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Support vector machine with discriminative low-rank embedding

CAAI Transactions on intelligence Technology 2024年第5期9卷 1249-1262页

作者： Guangfei Liang Zhihui Lai Heng Kong Computer Vision Institute College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina Shenzhen Institute of Artificial Intelligence and Robotics for Society ShenzhenChina Department of Breast and Thyroid Surgery BaoAn Central Hospital of ShenzhenShenzhenChina

Support vector machine(SVM)is a binary classifier widely used in machine ***,neglecting the latent data structure in previous SVM can limit the performance of SVM and its *** address this issue,the authors propose a novel SVM with discriminative low-rank embedding(LRSVM)that finds a discriminative latent low-rank subspace more suitable for SVM *** extension models of LRSVM are introduced by imposing different orthogonality constraints to prevent computational inaccuracies.A detailed derivation of the authors’iterative algorithms are given that is essentially for solving the SVM on the low-rank ***,some theorems and properties of the proposed models are presented by the *** is worth mentioning that the subproblems of the proposed algorithms are equivalent to the standard or the weighted linear discriminant analysis(LDA)*** indicates that the projection subspaces obtained by the authors’algorithms are more suitable for SVM classification compared to those from the LDA *** convergence analysis for the authors proposed algorithms are also ***,the authors conduct experiments on various machine learning data sets to evaluate the *** experiment results show that the authors’algorithms perform significantly better than other algorithms,which indicates their superior abilities on classification tasks.

关键词： iterative methods machine leaning support vector machunes

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Jacobian Regularizer-based Neural Granger Causality 41

Jacobian Regularizer-based Neural Granger Causality

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41st International Conference on Machine Learning, ICML 2024

作者： Zhou, Wanqi Bai, Shuanghao Yu, Shujian Zhao, Qibin Chen, Badong Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China RIKEN AIP Japan Vrije Universiteit Amsterdam Netherlands

With the advancement of neural networks, diverse methods for neural Granger causality have emerged, which demonstrate proficiency in handling complex data, and nonlinear ***, the existing framework of neural Granger causality has several *** requires the construction of separate predictive models for each target variable, and the relationship depends on the sparsity on the weights of the first layer, resulting in challenges in effectively modeling complex relationships between variables as well as unsatisfied estimation accuracy of Granger ***, most of them cannot grasp full-time Granger *** address these drawbacks, we propose a Jacobian Regularizer-based Neural Granger Causality (JRNGC) approach, a straightforward yet highly effective method for learning multivariate summary Granger causality and full-time Granger causality by constructing a single model for all target ***, our method eliminates the sparsity constraints of weights by leveraging an input-output Jacobian matrix regularizer, which can be subsequently represented as the weighted causal matrix in the post-hoc *** experiments show that our proposed approach achieves competitive performance with the state-of-the-art methods for learning summary Granger causality and full-time Granger causality while maintaining lower model complexity and high scalability. Copyright 2024 by the author(s)

关键词： Jacobian matrices

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Design of three-dimensional imaging lidar optical system for large field of view scanning

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Chinese Physics B 2022年第7期31卷 284-289页

作者： Qing-Yan Li Yu Zhang Shi-Yu Yan Bin Zhang Chun-Hui Wang National Key Laboratory of Tunable Laser Technology Harbin Institute of TechnologyHarbin 150001China Geling Institute of Artificial Intelligence and Robotics Shenzhen 518063China

Three-dimensional(3D)lidar has been widely used in various *** MEMS scanning system is one of its most important components,while the limitation of scanning angle is the main obstacle to improve the demerit for its application in various *** this paper,a folded large field of view scanning optical system is *** structure and parameters of the system are determined by theoretical derivation of ray *** optical design software Zemax is used to design the *** optimization,the final structure performs well in collimation and beam *** results show that the scan angle can be expanded from±5°to±26.5°,and finally the parallel light scanning is *** spot diagram at a distance of 100 mm from the exit surface shows that the maximum radius of the spot is 0.506 mm with a uniformly distributed *** maximum radius of the spot at 100 m is 19 cm,and the diffusion angle is less than 2 *** energy concentration in the spot range is greater than 90%with a high system energy concentration,and the parallelism is *** design overcomes the shortcoming of the small mechanical scanning angle of the MEMS lidar,and has good performance in collimation and beam *** provides a design method for large-scale application of MEMS lidar.

关键词： 3D lidar MEMS scanning system large field of view scanning Zemax

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Healthcare Monitoring Companion: The Social Robot 3

Healthcare Monitoring Companion: The Social Robot

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3rd International Conference on artificial intelligence For Internet of Things, AIIoT 2024

作者： Goraksha, Rahul Bongale, Arunkumar Sayyad, Sameer Kumar, Satish Symbiosis Institute of Technology Department of Robotics and Automation Pune412115 India Symbiosis Centre for Applied Artificial Intelligence Department of Robotics and Automation Pune412115 India

ISBN: (纸本)9798350372120

There is a rising scarcity of healthcare professionals to meet the demand of taking care of aged people. Social robots are becoming more and more popular because of the benefits they provide;during the pandemic, in particular, several of their primary applications are advantageous. The purpose of this work is to build the prototype of social robots and carry out the integration of those robots into healthcare monitoring using Thing Speak and Raspberry Pi. This prototype of social robots with Thing Speak and Raspberry Pi hosts an array of sensors (for measuring a multitude of data from the body of humans) like heartbeat sensor, Dallas temperature sensor, Radio-Frequency Identification (RFID), ultrasonic sensor, and Infrared (IR) sensor along with other components like motor controlling module and DC motor. The data from these sensors are transmitted to the cloud platform Thing Speak, offering remote monitoring and data analysis capabilities and the Raspberry Pi acts as the central processing element in our design, which additionally helps in triggering corresponding alerts regarding the patient status. Finally, we experimentally validate our design using 2 cases and then we present the assessment of the patient alerting feature. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Neural network-based adaptive sliding mode control for underactuated dual overhead cranes suffering from matched and unmatched disturbances

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Autonomous Intelligent Systems 2022年第1期2卷 1-15页

作者： Tianci Wen Yongchun Fang Biao Lu Institute of Robotics and Automatic Information System College of Artificial IntelligenceNankai UniversityTianjin300353China

To improve transportation capacity,dual overhead crane systems(DOCSs)are playing an increasingly important role in the transportation of large/heavy cargos and ***,when trying to deal with the control problem,current methods fail to fully consider such factors as external disturbances,input dead zones,parameter uncertainties,and other unmodeled dynamics that DOCSs usually suffer *** a result,dramatic degradation is caused in the control performance,which badly hinders the practical applications of *** by this fact,this paper designs a neural network-based adaptive sliding mode control(SMC)method for DOCS to solve the aforementioned issues,which achieves satisfactory control performance for both actuated and underactuated state variables,even in the presence of matched and mismatched *** asymptotic stability of the desired equilibrium point is proved with rigorous Lyapunov-based ***,extensive hardware experimental results are collected to verify the efficiency and robustness of the proposed method.

关键词： Asymptotic stability Sliding mode control Neural networks Unmatched disturbances Underactuated system

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D2S: Towards Efficient Sparse 3D Object Detection via Dense to Sparse Knowledge Distillation

D2S: Towards Efficient Sparse 3D Object Detection via Dense ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Yuqi Liu, Longjun Gao, Yingke Zhang, Haonan Li, Haoteng Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China Beijing Institute of Control Engineering China Academy of Space Technology Beijing China

ISBN: (纸本)9798350368741

LiDAR-based 3D object detection is widely used in high-level autonomous driving schemes. However, the cumbersome modules in most 3D detectors lead to substantial computational overhead. Despite knowledge distillation (KD) is an effective approach for compressing models, previous methods cannot be extended to the dense-to-sparse paradigm. To this end, we propose a simple yet effective Dense to Sparse Knowledge Distillation (D2S) framework for accelerating 3D detectors. Firstly, to compensate for the difference in predicted location between dense and sparse detectors, we introduce a lightweight feature diffusion (FeaD) module for spreading important features. Secondly, to achieve high performance, we propose a dual-stream distillation scheme to transfer knowledge. In this scheme, we align both of the feature and category prediction between distillation pairs at important positions. Extensive experiments on KITTI and Waymo Open Dataset demonstrate the effectiveness of our method. For example, on KITTI dataset, the sparse detector we obtained surpasses VoxelNeXt with around 2.0× fewer parameters and 1.6× fewer FLOPs. © 2025 IEEE.

关键词： Feature Diffusion Knowledge Distillation Model Compression Sparse 3D Object Detection

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Multi-Beams Forming and Steering Based on Optical Phased Array 8

Multi-Beams Forming and Steering Based on Optical Phased Arr...

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8th Optoelectronics Global Conference, OGC 2023

作者： Luo, Haipeng Sun, Caiming Li, Binghui Ding, Ning Zhang, Aidong The Chinese University of Hong Kong Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

In this work, the use of a single sparse Optical Phased Array (OPA) to achieve arbitrary multi-beams forming and steering is studied and demonstrated. This approach can be efficient and cost-effective for multiple-use... 详细信息

ISBN: (纸本)9798350325324

关键词： Silicon photonics

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RoboCup@Home 2024 OPL Winner NimbRo: Anthropomorphic Service Robots Using Foundation Models for Perception and Planning

RoboCup@Home 2024 OPL Winner NimbRo: Anthropomorphic Service...

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27th RoboCup International Symposium, 2024

作者： Memmesheimer, Raphael Nogga, Jan Pätzold, Bastian Kruzhkov, Evgenii Bultmann, Simon Schreiber, Michael Bode, Jonas Karacora, Bertan Park, Juhui Savinykh, Alena Behnke, Sven Autonomous Intelligent Systems Computer Science Institute VI Lamarr Institute for Machine Learning and Artificial Intelligence and Center for Robotics University of Bonn Bonn Germany

ISBN: (纸本)9783031858581

We present the approaches and contributions of the winning team NimbRo@Home at the RoboCup@Home 2024 competition in the Open Platform League held in Eindhoven, NL. Further, we describe our hardware setup and give an overview of the results for the task stages and the final demonstration. For this year’s competition, we put a special emphasis on open-vocabulary object segmentation and grasping approaches that overcome the labeling overhead of supervised vision approaches, commonly used in RoboCup@Home. We successfully demonstrated that we can segment and grasp non-labeled objects by text descriptions. Further, we extensively employed Large Language Model (LLMs) for natural language understanding and task planning. Throughout the competition, our approaches showed robustness and generalization capabilities. A video of our performance can be found online (https://***/videos/RoboCup_2024). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Robot programming

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Object Stiffness Discrimination from Fingertip Forces 23

Object Stiffness Discrimination from Fingertip Forces

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6th International Conference on Advances in robotics, AIR 2023

作者： Dey, Abhijit Hazarika, Shyamanta Biomimetic Robotics and Artificial Intelligence Lab Indian Institute of Technology - Guwahati Assam Guwahati India

ISBN: (纸本)9781450399807

Tactile perception is crucial for detecting contact and identifying the attributes associated with it. In this research, we demonstrate that the mechanical properties of objects may be inferred by employing data-driven algorithms from fingertip contact. We collected information from a fingertip pressure sensor as it established contact with several objects using a power prehensile circular grasp. This study establishes a correlation between fingertip force and object stiffness by incorporating a clustering algorithm to assess two phases of force data for dynamic and steady state analysis. We employed the dynamic time warping (DTW) distance-based metric and the K-means clustering technique. For the two phases, we have identified three main clusters that may be characterized as pseudo labels: Soft, Medium, and Hard. To demonstrate the robustness of the model evaluation, we performed 10-fold cross-validation. For each fold, we calculated the Bouldin Index and Silhouette Coefficient and reported the mean value to evaluate the efficacy of the clusters. © 2023 ACM.

关键词： Stiffness

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DE-Unet: Dual-encoder U-Net for Ultra-high Resolution Remote Sensing Image Segmentation

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12290-12302页

作者： Liu, Ye Song, Shitao Wang, Miaohui Gao, Hao Liu, Jun Nanjing University of Posts and Telecommunications School of Automation and Artificial Intelligence China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Lancaster University School of Computing and Communications United Kingdom

In recent years, there has been a growing demand for remote sensing image semantic segmentation in various applications. The key to semantic segmentation lies in the ability to globally comprehend the input image. While recent transformer-based methods can effectively capture global contextual information, they suffer from high computational complexity, particularly when it comes to ultra-high resolution (UHR) remote sensing images, it is even more challenging for these methods to achieve a satisfactory balance between accuracy and computation speed. To address these issues, we propose in this paper a CNN-based dual-encoder U-Net for effective and efficient UHR image segmentation. Our method incorporates dual encoders into the symmetrical framework of U-Net. The dual encoders endow the network with strong global and local perception capabilities simultaneously, while the U-Net's symmetrical structure guarantees the network's robust decoding ability. Additionally, multi-path skip connections ensure ample information exchange between the dual encoders, as well as between the encoders and decoders. Furthermore, we proposes a context-aware modulation fusion module that guides the encoder-encoder and encoder-decoder data fusion through global receptive fields. Experiments conducted on public UHR remote sensing datasets such as the Inria Aerial and DeepGlobe have demonstrated the effectiveness of proposed method. Specifically on the Inria Aerial dataset, our method achieves a 77.42% mIoU which outperforms the baseline [1] by 3.14% while maintaining comparable inference speed. © 2008-2012 IEEE.

关键词： Semantic Segmentation

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