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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Cong, Weiqi Sun, Junqing Sun, Hao School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China College of Artificial Intelligence Nankai University Tianjin300350 China

ISBN: (纸本)9798350361674

Deep learning methods are widely used in the task of detecting tumor cells. However, due to the small tumor cells and the presence of impurity interference, it results in the inability to meet the accuracy requirements for assisted diagnosis. To address this problem, this paper proposes an improved YOLOv5 algorithm, the specific process is as follows: use Mosaic image enhancement technology to improve the accuracy and robustness of neural network training, use the self-attention mechanism bottleneck transformers module to replace the original C3 module, and improve the performance of the detection model through effective feature extraction and feature fusion techniques. Then, the Squeeze-and-Excitation (SE) module is added to enhance the extraction of object features between channels, which can adaptively correct the channel features and help the detection of small objects. The experimental results show that the mAP_0.5 of the improved YOLOv5 algorithm reaches 75.87%, which is a significant improvement over the original algorithm for small object detection. © 2024 ieee.

关键词： attention mechanism Deep learning small object detection tumor cells YOLOv5

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Decision Making for Multi-Robot Fixture Planning Using Multi-Agent Reinforcement learning

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ieee TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 5578-5589页

作者： Canzini, Ethan Auledas-Noguera, Marc Pope, Simon Tiwari, Ashutosh Univ Sheffield Dept Automat Control & Syst Engn Sheffield S10 2TN England Airbus Robot Broughton CH4 0DR England

Within the realm of flexible manufacturing, fixture layout planning allows manufacturers to rapidly deploy optimal fixturing plans that can reduce surface deformation that leads to crack propagation in components during manufacturing tasks. The role of fixture layout planning has evolved from being performed by experienced engineers to computational methods due to the number of possible configurations for components. Current optimisation methods commonly fall into sub-optimal positions due to the existence of local optima, with data-driven machine learning techniques relying on costly to collect labelled training data. In this paper, we present a framework for multi-agent reinforcement learning with team decision theory to find optimal fixturing plans for manufacturing tasks. We demonstrate our approach on two representative aerospace components with complex geometries across a set of drilling tasks, illustrating the capabilities of our method;we will compare this against state of the art methods to showcase our method's improvement at finding optimal fixturing plans with 3 times the improvement in deformation control within tolerance bounds.

关键词： Fixtures Planning Task analysis Manufacturing Reinforcement learning Robots Layout Multi-agent systems reinforcement learning aerospace manufacturing fixture planning robotic fixturing

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data-driven Modeling and Experimental Validation of Autonomous Vehicles using Koopman Operator

Data-Driven Modeling and Experimental Validation of Autonomo...

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ieee/RSJ International conference on Intelligent Robots and systems (IROS)

作者： Joglekar, Ajinkya Sutavani, Sarang Samak, Chinmay Samak, Tanmay Kosaraju, Krishna Chaitanya Smereka, Jonathon Gorsich, David Vaidya, Umesh Krovi, Venkat Clemson Univ Dept Automot Engn Int Ctr Automot Res Greenville SC 29607 USA Clemson Univ Dept Mech Engn Clemson SC 29634 USA

ISBN: (纸本)9781665491907

This paper presents a data-driven framework to discover underlying dynamics on a scaled F1TENTH vehicle using the Koopman operator linear predictor. Traditionally, a range of white, gray, or black-box models are used to develop controllers for vehicle path tracking. However, these models are constrained to either linearized operational domains, unable to handle significant variability or lose explainability through end-2-end operational settings. The Koopman Extended Dynamic Mode Decomposition (EDMD) linear predictor seeks to utilize data-driven model learning whilst providing benefits like explainability, model analysis and the ability to utilize linear model-based control techniques. Consider a trajectory-tracking problem for our scaled vehicle platform. We collect pose measurements of our F1TENTH car undergoing standard vehicle dynamics benchmark maneuvers with an OptiTrack indoor localization system. Utilizing these uniformly spaced temporal snapshots of the states and control inputs, a data-driven Koopman EDMD model is identified. This model serves as a linear predictor for state propagation, upon which an MPC feedback law is designed to enable trajectory tracking. The prediction and control capabilities of our framework are highlighted through real-time deployment on our scaled vehicle.

关键词： Dynamic mode decomposition

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On Feedback Error learning for Adaptive Soft Robot control 7

On Feedback Error Learning for Adaptive Soft Robot Control

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7th International conference on Soft Robotics (ROBOSOFT)

作者： Veronese, Niccolo Enrico Albini, Alessandro Yao, Yao Rocco, Paolo Maiolino, Perla Forni, Fulvio Polytech Univ Milan Milan Italy Univ Oxford Oxford Robot Inst Oxford England Univ Genoa Genoa Italy Univ Cambridge Dept Engn Cambridge England

ISBN: (纸本)9798350381818

Soft robots are appealing in a wide variety of tasks thanks to their inherent advantages in safety, compliance, and adaptability. However, accurate modelling and control of soft robots are still significantly challenging. This paper proposes a control scheme implementing an online learning strategy. The architecture is composed of (i) a data-driven model generating a feedforward signal, and (ii) a feedback controller. The latter has two roles. Firstly, it corrects the action of the feedforward controller when the tracking error increases. Secondly, it generated a learning signal to train the data-driven model, allowing for online adaptation of the feedforward signal with respect to changes in the dynamic of the system. Experimental results show that the proposed method provides better performance compared with a PID controller when applied to a trajectory following task. Furthermore, our controller is shown to be capable of online adaptation to sudden changes in the dynamics of the soft robot due to a variable payload.

关键词： controllers

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Application of CPU Parallel Computing in MATLAB Environment for Analysis of Anti-Saturation and Gain Effects in PID controller 13

Application of CPU Parallel Computing in MATLAB Environment ...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Huang, Ruirui Wu, Zhenhui Hou, Yandong Cheng, Qianshuai Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Technology and Media University of Henan Kaifeng Kaifeng475004 China School of Artificial Intelligence Henan University Zhengzhou450000 China

ISBN: (纸本)9798350361674

With the continuous increase in data size and model complexity, the computational workload has grown rapidly, posing a significant challenge to the capabilities of computer data processing and simulation calculations. Therefore, parallel programming based on multicore and cluster architectures has become one of the mainstream technologies to enhance program execution efficiency and numerical computation efficiency. The theoretical foundations of parallel program computing have been applied in various aspects of engineering applications and theoretical simulations. In this paper, a new parallel PID anti-integral saturation controller is designed for a second-order closed-loop control system of unmanned aerial vehicles (UAVs). It compares and analyzes the runtime, execution efficiency, and speedup ratio of parallel programs and general serial programs under the same scenario. The experimental results demonstrate that parallel computing significantly improves the simulation program's efficiency for PID controller anti-saturation control systems under identical scenarios, exhibiting a high speedup ratio on the existing computing platform. Additionally, this study consolidates common issues encountered in MATLAB parallel program design, offering valuable insights into overcoming challenges in this domain. © 2024 ieee.

关键词： MATLAB

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Effective Analysis of Machine and Deep learning Methods for Diagnosing Mental Health Using Social Media Conversations

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ieee TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2025年第1期12卷 274-294页

作者： Kasanneni, Yashwanth Duggal, Achyut Sathyaraj, R. Raja, S. P. Vellore Inst Technol Sch Comp Sci & Engn SCOPE Vellore 632014 India

The increasing incidence of mental health issues demands innovative diagnostic methods, especially within digital communication. Traditional assessments are challenged by the sheer volume of data and the nuanced language found on social media and other text-based platforms. This study seeks to apply machine learning (ML) to interpret these digital narratives and identify patterns that signal mental health conditions. We apply natural language processing (NLP) techniques to analyze sentiments and emotional cues across datasets from social media and other text-based communication. Using ML, deep learning, and transfer learning models such as bidirectional encoder representations (BERTs), robustly optimized BERT approach (RoBERTa), distilled BERT (DistilBERT), and generalized autoregressive pretraining for language understanding (XLNet), we assess their ability to detect early signs of mental health concerns. The results show that BERT, RoBERTa, and XLNet consistently achieve over 95% accuracy, highlighting their strong contextual understanding and effectiveness in this application. The significance of this research lies in its potential to revolutionize mental health diagnostics by providing a scalable, data-driven approach to early detection. By harnessing the power of advanced NLP models, this study offers a pathway to more timely and accurate identification of individuals in need of mental health support, thereby contributing to better outcomes in public health.

关键词： Mental health Accuracy Social networking (online) Analytical models Computational modeling Linguistics Encoding data models Bidirectional control Natural language processing Bidirectional encoder representation (BERT) deep learning (DL) distilled BERT (DistilBERT) generalized autoregressive pretraining for language understanding (XLNet) machine learning (ML) mental health prediction natural language processing (NLP) robustly optimized BERT approach (RoBERTa) social media analytics transfer learning

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Adaptive Parameter Identification Based Sliding Mode control for Servo Mechanism Using Nonlinear Disturbance Observer 13

Adaptive Parameter Identification Based Sliding Mode Control...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Du, Hailan Tao, Liang Tao, Meiling Xu, Binzi Deng, Xiongfeng College of Electrical Engineering Anhui Polytechnic University Wuhu241000 China

ISBN: (纸本)9798350361674

In this paper, a friction compensation method is proposed based on adaptive parameter identification and nonlinear disturbance observer for servo mechanism system to achieve desired tracking performance. Firstly, the traditional nonlinear friction model is transformed into a continuously differentiable linear model. An adaptive parameter identifier is then designed using a state observer to facilitate finite-time estimation of friction parameters, thereby enhancing the overall performance of the control system. Subsequently, a nonlinear disturbance observer is utilized to enable real-time estimation and compensation of unknown disturbances in the system, as well as errors in parameter identification. Additionally, a sliding mode controller is developed to achieve robust tracking control. Finally, stability analysis is performed using Lyapunov stability theory, and the feasibility of the proposed method is validated through simulation. © 2024 ieee.

关键词： Sliding mode control

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A data-based adaptive consensus control algorithm of discrete-time multi-agent systems 14

A data-based adaptive consensus control algorithm of discret...

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14th Asian control conference (ASCC)

作者： Xiang, Aoxue Zhao, Xinyuan Beijing Univ Technol Sch Math Stat & Mech Beijing 100124 Peoples R China

ISBN: (纸本)9798331540845;9789887581598

This paper addresses the consensus tracking problem of leader-follower discrete-time multi-agent systems. To overcome the shortcomings of existing reinforcement learning (RL) algorithms, we propose a novel data-based adaptive reinforcement learning algorithm by introducing adaptive parameter. Distinguishing itself from most existing work, the study assumes that the dynamics of all agents are entirely unknown and the complete state vector is not accessible, the algorithm proposed only requires some input-output data relevant to the agents to be available. Based on this representation, the algorithm strikes a balance between data-based value iteration and data-based policy iteration, obviates the need for the limitation of initial control, exhibits a fast rate of convergence, and achieves the consensus in multi-agent systems. Through comprehensive simulation research, the effectiveness of the proposed algorithm is verified.

关键词： Multi-agent systems Consensus control data-based control Reinforcement learning

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Model-free Adaptive Heading control of Hovercraft Based on data-driven 21

Model-free Adaptive Heading Control of Hovercraft Based on D...

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21st ieee International conference on Mechatronics and Automation (ieee ICMA)

作者： Wu, Wenjie Su, Dayong Han, Shuyi Gao, Song Marine Design & Res Inst China 168 Zhongshan Nanyi Rd Shanghai 200011 Peoples R China

ISBN: (纸本)9798350388084;9798350388077

This paper addresses the heading control challenge of hovercraft, considering their unique navigational characteristics such as a minimal underwater physical field, limited resistance to wind and waves, and unstable regions. To tackle these issues, an enhanced data-driven control approach has been utilized to investigate the heading control issue in hovercrafts. Initially, the dynamic uncertainty model of the hovercraft is linearized into an equivalent data model using pseudo-partial derivatives. Subsequently, a model-free self-adaptive PD control algorithm for hovercraft heading is developed in conjunction with the PD feedback control law. Additionally, an incremental PID controller is designed to regulate the hovercraft's speed to manage the numerous unstable areas. The efficacy of the proposed data-driven heading control algorithm is substantiated through simulation tests. A distinctive aspect of this research is the development of a hovercraft heading and speed control method that operates without relying on any model information, suitable for control systems that are highly nonlinear and strongly coupled, where establishing an accurate model is challenging. Notably, the control system design is based solely on the input and output data such as the hovercraft's pitch, rudder angle, speed, and heading.

关键词： hovercraft data-driven control adaptive PD control

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data-driven Decision-Making for SCUC: An Improved Deep learning Approach Based on Sample Coding and Seq2Seq Technique

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PROTECTION AND control OF MODERN POWER systems 2025年第2期10卷 13-24页

作者： Yang, Nan Hao, Juncong Li, Zhengmao Ye, Di Xing, Chao Zhang, Zhi Wang, Can Huang, Yuehua Zhang, Lei China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China Aalto Univ Sch Elect Engn Espoo 02150 Finland State Grid Fujian Elect Power Co Ltd Fuzhou Power Supply Co Fuzhou 350009 Peoples R China Yunnan Power Grid Co Ltd Elect Power Res Inst Kunming 650217 Yunnan Peoples R China State Grid Corp China Beijing 100031 Peoples R China

The electricity industry has witnessed increasing challenges in power system operation and rapid developments of artificial intelligence technologies in the last decades. In this context, studying the approach of security-constrained unit commitment (SCUC) decision-making with high adaptability and precision is of great importance. This paper proposes an improved data-driven deep learning (DL) approach, following the sample coding and Sequence to Sequence (Seq2Seq) technique. First, an encoding and decoding strategy is utilized for high-dimensional sample matrix dimension compression. A DL SCUC decision model based on a Seq2Seq network with gated recurrent units as neurons is then constructed, and the mapping between load and unit on/off scheme is established through massive data from historical scheduling. Numerical simulation results based on the ieee 118-bus test system demonstrate the correctness and effectiveness of the proposed approach.

关键词： data-driven gated recurrent unit sam- ple coding sam- ple coding Sequence to Sequence Sequence to Sequence

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