moving beyond conventional methods. The analysis focuses on four key indicators: stick deflection, rudder deflection, and the positions of the left and right throttle levers, which are used to classify flight control as proper or improper. Environmental factors influencing improper controls are quantified using Pearson correlation analysis and random forest regression. A quantitative evaluation system is then developed using the TOPSIS method with entropy weighting. This innovative, data-driven approach addresses the limitations of traditional methods, offering a comprehensive framework for evaluating aircraft control proficiency. © 2024 IEEE.

关键词： Aircraft control

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Application and Prospects of Artificial intelligence in Intelligent Transportation Systems

Application and Prospects of Artificial Intelligence in Inte...

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2023 international conference on Internet of Things, robotics and Distributed Computing, ICIRDC 2023

作者： Sun, Yan Liu, Yunna Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350375275

With the rapid development of artificial intelligence technology, its application in the field of intelligent transportation system has gradually become a hot spot in research and industry. This thesis discusses the current status of AI application in ITS and provides an in-depth analysis of the current challenges. We also look into the potential future directions of AI in ITS, including autonomous driving, traffic flow optimization, and intelligent safety monitoring. The goal of this paper is to provide researchers and decision makers with a comprehensive understanding of the application of AI in ITS in order to promote the further development of the field. © 2023 IEEE.

关键词： Industrial research

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Dynamic Semantic analysis for Rumor Detection (DSARD): A suggestion 10

Dynamic Semantic Analysis for Rumor Detection (DSARD): A sug...

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10th international conference on Artificial intelligence and robotics, QICAR 2024

作者： Mohammadi, Fereshteh Keyvanpour, MohammadReza Masoumi, Behrouz faculty of ECB Azad Qazvin university Qazvin Iran

ISBN: (纸本)9798350348873

In the age of rapid information dissemination through online social networks, the detection of rumors has become a critical challenge. To address this, we introduce a novel approach called Dynamic Semantic analysis for Rumor Detection (DSARD). DSARD harnesses the intricate dynamics of rumor propagation by meticulously tracking shifts in semantic features within textual content over time. Through an amalgamation of advanced natural language processing techniques and temporal analysis, DSARD identifies abrupt semantic shifts that often signal the emergence or evolution of rumors. Moreover, DSARD extends its analysis to encompass the influence of network structures and multi-modal content, creating a comprehensive understanding of the evolving narrative. A machine learning classification model, trained on these multi-faceted features, accurately distinguishes rumors from non-rumors. DSARD's unique continuous learning mechanism further enhances its adaptability to evolving rumor-spreading strategies, ensuring timely and accurate identification of rumors in the complex landscape of online social networks. By addressing the limitations of existing rumor detection methods, DSARD presents a robust framework for combating the proliferation of misinformation. Its dynamic perspective, comprehensive analysis, and adaptability make it a potent tool for online platforms and social media watchdogs striving to maintain the integrity of information exchange. Through its integration of semantic, temporal, network, and multi-modal features, DSARD offers a paradigm shift in rumor detection, enabling proactive intervention and fostering a more informed and discerning online community. ©2024 IEEE.

关键词： Social networking (online)

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Proceeding - 2023 international conference on Artificial intelligence robotics, Signal and Image Processing, AIRoSIP 2023

Proceeding - 2023 International Conference on Artificial Int...

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2023 international conference on Artificial intelligence robotics, Signal and Image Processing, AIRoSIP 2023

ISBN: (纸本)9798350327472

The proceedings contain 78 papers. The topics discussed include: an exploratory study on healthcare applications for remote patients;common mode noise reduction strategies in radar devices;development of an automatic rice washing and cooking system;classification of abnormalities in spine curvature based on shape features using machine learning;deep learning optimizer evaluation in blur detection of digital breast tomosynthesis images using CNN constructed from scratch;automated thorax classification system in computed tomography images using deep convolutional neural network for lung cancer detection;Internet of things-based botnet traffic detection and analysis using deep convolutional neural network;and monkeypox skin lesion detection using transfer learning methods.

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Evaluating Accuracy and Efficiency of Fruit Image Generation Using Generative AI Diffusion Models for Agricultural robotics 39

Evaluating Accuracy and Efficiency of Fruit Image Generation...

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39th international conference on Image and Vision Computing New Zealand

作者： Zhao, Kun Minh Nguyen Yan, Weiqi Auckland Univ Technol Dept Comp Sci & Software Engn Auckland New Zealand

ISBN: (纸本)9798331518783;9798331518776

The automation of fruit picking using robotic systems is rapidly advancing within precision agriculture, relying heavily on the accurate detection and segmentation of fruit objects through computer vision. This study explores the use of generative artificial intelligence (AI) to create extensive fruit image datasets, concentrating on cherries, bananas, oranges, apples, and pineapples. Five generative models-Stable Diffusion v1.4, Stable Diffusion v1.5, Stable Diffusion v2, Stable Diffusion XL Refiner 1.0, and SDXL Turbo-were employed to generate 1000 images per fruit type, each set against a white background. Thresholding techniques were used to automatically extract fruit boundaries, which were then utilized to train segmentation models. The performance of these models was evaluated based on their accuracy and processing speed. SDXL Turbo consistently delivered the highest accuracy across all fruit types, though it required more processing time per image. Stable Diffusion XL Refiner 1.0 also exhibited strong accuracy but balanced performance differently. In contrast, Stable Diffusion v2 showed significant shortcomings, particularly in producing accurate images for cherries and oranges. This comparative analysis highlights the potential of advanced generative models in enhancing synthetic dataset creation for fruit object detection and segmentation in agricultural robotics. Future research will focus on refining these models to improve accuracy, broaden their applicability to a wider variety of fruits and environments, and optimize the trade-off between image quality and generation

关键词： Precision agriculture fruit detection object segmentation generative AI synthetic dataset Stable Diffusion agricultural robotics image processing computer vision automation

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Delving into Feature Space: Improving Adversarial Robustness by Feature Spectral Regularization 27th

Delving into Feature Space: Improving Adversarial Robustness...

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27th international conference on pattern Recognition, ICPR 2024

作者： Cheng, Zhen Zhu, Fei Zhang, Xu-Yao Liu, Cheng-Lin State Key Laboratory of Multimodal Artificial Intelligence Systems CASIA Beijing China School of Artificial Intelligence UCAS Beijing China Centre for Artificial Intelligence and Robotics HKISI-CAS Hong Kong

ISBN: (纸本)9783031783944

The study of adversarial examples in deep neural networks has attracted great attention. Numerous methods improve adversarial robustness via shrinking the gap of features between natural examples and adversarial examples. Nevertheless, the role of individual features in adversarial robustness has not been explored adequately. In this paper, we delve into this problem from the perspective of spectral analysis in feature space. We find that while standardly trained deep models have features distributed dominantly along eigenvectors with large eigenvalues, eigenvectors with smaller eigenvalues are more sensitive to adversarial attacks. We attribute this phenomenon to the dominance of the top eigenvalues, linked to the concept of intrinsic dimensionality. The extracted features possess a small intrinsic dimensionality, enhancing generalization but resulting in the model overlooking diverse features. We propose a method called Feature Spectral Regularization (FSR) to penalize the largest eigenvalue, so as to spread the distribution of eigenvalues. Comprehensive experiments demonstrate that FSR is effective to alleviate the dominance of larger eigenvalues, increase the intrinsic dimensionality, and improve adversarial robustness on multiple datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Study of Kinematics and Trajectory Planning for the Pneumatic Soft Manipulator 17th

Study of Kinematics and Trajectory Planning for the Pneumati...

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17th international conference on Intelligent robotics and Applications

作者： Che, Lu Chen, He Li, Zhaoqi Hebei Univ Technol Sch Artificial Intelligence Tianjin 300401 Peoples R China Wuhan Text Univ Hubei Key Lab Digital Textile Equipment Wuhan 430200 Peoples R China

ISBN: (纸本)9789819607976;9789819607983

The pneumatic soft manipulator is made of flexible materials, which has the characteristics of strong environmental adaptability, safe human-machine interaction, etc. In practice, the pneumatic soft manipulator has more and more applications in medical rehabilitation, aquaculture, pipeline maintenance, search and rescue work, etc. In recent years, the demand for pneumatic soft manipulators has increased, and higher requirements have been put forward for their flexibility and operation accuracy. However, due to the large deformation nonlinear characteristics of the pneumatic soft manipulator, its kinematic modeling is difficult. Considering this fact, this paper focuses on the kinematic modeling and trajectory planning for the pneumatic soft manipulator with three chambers. Firstly, we obtain the forward kinematic and inverse kinematic models of the pneumatic soft manipulator with three chambers through rigorous analysis and mathematical derivation. After that, based on the kinematic model, we propose a trajectory planning method for the pneumatic soft manipulator with three chambers. Finally, simulation results are given to test the satisfactory performance of the proposed method.

关键词： Pneumatic soft manipulator Kinematic model Trajectory planning

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A ROS-Based Data-Driven Motion Self-Recognition System Using Deep-Learning Convolutional Neural Networks in a Military Unmanned Ground Vehicle

A ROS-Based Data-Driven Motion Self-Recognition System Using...

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international Joint conference on Neural Networks (IJCNN)

作者： Santoso, Fendy Finn, Anthony Charles Sturt Univ Artificial Intelligence & Cyber Futures Inst Canberra ACT 2600 Australia Univ South Australia UniSA STEM Adelaide SA 5095 Australia

ISBN: (纸本)9798350359329;9798350359312

The ability to recognize motions is an important feature in cutting-edge robotics or autonomous systems, such as self-driving cars, humanoid robots, and human-robot interactions, resulting in improved safety and efficiency. Addressing this critical issue, we introduce a simple framework leveraging the benefits of the normalized real-time network traffic data of the middleware ROS platform formulated in the form of RGB or grayscale images to train the Convolutional Neural Network (CNN) system in order to learn the motion pattern of the robot. For our experimental platform, we employ the GVRBOT Unmanned Ground Vehicle (UGV), developed by the U.S. Army Combat Capabilities Development Command (CCDC), Ground Vehicle Systems Center (GVSC). We rigorously study the performance of our motion recognition system under several different lengths of data (epochs). In addition, we compare the relative merits of our proposed system with respect to the performance of the well-known 'Bag-of-Features' (BoFs) detection algorithm widely implemented in computer vision. Our research indicates the efficacy of the proposed motion recognition system as we can achieve a reasonably high detection accuracy >=psi 0.97 within a minimum detection time of two epochs highlighting its real-time benefits. Overall, our recognition system can also achieve superior detection performance compared to the efficacy of the BoFs algorithm.

关键词： Motion Self-Recognition Convolutional NeuralNetwork (CNN) Unmanned Ground Vehicle Robot Operating System (ROS).

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Muscle Synergy analysis for Human-Body Walking Balance Mechanism withWearable Capacitive Sensing 17th

Muscle Synergy Analysis for Human-Body Walking Balance Mecha...

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17th international conference on Intelligent robotics and Applications

作者： Guo, Chuxuan Tong, Yiran Liu, Zijie Liu, Yuchao Chen, Yibin Yu, Tianhao Guo, Jiajie Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Inst Med Equipment Sci & Engn Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9789819607853;9789819607860

Balance maintenance during human walking, as an energy efficient and obstacle adaptive motion, provides design guidelines and control strategies to develop biped robots. Synergy of muscle contractions, as the main power source for human voluntary motions, generates joint torques for the gait balance. However, the traditional electromyography approach is subjected to serious environmental disturbances and personal variations, and it is desired to develop a robust sensing method to monitor muscle motions. This paper proposes a sensing system based on muscle deformations to investigate muscle synergy for steady and unsteady human walking, revealing the contribution of gluteus maximus, rectus femoris and medial gastrocnemius to the lower limb joint motions in a gait cycle. The consistency and reliability of the muscle synergy analysis is demonstrated with nine tested subjects, and it is expected to uncover the balance mechanism of bipedal walking that is valuable to research of gait biomechanics, wearable robotics and humanoid robots.

关键词： Flexible sensor muscle synergy unsteady-state gait balance control

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Exploring Muscle Synergies for Performance Enhancement and Learning in Myoelectric Control Maps

Exploring Muscle Synergies for Performance Enhancement and L...

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international conference on Rehabilitation robotics (ICORR)

作者： Tse, K. C. Capsi-Morales, P. Castaneda, T. Spiegeler Piazza, C. Tech Univ Munich Sch Computat Informt & Technol Dept Comp Engn Munich Germany Tech Univ Munich Munich Inst Robot & Machine Intelligence Munich Germany

ISBN: (纸本)9798350342758

This work proposes two myoelectric control maps based on a DoF-wise synergy algorithm, inspired by human motor control studies. One map, called intuitive, matches control outputs with body movement directions. The second one, named non-intuitive, takes advantage of different synergies contribution to each DoF, without specific correlation to body movement directions. The effectiveness and learning process for the two maps is evaluated through performance metrics in ten able-bodied individuals. The analysis was conducted using a 2-DoFs center-reach-out task and a survey. Results showed equivalent performance and perception for both mappings. However, learning is only visible in subjects that performed better in non-intuitive mapping, that required some familiarization to then exploit its features. Most of the myoelectric control designs use intuitive mappings. Nevertheless, non-intuitive mapping could provide more design flexibility, which can be especially interesting for patients with motor disabilities.

关键词： Mapping

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