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Computer Engineering and Intelligent Communications (ISCEIC), International Symposium on

作者： Ting Liu Yafeng Zhu Department of Robotics and Artificial Intelligence Guangzhou Institute of Science and Technology Guangzhou China Vice Dean of the School of Intelligent Manufacturing and Electrical Engineering Guangzhou Institute of Science and Technology Guangzhou China

ISBN: (数字)9798331518677

ISBN: (纸本)9798331518684

Intelligent recommendation system is a service that is widely studied in the field of data science, implemented through machine learning technology. This article proposes a design scheme for an intelligent recommendation system based on collaborative filtering, providing users with personalized and accurate recommendations. Label classification is crucial in intelligent recommendation, but there are still issues such as diversity, ambiguity, and spam labels. This article first introduces the traditional intelligent recommendation system and improves it a machine learning based intelligent recommendation system design scheme was proposed, and the effectiveness of the improved intelligent recommendation system was verified through experiments. The experimental results show that the intelligent recommendation system design proposed in this article can effectively solve some of the problems existing in traditional intelligent recommendation systems.

关键词： Accuracy Collaborative filtering System performance Machine learning Information filters User experience Electronic commerce Recommender systems Optimization Testing

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Adaptive Prediction Control for Parallel Robots Actuated by Pneumatic artificial Muscles With Motion Constraints and Input Saturation

Adaptive Prediction Control for Parallel Robots Actuated by ...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Tong Yang Qihang Wang Shuzhen Diao Yongchun Fang Ning Sun College of Artificial Intelligence Institute of Robotics and Automatic Information Systems (IRAIS) Nankai University Tianjin China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen China

Benefitting from the inherent characteristics of high compliance, strong flexibility, and lightweight, pneumatic artificial muscles (PAMs), as a kind of soft actuators, are widely used in industrial logistics, medical rehabilitation, bionic engineering, etc. In addition to the basic issues in PAMs, e.g., unmodeled dynamics, input hysteresis, and unidirectional saturation constraints, parallel PAMs also suffer from more complex coupling relationships and antagonistic effects. Hence, it is necessary to ensure safety constraints for independent muscles and their input air pressure. To simultaneously deal with the above-mentioned problems, this paper proposes a dual-layer control structure for parallel PAM-actuated robot manipulators. The lower-layer controller handles uncertain dynamics and ensures accurate tracking of parallel PAMs, and the corresponding Lyapunov-based stability analysis also proves its effectiveness. Meanwhile, the lower-layer controller and parallel PAMs are approximated together by an optimal reference model, and necessary control parameters are flexibly adjusted during the optimization process, which make the upper-layer prediction controller not depend on any plant knowledge, to realize input/output constraints by multi-period motion prediction and horizon optimization. Moreover, experimental results in different cases are provided to illustrate the performance of the presented controller.

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Continuous-Time Range-Only Pose Estimation

Continuous-Time Range-Only Pose Estimation

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Canadian Conference on Computer and Robot Vision, CRV

作者： Abhishek Goudar Timothy D. Barfoot Angela P. Schoellig University of Toronto Institute for Aerospace Studies (UTIAS) Toronto Canada Vector Institute for Artificial Intelligence Toronto Canada Munich Institute for Robotics and Machine Intelligence (MIRMI) Technical University of Munich Munich Germany

Range-only (RO) localization involves determining the position of a mobile robot by measuring the distance to specific anchors. RO localization is challenging since the measurements are low-dimensional and a single range sensor does not have enough information to estimate the full pose of the robot. As such, range sensors are typically coupled with other sensing modalities such as wheel encoders or inertial measurement units (IMUs) to estimate the full pose. In this work, we propose a continuous-time Gaussian process (GP)-based trajectory estimation method to estimate the full pose of a robot using only range measurements from multiple range sensors. Results from simulation and real experiments show that our proposed method, using off-the-shelf range sensors, is able to achieve comparable performance and in some cases outperform alternative state-of-the-art sensor-fusion methods that use additional sensing modalities.

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Swing Suppression and Accurate Positioning Control for Underactuated Offshore Crane Systems Suffering From Disturbances

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IEEE/CAA Journal of Automatica Sinica 2020年第3期7卷 892-900页

作者： Tong Yang Ning Sun He Chen Yongchun Fang Institute of Robotics and Automatic Information Systems(IRAIS) College of Artificial IntelligenceTianjin 300350China Tianjin Key Laboratory of Intelligent Robotics(tjKLIR) Nankai UniversityTianjin 300350China IEEE School of Artificial Intelligence Hebei University of TechnologyTianjin 300401China

Offshore cranes are widely applied to transfer largescale cargoes and it is challenging to develop effective control for them with sea wave disturbances. However, most existing controllers can only yield ultimate uniform boundedness or asymptotical stability results for the system's equilibrium point, and the state variables' convergence time cannot be theoretically guaranteed. To address these problems, a nonlinear sliding mode-based controller is suggested to accurately drive the boom/rope to their desired positions. Simultaneously, payload swing can be eliminated rapidly with sea waves. As we know, this paper firstly presents a controller by introducing error-related bounded functions into a sliding surface, which can realize boom/rope positioning within a finite time, and both controller design and analysis based on the nonlinear dynamics are implemented without any linearization manipulations. Moreover, the stability analysis is theoretically ensured with the Lyapunov method. Finally, we employ some experiments to validate the effectiveness of the proposed controller.

关键词： Boom/rope positioning offshore cranes swing elimination underactuated systems

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SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models

arXiv

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arXiv 2023年

作者： Li, Hongxin Su, Jingran Chen, Yuntao Li, Qing Zhang, Zhaoxiang China Institute of Automation Chinese Academy of Sciences China Center for Artificial Intelligence and Robotics HKISI CAS Hong Kong The Hong Kong Polytechnic University Hong Kong Shanghai Artificial Intelligence Laboratory China

Computer end users have spent billions of hours completing daily tasks like tabular data processing and project timeline scheduling. Most of these tasks are repetitive and error-prone, yet most end users lack the skill to automate these burdensome works. With the advent of large language models (LLMs), directing software with natural language user requests become a reachable goal. In this work, we propose a SheetCopilot agent that takes natural language task and control spreadsheet to fulfill the requirements. We propose a set of atomic actions as an abstraction of spreadsheet software functionalities. We further design a state machine-based task planning framework for LLMs to robustly interact with spreadsheets. We curate a representative dataset containing 221 spreadsheet control tasks and establish a fully automated evaluation pipeline for rigorously benchmarking the ability of LLMs in software control tasks. Our SheetCopilot correctly completes 44.3% of tasks for a single generation, outperforming the strong code generation baseline by a wide margin. Our project page: https://***/. Copyright © 2023, The Authors. All rights reserved.

关键词： Spreadsheets

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Clustering Algorithm with Learnable Distance for Categorical Data with Nominal and Ordinal Attributes 5

Clustering Algorithm with Learnable Distance for Categorical...

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5th International Conference on Data Science and Information Technology, DSIT 2022

作者： Jia, Hong Zhong, Weiwei College of Electronics and Information Engineering Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

For clustering analysis on categorical data, the distance measurement between two objects often plays a very important role. However, most of the existing categorical distance metrics do not distinguish between nominal attributes and ordinal attributes. That is, these metrics do not explore the information contained in ordinal values, and ignore the order relationship among them. Therefore, this paper proposes a novel clustering algorithm, which uses a united framework to measure the distance between nominal attributes and ordinal attributes while distinguishing the different characteristics between them. The basic idea of the proposed method is that, the attribute value pairs with larger co-occurrence probability in the same cluster may have smaller distances. Therefore, the distances between different categories are dynamically evaluated based on the current cluster structure of the data samples. Subsequently, the distances and cluster relationship are alternately learned until convergence. Experimental results show that the proposed algorithm has better robustness and performance than the existing counterparts on different kinds of categorical data sets. © 2022 IEEE.

关键词： Clustering algorithms

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SEPL-Net: A Semantics-Enhanced Pseudo Labeling Network for Semi-Supervised Image Analysis

SEPL-Net: A Semantics-Enhanced Pseudo Labeling Network for S...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Xiao, Wenjing Hwang, Kai Chen, Min Li, Xianzhi School of Computer Science and Technology Huazhong University of Science and Technology China School of Data Science Chinese University of Hong Kong Hong Kong The Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

As the mainstream solution for semi-supervised learning (SSL), pseudo-labeling-based approaches have achieved re-markable success. However, an obvious drawback of existing methods is that the valuable semantic relationships among categories are often ignored, thus leading to suboptimal encoded embeddings. To address this, we present a novel Semantics-Enhanced Pseudo Labeling Network, called SEPL-Net, for image analysis in a semi-supervised manner. SEPL-Net explores the prior knowledge of visual similarity between different classes to improve the quality of pseudo label decision making. Particularly, we encode semantic labels combined with the one-hot label to jointly train our network by exploiting their disagreement. To alleviate the difficulty of labeling unlabeled images due to the introduction of semantic labels, we further design different classifiers with differentiated strong augmentation modes to enable cooperative pseudo labeling. Extensive experimental results show that our SEPL-Net outperforms existing SSL methods with the averaged 1.84% accuracy improvement on image classification task. Code is available at https://***/sweetvicky/***. © 2022 IEEE.

关键词： Embeddings

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Automated Underground Mapping of Buried Utilities: A Review of Robotic Solutions and Sensor Technologies

Automated Underground Mapping of Buried Utilities: A Review ...

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IEEE International Conference on Control and robotics Engineering (ICCRE)

作者： Mohit Vohra Ayush Gupta Mian Muhammad Umair Amit Shukla Jayakumar Vandavasi Karunamurthy Aditi Gupta 4IR-Robotics Dubai Electricity and Water Authority Dubai UAE School of Mechanical and Materials Engineering Indian Institute of Technology Mandi Mandi India Centre for Artificial Intelligence and Robotics CIndian Institute Of Technology Mandi Mandi India

ISBN: (数字)9798350372694

ISBN: (纸本)9798350372700

In smart cities, utility infrastructures are strategically placed underground for reasons of convenience, safety, and aesthetics. The aging of these underground utilities emphasizes the importance of accurate mapping for inspections and maintenance. This survey paper extensively explores a range of sensors specifically designed for the detection of underground utilities. The manual operation of these sensors, requiring skilled personnel, is characterized by its slow pace, susceptibility to human errors, and safety concerns in potentially hazardous environments. To overcome these challenges, researchers are actively engaged in developing robotic solutions that seamlessly incorporate machine learning algorithms, aiming to achieve both rapid and accurate underground mapping. The paper offers a comprehensive overview of these robotic solutions, emphasizing their integration with diverse sensors to enable autonomous and efficient mapping of underground infrastructure. The shift toward automated solutions is deemed crucial to meet the growing survey demands in rapidly developing urban environments, ensuring safety, accuracy, and speed in modern underground utility mapping practices.

关键词： Surveys Accuracy Service robots Power cables Magnetic sensors Thermal sensors Sensor phenomena and characterization

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Revolutionizing Tuberculosis Treatment: Smart Technology Innovations for Mycobacterium tuberculosis Management

Revolutionizing Tuberculosis Treatment: Smart Technology Inn...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Pramanik, Atreyi Singh, Ajay Rani, Gouri Dubey, Shival Aluvala, Srinivas Uttaranchal University School of Applied and Life Sciences Division of Research and Innovation Dehradun India Uttaranchal University School of Applied and Life Sciences Dehradun India Uttaranchal University India University of Leeds Institute of Design Optimization and Robotics School of Mechanical Engineering United Kingdom SR University School of Computer Science & Artificial Intelligence India

ISBN: (纸本)9798350394412

Tuberculosis remains a significant global problem. The modification in smart technology (ST) invention generates novel chances to reform tuberculosis (TB) controlling. This work explores the potential of ST for filtering MTB diagnostics and therapy. ST includes telemedicine, mobile health uses (mHealth), wearable devices (WD) and data analytical that deliver many options of improving TB control. Telemedicine licenses distant consulting thus empowering medics to access patients in remote areas, offer quick detects as well as recommend treatment. mHealth apps certify that individuals have right of entry to education materials, can remind them when it's time for medication and offers self-monitoring tools which enhance adherence to the therapy by keeping the patient involved during the course of treatment. WD support real-time (RT) monitoring of heart rates, blood pressure level, physical activity and other vital signs that are important in coming up with modified remedy plans while also detecting response to cure earlier enough. Data analytics combined with AI can examine large amounts of patient data to identify patterns, forecast drug effects and optimize treatment strategies resulting in more efficient individualized care. These novelties through intellectual technologies may be used as solutions for tasks experienced during TB management, leading to improved patient outcomes and a reduction in the TB burden. © 2024 IEEE.

关键词： Telemedicine

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SSF: Accelerating Training of Spiking Neural Networks with Stabilized Spiking Flow

SSF: Accelerating Training of Spiking Neural Networks with S...

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International Conference on Computer Vision (ICCV)

作者： Jingtao Wang Zengjie Song Yuxi Wang Jun Xiao Yuran Yang Shuqi Mei Zhaoxiang Zhang University of Chinese Academy of Sciences Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Xi’an Jiaotong University Centre for Artificial Intelligence and Robotics HKISI-CAS Tencent

Surrogate gradient (SG) is one of the most effective approaches for training spiking neural networks (SNNs). While assisting SNNs to achieve classification performance comparable to artificial neural networks, SG suffers from the problem of time-consuming training, preventing it from efficient learning. In this paper, we formally analyze the backward process of classic SG and find that the membrane accumulation through time leads to exponential growth of training time. With this discovery, we propose Stabilized Spiking Flow (SSF), a simple yet effective approach to accelerate training of SG-based SNNs. For each spiking neuron, SSF averages its input and output activations over time to yield stabilized input and output, respectively. Then, instead of back propagating all errors that are related to current neuron and inherently entangled in time domain, the auxiliary gradient is directly propagated from the stabilized output to input through a devised relationship mapping. Additionally, SSF method is suitable to different neuron models. Extensive experiments on both static and neuromorphic datasets demonstrate that SNNs trained with SSF approach can achieve performance comparable to the original counterparts, while reducing the training time significantly. In particular, SSF speeds up the training process of state-of-the-art SNN models up to 10× when time steps equal to 80.

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