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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Igor Volkov Yuri Andreev Faculty of Control Systems and Robotics ITMO University Saint- Petersburg Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

Ensuring the quality of CNC-machined parts is a challenging task in modern manufacturing due to frequent shifts in production conditions and the high costs of traditional inspection methods. In this study, we propose a data-driven approach that integrates machine learning (ML) models with statistical acceptance sampling to dynamically optimize quality control processes. Production data from ERP, MES, and IIoT systems were analyzed with Gradient Boosting model. By continuously retraining the model on newly completed batches, the system adapts to evolving conditions, maintaining predictive accuracy. A key innovation of this work is the timely adjustment of sampling parameters (e.g., sample size, acceptance number) according to predicted defect levels. This adaptive strategy conserves inspection resources when defect risks are low and intensifies monitoring when risks are high. The results demonstrate a marked reduction in inspection costs and a robust capacity to detect defects promptly, aligning with industry goals of minimizing scrap and rework. Overall, this study validates the effectiveness of applying machine learning methods to production data analysis, laying a foundation for future enhancements in adaptive, proactive quality control systems.

关键词： Industries Adaptation models Technological innovation Data analysis Costs Process control Production Quality control Inspection Predictive models

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Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder's Trajectory

Field Robotics

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Field robotics 2025年 2卷 222-240页

作者： Antonella Barišić Frano Petric Stjepan Bogdan Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb Croatia

This paper presents our approach to intercepting a faster intruder UAV, inspired by the MBZIRC 2020 Challenge 1. By utilizing a priori knowledge of the shape of the intruder's trajectory, we can calculate an interception point. Target tracking is based on image processing by a YOLOv3 Tiny convolutional neural network, combined with depth calculation using a gimbal-mounted ZED Mini stereo camera. We use RGB and depth data from the camera, devising a noise-reducing histogram-filter to extract the target's 3D position. Obtained 3D measurements of target's position are used to calculate the position, orientation, and size of a figure-eight shaped trajectory, which we approximate using a Bernoulli lemniscate. Once the approximation is deemed sufficiently precise, as measured by the distance between observations and estimate, we calculate an interception point to position the interceptor UAV directly on the intruder's path. Our method, which we have significantly improved based on the experience gathered during the MBZIRC competition, has been validated in simulation and through field experiments. Our results confirm that we have developed an efficient, visual-perception module that can extract information describing the intruder UAV's motion with precision sufficient to support interception planning. In a majority of our simulated encounters, we can track and intercept a target that moves 30% faster than the interceptor. Corresponding tests in an unstructured environment yielded 9 out of 12 successful results.

关键词： Autonomous aerial vehicles Trajectory Target tracking Cameras Three-dimensional displays Robots Radar tracking Image reconstruction Estimation Detectors

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Autonomous, Mobile Manipulation in a Wall-Building Scenario: Team LARICS at MBZIRC 2020

Field Robotics

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Field robotics 2025年 2卷 201-221页

作者： Ivo Vatavuk Marsela Polic Ivan Hrabar Frano Petric Matko Orsag Stjepan Bogdan Laboratory for Robotics and Intelligent Control systems University of Zagreb Faculty of Electrical Engineering and Computing Unska 3 Zagreb Croatia

In this paper we present our hardware design and control approaches for a mobile manipulation platform used in Challenge 2 of the MBZIRC 2020 competition. In this challenge, a team of UAVs and a single UGV collaborate in an autonomous, wall-building scenario, motivated by construction automation and large-scale robotic 3D printing. The robots must be able, autonomously, to detect, manipulate, and transport bricks in an unstructured, outdoor environment. Our control approach is based on a state machine that dictates which controllers are active at each stage of the Challenge. In the first stage our UGV uses visual servoing and local controllers to approach the target object without considering its orientation. The second stage consists of detecting the object's global pose using OpenCV-based processing of RGB-D image and point-cloud data, and calculating an alignment goal within a global map. The map is built with Google Cartographer and is based on onboard LIDAR, IMU, and GPS data. Motion control in the second stage is realized using the ROS Move Base package with Time-Elastic Band trajectory optimization. Visual servo algorithms guide the vehicle in local object-approach movement and the arm in manipulating bricks. To ensure a stable grasp of the brick's magnetic patch, we developed a passively-compliant, electromagnetic gripper with tactile feedback. Our fully-autonomous UGV performed well in Challenge 2 and in post-competition evaluations of its brick pick-and-place algorithms.

关键词： Robots Robot kinematics Navigation Manipulators Cameras Buildings Grippers Automation Service robots Laser radar

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Cascade LTI System for Acoustic Emission Digital Signal Processing: Result Visualization via Scalogram for Defect Pattern Representation

Cascade LTI System for Acoustic Emission Digital Signal Proc...

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Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Yeldos A. Altay Alexey V. Fedorov Department of Robotics and Technical Means of Automation Satbayev University Almaty Kazakhstan Faculty of Control Systems and Robotics ITMO University Saint-Petersburg Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

Modern acoustic emission (AE) diagnostic systems are sensitive instruments for detecting developing defects. A significant limitation of the AE method is the difficulty of isolating signals against the background of interference, which significantly reduces the signal-to-interference ratio. Therefore, in this article a cascade filtering scheme for visualizing acoustic signatures through a scalogram is proposed to isolate AE signals that characterize the process of defect formation. Acoustic portraits of the isolated signals characterizing the process of defect formation are given. It is shown that the amplitude of the AE signal from a defective structure during crack formation is characterized by the highest values of the signal amplitude and has a rich frequency-time structure, the signal shape of which is determined by discrete AE.

关键词： Linear systems Visualization Time-frequency analysis Filtering Shape Instruments Interference Digital signal processing Acoustic emission Signal to noise ratio

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Parameter Estimation of Autonomous Underwater Vehicles Dynamics. A Data-Driven Optimization Framework

Parameter Estimation of Autonomous Underwater Vehicles Dynam...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Radda Iureva Alexey Margun Egor Lykoyanov Faculty of Control Systems and Robotics ITMO Univresity Saint Petersburg Russia State Research Center of the Russian Federation Concern CSRI Elektropribor JSC ITMO University Saint Petersburg Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

Accurate parameter estimation in ship dynamics models is pivotal for enhancing navigation precision, optimizing control systems, and ensuring maritime safety. This study explores two distinct methodologies: the “Grey Box” approach, where the governing equations of ship dynamics are known but certain parameters remain unidentified, and the “Black Box” approach, which relies entirely on trajectory data without any prior knowledge of the underlying mathematical models. Leveraging machine learning techniques, specifically Long Short-Term Memory (LSTM) networks, alongside optimization algorithms like L-BFGS-B, this research aims to evaluate and compare the efficacy of both approaches in estimating model parameters and predicting ship trajectories. The findings demonstrate that while the Grey Box approach benefits from incorporating physical laws for parameter tuning, the Black Box method offers flexibility in modeling complex, nonlinear dynamics purely based on empirical data. The integrated use of these methodologies provides a robust framework for enhancing the accuracy and reliability of ship dynamics models, contributing significantly to maritime engineering applications.

关键词： Parameter estimation Accuracy Predictive models Prediction algorithms Mathematical models Trajectory Vehicle dynamics Marine vehicles Optimization Long short term memory

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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Hybrid adaptive Sine Cosine Algorithm with Finite-Time Prescribed Performance PID control for pneumatic servo systems

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IFAC Journal of systems and control 2025年 32卷

作者： Irawan, Addie Suid, Mohd Helmi Ismail, R.M.T. Raja Jusof, Mohd Falfazli Mat Azahar, Mohd Iskandar Putra Nasir, Ahmad Nor Kasruddin Robotics Intelligent Systems and Control Engineering (RiSC) research group Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan 26600 Malaysia

This paper addresses the challenge of enhancing pressure regulation in pneumatic servo systems, specifically for proportional valve-controlled double-acting pneumatic cylinders (PPVDC). A Hybrid Nonlinear Sine Cosine Algorithm (HNSCA) is proposed to optimize a Finite-Time Prescribed Performance control (FT-PPC) integrated with a PID controller. The HNSCA combines the Nonlinear Sine Cosine Algorithm (NSCA) with Adaptive Safe Experimentation Dynamics (ASED) to fine-tune FT-PPC-PID parameters, achieving rapid transient response and system stability. Simulation results demonstrate significant improvements over other optimization variants like ESCA and ASCA, including a 96% faster rise time, 61.9% reduction in settling time, and 6.4% lower overshoot. Additionally, HNSCA reduced pressure oscillations by 25%–30%, lowered power consumption by 20%–30%, and achieved up to a 50% reduction in energy consumption under a 10 kg load. It also enhanced subsonic flow stability by 10%–15% under choked flow conditions. These advancements offer practical benefits for industries utilizing pneumatic systems, such as manufacturing and robotics, by providing more precise control, reducing energy costs, and extending equipment lifespan. The findings highlight the effectiveness of the proposed approach in error minimization and long-term stability for pneumatic servo systems. © 2025 Elsevier Ltd

关键词： Metaheuristic Nonlinear sine cosine algorithm Pneumatic actuator Prescribed performance control Safe experimentation dynamics

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Machine Learning in Predicting the Impact of Filter Parameters on the Accuracy of Biosignal Measurement

Machine Learning in Predicting the Impact of Filter Paramete...

引用

Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Yeldos A. Altay Raisa K. Uskenbayeva Abu-Alim E. Ayazbay Alexey V. Fedorov Department of Robotics and Technical Means of Automation Satbayev University Almaty Kazakhstan Institute of Automation and Information Technologies Vice-Rector Satbayev University Almaty Kazakhstan Electronic engineering Almaty University of Power Engineering and Telecomunications Almaty Kazakhstan Faculty of Control Systems and Robotics ITMO University Saint-Petersburg Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

In this scientific article under consideration, a machine learning model has been developed that allows predicting the impact of filter parameters on the accuracy of measuring a biosignal - an ECG (electrocardiography) signal. For the first time, quantitative results have shown the degree of influence and the error introduced into the measured biosignal and described through dependence. The results of assessing the obtained relationship of indicators demonstrate an inverse correlation relationship that characterizes a decrease in the accuracy of measurement at the output of a high-order digital filtering system. The machine learning model is based on the linear regression method the dependence of which is approximated by the method based on least squares and visualized using a scattergram. The adequacy of the developed machine learning model was confirmed by calculating the coefficient of determination and checking its statistical significance. The developed model can be used to create new or improve existing biomedical data processing systems to increase the reliability of the results.

关键词： Accuracy Shape Filtering Biological system modeling Machine learning Biomedical measurement Electrocardiography Distortion Distortion measurement Digital filters

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Recommendations and Typical Errors on Usage of Wirewound Resistors in Design of Power Converters

IEEE Open Journal of the Industrial Electronics Society

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IEEE Open Journal of the Industrial Electronics Society 2025年 6卷 669-684页

作者： Dianov, Anton Sun, Xiaodong Kang, Ji-Won Demidova, Galina Prakht, Vladimir Tarlavin, Nikolay Xiang, Jiawei Wenzhou University Pingyang Institute of Intelligent Manufacturing Wenzhou325035 China Jiangsu University Automotive Engineering Research Institute Zhenjiang212013 China Dankook University Department of Science and Technology Policy Convergence Yongin16890 Korea Republic of Donghae Engineering & Consultants Technology Research Institute Seoul05203 Korea Republic of ITMO University Faculty of Control Systems and Robotics St. Petersburg197101 Russia Ural Federal University Department of Electrical Engineering Ekaterinburg620075 Russia Saint Petersburg State University of Veterinary Medicine St. Petersburg196084 Russia Wenzhou University College of Mechanical and Electrical Engineering Wenzhou325035 China

The wirewound resistors (WRs) are one of the most popular passive components in modern power converters. They are used for many purposes, including sensing, current limitation, discharging, and braking, therefore, depending on their main target, they are constructed prioritizing one of its characteristics. At the same time, one of the specifics of WR is significant parasitic reactance, which must be taken into account. This feature significantly impacts the operation of power converters, therefore, all side effects have to be carefully analyzed and countermeasures have to be implemented. In order to eliminate gap in this area, the authors provide a detailed insight into the design of various circuits with WRs in power converters and formalize the design process, highlighting 11 checkpoints. The authors share their 20-years experience in this field and illustrate five the most popular mistakes with oscillograms, which simplifies the misstep localization. It saves at least one iteration of prototyping and saves significant resources. This article is mainly addressed to young researchers, however, it could also be useful for experienced engineers without solid background in design with WRs. © 2020 IEEE.

关键词： Printed circuit design

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Model-Free Predictive control for Harmonic Suppression of PMSMs Based on Adaptive Resonant controller

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IEEE Transactions on Energy Conversion 2025年

作者： Li, Teng Sun, Xiaodong Su, Zonghao Zha, Xuhao Dianov, Anton Prakht, Vladimir Demidova, Galina Ma, Jinlin Jiangsu University Automotive Engineering Research Institute Zhenjiang212013 China Wenzhou University Pingyang Institute of Intelligent Manufacturing Wenzhou325035 China Ural Federal University Department of Electrical Engineering Ekaterinburg620062 Russia ITMO University Faculty of Control Systems and Robotics Petersburg197101 Russia Jiangsu University School of Automotive and Traffic Engineering Zhenjiang212013 China

To simultaneously suppress the periodic and aperiodic disturbances experienced by permanent magnet synchronous motors in model predictive control, a model-free predictive current control with adaptive resonance control based on an ultra-local model is proposed. Firstly, the ultra-local models of the speed loop and the current loop are established respectively, and the unmodeled and uncertain unknown part F in the ultra-local model is estimated by using a high-order fast terminal sliding mode observer, and its stability is analyzed. Then, an adaptive resonant controller (ARC) is designed to suppress the periodic disturbance caused by the dead zone effect. The ARC and the model-free control based on the ultra-local model are combined to compensate for their respective shortcomings, achieving the suppression of periodic and aperiodic disturbances in the system. Finally, by comparing with the traditional duty cycle model predictive control and the model-free model predictive control without ARC, the proposed scheme is proved to be robust to periodic and aperiodic disturbances. © 2025 IEEE. All rights reserved.

关键词： Permanent magnets

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