检索结果-内蒙古大学图书馆

2022 International Conference on Innovations and Development of Information Technologies and robotics, IDITR 2022

作者： Haider, Muhammad Husnain Ali, Hub Khan, Abdullah Aman Zheng, Hao Bhutta, M. Usman Maqbool Usman, Shaban Zhi, Pengpeng Wang, Zhonglai University of Electronic Science and Technology of China Chengdu China Yangtze Delta Region Institute Huzhou China Casia State Key Laboratory for Management & Control of Complex Systems Beijing China Sichuan Artificial Intelligence Research Institute Yibin China The Chinese University of Hong Kong Robotics Lab Mae Hong Kong

ISBN: (纸本)9781665409322

Navigation of autonomous robots in unknown and cluttered environments lies among the marked trends in robotics. Unlike animals and humans, the collision-free movement of a robot is challenging and requires processing complex information. An autonomous robot needs to cope with a large amount of uncertainty while navigating. The previous methods have limitations, such as lacking obstacle avoidance behaviour, having a large number of governing rules, designing a separate controller for each navigation and obstacle avoidance, not considering the robot's dynamics, computationally expensive training, and poor performance in a cluttered environment. This paper proposes a method that comprises a single adaptive neuro fuzzy inference system (ANFIS) based controller with 16 rules compared to hundred of rules used by previous methods to address such problems. Our method takes heading angle along with distance sensors data as input. AU the inputs are fuzzified into linguistic variables such as near-far and left-right. Additionally, a fuzzy inference system (FIS) is designed and trained using the generated dataset for optimum performance of ANFIS. The proposed method efficiently provides collision-free navigation of the mobile robot in densely cluttered environments. Comprehensive experiments are performed to prove the robustness and potency of the proposed ANFIS controller. Moreover, the performance of the proposed method is compared with various previous methods. The results of these comparisons indicate our proposed method's superiority in finding a near-optimal path. © 2022 IEEE.

关键词： Navigation

来源：评论

学校读者我要写书评

暂无评论

MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficient Crowdsourcing for Video Analytics 40

MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficien...

引用

40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Chen, Yu Zhang, Sheng Zhou, Ziying Wang, Xiaokun Liang, Yu Chen, Ning Yan, Yuting Xiao, Mingjun Wu, Jie Qian, Zhuzhong Xu, Harry Nanjing University State Key Lab. for Novel Software Technology China School of Systems Science Beijing Normal University China School of Computer and Electronic Information School of Artificial Intelligence Nanjing Normal University China School of Computer Science and Technology University of Science and Technology of China China Temple University Center for Networked Computing United States Samueli School of Engineering University of California Computer Science Department Los Angeles United States

ISBN: (纸本)9798350317152

In recent years, many crowdsourcing platforms have emerged, using the resources of recruited workers to perform diverse outsourcing tasks, where the video analytics attracts much attention due to its practical implications. For maximum profits, platforms carefully choose the workers and determine the video analytics configurations to ensure accuracy;meanwhile, workers possess the flexibility to tailor the configurations for their indivi-dual gains, which makes it hard for platforms to optimize their profits considering the platform-worker conflicts. In this paper, we design an incentive mechanism for Multi-leader game-based video Analytics upon CROwdsourcing, named MACRO, to over-come the above situation. Under that mechanism, we first formu-late the utility optimization problems for platforms and workers, respectively. We then propose a dual ascent-based method to op-timally determine the video analytics configurations for a multi-platform game, ensuring Pareto efficiency. Moreover, in the context of a multi-leader game involving platform-worker conflicts, we design an incentive function with its incentive factor update strategy and propose an ADMM-based approach for maximizing incentives that motivate workers to contribute to the platforms' profits. Rigorous proofs demonstrate the linear convergence of the MACRO to the multi-leader Stackelberg equilibrium. Trace-driven experiments show that MACRO improves the Pareto efficiency by 26.3%, outperforming other approaches. © 2024 IEEE.

关键词： Profitability

来源：评论

学校读者我要写书评

暂无评论

Robotic Autonomous Grasping Technique: A Survey 5

Robotic Autonomous Grasping Technique: A Survey

引用

5th Asian Conference on Artificial Intelligence Technology, ACAIT 2021

作者： Wang, Lili Zhang, Zhen Su, Jianhua Gu, Qipeng School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Lab. of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426305

This paper provides a comprehensive survey of robotic autonomous grasping techniques. We summarize three key tasks: grasp detection, affordance detection, and model migration. Grasp detection determines the graspable area and grasping posture of the manipulator, so that the robot can successfully perform the grasps. The grasp detection methods based on deep learning are divided into 3DoF grasp and 6DoF grasp. The object affordances based grasping methods can further improve the robot's understanding of objects and environment, thereby improving the robot's intelligence and autonomy. Methods for object affordances detection are classified as learning-based, knowledge-based, and simulation-based. Model migration means that when the grasping model is migrated to other scenes where lightness and background changes, only little or no lab.l data is required, so that the grasping model can be used in the target scene quickly and efficiently. This paper focuses on domain adaptation (DA) methods in model migration. © 2021 IEEE.

关键词： Surveys

来源：评论

学校读者我要写书评

暂无评论

TransForce: Transferable Force Prediction for Vision-based Tactile Sensors with Sequential Image Translation

arXiv

引用

arXiv 2024年

作者： Chen, Zhuo Ou, Ni Zhang, Xuyang Luo, Shan The Robot Perception Lab Centre for Robotics Research Department of Engineering King's College London LondonWC2R 2LS United Kingdom The State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

Vision-based tactile sensors (VBTSs) provide high-resolution tactile images crucial for robot in-hand manipulation. However, force sensing in VBTSs is underutilized due to the costly and time-intensive process of acquiring paired tactile images and force lab.ls. In this study, we introduce a transferable force prediction model, TransForce, designed to leverage collected image-force paired data for new sensors under varying illumination colors and marker patterns while improving the accuracy of predicted forces, especially in the shear direction. Our model effectively achieves translation of tactile images from the source domain to the target domain, ensuring that the generated tactile images reflect the illumination colors and marker patterns of the new sensors while accurately aligning the elastomer deformation observed in existing sensors, which is beneficial to force prediction of new sensors. As such, a recurrent force prediction model trained with generated sequential tactile images and existing force lab.ls is employed to estimate higher-accuracy forces for new sensors with lowest average errors of 0.69N (5.8% in full work range) in x-axis, 0.70N (5.8%) in y-axis, and 1.11N (6.9%) in z-axis compared with models trained with single images. The experimental results also reveal that pure marker modality is more helpful than the RGB modality in improving the accuracy of force in the shear direction, while the RGB modality show better performance in the normal direction. Copyright © 2024, The Authors. All rights reserved.

关键词： RGB color model

来源：评论

学校读者我要写书评

暂无评论

Deep Domain Adaptation Regression for Force Calibration of Optical Tactile Sensors

arXiv

引用

arXiv 2024年

作者： Chen, Zhuo Ou, Ni Jiang, Jiaqi Luo, Shan Robot Perception Lab Centre for Robotics Research Department of Engineering King’s College London LondonWC2R 2LS United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-lab.led tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4% in full force range) for normal force, and 0.095N (6.3%) and 0.062N (4.1%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Evolution of Cooperation in Snowdrift Game with Multi-Memory Mechanism

Evolution of Cooperation in Snowdrift Game with Multi-Memory...

引用

Chinese Control Conference (CCC)

作者： Yaojun Liu Xingwen Liu Nyima Tashi Lab. of Complex Systems & Intelligent Control Key Lab. of Electronic Information of State Ethnic Affairs Commission College of Electrical Engineering Southwest Minzu University Chengdu Sichuan China Engineering Research Center for Tibetan Information Processing School of Information Science and Technology Tibet University Lhasa China

Cooperative behavior has been of great concern in evolutionary game researches because of its important role in social life and natural evolution. Since memory can greatly influence the emergence of cooperative behavior, memory-based research on evolutionary games has proliferated in recent years. In the present paper, inspired by the well-known “Stanford marshmallow experiment” (SME), we proposed a multi-memory mechanism in Snowdrift game (SDG) on spatial lattice networks whose core lies in what follows: Players are divided into two groups according to individual self-control; different memory lengths are set by group and by time in a single realization. The simulation results show that our proposed mechanism promotes cooperation level of the considered evolutionary game. More specifically, as memory length increases to an intermediate length, the cooperation level increases as well. For mechanism with short memory length, the effect of population structure is obvious and small values of adjusting factor can intensively facilitate high-frequency cooperative behavior. These findings may be helpful to understand the connection between population heterogeneity and the emergence of high cooperation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Comparison of Machine Learning Models for Predicting Indoor Materials from Channel Impulse Response 30

Comparison of Machine Learning Models for Predicting Indoor ...

引用

30th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2022

作者： Kocevska, Teodora Javornik, Tomaz Svigelj, Ales Guan, Ke Rashkovska, Aleksandra Hrovat, Andrej Jožef Stefan Institute J. Stefan Int. Postgraduate School Ljubljana Slovenia Jožef Stefan Institute State Key Lab. of Rail Traffic Contr and Safety Ljubljana Slovenia Jožef Stefan Institute Depart. of Com. Systems Ljubljana Slovenia

ISBN: (数字)9789532901177

ISBN: (纸本)9789532901177

Integrated sensing and communication in future networks will enable enhanced indoor awareness which will offer new possibilities for smart city environments. The use of machine learning (ML) approaches for processing the reflections of the propagating waves in the emerging wireless networks, to yield knowledge about the materials of the surfaces bounding the indoor environment, is a possible research direction. In this work, we formalized the problem as a ML task, i.e. multi-target classification task, and we decomposed it to multiple single-target tasks. We focused on comparison of the optimized performances of size-specific and general models, learned with Nearest Neigh-bors, Multi-Layer Perceptron, Decision Tree, and Random Forest classifiers, on channel impulse response (CIR) data of traced rays on radio links in empty rooms. The results have shown that the performances of the models build for different surfaces and room sizes vary, indicating that the materials of all surfaces should be predicted simultaneously, with single model, based on data from radio links that are placed relatively to the room size. © 2022 University of Split, FESB.

关键词： Radio links

来源：评论

学校读者我要写书评

暂无评论

GazeOnce: Real-Time Multi-Person Gaze Estimation

arXiv

引用

arXiv 2022年

作者： Zhang, Mingfang Liu, Yunfei Lu, Feng Peng Cheng Laboratory China The University of Tokyo Japan State Key Lab. of VR Technology and Systems School of CSE Beihang University China

Appearance-based gaze estimation aims to predict the 3D eye gaze direction from a single image. While recent deep learning-based approaches have demonstrated excellent performance, they usually assume one calibrated face in each input image and cannot output multi-person gaze in real time. However, simultaneous gaze estimation for multiple people in the wild is necessary for real-world applications. In this paper, we propose the first one-stage end-to-end gaze estimation method, GazeOnce, which is capable of simultaneously predicting gaze directions for multiple faces (>10) in an image. In addition, we design a sophisticated data generation pipeline and propose a new dataset, MPSGaze, which contains full images of multiple people with 3D gaze ground truth. Experimental results demonstrate that our unified framework not only offers a faster speed, but also provides a lower gaze estimation error compared with state-of-the-art methods. This technique can be useful in real-time applications with multiple users. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Crossband Generalized Memory Polynomial for Nonlinear Target Modeling and its Identification with Two-Tone Signals

Crossband Generalized Memory Polynomial for Nonlinear Target...

引用

2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021

作者： Li, Yuanzhe Hu, Weidong Du, Xiaoyong Zhu, Chang Fan, Hongqi National University of Defense Technology National Key Laboratory of Science and Technology on Automatic Target Recognition Changsha China National University of Defense Technology State Key Lab. of Complex Electromagnetic Environment Effect on Electronics and Information Systems Changsha China

ISBN: (纸本)9781665429184

Nonlinear detection systems or radars have received sustained attention in recent years. However, the describing model for nonlinear scattering characterization remains to be the memoryless polynomial since the 1970s. For finer characterization of complicated nonlinear targets and future wideband applications, we introduced memorial structure in this field for the first time and proposed the crossband generalized memory polynomial model. Moreover, identification of the model coefficients based on two-Tone inputs was discussed, which demonstrated robustness to the unwanted nonlinear distortion compared with traditional white noisy inputs. Besides, we further explored the impact of frequency configuration, input SNR, output SNR and numbers of required and extra inputs on estimation performance. Several simulations were carried out to validate the analysis, which could serve as valuable guidance to further measuring experiment. © 2021 IEEE.

关键词： Polynomials

来源：评论

学校读者我要写书评

暂无评论

MuST: Maximizing the Latent Capacity of Spatial Transcriptomics Data with Multi-modality Structure Transformation

MuST: Maximizing the Latent Capacity of Spatial Transcriptom...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zelin Zang Liangyu Li Yongjie Xu Jiahao Li Chenrui Duan Yi Sun Zhen Lei Stan Z. Li Centre for Artificial Intelligence and Robotics (CAIR) HKISI-CAS AI Lab Research Center for Industries of the Future Westlake University Hangzhou State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS) Institute of Automation Chinese Academy of Sciences (CASIA) School of Artificial Intelligence University of Chinese Academy of Sciences (UCAS)

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Spatial transcriptomics (ST) technologies have revolutionized the study of gene expression patterns in tissues by providing multimodality data in transcriptomic, spatial, and morphological, offering opportunities for understanding tissue biology beyond transcriptomics. However, we identify the modality bias phenomenon in ST data species, i.e., the inconsistent contribution of different modalities to the lab.ls leads to a tendency for the analysis methods to retain the information of the dominant modality. How to mitigate the adverse effects of modality bias to satisfy various downstream tasks remains a fundamental challenge. This paper introduces Multiple-modality Structure Transformation, named MuST, a novel methodology to tackle the challenge. MuST integrates the multi-modality information contained in the ST data effectively into a uniform latent space to provide a foundation for all the downstream tasks. It learns intrinsic local structures by topology discovery strategy and topology fusion loss function to solve the inconsistencies among different modalities. Thus, these topology-based and deep learning techniques provide a solid foundation for a variety of analytical tasks while coordinating different modalities. The effectiveness of MuST is assessed by performance metrics and biological significance. The results show that it outperforms existing state-of-the-art methods with clear advantages in the precision of identifying and preserving structures of tissues and biomarkers. MuST offers a versatile toolkit for the intricate analysis of complex biological systems. The code is availab.e at https://***/zangzelin/code_Must.

关键词： Analytical models Biological system modeling Transcriptomics Biological tissues Spatial databases Data models Trajectory Topology Gene expression Biological information theory

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：