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arXiv 2024年

作者： Ren, Yilong Chen, Yue Liu, Shuai Wang, Boyue Yu, Haiyang Cui, Zhiyong The State Key Laboratory of Intelligent Transportation Systems School of Transportation Science and Engineering Beihang University Beijing100191 China The Faculty of Information Technology Beijing University of Technology Beijing100124 China

Traffic prediction constitutes a pivotal facet within the purview of intelligent Transportation systems (ITS), and the attainment of highly precise predictions holds profound significance for efficacious traffic management. The precision of prevailing deep learning-driven traffic prediction models typically sees an upward trend with a rise in the volume of training data. However, the procurement of comprehensive spatiotemporal datasets for traffic is often fraught with challenges, primarily stemming from the substantial costs associated with data collection and retention. Consequently, developing a model that can achieve accurate predictions and good generalization ability in areas with limited historical traffic data is a challenging problem. It is noteworthy that the rapidly advancing pretrained Large Language Models (LLMs) of recent years have demonstrated exceptional proficiency in cross-modality knowledge transfer and few-shot learning. Recognizing the sequential nature of traffic data, similar to language, we introduce TPLLM, a novel traffic prediction framework leveraging LLMs. In this framework, we construct a sequence embedding layer based on Convolutional Neural Networks (CNNs) and a graph embedding layer based on Graph Convolutional Networks (GCNs) to extract sequence features and spatial features, respectively. These are subsequently integrated to form inputs that are suitable for LLMs. A Low-Rank Adaptation (LoRA) fine-tuning approach is applied to TPLLM, thereby facilitating efficient learning and minimizing computational demands. Experiments on two real-world datasets demonstrate that TPLLM exhibits commendable performance in both full-sample and few-shot prediction scenarios, effectively supporting the development of ITS in regions with scarce historical traffic data. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph embeddings

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Drone Gesture Control using OpenCV and Tello

Drone Gesture Control using OpenCV and Tello

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Conference on Information Communication Technology and Society (ICTAS)

作者： Nokuthula Khoza Pius Owolawi Vusumuzi Malele Department of Computer Systems Engineering Faculty of Information and Communication Technology Soshanguve Campus Tshwane University of Technology Pretoria South Africa Unit for Data Science and Computing School of Computer Science and Information Systems Vanderbijlpark Campus North-West University Vanderbijlpark South Africa

Tracking a person with an onboard camera is a very difficult and perhaps technically impossible if one camera is used. In this regard, real-life projects use a series of cameras to achieve the task. The advent of camera installed drone technology has made it possible for researchers and designers to resolve the tracking issues of a single or multiple stagnant devices. This paper presents a method that makes it possible for a drone to recognize hand gestures as control mechanism and follow the person who control it. Furthermore, the method assists in drone object detection using an integrated depth camera that provides video capturing. The OpenCV and Tello were used in this project. A stabilized depth video is for use with current person trackers like the OpenCV tracker. The estimated Tello pose deduced from vision to stabilize the depth image by warping it to a virtual-static camera. This method allows the Tello to obtain not only the tracked person's position and orientation but also their full body pose, which can then be used, for instance, to recognize hand gestures and alter the Tello's behavior. This work has a potential of contributing or enhancing cutting-edge applications such as hands-free photography and videography.

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Cooperative Global K-exponential Tracking Control of Multiple Mobile Robots-Extended Version

arXiv

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arXiv 2024年

作者： Xu, Liang Su, Youfeng Cai, He College of Computer and Data Science Fuzhou University Fuzhou350116 China Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen518055 China School of Automation Science and Engineering South China University of Technology Guangzhou510641 China

This paper studies the cooperative tracking control problem for multiple mobile robots over a directed communication network. First, it is shown that the closed-loop system is uniformly globally asymptotically stable under the proposed distributed continuous feedback control law, where an explicit strict Lyapunov function is constructed. Then, by investigating the convergence rate, it is further proven that the closed-loop system is globally K-exponentially stable. Moreover, to make the proposed control law more practical, the distributed continuous feedback control law is generalized to a distributed sampled-data feedback control law using the emulation approach, based on the strong integral input-to-state stable Lyapunov function. Numerical simulations are presented to validate the effectiveness of the proposed control methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Lyapunov functions

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Distributed Constrained Online Nonconvex Optimization with Compressed Communication

arXiv

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arXiv 2025年

作者： Zhang, Kunpeng Xu, Lei Yi, Xinlei Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai201210 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online nonconvex optimization with time-varying inequality constraints over a network of agents. For a time-varying graph, we propose a distributed online primal–dual algorithm with compressed communication to efficiently utilize communication resources. We show that the proposed algorithm establishes an O(T max{1−θ1,θ1}) network regret bound and an O(T 1−θ1/2) network cumulative constraint violation bound, where T is the number of iterations and θ1 ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints at all iterations), the network cumulative constraint violation bound is reduced to O(T 1−θ1). These bounds are comparable to the state-of-the-art results established by existing distributed online algorithms with perfect communication for distributed online convex optimization with (time-varying) inequality constraints. Finally, a simulation example is presented to validate the theoretical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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Adaptive neural tracking control for nonlinear servo system with unknown time-varying parameters and uncertainties

Adaptive neural tracking control for nonlinear servo system ...

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第42届中国控制会议

作者： Faxiang Zhang Guanbin Gao Jing Na Jiande Wu Faculty of Mechanical and Electrical Engineering and Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University

This paper solves the neural network(NN) tracking control problem for uncertain nonlinear servo system with timevarying parameters and *** address the uncertain nonlinear function with unknown time-varying parameters(i.e.,unknown nonlinear spatiotemporal function),the time-varying parameter extraction method is used to separate the time-varying puameters from uncertain nonlinear spatiotemporal function,which yields an unknown state-based boundary *** the tools of NN and adaptive technology,an adaptive neural tracking controller is designed,which guarantees the uniformly ultimately bounded(UUB) performance of the resulting closed-loop *** effectiveness of the designed method is verified by simulations.

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Predictive Modeling of Portuguese Student Performance: Comparative Machine Learning Analysis

Predictive Modeling of Portuguese Student Performance: Compa...

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Telecommunications Conference (ITC-Egypt), International

作者： Faris H. Rizk Mahmoud Elshabrawy Mohamed Basant Sameh Ahmed Mohamed Zaki Marwa M. Eid El-Sayed M. El-kenawy Computer Science and Intelligent Systems Research Center Blacksburg Virginia USA Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

Such an analysis of different machine learning methods for predicting the achievement levels of students in Portuguese secondary education makes this essay. The research highlights the importance of accurate expectations of learners' results for education system administrations and respective policymakers. The current study makes use of the “Student Performance in Portuguese Secondary education” dataset and employs machine learning algorithms, namely MLPRegressor, XGBoost, DecisionTreeRegressor, CatBoost, and KNeighborsRe-gressor, to the corpus. Performance metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), etc., are used to judge every model's performance. The conclusion that can be drawn from the data is that the MLPRegressor model leads among the competitors, having an MSE equivalent of 0.0103, which is superior to others. The findings of this study are of great significance for educational institutions and policymakers as they work to make appropriate contact with students' performance prediction.

关键词： Measurement Machine learning algorithms Education Machine learning Predictive models Market research data models

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Efficacy of Local Cache for Performance Improvement of Reliable data Transmission in Aggregated Robot Processing Architecture

Efficacy of Local Cache for Performance Improvement of Relia...

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International Conference on Control, Automation and systems ( ICCAS)

作者： Abdul Jalil Jun Kobayashi Department of Computer Science and Systems Engineering Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Department of Intelligent and Control Systems Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan

This study aims to improve the performance of message data transmission in Aggregated Robot Processing (ARP) architecture where the RELIABLE option of the RELIABILITY QoS (Quality of Service) policy is applied to a ROS 2 (Robot Operating System 2) node communication. Basically, the RELIABLE option guarantees that a publisher properly send all message data to a subscriber. However, the publisher could fail to transmit some message data to the subscriber even in the RELIABLE QoS option unless the buffer size of the publisher is enough. We introduce local cache to a sensing component to alleviate the issue in the case that the sensors output the same value in a row. The experimental results showed that the local cache improved the latency and reduced the message loss in node communication.

关键词： Performance evaluation Operating systems Process control Quality of service Robot sensing systems Control systems Sensors

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Preliminary Research on Super-Regenerative Receiver for Intracardiac Communication

Preliminary Research on Super-Regenerative Receiver for Intr...

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2023 Cross Strait Radio science and Wireless Technology Conference, CSRSWTC 2023

作者： Weng, Xueyu Li, Dongming Zhao, Wenxiang Pun, Sio Hang Vai, Mang I. Gao, Yueming College of Advanced Manufacturing Fuzhou University China Fuzhou University The International Joint Laboratory on Health Intelligent Monitoring Systems China University of Macau State Key Laboratory of Analog and Mixed-Signal VLSI China University of Macau Faculty of Science and Technology China College of Physics and Information Engineering Fuzhou University China

ISBN: (纸本)9798350358971

Existing commercial pacemakers only provide single-chamber pacing and cannot achieve multi-chamber sequential pacing. Conduction Intracardiac Communication(CIC) has the characteristics of low power consumption and high security, which is very suitable for communication between multi-chamber leadless pacemakers. Because the pacemaker needs to be implanted into the heart, which limits its volume, power consumption, and other related parameters, and the super-regenerative circuit has its low power consumption and simple circuit structure, the super-regenerative receiver scheme is a good choice for intracardial communication where power consumption is the main problem. In this paper, a prototype super-regenerative receiver suitable for intracardiac communication is proposed. The differential instrument amplifier with high input impedance is used to filter out the external electromagnetic interference, reduce the input equivalent noise, and improve the signal-to-noise ratio. A super-regenerative oscillator and a filter amplifier circuit are designed to enhance the effectiveness and reliability of the communication. The experimental results show that the bit error rate of the circuit is lower than 1 × 10-5 for the received signal of -92 dBm. © 2023 IEEE.

关键词： CIC Leadless pacemaker super-regenerative

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NcoVis: A Visual Analysis Framework for Exploring Academic Collaboration Networks under New Collaborative Relationships 25

NcoVis: A Visual Analysis Framework for Exploring Academic C...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Yu, Ye Wu, Yao Liang, Xi Ma, Cheng Lu, Qiang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (纸本)9781665405270

Academic collaboration has become one of the topics of large-scale social network analysis. The information extracted from academic collaboration also shows the contextual value in combination with other data. For academic collaborative social networks with intricate collaborative relationships, previous research methods mainly focused on network structure and community discovery, lacking in mining the characteristics of relationships in collaborative networks. There were some limitations in the research perspective. In this work, we propose a collaborative relationship representation method that is more suitable for academic collaborative social networks. It marks the attribute characteristics of nodes in collaborative relationships and highlights collaborative relationships in visualization. By adopting this new representation of collaborative relationships, we designed a visual analysis framework NcoVis, to visualize academic collaborative social networks. We further visually analyze the link relationship groups guided by nodes, and explore the potential representation of the structure and attribute characteristics of link relationships, so that analysts can explore the theme and structural change characteristics of link relationships, communities and the whole network in collaborative social networks. To prove the proposed framework, we conducted a case study on the academic collaboration literature data set, and analyzed the questionnaire with Likert scale through the user evaluation experiment, which verified the effectiveness of our method and the practicability of NcoVis. © 2022 IEEE.

关键词： Visualization

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A Sensing Platform to Monitor Sleep Efficiency 11th

A Sensing Platform to Monitor Sleep Efficiency

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11th Italian Forum on Ambient Assisted Living, ForItAAL 2020

作者： Crivello, Antonino La Rosa, Davide Wilhelm, Elisabeth Palumbo, Filippo 1 Via G. Moruzzi Pisa56124 Italy Department of Health Sciences and Technology Istitute of Robotics and Intelligent Systems Sensory Motor Systems Lab ETH Zurich Zurich8092 Switzerland Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands

ISBN: (纸本)9783031088377

Sleep plays a fundamental role in the human life. Sleep research is mainly focused on the understanding of the sleep patterns, stages and duration. An accurate sleep monitoring can detect early signs of sleep deprivation and insomnia consequentially implementing mechanisms for preventing and overcoming these problems. Recently, sleep monitoring has been achieved using wearable technologies, able to analyse also the body movements, but old people can encounter some difficulties in using and maintaining these devices. In this paper, we propose an unobtrusive sensing platform able to analyze body movements, infer sleep duration and awakenings occurred along the night, and evaluating the sleep efficiency index. To prove the feasibility of the suggested method we did a pilot trial in which several healthy users have been involved. The sensors were installed within the bed and, on each day, each user was administered with the Groningen Sleep Quality Scale questionnaire to evaluate the user’s perceived sleep quality. Finally, we show potential correlation between a perceived evaluation with an objective index as the sleep efficiency. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sleep research

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