检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Li, Wenhao Qiao, Dan Wang, Baoxiang Wang, Xiangfeng Jin, Bo Zha, Hongyuan School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen518172 China School of Computer Science and Technology East China Normal University Shanghai200062 China School of Software Engineering Tongji University Shanghai200062 China

The difficulty of appropriately assigning credit is particularly heightened in cooperative MARL with sparse reward, due to the concurrent time and structural scales involved. Automatic subgoal generation (ASG) has recently emerged as a viable MARL approach inspired by utilizing sub-goals in intrinsically motivated reinforcement learning. However, end-to-end learning of complex task planning from sparse rewards without prior knowledge, undoubtedly requires massive training samples. Moreover, the diversity-promoting nature of existing ASG methods can lead to the "over-representation" of sub-goals, generating numerous spurious sub-goals of limited relevance to the actual task reward and thus decreasing the sample efficiency of the algorithm. To address this problem and inspired by the disentangled representation learning, we propose a novel "disentangled" decision-making method, Semantically Aligned task decomposition in MARL (SAMA), that prompts pretrained language models with chain-of-thought that can suggest potential goals, provide suitable goal decomposition and subgoal allocation as well as self-reflection-based replanning. Additionally, SAMA incorporates language-grounded RL to train each agent's subgoal-conditioned policy. SAMA demonstrates considerable advantages in sample efficiency compared to state-of-the-art ASG methods, as evidenced by its performance on two challenging sparse-reward tasks, Overcooked and MiniRTS. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

Water Quality Index (WQI) Prediction Using Machine Learning Algorithms

Water Quality Index (WQI) Prediction Using Machine Learning ...

引用

International Conference on Computational science and Computational Intelligence (CSCI)

作者： Kunyanuth Kularbphettong Phanu Waraporn Nareenart Raksuntorn Rujijan Vivhivanives Chanyapat Sangsuwon Chongrag Boonseng Computer Science Program Suan Sunandha Rajabhat University Bangkok Thailand Robotics Engineering Program Suan Sunandha Rajabhat University Bangkok Thailand Faculty of Science and Technology Suan Sunandha Rajabhat Universit Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

Water resources used by human activities ranges typically from personal and household, agricultural, industrial, recreational to environmental pursuits. The effects of these water utilizations are actually of great concerns by many due to various threats created by human functions and the nature itself, for instance, climate change, pollution, scarcity, and even conflicts. To mitigate these threats, implementation of water quality management based on recognized standards and guidelines not only will they provide solid framework and benchmark used in relation to the assessment of the water quality but will also enable the identification of corresponding classification indicated by the water quality index (WQI) pertinent and relevant to the surface water dataset. This paper aims at applying selected predictive modeling techniques that are highly optimized for use in semi-automating the work of the water quality classification (WQC) and the water quality index (WQI) that subsequently can be used in assisting the planning, problem-solving and/or decision-making processes. The preliminary results obtained are quite satisfactory as follow: predicting WQI using neural network model (NN) outperforms both the Multiple Linear Regression (MLR) and the Support Vector Machine (SVM) based on a mean absolute error (MAE) lower than the two models and predicting WQC using SVM, and ANN models based on accuracy score with SVM returns a favorable accuracy score higher than two others.

关键词： Water resources Water quality Indexes Classification algorithms Support vector machines Linear regression Climate change Predictive models Environmental monitoring Quality assessment Surface treatment

来源：评论

学校读者我要写书评

暂无评论

DPM-OT: A New Diffusion Probabilistic Model Based on Optimal Transport

DPM-OT: A New Diffusion Probabilistic Model Based on Optimal...

引用

International Conference on computer Vision (ICCV)

作者： Zezeng Li Shenghao Li Zhanpeng Wang Na Lei Zhongxuan Luo David Xianfeng Gu School of Software Dalian University of Technology China Beijing Key Laboratory of Light-Field Imaging and Digital Geometry Capital Normal University China School of Mathematical Sciences University of the Chinese Academy of Sciences China Computer Science and Applied Mathematics State University of New York at Stony Brook USA

Sampling from diffusion probabilistic models (DPMs) can be viewed as a piecewise distribution transformation, which generally requires hundreds or thousands of steps of the inverse diffusion trajectory to get a high-quality image. Recent progress in designing fast samplers for DPMs achieves a trade-off between sampling speed and sample quality by knowledge distillation or adjusting the variance schedule or the denoising equation. However, it can’t be optimal in both aspects and often suffer from mode mixture in short steps. To tackle this problem, we innovatively regard inverse diffusion as an optimal transport (OT) problem between latents at different stages and propose the DPM-OT, a unified learning framework for fast DPMs with a direct expressway represented by OT map, which can generate high-quality samples within around 10 function evaluations. By calculating the semi-discrete optimal transport map between the data latents and the white noise, we obtain an expressway from the prior distribution to the data distribution, while significantly alleviating the problem of mode mixture. In addition, we give the error bound of the proposed method, which theoretically guarantees the stability of the algorithm. Extensive experiments validate the effectiveness and advantages of DPM-OT in terms of speed and quality (FID and mode mixture), thus representing an efficient solution for generative modeling. Source codes are available at https://***/cognaclee/DPM-OT.

关键词：

来源：评论

学校读者我要写书评

暂无评论

DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map Construction

arXiv

引用

arXiv 2024年

作者： Li, Siyu Lin, Jiacheng Shi, Hao Zhang, Jiaming Wang, Song Yao, You Li, Zhiyong Yang, Kailun The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The State Key Laboratory of Extreme Photonics and Instrumentation The National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou310027 China The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany The College of Computer Science Zhejiang University Hangzhou310027 China The USC Viterbi School of Engineering The University of Southern California Los AngelesCA90089 United States

Temporal information plays a pivotal role in Bird’s-Eye-View (BEV) driving scene understanding, which can alleviate the visual information sparsity. However, the indiscriminate temporal fusion method will cause the barrier of feature redundancy when constructing vectorized High-Definition (HD) maps. In this paper, we revisit the temporal fusion of vectorized HD maps, focusing on temporal instance consistency and temporal map consistency learning. To improve the representation of instances in single-frame maps, we introduce a novel method, DTCLMapper. This approach uses a dual-stream temporal consistency learning module that combines instance embedding with geometry maps. In the instance embedding component, our approach integrates temporal Instance Consistency Learning (ICL), ensuring consistency from vector points and instance features aggregated from points. A vectorized points pre-selection module is employed to enhance the regression efficiency of vector points from each instance. Then aggregated instance features obtained from the vectorized points preselection module are grounded in contrastive learning to realize temporal consistency, where positive and negative samples are selected based on position and semantic information. The geometry mapping component introduces Map Consistency Learning (MCL) designed with self-supervised learning. The MCL enhances the generalization capability of our consistent learning approach by concentrating on the global location and distribution constraints of the instances. Extensive experiments on well-recognized benchmarks indicate that the proposed DTCLMapper achieves state-of-the-art performance in vectorized mapping tasks, reaching 61.9% and 65.1% mAP scores on the nuScenes and Argoverse datasets, respectively. The source code is available at https://***/lynn-yu/DTCLMapper. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Cooperative Control Strategy of Dual Manipulators Based on Model Reference Adaptive Admittance Control

Cooperative Control Strategy of Dual Manipulators Based on M...

引用

Automation, Control and robotics Engineering (CACRE), International Conference on

作者： Hongmin Wang Meng Jiang Longhua Wu Junlin Huang Zengxi Pan Dagang Li Yingying Song Hui Huang School of Mechanical and Automation Engineering Institute of Carbon Peaking and Carbon Neutralization Wuyi University Jiang Men China School of Mechanical and Automation Engineering Wuyi University Jiang Men China Robotics and Welding Automation Laboratory University of Wollongong Australia School of Computer Science and Engineering Macau University of Science and Technology Macau China Faculty of Innovation and Entrepreneurship Wuyi University Jiang Men China

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

This study aims to solve the dual robotic arm collaboration problem by proposing a method based on an adaptive conductivity control algorithm for nondestructive grasping and handling of unknown manipulated objects by dual robotic arms. The method is capable of dynamically tracking the intended force of the unknown entity while maintaining the desired trajectory and compensating for the error in the state of the unknown object. In this paper, adopting a two-arm collaborative robotic arm motion planning method oriented to the manipulated object, the kinematics of a singular appendage is calculated by closed-loop constraints. The collaborative control strategy of the dual robotic arms for the operated object is established, which facilitating the pursuit of the intended force and the rectification of the positional path deviation induced by external interference. Through simulation and experimental verification, the method demonstrates satisfactory performance regarding the error in tracking position and the steady state deviation in force, offering a viable solution for attaining heightened precision in tracking both position and force.

关键词： Trajectory tracking Force Collaboration Kinematics Manipulators Trajectory Steady-state

来源：评论

学校读者我要写书评

暂无评论

FireXnet: an explainable AI-based tailored deep learning model for wildfire detection on resource-constrained devices

引用

Fire Ecology 2023年第1期19卷 1-19页

作者： Ahmad, Khubab Khan, Muhammad Shahbaz Ahmed, Fawad Driss, Maha Boulila, Wadii Alazeb, Abdulwahab Alsulami, Mohammad Alshehri, Mohammed S. Ghadi, Yazeed Yasin Ahmad, Jawad Faculty of Engineering and Technology Multimedia University Melaka Malaysia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK Department of Cyber Security Pakistan Navy Engineering College NUST Karachi Pakistan Robotics and Internet of Things Laboratory Prince Sultan University Riyadh Saudi Arabia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia Department of Computer Science College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Software Engineering Computer Science Al Ain University Abu Dhabi United Arab Emirates

Forests cover nearly one-third of the Earth’s land and are some of our most biodiverse ecosystems. Due to climate change, these essential habitats are endangered by increasing wildfires. Wildfires are not just a risk to the environment, but they also pose public health risks. Given these issues, there is an indispensable need for efficient and early detection methods. Conventional detection approaches fall short due to spatial limitations and manual feature engineering, which calls for the exploration and development of data-driven deep learning solutions. This paper, in this regard, proposes 'FireXnet', a tailored deep learning model designed for improved efficiency and accuracy in wildfire detection. FireXnet is tailored to have a lightweight architecture that exhibits high accuracy with significantly less training and testing time. It contains considerably reduced trainable and non-trainable parameters, which makes it suitable for resource-constrained devices. To make the FireXnet model visually explainable and trustable, a powerful explainable artificial intelligence (AI) tool, SHAP (SHapley Additive exPlanations) has been incorporated. It interprets FireXnet’s decisions by computing the contribution of each feature to the prediction. Furthermore, the performance of FireXnet is compared against five pre-trained models — VGG16, InceptionResNetV2, InceptionV3, DenseNet201, and MobileNetV2 — to benchmark its efficiency. For a fair comparison, transfer learning and fine-tuning have been applied to the aforementioned models to retrain the models on our dataset. The test accuracy of the proposed FireXnet model is 98.42%, which is greater than all other models used for comparison. Furthermore, results of reliability parameters confirm the model’s reliability, i.e., a confidence interval of [0.97, 1.00] validates the certainty of the proposed model’s estimates and a Cohen’s kappa coefficient of 0.98 proves that decisions of FireXnet are in considerable accordance with t

关键词：

来源：评论

学校读者我要写书评

暂无评论

HIERARCHICALLY ENCAPSULATED REPRESENTATION FOR PROTOCOL DESIGN IN SELF-DRIVING LABS

arXiv

引用

arXiv 2025年

作者： Shi, Yu-Zhe Liu, Mingchen Meng, Fanxu Xu, Qiao Bi, Zhangqian He, Kun Ruan, Lecheng Wang, Qining Department of Advanced Manufacturing and Robotics Peking University China School of Computer Science and Technology Huazhong University of Science and Technology China

Self-driving laboratories have begun to replace human experimenters in performing single experimental skills or predetermined experimental protocols. However, as the pace of idea iteration in scientific research has been intensified by Artificial Intelligence, the demand for rapid design of new protocols for new discoveries become evident. Efforts to automate protocol design have been initiated, but the capabilities of knowledge-based machine designers, such as Large Language Models, have not been fully elicited, probably for the absence of a systematic representation of experimental knowledge, as opposed to isolated, flatten pieces of information. To tackle this issue, we propose a multi-faceted, multi-scale representation, where instance actions, generalized operations, and product flow models are hierarchically encapsulated using Domain-Specific Languages. We further develop a data-driven algorithm based on non-parametric modeling that autonomously customizes these representations for specific domains. The proposed representation is equipped with various machine designers to manage protocol design tasks, including planning, modification, and adjustment. The results demonstrate that the proposed method could effectively complement Large Language Models in the protocol design process, serving as an auxiliary module in the realm of machine-assisted scientific exploration. © 2025, CC BY-NC-ND.

关键词： Domain Knowledge

来源：评论

学校读者我要写书评

暂无评论

MV-BMR: A real-time Motion and Vision Sensing Integration based Agile Badminton Robot

引用

Information Fusion 2025年 123卷

作者： Shi, Zhiwei Zhang, Xingyu Zhu, Chengxi Wang, Haochen Yan, Jun Yang, Fan Xuan, Dong School of Computer Science and Technology Shandong University 72 Binhai Highway Qingdao Shandong Province Shandong Qingdao266000 China College of Electronic and Information Engineering Tongji University 4800 Cao'an Highway Jiading District Shanghai Shanghai201804 China DeepCode Robotics No. 488 Hongxing Road Xiaoshan District Zhejiang Hangzhou310000 China

This paper presents the Motion and Vision Sensing Integration-based Agile Badminton Robot (MV-BMR), a real-time system that plays badminton with human players. Current badminton robots excel at handling low-speed strikes, such as high clears and net shots, but struggle with high-speed cases, particularly short shots. This challenge arises from two key factors: the shuttlecock's short flight time, which ranges from 500 to 1000 ms for flat and low flat shots, and the extensive range of the robot's hit zone. This lingering problem highlights the necessity of designing a dynamic and precise badminton robot. We propose an innovative two-stage approach that incorporates trajectory prediction and control modules to address this challenge. In the first stage, we design the Shuttlecock Early Prediction Network (SEPNet) to estimate the robot's hit zone with an Inertial Measurement Unit (IMU) mounted on the racket so that the robot can move immediately after a player hits the shuttlecock. In the second stage, we employ a data-driven method, which exploits detected trajectories of shuttlecocks to determine hit points and control the corresponding robot to accurately hit the shuttlecock with Nonlinear Model Predictive Control (NMPC). We have implemented such a real-time system and conducted extensive experiments. The average successful hit rate for short shots of 92.2% and the most extended rallies of 68 demonstrates that our design effectively overcomes the challenges. The video demonstration is available at: https://***/lQo1Ls5Rj3o. © 2025 Elsevier B.V.

关键词： Predictive control systems

来源：评论

学校读者我要写书评

暂无评论

Cybersecurity in Local Governments: A Review and Framework of Key Challenges

SSRN

引用

SSRN 2023年

作者： Hossain, Sk Tahsin Yigitcanlar, Tan Nguyen, Kien Xu, Yue City 4.0 Lab School of Architecture and Built Environment Queensland University of Technology 2 George Street BrisbaneQLD4000 Australia School of Electrical Engineering and Robotics Queensland University of Technology 2 George Street BrisbaneQLD4000 Australia School of Computer Science Queensland University of Technology 2 George Street BrisbaneQLD4000 Australia

In an era characterised by rapid technological advancements and a march towards smarter cities, local governments are increasingly adopting sophisticated technologies to enhance service delivery and governance. This transformation elevates their role as custodians of a broad spectrum of confidential and critical data, from personal identification to public safety records, and makes them a prime target for cyber-attacks. Despite the importance of robust cybersecurity measures, there is a concerning gap—i.e., local governments often exhibit a cybersecurity posture that is inadequately prepared to counter the rising cyber threats. This discrepancy underscores the urgency for a comprehensive understanding of the challenges restricting effective cybersecurity in local governmental settings. This paper aims to elucidate local governments’ distinct challenges to ensure adequate cybersecurity measures. To accomplish the aim, this study utilises a systematic literature review method named the PRISMA protocol. The study findings underscore that the stakes are excessive when it comes to cybersecurity for local governments, with the key challenges in financial resource constraints, technological vulnerabilities, human factors, and regulatory framework. The study is intended to serve as a resource for urban planners, policymakers, and researchers, guiding efforts to strengthen cybersecurity measures in local government settings. © 2023, The Authors. All rights reserved.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Fast Detection of Multi-Direction Remote Sensing Ship Object Based on Scale Space Pyramid

Fast Detection of Multi-Direction Remote Sensing Ship Object...

引用

International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Ziying Song Li Wang Guoxin Zhang Caiyan Jia Jiangfeng Bi Haiyue Wei Yongchao Xia Chao Zhang Lijun Zhao School of Computer and Information Technology Beijing Jiaotong University Beijing China School of Vehicle and Mobility Tsinghua University Beijing China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang China

Ships in remote sensing images are usually arranged in arbitrary direction, small in size, and densely arranged. As a result, existing object detection algorithms cannot detect ships quickly and accurately. In order to solve the above problems, a lightweight object detection network for fast detection of ships is proposed. The network is composed of backbone network, four-scale fusion network and rotation branch. First, a lightweight network unit S-LeanNet is designed and used to build a low-computing and accurate backbone network. Then, a four-scale feature fusion module is designed to generate a four-scale feature pyramid, which contains more features such as ship shape and texture, and at the same time is conducive to the detection of small ships. Finally, a novel rotation branch module is designed, using balance L1 loss function and R-NMS for post-processing, to realize the precise positioning and regression of the rotating bounding box in one step. Experimental results show that the detection precision of our method in the DOT A remote sensing data set is compared with the latest SCRDet detection method, the precision is increased by 1.1%, and the operating speed is increased by 8 times, which can meet the fast detection requirements of ships.

关键词： Sea surface Shape Object detection US Department of Transportation Feature extraction Sensors Marine vehicles

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：