检索结果-内蒙古大学图书馆

Machinability of graphene filler abaca/sisal hybrid composite during AWJM process

Materials and Manufacturing Processes 2025年

作者： Arunkumar, K. Sudhakar, R. Kanagaraj, R. Om Prakasam, S. Department of Mechanical Engineering MVJ College of Engineering Bengaluru India Department of Robotics and Automation Sri Ramakrishna Engineering College Coimbatore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Mechanical Engineering Sri Ramakrishna Engineering College Coimbatore India

Composites prepared with natural fiber reinforcement (NFR) have gained popularity in a variety of applications. The addition of fillers to NFR composites strengthens the matrix–fiber interface, posingmachining challenges. In this work, a hybrid composite has been prepared using abaca and sisal fibers with a distinct range of graphene as a nanofiller (1 Wt. % to 3 Wt. %). The mechanical characteristics of the produced hybrid composite have been examined. In addition, how graphene affected the surface quality throughout the abrasive water jet machining (AWJM) procedure. The use of graphene in the hybrid composite enhances both the mechanical characteristics and the surface quality. Water pressure was the most considerable parameter at 1 weight percentage of graphene. The amount of graphene increased, and the nozzle transfer speed had the most impact parameter, contributing 52.78% at 3 weight percentage (R3) of graphene addition. The optimal parameters of P3, v1, and d1 of R3, which yield the lowest Ra 2.75 μm, indicate that introducing graphene during the ASGHC’s AWJM process enhances the surface quality. © 2025 Taylor & Francis Group, LLC.

关键词： Flax

来源：评论

学校读者我要写书评

暂无评论

Don’t Shake the Wheel: Momentum-Aware Planning in End-to-End Autonomous Driving

arXiv

引用

arXiv 2025年

作者： Song, Ziying Jia, Caiyan Liu, Lin Pan, Hongyu Zhang, Yongchang Wang, Junming Zhang, Xingyu Xu, Shaoqing Yang, Lei Luo, Yadan School of Computer Science and Technology Beijing Jiaotong University China Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence China Horizon Robotics China University of Macau China THU The University of Queensland Australia HKU Hong Kong

End-to-end autonomous driving frameworks enable seamless integration of perception and planning but often rely on one-shot trajectory prediction, which may lead to unstable control and vulnerability to occlusions in single-frame perception. To address this, we propose the Momentum-Aware Driving (MomAD) framework, which introduces trajectory momentum and perception momentum to stabilize and refine trajectory predictions. MomAD comprises two core components: (1) Topological Trajectory Matching (TTM) employs Hausdorff Distance to select the optimal planning query that aligns with prior paths to ensure coherence;(2) Momentum Planning Interactor (MPI) cross-attends the selected planning query with historical queries to expand static and dynamic perception files. This enriched query, in turn, helps regenerate long-horizon trajectory and reduce collision risks. To mitigate noise arising from dynamic environments and detection errors, we introduce robust instance denoising during training, enabling the planning model to focus on critical signals and improve its robustness. We also propose a novel Trajectory Prediction Consistency (TPC) metric to quantitatively assess planning stability. Experiments on the nuScenes dataset demonstrate that MomAD achieves superior long-term consistency (≥ 3s) compared to SOTA methods. Moreover, evaluations on the curated Turning-nuScenes shows that MomAD reduces the collision rate by 26% and improves TPC by 0.97m (33.45%) over a 6s prediction horizon, while closed-loop on Bench2Drive demonstrates an up to 16.3% improvement in success rate. The source code is available at https://***/adept-thu/MomAD. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization

Federated Learning While Providing Model as a Service: Joint...

引用

IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Pengchao Han Shiqiang Wang Yang Jiao Jianwei Huang School of Information Engineering Guangdong University of Technology Guangzhou China IBM T. J. Watson Research Center NY USA Department of Computer Science and Technology Tongji University Shanghai China School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Shenzhen China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

While providing machine learning model as a service to process users’ inference requests, online applications can periodically upgrade the model utilizing newly collected data. Federated learning (FL) is beneficial for enabling the training of models across distributed clients while keeping the data locally. However, existing work has overlooked the coexistence of model training and inference under clients’ limited resources. This paper focuses on the joint optimization of model training and inference to maximize inference performance at clients. Such an optimization faces several challenges. The first challenge is to characterize the clients’ inference performance when clients may partially participate in FL. To resolve this challenge, we introduce a new notion of age of model (AoM) to quantify client-side model freshness, based on which we use FL’s global model convergence error as an approximate measure of inference performance. The second challenge is the tight coupling among clients’ decisions, including participation probability in FL, model download probability, and service rates. Toward the challenges, we propose an online problem approximation to reduce the problem complexity and optimize the resources to balance the needs of model training and inference. Experimental results demonstrate that the proposed algorithm improves the average inference accuracy by up to 12%.

关键词： Training Adaptation models Accuracy Federated learning Computational modeling Approximation algorithms Data models

来源：评论

学校读者我要写书评

暂无评论

QUADFormer: Learning-based Detection of Cyber Attacks in Quadrotor UAVs

arXiv

引用

arXiv 2024年

作者： Wang, Pengyu Yang, Zhaohua Yang, Nachuan Wang, Zikai Li, Jialu Zhang, Fan Wang, Chaoqun Wang, Jiankun Meng, Max Q.-H. Shi, Ling Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The School of Control Science and Engineering Shandong University Shandong China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Department of Electrical and Computer Engineering The University of Alberta Canada

Safety-critical intelligent cyber-physical systems, such as quadrotor unmanned aerial vehicles (UAVs), are vulnerable to different types of cyber attacks, and the absence of timely and accurate attack detection can lead to severe consequences. When UAVs are engaged in large outdoor maneuvering flights, their system constitutes highly nonlinear dynamics that include non-Gaussian noises. Therefore, the commonly employed traditional statistics-based and emerging learning-based attack detection methods do not yield satisfactory results. In response to the above challenges, we propose QUADFormer, a novel Quadrotor UAV Attack Detection framework with transFormer-based architecture. This framework includes a residue generator designed to generate a residue sequence sensitive to anomalies. Subsequently, this sequence is fed into a transformer structure with disparity in correlation to specifically learn its statistical characteristics for the purpose of classification and attack detection. Finally, we design an alert module to ensure the safe execution of tasks by UAVs under attack conditions. We conduct extensive simulations and real-world experiments, and the results show that our method has achieved superior detection performance compared with many state-of-the-art methods. (Video1) Copyright © 2024, The Authors. All rights reserved.

关键词： Antennas

来源：评论

学校读者我要写书评

暂无评论

Diagnosis of Faults in Edge-PLC using Optimized Deep Learning models in Industrial IoT

Diagnosis of Faults in Edge-PLC using Optimized Deep Learnin...

引用

International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： A. Kishore Kumar T Rajesh Kumar R. Balamanigandan A. Sasi Kumar Mahaveerakannan R Department of Robotics and Automation Sri Ramakrishna Engineering College Coimbatore Tamilnadu Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu Department Of Cloud Technology & Data Science Institute Of Engineering & Technology Srinivas University Surathkal

Industrial Internet of Things (IIoT) may allow for the state of equipment to be monitored and defects to be detected before they become serious, helping smart factories keep up with the rising demands for safety and efficiency brought on by worldwide competition. In the case of Cloud Computing, data-driven approaches implemented in the cloud are used to first train the model using a vast quantity of raw data, and then end-machines send their real-time readings for processing at the cloud data centre. It is possible, however, that this will impose a significant delay and a high computational cost. In this study, we take into account a hierarchical structure in which edge-PLCs are used to collect sensed data locally and lessen the burden on long-distance connections. Due to the fact that a single responsibility may be associated with multiple swaying topographies, we aim to first reduce the sum of features required to identify a fault, and then identify the smallest possible set of edge-PLCs that still accounts for all of the most important ones, all while keeping deployment costs to a minimum. To address this, we present an Enhanced Harris Hawkins Optimization (EHHO) approach to tackling the features selection issue, followed by the assortment of edge-PLCs through the set coverage problematic. The model named Residual Attention Neural Network (RANN) is proposed the in this study to forecast the fault detection procedure. Residual Neural Network (ResNet) is used by RANN to help diagnose problems, alongside the knowledge of experts. We evaluate the performance of our technique in comparison to existing AI-based approaches, such as the lregression model and its expansions, by simulating on a real data trail. The findings confirm competence and presentation of the suggested approach, which meets or even surpasses the correctness of approaches employing the entire collection of data.

关键词： Fault diagnosis Deep learning Solid modeling Cloud computing Image edge detection Computational modeling Fault detection

来源：评论

学校读者我要写书评

暂无评论

Toward Closed-Loop Additive Manufacturing: Paradigm Shift in Fabrication, Inspection, and Repair

Toward Closed-Loop Additive Manufacturing: Paradigm Shift in...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Manpreet Singh Fujun Ruan Albert Xu Yuchen Wu Archit Rungta Luyuan Wang Kevin Song Howie Choset Lu Li Department of Mechanical Engineering Carnegie Mellon University Pittsburgh USA CMU The Robotics Institute School of Computer Science USA Department of Electrical Engineering and Computer Science University of Michigan USA Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani India Department of Electrical and Computer Engineering CMU USA

Increased usage of additive manufacturing (AM) in various industries has solidified its role as an advanced manufacturing technique. However, there is an inherent lack of reliability in AM processes, particularly common in extrusion or deposition-based methods due to the stochastic nature of ma-terial deposition. This necessitates an intelligent manufacturing solution to address the drawbacks of AM. Thus, we propose a novel layer-wise approach toward closed-loop AM, which is capable of in-situ monitoring and repairing geometric defects. In this paper, we present a system that uses a robotic AM experimental platform that mimics a conventional open-loop fabrication setup, which we augment into a closed-loop system using two add-ons: in-situ inspection subsystem and online process correction subsystem. The in-situ inspection subsystem collects 3D point cloud scans and compares them against a reference CAD model, categorizing geometric deviations as positive or negative defects. Then the subsequent online process correction subsystem uses a re-plan and/or repair strategy to address the positive and/or negative defects, respectively. To evaluate this idea, we conducted three experiments on parts with manually induced defects to investigate the system's ability to repair those parts, thereby reducing defects, improving part accuracy, and enhancing mechanical properties. Comparing the defective and repaired parts, we observe a reduction in defect percent by volume from 10.7% to 1.3%, an improvement in geometric tolerance from 3.86% error to 0.08% error, and an increase in the part's breaking load from 4.77 kN to 6.31 kN. These experiments prove that our layer-wise closed-loop additive manufacturing approach improves the quality, tolerance, and reliability of plastic 3D printed parts, with the potential to extend to other extrusion/deposition-based AM processes, or even subtractive manufacturing and hybrid manufacturing methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Rediscovering BCE Loss for Uniform Classification

arXiv

引用

arXiv 2024年

作者： Li, Qiufu Jia, Xi Zhou, Jiancan Shen, Linlin Duan, Jinming National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Computer Vision Institute Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China School of Computer Science University of Birmingham United Kingdom Aqara Lumi United Technology Co. Ltd China

This paper introduces the concept of uniform classification, which employs a unified threshold to classify all samples rather than adaptive threshold classifying each individual sample. We also propose the uniform classification accuracy as a metric to measure the model’s performance in uniform classification. Furthermore, begin with a naive loss, we mathematically derive a loss function suitable for the uniform classification, which is the BCE function integrated with a unified bias. We demonstrate the unified threshold could be learned via the bias. The extensive experiments on six classification datasets and three feature extraction models show that, compared to the SoftMax loss, the models trained with the BCE loss not only exhibit higher uniform classification accuracy but also higher sample-wise classification accuracy. In addition, the learned bias from BCE loss is very close to the unified threshold used in the uniform classification. The features extracted by the models trained with BCE loss not only possess uniformity but also demonstrate better intra-class compactness and inter-class distinctiveness, yielding superior performance on open-set tasks such as face recognition. © 2024, CC BY.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Improvement of Depth Mapping and Occlusion Removal Method Using Integral Imaging

Improvement of Depth Mapping and Occlusion Removal Method Us...

引用

International Conference on Control, Automation and Systems ( ICCAS)

作者： Haruka Sakanoue Hyun-Woo Kim Myungjin Cho Min-Chul Lee Department of Computer Science and Networks Kyushu Institute of Technology Iizuka Fukuoka Japan School of ICT Robotics and Mechanical Engineering IITC Hankyong National University Anseong Kyonggi-do South Korea

In this paper, we propose the depth mapping and occlusion removal method using integral imaging (InIm). InIm is a method to obtain 3D information by capturing and reconstructing an object from multiple viewpoints. InIm can remove occlusion using parallax obtained by photographing an object from multiple viewpoints. However, occlusion removal uses a unit matrix to calculate the number of overlaps, which results in an overall darkening of the image. To solve this problem, this paper aims to improve occlusion removal by using the matrix of the mask image to calculate the number of overlaps. InIm can provide depth mapping that shows the distance from an object as pixel values. However, when there is an occlusion in front of the object, it is difficult to provide depth mapping object due to the influence of the occlusion. To solve this problem, we propose a method of depth mapping and occlusion removal method using InIm. Our method is expected to make great progress in improving safety in the field of autonomous driving.

关键词：

来源：评论

学校读者我要写书评

暂无评论

POMDP-Guided Active Force-Based Search for Robotic Insertion

POMDP-Guided Active Force-Based Search for Robotic Insertion

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Chen Wang Haoxiang Luo Kun Zhang Hua Chen Jia Pan Wei Zhang School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Computer Science The University of Hong Kong Hong Kong Cheng Kar-Shun Robotics Institute Hong Kong University of Science and Technology Clear Water Bay Hong Kong

In robotic insertion tasks where the uncertainty exceeds the allowable tolerance, a good search strategy is essential for successful insertion and significantly influences efficiency. The commonly used blind search method is time-consuming and does not exploit the rich contact information. In this paper, we propose a novel search strategy that actively utilizes the information contained in the contact configuration and shows high efficiency. In particular, we formulate this problem as a Partially Observable Markov Decision Process (POMDP) with carefully designed primitives based on an in-depth analysis of the contact configuration's static stability. From the formulated POMDP, we can derive a novel search strategy. Thanks to its simplicity, this search strategy can be incorporated into a Finite-State-Machine (FSM) controller. The behaviors of the FSM controller are realized through a low-level Cartesian Impedance Controller. Our method is based purely on the robot's proprioceptive sensing and does not need visual or tactile sensors. To evaluate the effectiveness of our proposed strategy and control framework, we conduct extensive comparison experiments in simulation, where we compare our method with the baseline approach. The results demonstrate that our proposed method achieves a higher success rate with a shorter search time and search trajectory length compared to the baseline method. Additionally, we show that our method is robust to various initial displacement errors.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Noise Removal in Scattering Media Enviorment Using Peploaraphy and DCLGAN

Noise Removal in Scattering Media Enviorment Using Peploarap...

引用

International Conference on Control, Automation and Systems ( ICCAS)

作者： Riki Numata Hyun-Woo Kim Seiya Ono Myungjin Cho Min-Chul Lee Department of Computer Science and Networks Kyushu Institute of Technology Iizuka Fukuoka Japan School of ICT Robotics and Mechanical Engineering IITC Hankyong National University Anseong Kyonggi-do Republic of Korea

Noise removal in scattering media environments such as fog and smoke are an important task in reality. For example, it can be applied in many fields such as searching under turbid water and rescuing in smoke. Therefore, the purpose of this research is to remove the noise under the scattering medium environment and to visualize the object. As a conventional method for removing noise, there is a technique called peplography. Peplograhy enables the denoising of dense fog and reconstructing the image in scattering media environments. However, peplography has a problem that complex noise exists after removing particularly deep noise, and as a result, it becomes difficult to visualize the object. This research aims to solve this problem. We propose a new denoising technology that combines peplography with DCLGAN. DCLGAN is an image transformation algorithm using deep learning and an improved model of CycleGAN which is also used in the field of noise reduction. In this study, we evaluate the conventional method and the proposed method for scattering media images. As the evaluation method, visual evaluation and numerical evaluation using the PSNR index are performed to demonstrate the effectiveness of the proposed method. We believe that the proposed method can be applied in various fields such as object visualization under turbid water and automatic driving technology with fog removed.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：