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Intelligent Synthesis of Individual Rehabilitation Plans for Patients after Joint Endoprosthesis Based on Computer Vision Technologies

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Intelligent Synthesis of Individual Rehabilitation Plans for...

International Conference on Information Technologies in engineering Education (Inforino)

作者： D.G Arseniev V.V. Borisov A.E. Misnik E.A Gomzhina M.A. Shalukhova Scientific Innovation Institute “Mathematical Modeling and Intelligent Control Systems” Peter the Great St. Petersburg Polytechnic University St. Petersburg Russian Federation Department of Computer Engineering National Research University “MPEI” Branch in Smolensk Smolensk Russia Information Technology Software Inter-State Educational Institution of Higher Education ‘Belarusian-Russian University” Mogilev Belarus Early Medical Rehabilitation FSBI “FCTOE” of the Ministry of Health of the Russian Federation Smolensk Russian Federation Information Technology Software Inter-State Educational Institution of Higher Education ‘Belarusian-Russian University Peter the Great St. Petersburg Polytechnic University Mogilev Belarus

ISBN: (数字)9798350371369

ISBN: (纸本)9798350371376

The article presents an innovative approach to the rehabilitation process for patients with joint endoprosthetics, focusing on the integration of a specialized software module based on computer vision technologies and the ontological approach. Developed for the Federal Centre for Traumatology, Orthopaedics, and Endoprosthesis in Smolensk, Russia, this system is designed to improve the quality of life for patients by offering personalized rehabilitation plans. The core of the study lies in the application of ontological models and meta-associative graphs for synthesizing individual rehabilitation plans, which streamline the recovery process and reduce the workload on healthcare professionals. This method allows for the processing of large amounts of information, providing detailed conclusions and recommendations for functional correction of the musculoskeletal system. The software utilizes computer vision to analyze patient movement and develop tailored rehabilitation strategies, overcoming the limitations of traditional diagnostic tools like MRI, which may not be suitable for all patients. By incorporating artificial intelligence and augmented reality, the system enhances the accuracy of patient assessments and the effectiveness of rehabilitation plans. Furthermore, the article discusses the application of this technology in the educational process at the Belarusian-Russian university, where students are introduced to cutting-edge software tools that assist in creating personalized rehabilitation plans. This educational integration not only enriches the students’ learning experience but also fosters innovation in the development of new applications for computer vision in medical rehabilitation. The ontological approach, combined with modern technology, represents a significant advancement in the management of musculoskeletal system diseases, offering a more efficient, personalized approach to rehabilitation and recovery.

关键词： Computer vision Musculoskeletal system Technological innovation Computational modeling Medical services Propulsion Trajectory

Frequency Measurement Research with Weight Averaging of Pulse Output Signal of Voltage-to-Frequency Converter

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SSRN

SSRN 2022年

作者： Dorozhovets, Mykhaylo Pawlowski, Eligiusz Świsulski, Dariusz Department of Metrology and Diagnostic Systems Faculty of Electrical and Computer Engineering Rzeszow University of Technology ul. Wincentego Pola 2A Rzeszów35-959 Poland Department of Information and Measuring Technology National University Lviv Polytechnic Lviv Ukraine Faculty of Electrical Engineering and Computer Science Lublin University of Technology Lublin Poland Faculty of Electrical and Control Engineering Gdańsk University of Technology Gdańsk Poland

The paper presents investigation of the efficiency of weight averaging of a pulse frequency modulated signal. The sampling effects and the interference influence in the input signal of the voltage-to frequency converter on the result of output frequency weight averaging are analyzed. A general form of the averaging result dependence on the used weighting function, signal value, interference level and modulation parameters is derived. In the case of high interferences whose frequencies are unstable or may change over a wide range, the specified level of their suppression, together with the reduction of the sampling effect, can be achieved using weighting functions, such as trigonometric, with an appropriate level of side lobes. The interference suppression dependently on frequency ranges using selected weighing functions are tested using both the simulation and experimental methods. The obtained results show very good convergence with the values calculated from theoretical formulas. © 2022, The Authors. All rights reserved.

关键词： Weighing

Discrete-time fractional-order dynamical networks minimum-energy state estimation

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arXiv 2021年

作者： Chatterjee, Sarthak Alessandretti, Andrea Aguiar, A. Pedro Pequito, Sérgio The Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Magneti Marelli S.p.A. Italy The Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Porto Portugal The Delft Center for Systems and Control Delft University of Technology Delft Netherlands

Fractional-order dynamical networks are increasingly being used to model and describe processes demonstrating long-term memory or complex interlaced dependencies amongst the spatial and temporal components of a wide variety of dynamical networks. Notable examples include networked control systems or neurophysiological networks which are created using electroencephalographic (EEG) or blood-oxygen-level-dependent (BOLD) data. As a result, the estimation of the states of fractional-order dynamical networks poses an important problem. To this effect, this paper addresses the problem of minimum-energy state estimation for discrete-time fractional-order dynamical networks (DT-FODN), where the state and output equations are affected by an additive noise that is considered to be deterministic, bounded, and unknown. Specifically, we derive the corresponding estimator and show that the resulting estimation error is exponentially input-to-state stable with respect to the disturbances and to a signal that is decreasing with the increase of the accuracy of the adopted approximation model. An illustrative example shows the effectiveness of the proposed method on real-world neurophysiological networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Neurophysiology

A Sample-Based Algorithm for Approximately Testing r-Robustness of a Digraph

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arXiv 2022年

作者： Yi, Yuhao Wang, Yuan He, Xingkang Patterson, Stacy Johansson, Karl H. The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures StockholmSE-100 44 Sweden The Department of Electrical Engineering University of Notre Dame Notre Dame IN46556 United States The Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States

One of the intensely studied concepts of network robustness is r-robustness, which is a network topology property quantified by an integer r. It is required by mean subsequence reduced (MSR) algorithms and their variants to achieve resilient consensus. However, determining r-robustness is intractable for large networks. In this paper, we propose a sample-based algorithm to approximately test r-robustness of a digraph with n vertices and m edges. For a digraph with a moderate assumption on the minimum in-degree, and an error parameter 0 Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

Fractional cyber-neural systems - a brief survey

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arXiv 2021年

作者： Reed, Emily Chatterjee, Sarthak Ramos, Guilherme Bogdan, Paul Pequito, Sérgio Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Portugal Delft Center for Systems and Control Delft University of Technology Delft Netherlands

Neurotechnology has made great strides in the last 20 years. However, we still have a long way to go to commercialize many of these technologies as we lack a unified framework to study cyber-neural systems (CNS) that bring the hardware, software, and the neural system together. Dynamical systems play a key role in developing these technologies as they capture different aspects of the brain and provide insight into their function. Converging evidence suggests that fractional-order dynamical systems are advantageous in modeling neural systems because of their compact representation and accuracy in capturing the long-range memory exhibited in neural behavior. In this brief survey, we provide an overview of fractional CNS that entails fractional-order systems in the context of CNS. In particular, we introduce basic definitions required for the analysis and synthesis of fractional CNS, encompassing system identification, state estimation, and closed-loop control. Additionally, we provide an illustration of some applications in the context of CNS and draw some possible future research directions. Ultimately, advancements in these three areas will be critical in developing the next generation of CNS, which will, ultimately, improve people’s quality of life. Copyright © 2021, The Authors. All rights reserved.

关键词： Surveys

Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving

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arXiv 2024年

作者： Hu, Xuemin Chen, Pan Wen, Yijun Tang, Bo Chen, Long School of Artificial Intelligence Hubei University Wuhan430062 China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Hubei Wuhan430062 China Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States State Key Laboratory of Multimodal Artificial Intelligence Systems State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing100190 China WAYTOUS Inc. Beijing100083 China Shenzhen518107 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi’an710049 China

Reinforcement learning (RL) has been widely used in decision-making and control tasks, but the risk is very high for the agent in the training process due to the requirements of interaction with the environment, which seriously limits its industrial applications such as autonomous driving systems. Safe RL methods are developed to handle this issue by constraining the expected safety violation costs as a training objective, but the occurring probability of an unsafe state is still high, which is unacceptable in autonomous driving tasks. Moreover, these methods are difficult to achieve a balance between the cost and return expectations, which leads to learning performance degradation for the algorithms. In this paper, we propose a novel algorithm based on the long and short-term constraints (LSTC) for safe RL. The short-term constraint aims to enhance the shortterm state safety that the vehicle explores, while the long-term constraint enhances the overall safety of the vehicle throughout the decision-making process, both of which are jointly used to enhance the vehicle safety in the training process. In addition, we develop a safe RL method with dual-constraint optimization based on the Lagrange multiplier to optimize the training process for end-to-end autonomous driving. Comprehensive experiments were conducted on the MetaDrive simulator. Experimental results demonstrate that the proposed method achieves higher safety in continuous state and action tasks, and exhibits higher exploration performance in long-distance decision-making tasks compared with state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Lagrange multipliers

Unified resilience model using deep learning for assessing power system performance

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Heliyon 2025年第4期11卷 e42802页

作者： Artemchuk, Volodymyr Garbuz, Iurii Jamil Alsayaydeh, Jamil Abedalrahim Shkarupylo, Vadym Oliinyk, Andrii Bin Yusof, Mohd Faizal Herawan, Safarudin Gazali Department of Mathematical and Econometric Modelling G.E. Pukhov Institute for Modelling in Energy Engineering of the NAS of Ukraine Kyiv Ukraine Department of Environmental Protection Technologies and Radiation Safety Center for Information-analytical and Technical Support of Nuclear Power Facilities Monitoring of the NAS of Ukraine Kyiv Ukraine Department of Іnformation Systems in Economics Kyiv National Economic University Named After Vadym Hetman Kyiv Ukraine Department of Computerized Control System National Aviation University Kyiv Ukraine Department of Engineering Technology Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer (FTKEK) Universiti Teknikal Malaysia Melaka (UTeM) Melaka 76100 Malaysia Department of Computer Systems Networks and Cybersecurity of the National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Software Tools Faculty of Computer Science and Technology Zaporizhzhia Polytechnic National University Zaporizhzhia Ukraine Research Section Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia

Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model's fidelity in enhancing power system performance, with a specific focus on the impact of weather factors. © 2025 The Authors

关键词： Deep learning Energy resilience Fidelity Weather impact

Combined Extension and Torsion of Hydrogels with Chemo-Mechanical Coupling: Revealing Negative Poynting Effect

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SSRN

SSRN 2024年

作者： Zheng, Chengxiang Hu, Minghui Wu, Tao Pasquale, Ciarletta Li, Qian Deng, Zichen School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University Xi'An710072 China MIIT Key Laboratory of Dynamics and Control of Complex Systems Xi'An710072 China MOX Department di Mathematica Polytechnic di Milano piazza Leonardo da Vinci 32 Milano20133 Italy China Electronic Product Reliability and Environmental Testing Research Institute China Guangdong Provincial Key Laboratory of Electronic Information Products Reliability Technology China

In this paper, combined extension and torsion of hydrogel subject to a chemo-mechanical coupled loading is described in the framework of continuum mechanics, where the free energy density consists of the elastic, mixing and chemical contributions. A simplified, closed-form and exactly analytical solution to the mechanical response is obtained, which accounts for the coupling effect of external loading, chemical potential and microstructural parameters, such as crosslinking degree, Flory parameter, etc. In particular, the effect of free swelling and microscopic diffusion on deformation of the hydrogel is discussed, reaching some fundamental conclusions. The classic negative Poynting effect is revealed that the cylinder tends to shorten on twisting, and an inhomogeneous deformation, induced by torsion, along the radial direction is demonstrated. Furthermore, the dynamic competition between external loading and solvent environment is revealed and investigated, where the direct connection between internal micro-physical parameters and macroscopic deformation is demonstrated. The theoretical results presented in this paper may provide predictions and guidance for the mechanical analysis and design of hydrogel cylinder subject to extension and torsion in a solvent. © 2024, The Authors. All rights reserved.

关键词： Torsional stress

Defect Sample Generation System Based on DCGAN for Glass Package Electrical Connectors

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Defect Sample Generation System Based on DCGAN for Glass Pac...

Chinese Intelligent systems Conference, CISC 2020

作者： Liu, Qunpo Wang, Mengke Hanajima, Naohiko Department of Robotics Engineering Henan Polytechnic University 2001 Century Avenue JiaozuoHenan454003 China Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment 2001 Century Avenue JiaozuoHenan454003 China College of Information and Systems Muroran Institute of Technology 27-1 Mizumoto-cho Muroran-shiHokkaido050-8585 Japan

ISBN: (纸本)9789811584572

Precision glass packaged electrical connectors are widely used to connect precision modular electrical appliances. Manual detection is currently used due to its small size. In order to improve the detection efficiency and accuracy, deep learning is applied to the automatic detection of the defect. In this paper, in order to solve the problem of lack of defect samples, a defect sample generation system for micro-precision glass package electrical connectors based on deep convolutional generation confrontation network (DCGAN) is constructed. A network model of defect samples based on DCGAN is constructed, and algorithm design, parameter settings, network training and experiments are performed. More than 1,500 samples were generated. Finally, the generated samples were tested for defects (including missing blocks and bubbles). The results show that the samples generated by this method can be used as real samples in defect detection training. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Defects