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arXiv 2025年

作者： Doodeman, Sander Tang, Zhiqi Jacinto, Marcelo Cunha, Rita Silvestre, Carlos Department of Mechanical Engineering TU/e Eindhoven University of Technology Eindhoven Netherlands Division of Decision and Control Systems KTH Royal Institute of Technology Sweden Instituto Superior Técnico University of Lisbon Portugal Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China

This work addresses the practical problem of distributed formation tracking control of a group of quadrotor vehicles in a relaxed sensing graph topology with a very limited sensor set, where only one leader vehicle can access the global position. Other vehicles in the formation are assumed to only have access to inter-agent bearing (direction) measurements and relative velocities with respect to their neighbor agents. A hierarchical control architecture is adopted for each quadrotor, combining a high-gain attitude inner-loop and an outer-loop bearing-based formation controller with collision avoidance augmentation. The proposed method enables a group of quadrotors to track arbitrary bearing persistently exciting desired formations, including time-varying shapes and rotational maneuvers, such that each quadrotor only requires relative measurements to at least one neighboring quadrotor. The effective performance of the control strategy is validated by numerical simulations in MATLAB and real-world experiments with three quadrotors. © 2025, CC BY-NC-ND.

关键词： Unmanned aerial vehicles (UAV)

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Computationally Efficient Predictive control Based on ANN State-Space Models

Computationally Efficient Predictive Control Based on ANN St...

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IEEE Conference on Decision and control

作者： Jan H. Hoekstra Bence Cseppento Gerben I. Beintema Maarten Schoukens Zsolt Kollár Roland Tóth Dept. of Electrical Engineering at the Eindhoven Control Systems group University of Technology The Netherlands Dept. of Measurement and Information Systems Budapest University of Technology and Economics Hungary Institute for Computer Science and Control Budapest Hungary

Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for the identification of nonlinear state-space models. However, if the resulting models are used directly for nonlinear model predictive control (NMPC), the resulting nonlinear optimization problem is often overly complex due to the size of the network, requires the use of high-order observers to track the states of the ANN model, and the overall control scheme does not exploit the available autograd tools for these models. In this paper, we propose an efficient approach to auto-convert ANN statespace models to linear parameter-varying (LPV) form and solve predictive control problems by successive solutions of linear model predictive problems, corresponding to quadratic programs (QPs). Furthermore, we show how existing deep-learning methods, such as SUBNET that uses a state encoder, enable efficient implementation of MPCs on identified ANN models. Performance of the proposed approach is demonstrated by a simulation study on an unbalanced disc system.

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Medical Dialogue Generation via Intuitive-then-Analytical Differential Diagnosis

arXiv

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arXiv 2024年

作者： Xu, Kaishuai Hou, Wenjun Cheng, Yi Wang, Jian Li, Wenjie Department of Computing The Hong Kong Polytechnic University Hong Kong Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Medical dialogue systems have attracted growing research attention as they have the potential to provide rapid diagnoses, treatment plans, and health consultations. In medical dialogues, a proper diagnosis is crucial as it establishes the foundation for future consultations. Clinicians typically employ both intuitive and analytic reasoning to formulate a differential diagnosis. This reasoning process hypothesizes and verifies a variety of possible diseases and strives to generate a comprehensive and rigorous diagnosis. However, recent studies on medical dialogue generation have overlooked the significance of modeling a differential diagnosis, which hinders the practical application of these systems. To address the above issue, we propose a medical dialogue generation framework with the Intuitive-then-Analytic Differential Diagnosis (IADDx). Our method starts with a differential diagnosis via retrieval-based intuitive association and subsequently refines it through a graph-enhanced analytic procedure. The resulting differential diagnosis is then used to retrieve medical knowledge and guide response generation. Experimental results on two datasets validate the efficacy of our method. Besides, we demonstrate how our framework assists both clinicians and patients in understanding the diagnostic process, for instance, by producing intermediate results and graph-based diagnosis paths. © 2024, CC BY.

关键词： Diagnosis

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A Highly Scalable Instruction Scheduler Design based on CPU Stall Elimination

A Highly Scalable Instruction Scheduler Design based on CPU ...

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2021 Zooming Innovation in Consumer Technologies Conference, ZINC 2021

作者： Sari, Alparslan Butun, Ismail University of Delaware Computer Systems and Networking Department of Electrical and Computer Engineering NewarkDE19716 United States Chalmers University of Technology Network and Systems Division Department of Computer Science and Engineering Gothenburg412 96 Sweden

ISBN: (纸本)9781665404174

In this paper, by targeting low-level code optimization, an instruction scheduler is designed and experimented with a synergistic processor unit (SPU) to show its effectiveness on a basic block and data dependency graph (DDG) called compiler instruction scheduler (CIS). In our methodology, a source C/C++ file is converted to an assembly file via spu-gcc to detect stalls in basic code blocks and CIS generates the DDG of executable code to eliminate stalls to find optimization opportunities and increase the program performance. The CIS simply shuffles the instruction sequences of the assembly code to eliminate CPU stalls in a given basic instruction block. Random and sliding window schedulers are implemented to generate a new assembly code sequence based on DDG and a basic block in parallel. Finally, this paper describes how CIS finds the optimized code sequence for a given file without any conflicts and hazards. Compared to the original code compilation process, we have shown that CIS improves the code execution metrics, and also our evaluated speedup results are found to be promising. © 2021 IEEE.

关键词： Scheduling

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Classification of Wind Turbine Defects based on the SqueezeNet Neural Network

Classification of Wind Turbine Defects based on the SqueezeN...

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International computer sciences and Information Technologies (CSIT)

作者： Lesia Dubchak Anatoliy Sachenko Carsten Wolff Nadiia Vasylkiv Zenovii Bernas Department of Computer Engineering West Ukrainian National University Ternopil Ukraine Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine Department of Tele-Informatics Kazimierz Pulaski University of Technology and Humanities in Radom Radom Poland Dortmund University of Applied Science and Arts Dortmund Germany Department of Information Computer Systems and Control West Ukrainian National University Ternopil Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

The article presents a study on classifying wind turbine defects using the SqueezeNet neural network. Wind turbines are critical for renewable energy, but defects such as corrosion, erosion, and cracks can significantly reduce their efficiency. The study proposes using image classification techniques with neural networks, particularly SqueezeNet, to automate defect detection. The compact nature of SqueezeNet makes it suitable for real-time applications with limited computing resources. Through training and testing, the network achieved an accuracy of 89%, demonstrating its potential to improve wind turbine maintenance and reduce operational costs.

关键词： Training Accuracy Costs Neural networks Real-time systems Wind turbines Maintenance Defect detection Testing Image classification

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COVID19 Classification Using CT Images via Ensembles of Deep Learning Models

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computers, Materials & Continua 2021年第10期69卷 319-337页

作者： Abdul Majid Muhammad Attique Khan Yunyoung Nam Usman Tariq Sudipta Roy Reham R.Mostafa Rasha H.Sakr Department of Computer Science HITEC University TaxilaTaxila47080Pakistan Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia PRTTL Washington University in Saint LouisSaint LouisMO 63110USA Department of Information Systems Faculty of Computers and Information SciencesMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computers and Information SciencesMansoura UniversityMansoura35516Egypt

The recent COVID-19 pandemic caused by the novel coronavirus,severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),has had a significant impact on human life and the economy around the world.A reverse transcription polymerase chain reaction(RT-PCR)test is used to screen for this disease,but its low sensitivity means that it is not sufficient for early detection and *** RT-PCR is a time-consuming procedure,there is interest in the introduction of automated techniques for *** learning has a key role to play in the field of medical *** most important issue in this area is the choice of key ***,we propose a set of deep learning features based on a system for automated classification of computed tomography(CT)images to identify ***,this method was used to prepare a database of three classes:Pneumonia,COVID19,and *** dataset consisted of 6000 CT images refined by a hybrid contrast stretching *** the next step,two advanced deep learning models(ResNet50 and DarkNet53)were fine-tuned and trained through transfer *** features were extracted from the second last feature layer of both models and further optimized using a hybrid optimization *** each deep model,the Rao-1 algorithm and the PSO algorithm were combined in the hybrid ***,the selected features were merged using the new minimum parallel distance non-redundant(PMDNR)*** final fused vector was finally classified using the extreme machine *** experimental process was carried out on a set of prepared data with an overall accuracy of 95.6%.Comparing the different classification algorithms at the different levels of the features demonstrated the reliability of the proposed framework.

关键词： COVID19 preprocessing deep learning information fusion firefly algorithm extreme learning machine

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A Recurrent Neural Network Based Bicriteria Repetitive Motion Collision Avoidance Scheme for Motion Planning of Dual Redundant Manipulators 13

A Recurrent Neural Network Based Bicriteria Repetitive Motio...

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13th International Conference on Intelligent control and Information Processing, ICICIP 2025

作者： Chen, Shirui Gao, Huanli Hu, Shaolin Zhang, Zhijun The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China The School of Automation Guangdong University of Petrochemical Technology Maoming Guangzhou525000 China The Guangzhou Key Laboratory of Brain-Computer Interaction and Applications Guangzhou China The Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Guangzhou510555 China The Jiangxi Thousand Talents Plan Nanchang University Nanchang330031 China The College of Computer Science and Engineering Jishou University Jishou416000 China Guangzhou510335 China The Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shaanxi University of Technology Hanzhong723001 China The School of Information Science and Engineering Changsha Normal University Changsha410100 China The School of Automation Science and Engineering Maoming525000 China The Institute of Artificial Intelligence and Automation Guangdong University of Petrochemical Technology Maoming525000 China

ISBN: (纸本)9798331516147

To achieve low joint-angle drift and avoid mutual collision between dual redundant manipulators (DRMs) when they are doing collaboration works, a recurrent neural network based bicriteria repetitive motion collision avoidance (BRMCA) scheme is proposed for motion planning of DRMs. By designing a combined optimization objective encompassing both repetitive motion criterion and collision avoidance criterion, the BRMCA scheme has larger feasible region and less constraint contradiction issues compared with traditional inequality constraints based method. Furthermore, by incorporating velocity-level kinematic model and physical limits of DRMs into the scheme, a time varying quadratic programming (TVQP) problem is formulated. The linear variational inequality-based primal–dual neural network (LVI-PDNN) is used to solve the proposed scheme due to the high accuracy and low computation complexity. Finally, through comparison simulations of tracking Circle-shape and C-shape trajectories between BRMCA scheme and minimum velocity norm (MVN) scheme, it exhibits that the proposed BRMCA scheme can achieve low joint-angle drift, collision avoidance and high tracking accuracy when DRMs are doing cooperative tasks. © 2025 IEEE.

关键词： Recurrent neural networks

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Effectiveness of Using Deep Learning for Blister Blight Identification in Sri Lankan Tea

Effectiveness of Using Deep Learning for Blister Blight Iden...

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2023 International Research Conference on Smart Computing and systems engineering, SCSE 2023

作者： Hewawitharana, G.H.A.U. Nawarathne, U.M.L.A. Hassan, A.S.F. Wijerathna, Lochana M. Sinniah, G.D. Vidhanaarachchi, Samitha P. Wickramarathne, Jagath Wijekoon, Janaka L. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Tea Research Institute of Sri Lanka Plant Pathology Division Talawakelle Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Military Technological College Systems Engineering Department Muscat Oman

ISBN: (纸本)9798350341454

Ceylon tea industry faces a major challenge in the form of pathogen-induced crop loss, with Blister Blight (BB) caused by Exobasidium vexans posing the greatest threat, leading to harvest losses of over 30%. This fungus attacks the tender tea shoots, resulting in a direct negative impact on the tea harvest. This paper presents a system to identify the suspicious tea leaves and BB disease at its early stages along with an assessment of severity, offering a potential solution to this critical issue. By utilizing real-time object detection, the system filters out non-tea leaves from the captured initial image of a segment of a tea plant. The identified tea leaves are then subjected to BB identification and severity assessment based on differing visual symptoms of the BB stages. This approach enables the system to accurately identify BB in the initial stage and severity stage, allowing for timely and targeted intervention to minimize crop losses. The YOLOv8 model has been able to correctly identify 98% of the objects it has detected as relevant (precision), and it has been able to correctly identify 96% of all the relevant objects present in the scene (recall). The Residual Network 50 (Resnet50) convolutional neural network (CNN) model was selected as the final model, achieving an accuracy of 89.90% during the training phase and an accuracy of 88.26% during the testing phase. © 2023 IEEE.

关键词： Convolutional neural networks

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Integration of a multi-rate electron density profile observer in the plasma control system of TCV 49

Integration of a multi-rate electron density profile observe...

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49th EPS Conference on Plasma Physics, EPS 2023

作者： Pastore, F. Felici, F. Sauter, O. Bosman, T.O.S.J. Galperti, C. Vincent, B. Pau, A. Poels, Y. Ravensbergen, T. Vu, N.M.T. Lausanne Switzerland DIFFER - Dutch Institute for Fundamental Energy Research Energy Systems and Control Group Eindhoven Netherlands Eindhoven University of Technology Dept. of Mechanical Engineering Control Systems Technology Group Eindhoven Netherlands Eindhoven University of Technology Dept. of Mathematics and Computer Science Data Mining Group Eindhoven Netherlands ITER Organization Route de Vinon-sur-Verdon St. Paul Lez Durance13067 Cedex France

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MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN 2

MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Krishna, Tiruveedula Gopi Sreenivas, Nune Ajay Kumar, M. Reddy, Chandra Sekhar Rathee, Davinder Sing Urgessa, Teklu College of Electrical Engineering and Computing Adama Science and Technology University Department of Computer Science and Engineering Adama Ethiopia Andhra Loyala institute of Engineering and Technology Department of Electrical and Electronics Engineering Vijayawada India College of Electrical Engineering and Computing Adama Science and Technology University Department of Electrical Power and Control Engineering Adama Ethiopia College of Electrical Engineering and Computing Adama Science and Technology University Department of ECE Adama Ethiopia

ISBN: (纸本)9798331505745

The research presents a new efficient machine learning method to classify brain tumors because this task remains vital in fighting the high incidence of brain cancers. The proposed approach unites all its operations including image segmentation with feature extraction and classification into a single processing system. This study uses Particle Swarm Optimization (PSO) and Adaptive PSO (APSO) methodologies to optimize Locally Linear Radial Basis Function Neural Network (LLRBFNN) resulting in PSO-LLRBFNN and APSO-LLRBFNN model implementations. The research examined the optimized APSO-LLRBFNN models against Support Vector Machines (SVM) and Least Mean Squares (LMS) based LLRBFNN classifier methods. Results from experiments show that APSO-LLRBFNN gives superior outcomes with 98.93% accuracy and takes 12.23 seconds for execution, which surpasses currently available methods. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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