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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

共 24667 条记录，以下是131-140 订阅

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Mixture of Experts Fusion for Fake Audio Detection Using Frozen wav2vec 2.0

Mixture of Experts Fusion for Fake Audio Detection Using Fro...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Zhiyong Fu, Ruibo Wen, Zhengqi Tao, Jianhua Wang, Xiaopeng Xie, Yuankun Qi, Xin Shi, Shuchen Lu, Yi Liu, Yukun Li, Chenxing Liu, Xuefei Li, Guanjun University of Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Science Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China AI Lab Tencent Beijing China Communication University of China Beijing China Shanghai Polytechnic University Shanghai China

ISBN: (纸本)9798350368741

Speech synthesis technology has posed a serious threat to speaker verification systems. Currently, the most effective fake audio detection methods utilize pretrained models, and integrating features from various layers of pretrained model further enhances detection performance. However, most of the previously proposed fusion methods require fine-tuning the pretrained models, resulting in excessively long training times and hindering model iteration when facing new speech synthesis technology. To address this issue, this paper proposes a feature fusion method based on the Mixture of Experts, which extracts and integrates features relevant to fake audio detection from layer features, guided by a gating network based on the last layer feature, while freezing the pretrained model. Experiments conducted on the ASVspoof2019 and ASVspoof2021 datasets demonstrate that the proposed method achieves competitive performance compared to those requiring fine-tuning. © 2025 IEEE.

关键词： Audio DeepFake Detection Fake Audio Detection Mixture of Experts Pretrained Model Wav2Vec 2.0

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Late Breaking Results: Dynamically Scalable Pruning for Transformer-Based Large Language models

Late Breaking Results: Dynamically Scalable Pruning for Tran...

引用

Design, automation and Test in Europe conference and Exhibition

作者： Junyoung Lee Shinhyoung Jang Seohyun Kim Jongho Park Il Hong Suh Hoon Sung Chwa Yeseong Kim DGIST Coga Robotics

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

We propose Matryoshka, a novel framework for transformer model pruning, enabling dynamic runtime controls while maintaining competitive accuracy to modern large language models (LLMs). Matryoshka incrementally constructs submodels with varying complexities, allowing runtime adaptation without maintaining separate models. Our evaluations on LLaMA-7B demonstrate that Matryoshka achieves up to 34% speedup and outperforms the quality of state-of-the-art pruning methods, providing a flexible solution for deploying LLMs.

关键词： Adaptation models Runtime Accuracy Large language models Heuristic algorithms Computational modeling Europe Transformers Real-time systems Inference algorithms

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Machine Learning Techniques for GDP Per Capita Prediction in Developing Countries: A Case Study of Sub-Saharan Africa

Machine Learning Techniques for GDP Per Capita Prediction in...

引用

international conference on Artificial Intelligence in Renewable Energetic Systems, IC-AIRES 2024

作者： Zermane, Hanane Laboratory of Automation and Manufacturing Industrial Engineering Department Faculty of Technology Batna 2 University Batna Algeria

ISBN: (纸本)9783031803000

Solid biofuels are a crucial energy source for Sub-Saharan Africa, with a significant portion of the population relying on them for cooking and heating. The prediction of Gross Domestic Product (GDP) is a critical task for economic planning and policy making. Traditional statistical methods have been widely used for this purpose;however, the advent of machine learning techniques offers new opportunities for improving predictions. Several machine learning models are used to determine their effectiveness in predicting GDP per Capita. The models’ performance was assessed based on many metrics. Support Vector Regression achieved the lowest MAE of 0.81 and the highest R2 of 0.98. Ridge and Lasso Regression also performed well with an MAE of 0.89 and 0.88, respectively, and a high R2 of 0.98. In contrast, Decision Tree and Random Forest exhibited higher MAE values and lower R2, while XGBoost and TabNet provided intermediate performance metrics. Accurate GDP forecasts enable governments and organizations to make informed decisions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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Hybrid Deep Learning for Modeling Electro-Hydrostatic Actuators: A CNN-GRU Approach

Hybrid Deep Learning for Modeling Electro-Hydrostatic Actuat...

引用

robotics, Control and automation (ICRCA), international conference on

作者： Linhai Zhang Chen Jia Chengjie Han Wenbo Shen Juan Wang Institute of Aircraft System AVIC Jincheng Nanjing Engineering Nanjing China Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

Electro-hydrostatic actuators are difficult to model accurately due to their nonlinear and time-varying behavior. To address this issue, this paper proposes a hybrid deep learning-based modeling approach. First, the dynamic characteristics of the actuator are systematically analyzed, and a simulation model is developed. Experiments are conducted under various operating conditions to construct a dataset comprising multidimensional time-series data, including motor voltage, current, and rotational speed. Subsequently, a hybrid deep learning network integrating a convolutional neural network, gated recurrent unit, and attention mechanism is designed to enable high-precision predictions of both displacement and rotational speed. The proposed method is validated through comparative experiments under multiple operating conditions. Experimental results demonstrate that the proposed approach effectively models the characteristics of the actuator with high accuracy.

关键词： Deep learning Adaptation models Actuators Accuracy Attention mechanisms Computational modeling Velocity control Training data Nonlinear dynamical systems Convolutional neural networks

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Finite Element Simulation Study of Ultrasonic-Assisted Vibration Cutting of Carbon Fiber Reinforced Polyetherketoneketone (CF/PEKK)

Finite Element Simulation Study of Ultrasonic-Assisted Vibra...

引用

robotics, Control and automation (ICRCA), international conference on

作者： Ran Tao Liqiang Zhang Zeyu Zhou Yichen Jiang School of Mechanical and Automotive Engineering Shanghai University of Engineering Science Shanghai China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

In the past few decades, the demand for carbon fiber reinforced polymer (CFRP) composites has significantly increased in aerospace, biomedical, and marine engineering industries due to their outstanding properties. These properties include high strength, high stiffness, low density, and excellent corrosion resistance. However, the high hardness and rigidity of polyetherketoneketone (CF/PEKK) materials result in high cutting forces during machining, which can lead to vibration and tool wear, posing significant challenges for numerical modeling, simulation, and process testing of the machining process. This study develops an ultrasonic vibration cutting force simulation model for carbon fiber reinforced CF/PEKK materials and validates the model’s effectiveness in predicting cutting forces and temperatures, with prediction errors controlled within 15%. The results provide a reliable theoretical basis for simulating the elliptical ultrasonic vibration cutting process of CF/PEKK materials and lay the foundation for future research in machining surface quality optimization and process improvement.

关键词： Vibrations Force Machining Predictive models Reliability theory Acoustics Temperature control Numerical models Carbon Surface treatment

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Field Oriented Control of BLDC Motors for Unmanned Underwater Vehicle (UUV)

Field Oriented Control of BLDC Motors for Unmanned Underwate...

引用

international conference on Autonomous Robots and Agents, ICARA

作者： Basavaraj S Talikoti Devindra M Chitguppa Rahul Kumar Mohan Karnik Automation Solutions PDIC Bharat Electronics Limited Bengaluru India

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

This paper delves into the domain of Brushless Direct Current (BLDC) Motors that have become significantly popular in modern day applications like automotive, aerospace, robotics (unmanned vehicles) etc. because of the edge they have over their brushed counterpart. Unlike brushed DC motors, which use brush and commutator segments for commutation, BLDC motors use electronic commutation which eliminates wear and tear of mechanical parts, frictional loss etc. This paper discusses mathematical modelling of a BLDC motor, presents the insights about design and implementation of Field Oriented Control (FOC), initial calculation of Proportional Integral Differential (PID) gains and final implementation of FOC algorithm for BLDC Motors of UUV. This technique is far superior than other traditional methods, like Trapezoidal control and sinusoidal control, and implements independent control of torque and flux with the help of various transforms. It is a very useful and precise technique which results in a superior dynamic response, less torque ripple and high efficiency. The key objective is to dispense a holistic approach for designing PID controller for a BLDC motor. The performance of the controller was tested for no load and load condition. The final results demonstrated the anticipated superior dynamic response.

关键词： Dynamic response Motor drives Accuracy Heuristic algorithms Commutation Motors Real-time systems Mathematical models Tuning Robots

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A Distributed Task Allocation Method for Reducing the Number of UAV Communications

A Distributed Task Allocation Method for Reducing the Number...

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robotics, Control and automation (ICRCA), international conference on

作者： Shaokun Yan Tao Wu Fuming Wang Shuo Zhang Jiangsu Automation Research Institute Lianyungang China Jiangsu Nuclear Power Co. Ltd Lianyungang China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

The flexibility of UAVs and line-of-sight communication capabilities have made UAVs an effective solution to provide emergency services in many real-world applications. This paper solves the problem of invalid communication and excessive repeated communication between UAVs in distributed task allocation methods. Considering the time constraints of tasks and the range constraints of UAVs, the UAV clustering method is introduced into the Performance impact (PI) algorithm in this paper. The UAV clustering method can enable some UAVs to participate in consistency processing, solve task allocation conflicts between UAVs, and avoid repeated communication. Combined with the search and rescue application of UAVs, this paper studies the influence of the number of UAVs on the number of communications through numerical simulation and compares the simulation results with the PI algorithm. Simulation results show that the proposed algorithm can effectively reduce the number of communications between UAVs.

关键词： Simulation Clustering methods Clustering algorithms Line-of-sight propagation Autonomous aerial vehicles Numerical simulation Emergency services Resource management Time factors Robots

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Integrating Regression models and Deep Learning for Lung Cancer Risk Prediction 3

Integrating Regression Models and Deep Learning for Lung Can...

引用

3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Sharma, Divya Sunil, M.P. Yadav, Dhananjay Kumar Raghunathan, S. Singh, Preetjot Nayak, Soumen Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddil74103 India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Bengaluru India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331542375

Early diagnosis is vital for successful treatment of lung cancer, as the disease remains one of the most common causes of death by cancer worldwide. Historically, the prediction of lung cancer has been based on regression models and disemboweled by the fact that it cannot optimally handle complex data. In contrast, deep learning techniques have exhibited great potential in developing precise predictive models of disease based on more complicated forms of data;nevertheless, these methods have been less rigorously applied specifically for predicting lung cancer risks. This project aims to combine conventional regression models and deep learning techniques to achieve better performance with more generalizable accuracy for lung cancer risk prediction. A hybrid framework will be developed, exploiting the strengths of regression models in being interpretable and deep learning for its superior prediction. Our proposed methodology will also tackle the issue of high-dimensional imaging data needed for lung cancer risk prediction. These advances in notably conjunctional regression models and deep learning may contribute to better prediction models for lung cancer risk by enhancing the accuracy and translation ability of the developed scores. It will help improve the identification of people at high risk for lung cancer and, on that note, also target the population likely to benefit, thereby ensuring early controls with better outcomes. © 2025 IEEE.

关键词： Deep learning

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Visual Question Answering: In-Depth Analysis of methods, Datasets, and Emerging Trends 3

Visual Question Answering: In-Depth Analysis of Methods, Dat...

引用

3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Singh, Pooja Mehta, Munish Bathla, Gourav Batumalay, M. Gla University Department of Cae Mathura India School of Mech. Engg Lovely Professional University Jalandhar India Inti University Faculty of It Nilai Malaysia

ISBN: (纸本)9798331542375

Visual Question Answering (VQA) is a multi-modal technique that merges computer vision and natural language processing to enable systems to answer questions about images. Recently, several improvements have been made to VQA, driven by its wide applications. In this paper, we analyze and compare key studies, focusing on traditional and modern approaches to VQA. Traditional techniques mostly depend on Convolution Neural Networks (CNN), which extract visual features, while Recurrent Neural Networks (RNN) are used for text-based features. Further, in advanced models transformers, self-attention, and pre-trained CNN models are applied for better accuracy. In this paper, traditional and modern techniques are examined in detail for a better understanding of future researchers. Further, popular VQA datasets like VizWiz, COCO-QA, etc. are discussed for their strengths and limitations. In spite of improvements, VQA system faces issues like handling complex reasoning and achieving generalization among various domains. These open research problems and issues are presented in this paper. © 2025 IEEE.

关键词： Question answering

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D2S: Towards Efficient Sparse 3D Object Detection via Dense to Sparse Knowledge Distillation

D2S: Towards Efficient Sparse 3D Object Detection via Dense ...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Yuqi Liu, Longjun Gao, Yingke Zhang, Haonan Li, Haoteng Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China Beijing Institute of Control Engineering China Academy of Space Technology Beijing China

ISBN: (纸本)9798350368741

LiDAR-based 3D object detection is widely used in high-level autonomous driving schemes. However, the cumbersome modules in most 3D detectors lead to substantial computational overhead. Despite knowledge distillation (KD) is an effective approach for compressing models, previous methods cannot be extended to the dense-to-sparse paradigm. To this end, we propose a simple yet effective Dense to Sparse Knowledge Distillation (D2S) framework for accelerating 3D detectors. Firstly, to compensate for the difference in predicted location between dense and sparse detectors, we introduce a lightweight feature diffusion (FeaD) module for spreading important features. Secondly, to achieve high performance, we propose a dual-stream distillation scheme to transfer knowledge. In this scheme, we align both of the feature and category prediction between distillation pairs at important positions. Extensive experiments on KITTI and Waymo Open Dataset demonstrate the effectiveness of our method. For example, on KITTI dataset, the sparse detector we obtained surpasses VoxelNeXt with around 2.0× fewer parameters and 1.6× fewer FLOPs. © 2025 IEEE.

关键词： Feature Diffusion Knowledge Distillation Model Compression Sparse 3D Object Detection

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