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检索条件"任意字段=International Conference on Advances in Science, Engineering and Robotics Technology"

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VIBRATION MODE ANALYSIS OF LOCAL RESONANCE METAMATERIAL WITH CHIRAL SPIRAL ELASTIC BEAMS 14

VIBRATION MODE ANALYSIS OF LOCAL RESONANCE METAMATERIAL WITH...

引用

14th international conference on Quality, Reliability, Risk, Maintenance, and Safety engineering, QR2MSE 2024

作者： Zeng, Tan Ni, Hengtai Kong, Chuijian Liu, Jing School of Marine Science and Technology Northwestern Polytechnical University Xi'an China Laboratory for Unmanned Underwater Vehicle Northwestern Polytechnical University Xi'an China Chongqing Dajiang Industrial Co. LTD Chongqing China

ISBN: (纸本)9781837242672

Local resonant metamaterials are highly effective for managing low-frequency vibrations in practical engineering applications. However, the focus of existing research on locally resonant metamaterials has predominantly been on their structural attributes, limiting the expansion of bandgaps that are generated through local resonance mechanisms. This study introduces a chiral spiral locally resonant metamaterial (CSLRM) designed to attenuate low-frequency vibrations. The proposed vibration damper, crafted from homogeneous materials, exhibits robust adaptability, high stability, and manufacturability, making it ideal for use in demanding engineering environments. The incorporation of resonators, consisting of chiral helical beams and mass blocks, into the supporting structure facilitates vibration control within compact spaces. Additionally, a finite element model of the CSLRM structure was developed, and its dispersion curves and vibration modes were analyzed to explore the bandgap formation mechanism and characterize the bandgap features of the CSLRM structure. This research advances the understanding of how to broaden and tune the bandgaps in locally resonant metamaterials. © The Institution of engineering & technology 2024.

关键词： Vibration analysis

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Research Progress of Remanufacturing technology in the Field of Construction Machinery 8th

Research Progress of Remanufacturing Technology in the Field...

引用

8th international conference on advances in Construction Machinery and Vehicle engineering, ICACMVE 2023

作者： Wang, Kaiming Liu, Wei Pang, Xiaotong Hu, Yongle Tong, Yonggang College of Automotive and Mechanical Engineering Changsha University of Science and Technology Changsha410114 China

ISBN: (纸本)9789819718757

Excavator bucket teeth are often in direct contact with abrasive particles in the soil during work, resulting in the tip being severely worn. This study used laser cladding to synthesize (Ti, Nb)C reinforced coatings on the surface of Q550 steel of bucket teeth of commonly used excavators to improve the wear resistance, and researches the effects of different Ti, Nb, Cr3C2 powder ratios on the hardness, wear resistance, and wear loss of laser cladding (Ti, Nb)C reinforced coatings. The results show that the optimal powder ratio is 80% Ni-based+1.26% Ti+7.54% Nb+11.2% Cr3C2. Under the optimal powder ratio, the hardness of the coating is 213.4 HV, the wear amount is 32.7 mg, and the wear failure form is abrasive wear and slight adhesive wear. © The Author(s) 2024.

关键词： Laser cladding

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TSQ-2024: A Categorized Dataset of 2D LiDAR Images of Moving Dump Trucks in Various Environment Conditions 17

TSQ-2024: A Categorized Dataset of 2D LiDAR Images of Moving...

引用

17th international conference on Machine Vision, ICMV 2024

作者： Kokhan, V.V. Konyushenko, I.D. Bocharov, D.A. Seleznev, I.O. Nikolaev, I.P. Nikolaev, D.P. Institute for Information Transmission Problems Russian Academy of Sciences Moscow Russia Moscow Institute of Physics and Technology Dolgoprudny Russia GLAVKARIER LLC Borovsk Russia Federal Research Center "Computer Science and Control" Russian Academy of Sciences Moscow Russia Smart Engines Service LLC Moscow117312 Russia

ISBN: (纸本)9781510688278

LiDARs are powerful and common sensors used for complex 3D environment analysis tasks. They are widely utilized as data providers in industrial measurements, robotics and unmanned technologies. Due to physical properties of such sensors they suffer from environmental heterogenity resulting in distortions of obtained measurements because of natural clutter such as cloud dust, snow and fog. Nevertheless the problem of robust clutter filtering algorithms is of significant importance, we highlight the lack of LiDAR measurements datasets containing natural clutter. The current study presents a novel publicly available and expertly annotated 2D LiDAR measurements dataset TSQ-2024 that contains 120 measurements of passing trucks under natural conditions including sand dust clutter data. The proposed dataset was utilized in the task of truck body segmentation. The experiments section provides the performance evaluation of the proposed truck body segmentation algorithm on the TSQ-2024 dataset which is also published along with the data. © 2025 SPIE.

关键词： Radar clutter

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Machine Learning based Comparative Analysis of Cervical Cancer Risk Classifications Algorithms 2

Machine Learning based Comparative Analysis of Cervical Canc...

引用

2nd IEEE international conference on advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Ganguly, Tania Pati, Peeta Basa Deepa, K. Singh, Tripty Ozer, Tolga Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita School of Engineering Department of Electrical and Electronics Engineering Bengaluru India Afyon Kocatepe University Technology Faculty Electric Electronic Engineering Afyonkarahisar Turkey

ISBN: (纸本)9798350315905

Cervical cancer is a major health concern for women worldwide, and early detection is essential for successful treatment. Since symptoms often do not appear until later stages, early screening is necessary. Machine learning can help classify cervical cancer risk by analyzing patient datasets and identifying the important factors that predict the likelihood of emerging cervical cancer. This paper evaluates six different machine-learning approaches for analyzing risk factors associated with cervical cancer using a dataset of 838 instances with 36 features. Results show that the SVM classifier performs the best, with an accuracy of 99.60% which emphasize the possibility of utilizing machine learning to enhance the precision of cervical cancer risk assessment. This can result in the development of better screening and prevention techniques for cervical cancer, which can be more effective in identifying and managing this disease. © 2023 IEEE.

关键词： Logistic regression

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Brightness Attention Mechanism for Few-Shot Action Recognition 3

Brightness Attention Mechanism for Few-Shot Action Recogniti...

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3rd international conference on Electronic Information engineering and Computer science, EIECS 2023

作者： Tian, Zhiyuan Xue, Wanli Zhang, Ying Tianjin University of Technology School of Computer Science and Engineering Tianjin300384 China

ISBN: (纸本)9798350316674

Few-shot action recognition remains a challenging problem in computer vision, particularly in scenarios where the model needs to generalize well from limited examples. In this study, we present a novel concept known as the Brightness Attention Mechanism(BAM), seamlessly integrated into the ResNet50 architecture, to enhance the model's sensitivity to brighter regions in the image. Our approach begins with the use of an image segmentation model that isolates human figures, followed by blending these segmented images with original images to accentuate the subject. We then apply BAM to give more attention to these highlighted regions during the action recognition task. The data from our experiments reveal a marked increase in the accuracy of few-shot action recognition, surpassing both conventional techniques and the latest attention-based methods. Our methodology advances the state of the art for few-shot scenarios across Kinetics, HMDB51, and UCF101. © 2023 IEEE.

关键词： action recognition brightness attention computer vision image fusion

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Artificial Intelligence-Powered Underwater Communication and Sensing Networks

Artificial Intelligence-Powered Underwater Communication and...

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2023 international conference for Technological engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Gehlot, Anita Esanmurodova, N. Al-Farouni, Mohammed Agrawal, Shruti Rai, Anjani Kumar Preetha, M. Sravanthi, Jajimoggala Uttaranchal University Department of Electronics & Communication Engineering Uttaranchal Institute of Technology Dehradun248007 India National Research University Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Tashkent Uzbekistan The Islamic University Najaf Iraq IES College of Technology Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India GLA University Department of CEA Mathura India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India GRIET Telangana Hyderabad India

ISBN: (纸本)9798350336474

Ocean-related activities including ocean exploration, underwater surveillance, and environmental monitoring all depend on underwater communication. The peculiar characteristics of the underwater environment make it difficult to transfer information even with advances in underwater communication technology. By enabling the use of natural language for communication, natural language processing (NLP) techniques have the potential to increase the effectiveness and dependability of underwater communication. Huge volumes of data may be analysed using deep learning, which can also be used to spot patterns and make predictions. In order to improve the capabilities of underwater communication systems, this article suggests combining NLP and deep learning approaches. © 2023 IEEE.

关键词： Deep learning

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A Comparative Study of Machine Learning Approaches for Sentiment Classification of Bengali Book Reviews Using Word2Vec

A Comparative Study of Machine Learning Approaches for Senti...

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2024 IEEE international Women in engineering (WIE) conference on Electrical and Computer engineering, WIECON-ECE 2024

作者： Khalil, Mohammad Ibrahim Alam, Md Tanvir Islam, Akib Jayed Chowdhury, Nazifa Nuha Bhowmik, Subashis Roy Salehin, Sultanus Computer Science and Engineering Port City International University Chattogram Bangladesh Computational Colour and Spectral Imaging Norwegian University of Science and Technology Gjovik Norway University of Dhaka Department of Linguistics Dhaka Bangladesh Electrical and Computer Engineering The University of Alabama Tuscaloosa United States

ISBN: (纸本)9798331535476

This study explores sentiment analysis of Bengali book reviews, addressing a critical research gap in analyzing Bengali text for this domain. Customer reviews and ratings significantly influence online platforms, and automated sentiment analysis offers an efficient method to understand user emotions and opinions. To facilitate this, a dataset of 16,600 Bengali reviews was developed by compiling three datasets and augmenting them with data from online bookstores and Facebook pages. The Word2Vec technique was utilized for feature extraction, employing Continuous Bag of Words (CBOW) and Skip-gram models. Various machine learning classifiers were a pplied to classify sentiment polarity as positive or negative, including Logistic Regression (LR), k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), AdaBoost, XGBoost, Bernoulli Naive Bayes (BNB), Stochastic Gradient Descent (SGD), and a Hybrid model. The Skip-gram model paired with the Hybrid classifier achieved t he highest accuracy of 92%. Furthermore, classifiers were e valuated using metrics such as accuracy, recall, precision, Fl-score, and ROC curves. This research advances sentiment analysis for Bengali text, demonstrating effective techniques for predicting sentiment and contributing to broader natural language processing applications for underrepresented languages. © 2024 IEEE.

关键词： Decision trees

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3D Semantic Mapping based on RGB-D Camera and LiDAR Sensor in Beach Environment

3D Semantic Mapping based on RGB-D Camera and LiDAR Sensor i...

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robotics, engineering, science, and technology (RESTCON), international conference on

作者： Chi Jie Tan Shintaro Ogawa Takamasa Hayashi Titan Janthori Ayumu Tominaga Eiji Hayashi Department of Creative Informatics Kyushu Institute of Technology Fukuoka Japan Department of Creative Engineering Robotics and Mechatronics National Institute of Technology Kitakyushu College Kitakyushu Japan

The increasing amount of sea garbage proving to be a huge problem not only for marine life but also causing a tremendous number of troubles to humans living along the coastlines. Especially Japan, that is surrounded by the ocean which has a long coastline that spans thousands of kilometers, and this provides ample opportunities for marine debris to accumulate. This paper proposes a 3D mapping system utilizing the semantic information from the RGBD sensor extracted from a specifically trained state-of-art deep learning neural network to merge with the spatial information from the LiDAR sensor using Max Fusion Technique. This paper also aims to contribute to the implementation of deep learning and autonomous driving in beach cleaning-related robots and spark interest and raise awareness of the public in beach clean-up activities. It is also shown that the proposed system can achieve a mean accuracy of 98% approximately with the uniquely trained deep learning model.

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The Utilization of DenseNet121 for Analyzing and Classification of Leaf Image 14

The Utilization of DenseNet121 for Analyzing and Classificat...

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14th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Geluvaraj, B. Anil Kumar, K.N. Vishveshvaran, R. Department of Computer Science and Engineering New Horizon College of Engineering Bangalore India Department of Information Science and Engineering Rajeev Institute of Technology Hassan India

Now the issue of agriculture has become a significant concern in our modern times. The growth of crops and plants is hindered by numerous challenges, and one of the most pressing concerns is ensuring their health. Agriculture plays a pivotal role in human life and the economy, and poor management practices can lead to significant losses in agricultural output. To address this problem, a deep neural network approach for detecting plant leaf diseases has been developed as an alternative to the Densenet121 neural network. This can easily detect the disease of plant leaf. First select the plant village dataset and apply into pre-processing method. In this part it is very useful to identify the disease, then it will process into model selection and classification. In classification it will train the dataset and the disease can analyse and show the status of plant leaf that is healthy or unhealthy. The disease can be detected in the image of plant leaf. In this study, a publicly available dataset of both healthy and diseased plants is employed to classify crop species and identify the presence of various illnesses across different classes. To achieve this, Densenet121 is utilized. The predicted result based on accuracy. © Grenze Scientific Society, 2023.

关键词： Crops

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Reinforcement Learning Based Soccer Kicking for Humanoid Robots 2nd

Reinforcement Learning Based Soccer Kicking for Humanoid Ro...

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2nd international conference on the Frontiers of robotics and Software engineering, FRSE 2024

作者： Yan, Bo Zhu, Haier Li, Xiu Li, Xiang Shenzhen Tsinghua International Graduate School Shenzhen China Department of Automation Tsinghua University Beijing China

ISBN: (纸本)9789819630127

The robot soccer game is a classic problem in robot control, which can design and verify new algorithms and migrate to other typical robotic applications. Most commonly applied methods are still rule-based, and their performance may be limited as the number of players increases and the interaction between players becomes more and more intensive. Reinforcement-learning-based methods are gaining attention and have been applied to deal with high-dimensional problems with various constraints, like robot soccer. This paper proposes a RL-based method to train a humanoid robot to learn and kick a soccer ball into a gate from scratch. To enhance performance, this paper designs a novel form of a reward function and generalizes the case to obstacle presence scenes. Simulation studies are performed based on Isaac Gym, and the result shows substantial gains in goal accuracy, efficiency, and speed compared with the baseline. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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