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Longitudinal velocity control of autonomous driving based on extended state observer

CAAI Transactions on Intelligence Technology 2025年第1期10卷 36-46页

作者： Hongbo Gao Hanqing Yang Xiaoyu Zhang Xiangyun Ren Fenghua Liang Ruidong Yan Qingchao Liu Mingmao Hu Fang Zhang Jiabing Gao Siyu Bao Keqiang Li Deyi Li Danwei Wang Department of Automation University of Science and Technology of ChinaHefeiChina State Key Laboratory of Intelligent Vehicle Safety Technology ChongqingChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Traffic and Transportation Beijing Jiaotong UniversityBeijingChina Automotive Engineering Research Institute Jiangsu UniversityZhenjiangChina School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingaporeSingapore School of Automotive Engineering Hubei University of Automotive TechnologyShiyanChina Beijing Idriverplus Technology Co. Ltd.BeijingChina Chery Automobile Co. Ltd.WuhuChina State Key Laboratory of Automotive Safety and Energy Tsinghua UniversityBeijingChina Department of Computer Science and Technology Tsinghua UniversityBeijingChina

Active Disturbance Rejection Control(ADRC)possesses robust disturbance rejection capabilities,making it well-suited for longitudinal velocity ***,the conventional Extended State Observer(ESO)in ADRC fails to fully exploit feedback from first-order and higher-order estimation errors and tracking error simultaneously,thereby diminishing the control performance of *** address this limitation,an enhanced car-following algorithm utilising ADRC is proposed,which integrates the improved ESO with a feedback *** comparison to the conventional ESO,the enhanced version effectively utilises multi-order estimation and tracking ***,it enhances convergence rates by incorporating feedback from higher-order estimation errors and ensures the estimated value converges to the reference value by utilising tracking error *** improved ESO significantly enhances the disturbance rejection performance of ***,the effectiveness of the proposed algorithm is validated through the Lyapunov approach and experiments.

关键词： active disturbance rejection control(ADRC) autonomous vehicle extended state observer(ESO) intelligent control longitudinal velocity control

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Gap minimization for knowledge sharing and transfer

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 1282-1338页

作者： Boyu Wang Jorge A. Mendez Changjian Shui Fan Zhou Di Wu Gezheng Xu Christian Gagné Eric Eaton Department of Computer Science University of Western Ontario Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology Institute Intelligence and Data Université Laval School of Transportation Science and Engineering Beihang University Department of Electrical and Computer Engineering McGill University Department of Computer and Information Science University of Pennsylvania

Learning from multiple related tasks by knowledge sharing and transfer has become increasingly relevant over the last two decades. In order to successfully transfer information from one task to another, it is critical to understand the similarities and differences between the domains. In this paper, we introduce the notion of performance gap, an intuitive and novel measure of the distance between learning tasks. Unlike existing measures which are used as tools to bound the difference of expected risks between tasks (e.g., H-divergence or discrepancy distance), we theoretically show that the performance gap can be viewed as a data- and algorithm-dependent regularizer, which controls the model complexity and leads to finer guarantees. More importantly, it also provides new insights and motivates a novel principle for designing strategies for knowledge sharing and transfer: gap minimization. We instantiate this principle with two algorithms: 1. gapBoost, a novel and principled boosting algorithm that explicitly minimizes the performance gap between source and target domains for transfer learning; and 2. gapMTNN, a representation learning algorithm that reformulates gap minimization as semantic conditional matching for multitask learning. Our extensive evaluation on both transfer learning and multitask learning benchmark data sets shows that our methods outperform existing baselines.

关键词： performance gap transfer learning multitask learning regularization algorithmic stability

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Comparison of Production-Triggering Strategies for Manufacturing Enterprises under Random Orders

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engineering 2021年第6期7卷 798-806页

作者： Longfei Zhou Lin Zhang Yajun Fang Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of TechnologyCambridgeMA 02139USA School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

Although new technologies have been deeply applied in manufacturing systems,manufacturing enterprises are still encountering difficulties in maintaining efficient and flexible production due to the random arrivals of diverse customer *** order delivery and low inventory cost are fundamentally contradictory to each *** to make a suitable production-triggering strategy is a critical issue for an enterprise to maintain a high level of competitiveness in a dynamic *** this paper,we focus on production-triggering strategies for manufacturing enterprises to satisfy randomly arriving orders and reduce inventory *** theoretical models and simulation models of different production strategies are proposed,including time-triggered strategies,event-triggered strategies,and hybrid-triggered *** each model,both part-production-triggering strategies and product-assembly-triggering strategies are considered and *** time-triggered models and hybrid-triggered models also consider the impact of the period on system *** results show that hybrid-triggered and time-triggered strategies yield faster order delivery and lower inventory costs than event-triggered strategies if the period is set appropriately.

关键词： Collaborative manufacturing Production triggering Optimization Simulation Enterprise

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Privacy-Preserving Data Evaluation via Functional Encryption, Revisited

Privacy-Preserving Data Evaluation via Functional Encryption...

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

作者： Xinyuan Qian Hongwei Li Guowen Xu Haoyong Wang Tianwei Zhang Xianhao Chen Yuguang Fang School of Computer Science and Engineering University of Electronic Science and Technology of China Department of Computer Science City University of Hong Kong Chongqing Key Laboratory of Automated Reasoning and Cognition School of Computer Science and Engineering Nanyang Technological University Department of Electrical and Electronic Engineering University of Hong Kong

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

In cloud-based data marketplaces, the cardinal objective lies in facilitating interactions between data shoppers and sellers. This engagement allows shoppers to augment their internal datasets with external data, consequently leading to significant enhancements in their machine learning models. Nonetheless, given the potential diversity of data values, it becomes critical for consumers to assess the value of data before cementing any transactions. Recently, Song et al. introduced Primal (publish in ACSAC), the pioneering cloud-assisted privacy-preserving data evaluation (PPDE) strategy. This strategy relies on variants of functional encryption (FE) as the underlying framework, conferring notable performance advantages over alternative cryptographic primitives such as secure multi-party computation and homomorphic encryption. However, in this paper, we regretfully highlight that Primal is susceptible to inadvertent misuse of FE, and leaves much-desired room for performance amelioration. To combat this, we introduce a novel cryptographic primitive known as labeled function-hiding inner-product encrypted. This new primitive serves as a remedy and forms the foundation for designing the concrete framework for PPDE. Furthermore, experiments conducted on real datasets demonstrate that our framework significantly reduces the overall computation cost of the current state-of-the-art secure PPDE scheme by roughly 10× and the communication cost for the data seller by about 2×.

关键词： Costs Scalability Cement industry Machine learning Benchmark testing Iron Multi-party computation

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Domain Adaptation for Large-Vocabulary Object Detectors 38

Domain Adaptation for Large-Vocabulary Object Detectors

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jiang, Kai Huang, Jiaxing Xie, Weiying Lei, Jie Li, Yunsong Shao, Ling Lu, Shijian State Key Laboratory of Integrated Services Networks Xidian University Xi'an710071 China S-Lab School of Computer Science and Engineering Nanyang Technological University Singapore School of Electrical and Data Engineering The University of Technology Sydney Australia UCAS-Terminus AI Lab University of Chinese Academy of Sciences China

Large-vocabulary object detectors (LVDs) aim to detect objects of many categories, which learn super objectness features and can locate objects accurately while applied to various downstream data. However, LVDs often struggle in recognizing the located objects due to domain discrepancy in data distribution and object vocabulary. At the other end, recent vision-language foundation models such as CLIP demonstrate superior open-vocabulary recognition capability. This paper presents KGD, a Knowledge Graph Distillation technique that exploits the implicit knowledge graphs (KG) in CLIP for effectively adapting LVDs to various downstream domains. KGD consists of two consecutive stages: 1) KG extraction that employs CLIP to encode downstream domain data as nodes and their feature distances as edges, constructing KG that inherits the rich semantic relations in CLIP explicitly;and 2) KG encapsulation that transfers the extracted KG into LVDs to enable accurate cross-domain object classification. In addition, KGD can extract both visual and textual KG independently, providing complementary vision and language knowledge for object localization and object classification in detection tasks over various downstream domains. Experiments over multiple widely adopted detection benchmarks show that KGD outperforms the state-of-the-art consistently by large margins. © 2024 Neural information processing systems foundation. All rights reserved.

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Research on CMUTs Design with Different Cell Arrangements

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Recent Patents on engineering 2024年第1期18卷 110-118页

作者： Cao, Xuewen Yu, Yuanyu Liu, Xin Ma, Jingyi Zhang, Hua Wang, Lijia Intelligent Manufacturing Institute Hebei Vocational University of Industry and Technology Shi Jiazhuang050091 China School of Artificial Intelligence Neijiang Normal University Neijiang641100 China State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau Macau 999078 China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau 999078 China

Background: Capacitive micromachined ultrasonic transducers (CMUTs) is a promising component of mechanical-electrical-acoustical conversion, which shows valuable applications in non-distructive testing and obstacle detection fields. In order to study the influence of dimension parameters on the directivity of CMUTs, the three-dimensional multi-physics coupling model is proposed for CMUTs in this paper. Methods: The model combines multiple physical fields through finite element method (FEM), which is more efficient and accurate. The influence of dimension parameters on the transducer is studied from two aspects: the pitch between adjacent cells and cell number, respectively. Results: When increasing the pitch, the directivity becomes better if the pitch is less than half of the wavelength, the grating lobe and side lobe are aroused. However, the bandwidth at-3dB is reduced by up to 71.1% at the same time. When increasing the cell number, the better the direcitvity, while the change of the cell number has little effect on the bandwidth of the array. However, 5.4% frequency shift was caused due to the effective mass change of the transducer. Conclusion: In this study, a 3D model based on FEM is proposed for the influence of dimension parameter on CMUTs. The simulation results indicate that the directivity can be enhanced by chang-ing the dimension. At the same time, it also introduces other performance issues, such as bandwidth decrease and frequency shift through this approach. Therefore, the simulation proposed the guidance of CMUTs performance optimization and patented technology application work in the future. © 2024, Bentham science Publishers. All rights reserved.

关键词： Finite element method

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Antenna-Coupled Micro-Electromechanical Bolometer for Terahertz Detection

Antenna-Coupled Micro-Electromechanical Bolometer for Terahe...

引用

Asia-Pacific Conference on Microwave

作者： Sakib Quader Christophe Fumeaux Withawat Withayachumnankul Terahertz Engineering Laboratory The University of Adelaide Adelaide Australia School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia

ISBN: (数字)9798350363548

ISBN: (纸本)9798350363555

The terahertz sensing and imaging applications require room temperature terahertz detectors that are fast and highly sensitive. In this paper, we introduce a microelectromechanical bolometer coupled with an antenna to improve detection sensitivity by increasing the effective absorption area for terahertz waves. The absorption of terahertz waves heats up the antenna element causing thermal expansion, which affects the natural mechanical resonance frequency of the antenna. The frequency shift can be measured using a laser vibrometer. Additionally, the spiral antenna structure is insensitive to the direction of linear polarization. Overall, the proposed bolometer can achieve improved responsivity with relatively simple design.

关键词： Micromechanical devices Terahertz wave imaging Spirals Absorption Vibrometers Resonant frequency Bolometers Frequency measurement Microwave antenna arrays Antennas

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Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip

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Light(science & Applications) 2021年第3期10卷 409-419页

作者： Elena Goi Xi Chen Qiming Zhang Benjamin P.Cumming Steffen schoenhardt Haitao Luan Min Gu Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Laboratory for Artificial-Intelligence Nanophotonics School of ScienceRMIT UniversityMelbourneVIC 3001Australia

Optical machine learning has emerged as an important research area that,by leveraging the advantages inherent to optical signals,such as parallelism and high speed,paves the way for a future where optical hardware can process data at the speed of *** this work,we present such optical devices for data processing in the form of single-layer nanoscale holographic perceptrons trained to perform optical inference *** experimentally show the functionality of these passive optical devices in the example of decryptors trained to perform optical inference of single or whole classes of keys through symmetric and asymmetric *** decryptors,designed for operation in the near-infrared region,are nanoprinted on complementary metal-oxide-semiconductor chips by galvo-dithered two-photon nanolithography with axial nanostepping of 10 nm achieving a neuron density of>500 million neurons per square *** power-efficient commixture of machine learning and on-chip integration may have a transformative impact on optical decryption3,sensing4,medical diagnostics5 and computing6,7.

关键词： holographic complementary optical

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Unsupervised HDR Image and Video Tone Mapping via Contrastive Learning

arXiv

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arXiv 2023年

作者： Cao, Cong Yue, Huanjing Liu, Xin Yang, Jingyu School of Electrical and Information Engineering Tianjin University China Computer Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland

Capturing high dynamic range (HDR) images (videos) is attractive because it can reveal the details in both dark and bright regions. Since the mainstream screens only support low dynamic range (LDR) content, tone mapping algorithm is required to compress the dynamic range of HDR images (videos). Although image tone mapping has been widely explored, video tone mapping is lagging behind, especially for the deep-learning-based methods, due to the lack of HDR-LDR video pairs. In this work, we propose a unified framework (IVTMNet) for unsupervised image and video tone mapping. To improve unsupervised training, we propose domain and instance based contrastive learning loss. Instead of using a universal feature extractor, such as VGG to extract the features for similarity measurement, we propose a novel latent code, which is an aggregation of the brightness and contrast of extracted features, to measure the similarity of different pairs. We totally construct two negative pairs and three positive pairs to constrain the latent codes of tone mapped results. For the network structure, we propose a spatial-feature-enhanced (SFE) module to enable information exchange and transformation of nonlocal regions. For video tone mapping, we propose a temporal-feature-replaced (TFR) module to efficiently utilize the temporal correlation and improve the temporal consistency of video tone-mapped results. We construct a large-scale unpaired HDR-LDR video dataset to facilitate the unsupervised training process for video tone mapping. Experimental results demonstrate that our method outperforms state-of-the-art image and video tone mapping methods. Our code and dataset are available at https://***/caocong/UnCLTMO. Copyright © 2023, The Authors. All rights reserved.

关键词： Conformal mapping

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System-level Security Solution for Heterogeneous D2D-underlaid Cellular Network 15

System-level Security Solution for Heterogeneous D2D-underla...

引用

15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Feng, Shaohan Lu, Xiao Niyato, Dusit Wu, Yuan Wang, Wenbo Zhejiang Gongshang University School of Information and Electronic Engineering Hangzhou China Ericsson Research and Development Canada Nanyang Technological University School of Computer Science and Engineering Singapore University of Macau State Key Laboratory of Internet of Things for Smart City China Kunming University of Science and Technology School of Mechanical and Electrical Engineering Kunming China

ISBN: (纸本)9798350324662

This paper focuses on the problem of secure communications in a D2D-underlaid cellular network, where device-to-device (D2D) communication operates in a hybrid manner using both millimeter-wave (mmWave) and microwave frequency bands. To combat vigilant adversaries, we employ the technique of covert communication to conceal the presence of legitimate D2D transmissions. The D2D transmitters employ power control, communication-mode switching, and leverage the cellular signals to evade detection by adversaries. The conflict situation between the D2D transmitters and the adversaries is modeled as a Stackelberg game. To mathematically characterize the network's spatial configuration, we utilize the stochastic geometry modeling and consider a large-scale D2D-underlaid network, enabling a system-level analysis of communication strategies. We analyze the game equilibrium and thereafter search for equilibrium by using a bi-level algorithm. Insightful findings are drawn from numerical simulation results. © 2023 IEEE.

关键词： covert communication hybrid device-to-device communication stochastic geometry Wireless security

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