检索结果-内蒙古大学图书馆

IEEE Symposium on intelligent Vehicle

作者： Peng Yi Wenyuan Wang Qingwen Liu Yiguang Hong Shanghai Research Institute for Intelligent Autonomous Systems Tongji University National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Department of Control Science and Engineering Tongji University

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Motion planning at intersections is a challenging problem in autonomous driving due to the complicated interactions. The existing pipeline of "planning after predicting" is too conservative, can reduce traffic efficiency. Using game theory to model the non-cooperative coupling relationships between multiple vehicles can resolve the above problems, but such methods cannot guarantee safety without collision. This paper presents motion planning for autonomous driving with safe differential games based on control Barrier Function (CBF), and also provides a safety-critical generalized Nash equilibrium seeking algorithm. We handle the hard CBF constraints through augmented Lagrangian multiplier method. Motivated by iterative Linear-Quadratic Game (iLQG) algorithm, we use the Taylor expansion method to approximate the model into an Linear-Quadratic (LQ) structure, and then incrementally solve this problem with an iterative feedback LQ game algorithm. Through Carla simulation and hardware testing, our results indicate that the algorithm can find a balance between safety and efficiency while maintaining real-time implementation performance.

关键词： Differential games Games Approximation algorithms Real-time systems Iterative algorithms Hardware Planning

来源：评论

学校读者我要写书评

暂无评论

Study of Ships Safety Smart Monitoring System in Inland Restricted Water Area 7

Study of Ships Safety Smart Monitoring System in Inland Rest...

引用

7th IEEE International Conference on Transportation Information and Safety, ICTIS 2023

作者： Xu, Haichao Xie, Bin Wan, Jian Liu, Feng Zhang, Hemu Huang, Xu Intelligent Transport Research Center China Design Group Co. Ltd. Nanjing China Jiangsu Key Laboratory of Intelligent Perception and Control of Integrated Transportation China Design Group Co. Ltd. Nanjing China Guangzhou Marine Engineering Corporation Guangzhou China

ISBN: (纸本)9798350308532

Presently, the ultra-high detection of ships technical approaches includes Automatic Identification System(AIS) with laser radar, infrared imaging, laser altimeter and infrared laser. However, these ultra-high detection technical approaches are not perfect, resulting in cost high, application range small and false alarm rate high. In this study, a ships safety smart monitoring system which is low false alarm, high reliability and automatic detection, is proposed to detect the ultra-high ships in inland restricted water area. Firstly, the performance of various ultra-high detection technologies is comparison and analysis. Secondly, a hardware platform of the ships safety smart monitoring system composed of system trigger module, ultra-high detection module, information processing module, alarm module and forensics module is constructed. Thirdly, the work flow and functions of the system are detailedly discussed. Next, the safety monitoring system in inland restricted water area has been proved in the pilot of Hai'an, the pilot shows that the system is able to improve economy, application range and data accuracy of detection. Finally, two factors which are improved are proposed, in order to indicate a direction for the following research. © 2023 IEEE.

关键词： Errors

来源：评论

学校读者我要写书评

暂无评论

Expression recognition based on convolutional difference contextual feature fusion

Expression recognition based on convolutional difference con...

引用

2022 International Conference on Electronic Information engineering and Computer Science, EIECS 2022

作者： Lu, Shengfu Yang, Chuang Li, Mi Kang, Jiaming Faculty of Information Technology Beijing University of Technology Beijing International Collaboration Base on Brain Informatics and Wisdom Services Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Department of Automation Faculty of Information Technology Beijing University of Technology Beijing China Department of Automation Faculty of Information Technology Beijing China

ISBN: (纸本)9781510663312

Facial expression recognition has always been a focus of study in emotion computing. With the aim of solving the problem that previous facial expression recognition studies are susceptible to individual differences and background noise, as well as the loss of facial expression features in deep networks of deep convolutional networks, we propose a facial expression method based on convolutional difference contextual feature fusion. The convolutional difference module can carry out difference operation between emotional image and neutral image in the input image, eliminate individual difference and background noise in expression features and improve the accuracy and generalization of the network. Finally, convolutional difference and contextual features are combined to improve the accuracy of the network further. Experiment results of 4 public facial expression datasets illustrate that compared with the existing facial expression recognition methods, this method has higher accuracy, can extract facial expression features more effectively, and has a better generalization ability. © 2023 SPIE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Distributed Generalized Nash Equilibria Learning for Online Stochastic Aggregative Games

arXiv

引用

arXiv 2025年

作者： Du, Kaixin Meng, Min Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai200092 China Department of Control Science and Engineering College of Electronics and Information Engineering National Key Laboratory of Autonomous Intelligent Unmanned System Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai Research Institute for Intelligent Autonomous Systems Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China

This paper investigates online stochastic aggregative games subject to local set constraints and time-varying coupled inequality constraints, where each player possesses a time-varying expectation-valued cost function relying on not only its own decision variable but also an aggregation of all the players’ variables. Each player can only access its local individual cost function and constraints, necessitating partial information exchanges with neighboring players through time-varying unbalanced networks. Additionally, local cost functions and constraint functions are not prior knowledge and only revealed gradually. To learn generalized Nash equilibria of such games, a novel distributed online stochastic algorithm is devised based on push-sum and primal-dual strategies. Through rigorous analysis, high probability bounds on the regret and constraint violation are provided by appropriately selecting decreasing stepsizes. Moreover, for a time-invariant stochastic strongly monotone game, it is shown that the generated sequence by the designed algorithm converges to its variational generalized Nash equilibrium (GNE) almost surely, and the time-averaged sequence converges sublinearly with high probability. Finally, the derived theoretical results are illustrated by numerical simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Nash equilibrium

来源：评论

学校读者我要写书评

暂无评论

Combining Transformer and Convolutional Neural Network for Smoke Detection 9th

Combining Transformer and Convolutional Neural Network for...

引用

9th International Forum on Digital Multimedia Communication, IFTC 2022

作者： Gong, Yafei Lian, Xinkang Ma, Xuanchao Xia, Zhifang Zhou, Chengxu Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Beijing Laboratory of Smart Environmental Protection Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China National Information Center Beijing China School of Electronic and Information Engineering Liaoning University of Technology Liaoning Jinzhou China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China

ISBN: (纸本)9789819908554

Smoke detection plays a crucial role in the safety production of petrochemical enterprises and fire prevention. Image-based machine learning and deep learning methods have been widely studied. Recently, many works have applied the transformer to solve problems faced by computer vision tasks (such as classification and object detection). To our knowledge, there are few studies using the transformer structure to detect smoke. In order to research the application potential and improve the performance of the transformer in the smoke detection field, we propose a model consisting of two transformer encoders and a convolutional neural network (CNN) module. The first transformer encoder can be used to establish the global relationship of an image, and the CNN structure can provide additional local information to the transformer. The fusion of global information and local information is conducive to the second transfer encoder to make better decisions. Experiments results on large-size dataset for industrial smoke detection illustrate the effectiveness of the proposed model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smoke

来源：评论

学校读者我要写书评

暂无评论

River Turbidity Monitoring Based on Semi-supervised Transfer Learning 9th

River Turbidity Monitoring Based on Semi-supervised Transfe...

引用

9th International Forum on Digital Multimedia Communication, IFTC 2022

作者： Xie, Shuangyi Liao, Xin Bai, Ruxue Zhou, Chengxu Gu, Ke Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Beijing Laboratory of Smart Environmental Protection Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China School of Electronic and Information Engineering Liaoning University of Technology Liaoning China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China

ISBN: (纸本)9789819908554

River turbidity is an important index to evaluate the quality of water environment, which is of great significance to environmental monitoring. Most of the existing monitoring methods rely on turbidity labels measured by river water quality monitoring stations. However, due to the limitation of spatial distribution of water quality monitoring stations, those turbidity data outside the monitoring range are difficult to obtain and the correlation of turbidity data between monitoring stations is poor. Using such data directly might weaken the generalization ability that the model has. In this paper, we propose a new river turbidity monitoring model based on semi-supervised transfer learning (RTMM-SSTL) to solve these problems. First, in the pre-training stage, we innovatively propose a semi-supervised training method combined with big data with pseudo labels for river turbidity monitoring to solve the problem of weakened model generalization ability. Then, the model is fine-tuned using GEE hyper spectral data with ground truth to further improve the monitoring capability of the model. Experiments conducted on the river turbidity monitoring task demonstrate that the proposed model is superior to the advanced learning models, and further proves that our semi-supervised transfer learning method performs better than the state-of-the-art supervision models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Rivers

来源：评论

学校读者我要写书评

暂无评论

Few-Reference Image Quality Assessment with Multiple Information Measurement Fusion 9th

Few-Reference Image Quality Assessment with Multiple Inform...

引用

9th International Forum on Digital Multimedia Communication, IFTC 2022

作者： Shi, Shuang Wang, Simeng Liu, Yuchen Zhou, Chengxu Gu, Ke Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Beijing Laboratory of Smart Environmental Protection Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China School of Electronic and Information Engineering Liaoning University of Technology Liaoning Jinzhou China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China

ISBN: (纸本)9789819908554

In the era of media information explosion, there is an urgent need for a fast and reliable image quality assessment (IQA) model to improve the actual application effect of images. To this end, we propose the multiple information measurement fusion metric (MMFM), which innovatively combines two types of information measures (IMs), i.e., local IM and global IM, using only a small number of references for IQA. First, inspired by the free energy theory, we combine 2-dimensional autoregressive model with sparse random sampling method as an inference engine on an input image to generate its associated predicted image. Second, by the inspiration of pixel-wise measurement, we obtain the local IM by calculating the information entropy of the residual error between the input image and its corresponding predicted one. Third, motivated by the histogram-based measurement, we acquire the global IM by computing the two kinds of divergences between the input image and its predicted one. Fourth, we systematically fuse three components, independently including one distance of local IM between the reference and corrupted images and two distances of global IMs between the reference and corrupted images, based on a linear function to derive the final IQA result. The results of experiment on the most popular LIVE database show that our designed algorithm with only one number used as few reference has achieved well performance as compared with several mainstream IQA models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Cooperative Formation control and Obstacle Avoidance of Multi-Robot Systems Based on Potential Field Method

Cooperative Formation Control and Obstacle Avoidance of Mult...

引用

Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Zixian Wang Heng Deng Liguo Zhang Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

Multi-robot systems (MRS) are widely used in military, industrial, and agricultural applications since they are capable of accomplishing tasks that individual robots cannot handle through collaborative cooperation. However, MRS often face challenges when considering formation characterization and obstacle avoidance simultaneously in real-world scenarios. In such context, a distributed cooperative formation and obstacle avoidance controller is proposed for wheeled mobile robots based on potential field method. In this paper, by using the leader-follower model, a consistency-based cooperative formation controller is designed, where the leader robot focuses on reaching the desired target point while the followers are responsible for maintaining the formation. Then, an obstacle avoidance controller based on potential field method is designed to avoid static and dynamic obstacles while preventing the formation from passing across the obstacles. Finally, the effectiveness and feasibility of the proposed cooperative method is verified through simulation tests and real experiments.

关键词： Shape Service robots Heuristic algorithms Simulation Formation control Mobile robots Multi-robot systems

来源：评论

学校读者我要写书评

暂无评论

Data Poisoning Attacks to Session-Based Recommender Systems 12

Data Poisoning Attacks to Session-Based Recommender Systems

引用

12th International Conference on Communication and Network Security, ICCNS 2022

作者： Zhang, Yueqi Yin, Ruiping Yang, Zhen Faculty of Information Technology Beijing University of Technology China Faculty of Information Technology Beijing University of Technology Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing China

ISBN: (纸本)9781450397520

Recommender systems are used to assist users in discovering their interests from websites. Companies deployed various types of recommender systems, including content-based, collaborative filtering-based, and session-based. Especially, session-based recommender systems have been deployed successfully in industry. In this work, we conduct the systematic study on data poisoning attacks to session-based recommender systems. An attacker's goal is to promote a target item such that it can be recommended to as many people as possible. Our attack injects fake users with carefully crafted interaction sessions (e.g., clicking sessions) into the recommender system to achieve this goal. The critical challenge is to choose and arrange the items in interaction sessions. We formulate our attack as an optimization problem to address this challenge, so that the injected sessions would maximize the number of users to whom the target items are recommended. We evaluate our experiments on several real-world datasets, which show that our attack methods outperform existing methods. © 2022 ACM.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Online, Target-Free LiDAR-Camera Extrinsic Calibration via Cross-Modal Mask Matching

arXiv

引用

arXiv 2024年

作者： Huang, Zhiwei Zhang, Yikang Chen, Qijun Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Institute of Intelligent Science and Technology Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China

LiDAR-camera extrinsic calibration (LCEC) is crucial for data fusion in intelligent vehicles. Offline, target-based approaches have long been the preferred choice in this field. However, they often demonstrate poor adaptability to real-world environments. This is largely because extrinsic parameters may change significantly due to moderate shocks or during extended operations in environments with vibrations. In contrast, online, target-free approaches provide greater adaptability yet typically lack robustness, primarily due to the challenges in cross-modal feature matching. Therefore, in this article, we unleash the full potential of large vision models (LVMs), which are emerging as a significant trend in the fields of computer vision and robotics, especially for embodied artificial intelligence, to achieve robust and accurate online, target-free LCEC across a variety of challenging scenarios. Our main contributions are threefold: we introduce a novel framework known as MIAS-LCEC, provide an open-source versatile calibration toolbox with an interactive visualization interface, and publish three real-world datasets captured from various indoor and outdoor environments. The cornerstone of our framework and toolbox is the cross-modal mask matching (C3M) algorithm, developed based on a state-of-the-art (SoTA) LVM and capable of generating sufficient and reliable matches. Extensive experiments conducted on these real-world datasets demonstrate the robustness of our approach and its superior performance compared to SoTA methods, particularly for the solid-state LiDARs with super-wide fields of view. Our toolbox and datasets are publicly available at ***/MIAS-LCEC. Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras

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：