检索结果-内蒙古大学图书馆

IEEE International Conference on Robotics and Automation (ICRA)

作者： Haicheng Liao Shangqian Liu Yongkang Li Zhenning Li Chengyue Wang Yunjian Li Shengbo Eben Li Chengzhong Xu State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science University of Macau Macau SAR China Department of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Departments of Civil and Environmental Engineering and Computer and Information Science State Key Laboratory of Internet of Things for Smart City University of Macau Macau SAR China State Key Laboratory of Internet of Things for Smart City and Departments of Civil and Environmental Engineering University of Macau Macau SAR China Faculty of Innovation Engineering Macau University of Science and Technology Macau SAR China School of Vehicle and Mobility Tsinghua University Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In the burgeoning field of autonomous vehicles (AVs), trajectory prediction remains a formidable challenge, especially in mixed autonomy environments. Traditional approaches often rely on computational methods such as time-series analysis. Our research diverges significantly by adopting an interdisciplinary approach that integrates principles of human cognition and observational behavior into trajectory prediction models for AVs. We introduce a novel “adaptive visual sector” mechanism that mimics the dynamic allocation of attention human drivers exhibit based on factors like spatial orientation, proximity, and driving speed. Additionally, we develop a “dynamic traffic graph” using Convolutional Neural Networks (CNN) and Graph Attention Networks (GAT) to capture spatio-temporal dependencies among agents. Benchmark tests on the NGSIM, HighD, and MoCAD datasets reveal that our model (GAVA) outperforms state-of-the-art baselines by at least 15.2%, 19.4%, and 12.0%, respectively. Our findings underscore the potential of leveraging human cognition principles to enhance the proficiency and adaptability of trajectory prediction algorithms in AVs.

关键词： Adaptation models Visualization Time series analysis Predictive models Cognition Trajectory Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Joint Beamforming and Power Allocation Design for Wirelessly Charged Unmanned Inspecting Vehicles in Power Transmission Systems

IEEE Transactions on Green Communications and Networking

引用

IEEE Transactions on Green Communications and Networking 2025年

作者： Huang, Yingjie Abdelhady, Amr M. Wang, Qianggang Ye, Jia Chi, Yuan Eltawil, Ahmed M. El-Sherif, Amr A. ElBatt, Tamer Chongqing University State Key Laboratory of Power Transmission Equipment Technology School of Electrical Engineering Chongqing400044 China King Abdullah University of Science and Technology Computer Electrical and Mathematical Science and Engineering Division Province Makkah23955-6900 Saudi Arabia Electronics and Communications Department Egypt University of Informatics Cairo Governorate Egypt Department of Computer Science and Engineering The American University Cairo Governorate Cairo Egypt

The rapid development of the smart grid has intensified research efforts towards automating power transmission system inspections using unmanned intelligent vehicles (UIVs). A major challenge hindering their large-scale deployment is the short operational endurance of these vehicles. This paper addresses this limitation by proposing the use of an energy transmitter (ET) and a reconfigurable intelligent surface for wirelessly charging the vehicles through radio frequency (RF) signals. Additionally, we tackle the nonlinearity of RF-to-direct current (DC) conversion, which shows efficiency saturation at high power levels, by introducing a novel power-splitting (PS) energy harvesting (EH) receiver architecture in the inspection vehicles. The inspection tasks are divided into multiple time slots, and a power transfer efficiency (PTE) maximization problem is formulated to jointly optimize the beamforming design at both the transmitter and the RIS, as well as the transmit power allocation and receiving power splitter settings for each time slot. This approach aims to meet the energy requirements of the unmanned inspection vehicles while adhering to the total available power constraint at the ET. To solve the non-convex PTE maximization problem, we propose an efficient solution utilizing alternating optimization, the Dinkelbach’s method, and successive convex approximation, considering the unique properties of channel structure and finite phase resolution. Numerical results demonstrate that our proposed system and design significantly extend the lifespan of inspection vehicles and achieve high energy efficiency in a low-complexity manner. © 2017 IEEE.

关键词： Inductive power transmission

来源：评论

学校读者我要写书评

暂无评论

Measuring discrimination to boost comparative testing for multiple deep learning models

arXiv

引用

arXiv 2021年

作者： Meng, Linghan Li, Yanhui Chen, Lin Wang, Zhi Wu, Di Zhou, Yuming Xu, Baowen State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China Momenta Suzhou China

The boom of DL technology leads to massive DL models built and shared, which facilitates the acquisition and reuse of DL models. For a given task, we encounter multiple DL models available with the same functionality, which are considered as candidates to achieve this task. Testers are expected to compare multiple DL models and select the more suitable ones w.r.t. the whole testing context. Due to the limitation of labeling effort, testers aim to select an efficient subset of samples to make an as precise rank estimation as possible for these models. To tackle this problem, we propose Sample Discrimination based Selection (SDS) to select efficient samples that could discriminate multiple models, i.e., the prediction behaviors (right/wrong) of these samples would be helpful to indicate the trend of model performance. To evaluate SDS, we conduct an extensive empirical study with three widely-used image datasets and 80 real world DL models. The experimental results show that, compared with state-of-the-art baseline methods, SDS is an effective and efficient sample selection method to rank multiple DL models. Copyright © 2021, 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

P-12.4: Low Voltage Switchable Bistable Twist Nematic Display

引用

SID Symposium Digest of Technical Papers 2023年第S1期54卷 866-869页

作者： Pengcheng Liu Man Chun Tseng Fion Sze Yan Yeung Hoi Sing Kwok State Key Laboratory of Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

The most common electronic paper technology is E-ink, which enables many novel applications such as e-readers, watches and electronic shelf labels. However, due to the TFT backplane driving and complex production processes, the cost of this technology is high, which limits its popularity. Recently, a low-cost π-bistable twist nematic (π-BTN) liquid crystal display with a high pretilt angle is proposed. It has the same structure and production method as an ordinary liquid crystal display. Passive driving enables the manufacture of high-resolution bistable displays without the use of thin-film transistors. Compared with the current bistable twist nematic display technology, the high pretilt angles structure helps to achieve a lowvoltage and fast-respond bistable display. This wide viewing angle, high contrast and TFT-free display is a promising candidate for flexible displays.

关键词： Bistable Pretilt BTN Electronic Shelf Label Green-technology

来源：评论

学校读者我要写书评

暂无评论

Revisiting dependence cluster metrics based defect prediction 32

Revisiting dependence cluster metrics based defect predictio...

引用

32nd International Conference on software Engineering and Knowledge Engineering, SEKE 2020

作者： Huang, Qiguo Chen, Xiang Li, Zhengliang Ni, Chao Gu, Qing State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China School of Computer Science and Technology Nantong University Nantong226019 China

ISBN: (纸本)1891706500

A dependence cluster is a set of program elements that all depend upon each other. Prior empirical studies have found that the dependence cluster based metrics are useful in effort-aware defect prediction. However, it is still unknown whether they are useful in non-effort-aware defect prediction. In this paper, we perform empirical studies to investigate this issue. We use the product, process, and network metrics to build the "B" model (baseline model), and then use the product, process, network and dependence cluster metrics to build the "B+C" model. Our experimental results, based on five well-known open-source systems, show that the dependence clusters are useful for non-effort-aware defect prediction. These findings help us better understand how dependence clusters influence non-effort-aware defect prediction. © 2020 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Implicit Multi-Scale Swin Transformer Network for Image Denoising

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Zhang, Qi Ding, Yuwei Zhang, Weiqi Zhu, Yian Zhang, Bob Lin, Jerry Chun-Wei Harbin Institute of Technology at Weihai School of Economics and Management Weihai China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Northwestern Polytechnical University School of Computer Science Xi’an China Northwestern Polytechnical University Yangtze River Delta Research Institute Taicang China University of Macau PAMI Research Group Department of Computer and Information Science Taipa China Department of Distributed Systems and IT Devices Electrical Engineering and Mathematical Sciences Silesian university of technology Poland

Image denoising has been used in various edge computing scenarios such as consumer electronics to improve the image quality and user experience. Existing image denoising methods based on Convolutional Neural Networks (CNNs) and vision Transformers achieve good performance by empirically utilizing residual neural network (ResNet) as basic component. However, ResNet lacks interpretability in network design and also long-term memory of features, potentially restricting the performance of denoising networks. In this paper, we propose an Implicit Multi-scale Swin Transformer Network (IMSNet) for image denoising, which introduces the implicit Euler scheme from the feature memory perspective. Specifically, densely connected implicit feature extraction blocks (IFEBs) are designed to learn the residual mapping between noisy and clean images. The IFEB reformulates the initial skip connection of ResNet based on the implicit Euler discretization, providing both network interpretability and long-term memory. In IFEB, the multi-scale swin Transformer block (MSB) is designed as the implicit layer to capture spatial details and non-local contextual information at different scales. Additionally, a cross-layer feature fusion block (CLFF) is proposed to further improve feature reuse capabilities. Compared to existing denoising networks, the extensive experiments demonstrate the superior performance of our IMSNet in various image denoising tasks, and the flexibility in the practical applications with proposed light model on resource-restricted platforms such as consumer electronic devices. © 1975-2011 IEEE.

关键词： Image denoising

来源：评论

学校读者我要写书评

暂无评论

Prediction Guided Meta-Learning for Multi-Objective Reinforcement Learning

Prediction Guided Meta-Learning for Multi-Objective Reinforc...

引用

Congress on Evolutionary Computation

作者： Fei-Yu Liu Chao Qian School of Computer Science and Technology University of Science and Technology of China Hefei China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Many real-world control problems consist of several different, possibly conflicting, objectives, which require finding a high-quality set of policies that are optimal for different objective preferences. Extensive research mainly focused on how to obtain a high-quality approximated Pareto set of policies, while another important research direction studies how to adapt to new objective preferences quickly. In this paper, we propose a new multi-objective reinforcement learning (MORL) algorithm so-called PG-Meta-MORL for achieving both goals. PG-Meta-MORL frames MORL as a meta-learning problem and iteratively optimizes a meta-policy using multiple tasks with objective preferences selected based on a prediction model, which is trained to guide the optimization process towards best improving the quality of the current Pareto set of policies. The empirical results on several multi-objective continuous control problems show that PG-Meta-MORL can find a high-quality approximated Pareto set of policies, and meanwhile, the obtained meta-policy can be adapted well to new objective preferences using few-shot interactions with the environment.

关键词： Adaptation models Reinforcement learning Evolutionary computation Predictive models Prediction algorithms Approximation algorithms Task analysis

来源：评论

学校读者我要写书评

暂无评论

Exploring Leximin Principle for Fair Core-Selecting Combinatorial Auctions: Payment Rule Design and Implementation

arXiv

引用

arXiv 2023年

作者： Cheng, Hao Kong, Shufeng Deng, Yanchen Liu, Caihua Wu, Xiaohu An, Bo Wang, Chongjun State Key Laboratory for Novel Software Technology Nanjing University China School of Software Engineering Sun Yat-Sen University China School of Computer Science and Engineering Nanyang Technological University Singapore School of Artificial Intelligence Guilin University of Electronic Technology China National Engineering Research Center of Mobile Network Technologies Beijing University of Posts and Telecommunications China

Core-selecting combinatorial auctions (CAs) restrict the auction result in the core such that no coalitions could improve their utilities by engaging in collusion. The minimum-revenue-core (MRC) rule is a widely used core-selecting payment rule to maximize the total utilities of all bidders. However, the MRC rule can suffer from severe unfairness since it ignores individuals’ utilities. To address this limitation, we propose to explore the leximin principle to achieve fairness in core-selecting CAs since the leximin principle prefers to maximize the utility of the worst-off;the resulting bidder-leximin-optimal (BLO) payment rule is then theoretically analyzed and an effective algorithm is further provided to compute the BLO outcome. Moreover, we conduct extensive experiments to show that our algorithm returns fairer utility distributions and is faster than existing algorithms of core-selecting payment rules. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Mutual Information Guided Backdoor Mitigation for Pre-trained Encoders

arXiv

引用

arXiv 2024年

作者： Han, Tingxu Sun, Weisong Ding, Ziqi Fang, Chunrong Qian, Hanwei Li, Jiaxun Chen, Zhenyu Zhang, Xiangyu The State Key Laboratory for Novel Software Technology Nanjing University Nanjing210093 China The School of Computer Science and Engineering Nanyang Technological University Nanyang639798 Singapore The School of Computer Science and Engineering University of New South Wales NSW2052 Australia The School of Mathematical Sciences Soochow University Suzhou215000 China The School of Computer Sciences Purdue University West Lafayette47907 United States

Self-supervised learning (SSL) is increasingly attractive for pre-training encoders without requiring labeled data. Downstream tasks built on top of those pre-trained encoders can achieve nearly state-of-the-art performance. The pre-trained encoders by SSL, however, are vulnerable to backdoor attacks as demonstrated by existing studies. Numerous backdoor mitigation techniques are designed for downstream task models. However, their effectiveness is impaired and limited when adapted to pre-trained encoders, due to the lack of label information when pre-training. To address backdoor attacks against pre-trained encoders, in this paper, we innovatively propose a mutual information guided backdoor mitigation technique, named MIMIC. MIMIC treats the potentially backdoored encoder as the teacher net and employs knowledge distillation to distill a clean student encoder from the teacher net. Different from existing knowledge distillation approaches, MIMIC initializes the student with random weights, inheriting no backdoors from teacher nets. Then MIMIC leverages mutual information between each layer and extracted features to locate where benign knowledge lies in the teacher net, with which distillation is deployed to clone clean features from teacher to student. We craft the distillation loss with two aspects, including clone loss and attention loss, aiming to mitigate backdoors and maintain encoder performance at the same time. Our evaluation conducted on two backdoor attacks in SSL demonstrates that MIMIC can significantly reduce the attack success rate by only utilizing Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

来源：评论

学校读者我要写书评

暂无评论

VeriQBench: A Benchmark for Multiple Types of Quantum Circuits

arXiv

引用

arXiv 2022年

作者： Chen, Kean Fang, Wang Guan, Ji Hong, Xin Huang, Mingyu Liu, Junyi Wang, Qisheng Ying, Mingsheng State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Department of Computer Science and Technology Tsinghua University China The University of Chinese Academy of Sciences China Centre for Quantum Software and Information University of Technology Sydney Australia

In this paper, we introduce VeriQBench-an open source benchmark for quantum circuits. It offers high-level quantum circuit abstractions of various circuit types, including: 1) combinational, 2) dynamic, 3) sequen-tial, and 4) variational quantum circuits, which cover almost all existing types of quantum circuits in the literature. Meanwhile, VeriQBench is a versatile benchmark which can be used in verifying quantum software for different applications, as is evidenced by the existing works includ-ing quantum circuit verification (e.g., equivalence checking [Hon+21a;WLY21] and model checking [Yin21]), simulation (e.g., fault simulation), testing (e.g., test pattern generation [CY22]) and debugging (e.g., run-time assertions [Li+20b]). All the circuits are described in OpenQASM and are validated on Qiskit and QCOR simulators. With the hope that it can be used by other researchers, VeriQBench is released at: https:// ***/Veri-Q/Benchmark. © 2022, CC BY.

关键词： Application programs

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：