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Lateral control of intelligent vehicles using radial basis function neural networks with sliding mode control based on fractional order calculus

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Journal of Intelligent Manufacturing and Special Equipment 2022年第2期3卷 117-133页

作者： Peiqing Li Taiping Yang Hao Zhang Lijun Wang Qipeng Li School of Mechanical and Energy Engineering Zhejiang University of Science and Technology Hangzhou China UK-China Joint Intelligent Center of Special Equipment and Robotics Ltd Ashford UK

Purpose This paper aimed a fractional-order sliding mode-based lateral lane-change control method that was proposed to improve the path-tracking accuracy of vehicle lateral motion. Design/methodology/approach In this paper the vehicle presighting and kinematic models were established, and a new sliding mode control isokinetic convergence law was devised based on the fractional order calculus to make the front wheel turning angle approach the desired value quickly. On this basis, a fractional gradient descent algorithm was proposed to adjust the radial basis function (RBF) neuron parameter update rules to improve the compensation speed of the neural network. Findings The simulation results revealed that, compared to the traditional sliding mode control strategy, the designed controller eliminated the jitter of the sliding mode control, sped up the response of the controller, reduced the overshoot of the system parameters and facilitated accurate and fast tracking of the desired path when the vehicle changed lanes at low speeds. Originality/value This paper combines the idea of fractional order calculus with gradient descent algorithm, proposed a fractional-order gradient descent method applied to RBF neural network and fast adjustment the position and width of neurons.

关键词： Trajectory tracking Lateral motion control Sliding mode control Radial basis neural network Fractional order calculus

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Correction of Read Biases Induced by Complex Reference Genome Regions for Improving Copy Number Variation Detection Using a Gaussian Mixture Model

Correction of Read Biases Induced by Complex Reference Genom...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Xuwen Chang, Zhili Wang, Shenjie Liu, Ruoyu Liu, Yuqian Zhu, Xiaoyan Shao, Yang Lai, Xin Yang, Shuanying Wang, Jiayin Second Affiliated Hospital Xi'an Jiaotong University Department of Respiratory Medicine Xi'an710003 China Xi'an Jiaotong University School of Computer Science and Technology Xi'an710049 China Xi'an Jiaotong University Shaanxi Engineering Research Center of Medical and Health Big Data Xi'an710049 China Nanjing Geneseeq Technology INC. China

ISBN: (纸本)9798350386226

Copy number variations are crucial in cancer research, but their detection through next-generation sequencing is often hindered by read biases, particularly in complex genomic regions. Existing bias-correction methods address common issues like GC content but often fail in regions with repetitive sequences or segmental duplications, leading to false-positive CNVs. We propose refMask, a hybrid Gaussian model-based method that dynamically identifies low-confidence regions in the reference genome, correcting read biases and improving CNV detection accuracy. By integrating features from hg38 and T2T genomes, refMask tailors a custom blacklist for each sequencing sample, enhancing the reliability of CNV detection across diverse conditions. Our method provides a more accurate and flexible solution compared to current fixed blacklists, offering improved performance in challenging genomic regions. © 2024 IEEE.

关键词： Copy number variation Gaussian model Next-generation sequencing Read biase

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Optimal camera-robot pose estimation in linear time from points and lines

arXiv

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arXiv 2024年

作者： Zeng, Guangyang Mu, Biqiang Zeng, Qingcheng Song, Yuchen Dai, Chulin Shi, Guodong Wu, Junfeng School of Data Science Chinese University of Hong Kong Shenzhen China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Guangzhou China Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney Sydney Australia

Camera pose estimation is a fundamental problem in robotics. This paper focuses on two issues of interest: First, point and line features have complementary advantages, and it is of great value to design a uniform algorithm that can fuse them effectively;Second, with the development of modern front-end techniques, a large number of features can exist in a single image, which presents a potential for highly accurate robot pose estimation. With these observations, we propose AOPnP(L), an optimal linear-time camera-robot pose estimation algorithm from points and lines. Specifically, we represent a line with two distinct points on it and unify the noise model for point and line measurements where noises are added to 2D points in the image. By utilizing Plücker coordinates for line parameterization, we formulate a maximum likelihood (ML) problem for combined point and line measurements. To optimally solve the ML problem, AOPnP(L) adopts a two-step estimation scheme. In the first step, a consistent estimate that can converge to the true pose is devised by virtue of bias elimination. In the second step, a single Gauss-Newton iteration is executed to refine the initial estimate. AOPnP(L) features theoretical optimality in the sense that its mean squared error converges to the Cramér-Rao lower bound. Moreover, it owns a linear time complexity. These properties make it well-suited for precision-demanding and real-time robot pose estimation. Extensive experiments are conducted to validate our theoretical developments and demonstrate the superiority of AOPnP(L) in both static localization and dynamic odometry systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

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Monocular depth recovery based on self-attention mechanism and transfer learning 4

Monocular depth recovery based on self-attention mechanism a...

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4th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2021

作者： Li, Zeqi Yuan, Chunmiao Sun, Yukuan Wang, Jianming Tiangong University School of Computer Science and Technology TianJin China Tiangong University Center for Engineering Intership and Training TianJin China

ISBN: (纸本)9781665413220

The task of depth recovery is to calculate the depth map using only the rgb images. The existing deep network greatly compresses the resolution in the process of feature extraction, resulting in serious loss of depth information in the edge area of the object with large depth changes and the area far away from the sensor. This paper proposes a convolutional neural network based on self-attention mechanism, which can use a single rgb image to calculate its corresponding high-resolution depth map. Specifically, we use the Fully Convolutional Network with jump connections and bilinear upsampling, and add a self-attention mechanism module inside it. The self-attention mechanism module aggregates the features at each location through the weighting and selective aggregation of the features at all locations. Compared with the baseline, our method has more obvious object edge information, and also has better results on some indicators. © 2021 IEEE.

关键词： Convolution

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Minimizing Carbon Emissions in Container Shipping Using Quantum-Inspired Approaches

Minimizing Carbon Emissions in Container Shipping Using Quan...

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IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

作者： Chia-Ho Ou Shi-Yu Liu Huai-Zhong She Wen-Hao Hong De-Lun Kao Chih-Yu Chen Krittin Siangprasertkij Department of Computer Science and Information Engineering National Pingtung University Pingtung Taiwan Quantum Information Center Chung Yuan Christian University Taoyuan Taiwan International School of Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

International trade heavily relies on shipping, yet container mobility and its varying demand create imbalances in container stocks, this results in frequent scheduling of empty containers by shipping companies, leading to unnecessary voyages that adversely impact marine ecosystems and exacerbate global warming. This study applies quantum and digital annealing to optimize empty container scheduling, aiming to minimize the carbon emissions resulting from the scheduling of empty containers to mitigate damage to marine ecosystems and the effects of global warming. Comparing to Simulated Annealing, Compal Quantix, and D-Wave Hybrid, Fujitsu DA3 stands out for its superior performance in both calculating the optimal solution and reducing computation time.

关键词： International trade Processor scheduling Carbon dioxide Simulated annealing Companies Containers Signal processing Marine ecosystems Quantum annealing Global warming

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CAD-Informed Uncertainty-Aware Sequence and Motion Planning for Robotic Assembly

CAD-Informed Uncertainty-Aware Sequence and Motion Planning ...

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IEEE International Conference on Automation science and engineering (CASE)

作者： Takuya Kiyokawa Ismael Rodriguez Korbinian Nottensteiner Peter Lehner Thomas Eiband Maximo A. Roa Kensuke Harada Department of Systems Innovation Graduate School of Engineering Science Osaka University Osaka Japan Institute of Robotics and Mechatronics German Aerospace Center (DLR) Oberpfaffenhofen Weßling Germany Industrial Cyber-Physical Systems Research Center The National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

This study addresses a multi-objective optimization problem in the planning of an uncertainty-aware sequence and motion for mechanical products with intricate structures and numerous contact areas. To generate an optimized sequence and motion that satisfies multiple conditions under mandatory requirements, we use a multi-objective optimization algorithm inspired by Non-Dominated Sorting Genetic Algorithm III, along with contact-rich robotic assembly-oriented constraints and objective functions. The proposed pipeline takes as input the CAD models of robot hardware, workspace, and assembled parts, conducts 3D geometrical and physical simulations of assembly motions, and then optimizes the assembly plan, including parts order, object placement pose, state transition, grasp, and trajectory for the real robot to execute. To obtain the uncertainty-aware sequence and motion, we incorporated a Contingent Contact-Exploring Rapidly-exploring Random Trees (ConCERRT)-based state transition planner and an objective function to evaluate the uncertainty in the multi-objective optimization algorithm. Our experiments on assembly planning for a chainsaw product demonstrated that the proposed method can generate constraint-satisfied assembly plans with a success rate of 99.2% while lowering the uncertainty in the simulations.

关键词： Robotic assembly Solid modeling Uncertainty Linear programming Planning Trajectory Mechanical products Robots Optimization Assembly

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Transformer-Based Material Recognition Via Short-Time Contact Sensing

SSRN

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SSRN 2024年

作者： Liu, Zhenyang Shao, Yitian Li, Qiliang Su, Jingyong School of Computer Science and Technology Harbin Institute of Technology Guangdong Shenzhen518055 China School of Computing and Information Technology Great Bay University Guangdong Dongguan523000 China Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing Beijing100871 China

Embodied intelligence needs haptic sensing for spontaneous and accurate material recognition. The haptic sensing module of an intelligent system can acquire material data through either sliding or tapping motions. Sliding movements are commonly adopted for collecting the spatial frequency features of the material but are less time-efficient than tapping. Here, we introduce a haptic sensing framework that can extract material features from short-time tapping signals. To improve the performance of material recognition, transfer learning is used by transferring the knowledge of pretrained model training on large-scale images into haptic sensing. The waveforms of the tapping signals are encoded as images to be input into a transformer model tailored for image recognition tasks. The encoding employs line graph image-point scaling, effectively accommodating signals that exhibit large variations in magnitude and temporal structures. Using the LMT haptic material database containing sliding and tapping data, our study showcases the efficacy of the proposed framework in material recognition tasks, especially for short-time (≤ 60 ms) sensing via tapping interactions. The findings provide fresh insights into haptic sensing technologies and may help improve the physical interaction capabilities of embodied intelligence, such as medical and rescue robots. © 2024, The Authors. All rights reserved.

关键词： Intelligent systems

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MoME: Mixture of Multimodal Experts for Cancer Survival Prediction

arXiv

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arXiv 2024年

作者： Xiong, Conghao Chen, Hao Zheng, Hao Wei, Dong Zheng, Yefeng Sung, Joseph J.Y. King, Irwin Dept. of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Dept. of Computer Science and Engineering Dept. of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong Jarvis Research Center Tencent YouTu Lab China Lee Kong Chian School of Medicine Nanyang Technological University Singapore

Survival analysis, as a challenging task, requires integrating Whole Slide Images (WSIs) and genomic data for comprehensive decision-making. There are two main challenges in this task: significant heterogeneity and complex inter- and intra-modal interactions between the two modalities. Previous approaches utilize co-attention methods, which fuse features from both modalities only once after separate encoding. However, these approaches are insufficient for modeling the complex task due to the heterogeneous nature between the modalities. To address these issues, we propose a Biased Progressive Encoding (BPE) paradigm, performing encoding and fusion simultaneously. This paradigm uses one modality as a reference when encoding the other. It enables deep fusion of the modalities through multiple alternating iterations, progressively reducing the cross-modal disparities and facilitating complementary interactions. Besides modality heterogeneity, survival analysis involves various biomarkers from WSIs, genomics, and their combinations. The critical biomarkers may exist in different modalities under individual variations, necessitating flexible adaptation of the models to specific scenarios. Therefore, we further propose a Mixture of Multimodal Experts (MoME) layer to dynamically selects tailored experts in each stage of the BPE paradigm. Experts incorporate reference information from another modality to varying degrees, enabling a balanced or biased focus on different modalities during the encoding process. Extensive experimental results demonstrate the superior performance of our method on various datasets, including TCGA-BLCA, TCGA-UCEC and TCGA-LUAD. Codes are available at https://***/BearCleverProud/MoME. © 2024, CC BY.

关键词： Decision making

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Rethinking the Video Sampling and Reasoning Strategies for Temporal Sentence Grounding

Rethinking the Video Sampling and Reasoning Strategies for T...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Zhu, Jiahao Liu, Daizong Zhou, Pan Di, Xing Cheng, Yu Yang, Song Xu, Wenzheng Xu, Zichuan Wan, Yao Sun, Lichao Xiong, Zeyu Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China ProtagoLabs Inc Microsoft Research Wangxuan Institute of Computer Technology Peking University China Beijing Institute of Technology China School of Sichuan University China Dalian University of Technology China Lehigh University United States School of Computer Sci. & Tech. Huazhong University of Science and Technology China

Temporal sentence grounding (TSG) aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query. All existing works first utilize a sparse sampling strategy to extract a fixed number of video frames and then conduct multimodal interactions with query sentence for reasoning. However, we argue that these methods have overlooked two indispensable issues: 1) Boundary-bias: The annotated target segment generally refers to two specific frames as corresponding start and end timestamps. The video downsampling process may lose these two frames and take the adjacent irrelevant frames as new boundaries. 2) Reasoning-bias: Such incorrect new boundary frames also lead to the reasoning bias during frame-query interaction, reducing the generalization ability of model. To alleviate above limitations, in this paper, we propose a novel Siamese Sampling and Reasoning Network (SSRN) for TSG, which introduces a siamese sampling mechanism to generate additional contextual frames to enrich and refine the new boundaries. Specifically, a reasoning strategy is developed to learn the inter-relationship among these frames and generate soft labels on boundaries for more accurate frame-query reasoning. Such mechanism is also able to supplement the absent consecutive visual semantics to the sampled sparse frames for fine-grained activity understanding. Extensive experiments demonstrate the effectiveness of SSRN on three challenging datasets. © 2022 Association for Computational Linguistics.

关键词： Semantics

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Massive MIMO Joint Communications and Sensing with MRT Beamforming

Massive MIMO Joint Communications and Sensing with MRT Beamf...

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IEEE National Conference on Radar

作者： Nhan T. Nguyen V.-Dinh Nguyen Hieu V. Nguyen Hien Q. Ngo A. L. Swindlehurst Markku Juntti Centre for Wireless Communications University of Oulu Finland Collese of Engineering and Computer Science VinUniversity Hanoi Vietnam Faculty of Electronics and Telecommunication Engineering University of Danang Vietnam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast UK Center for Pervasive Communications & Computing University of California Irvine CA USA

ISBN: (数字)9798350329209

ISBN: (纸本)9798350329216

Joint communications and sensing (JCAS) is en-visioned as a key feature in future wireless communications networks. In massive MIMO-JCAS systems, the very large number of antennas causes excessively high computational complexity in beamforming designs. In this work, we investigate a low-complexity massive multiple-input-multiple-output (MIMO)-JCAS system employing the maximum-ratio transmission (MRT) scheme for both communications and sensing. We first derive closed-form expressions for the achievable communications rate and Cramer-Rao bound (CRB) as functions of the large-scale fading channel coefficients. Then, we develop a power allocation strategy based on successive convex approximation to maximize the communications sum rate while guaranteeing the CRB con-straint and transmit power budget. Our analysis shows that the introduction of sensing functionality increases the beamforming uncertainty and inter-user interference on the communications side. However, these factors can be mitigated by deploying a very large number of antennas. The numerical results verify our findings and demonstrate the power allocation efficiency.

关键词： Wireless communication Closed-form solutions Uncertainty Array signal processing Massive MIMO Radar Interference

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