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Robust Reliability Design for Nonlinear Networked control Systems 40

Robust Reliability Design for Nonlinear Networked Control Sy...

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40th Chinese control conference (CCC)

作者： Huo, Zhihong Zhang, Zhixue Xu, Chang Zeng, Namei Hohai Univ Coll Energy & Elect Engn Nanjing 210098 Peoples R China NR Elect Co Ltd Nanjing 210098 Peoples R China

ISBN: (纸本)9789881563804

Network control system (NCS) is a kind of distributed control system, which is widely used in industrial control process. The communication network is used to exchange information and control signals among various physical components of subsystems. The transmission of data and information on the communication network to achieve system control will lead to the inevitable delay of information transmission time, which will reduce the performance and even make the system unstable. Reducing network traffic is one of the most effective methods to reduce the impact of network control system delay on system performance. This paper discusses the adjustable deadband as a method to reduce network traffic in networked control systems, and proposes a modeling method of networked control systems with random delay and communication constraints based on quasi T-S fuzzy model. Based on robust fault-tolerant control theory and information scheduling theory, the integrity design of this kind of networked control system is analyzed. After detailed theoretical analysis, simulation results are given to further verify the proposed scheme.

关键词： Networked control systems (NCS) Integrity design Deadband Random time delays T-S fuzzy modeling

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9th International Work-conference on Bioinformatics and Biomedical Engineering, IWBBIO 2022

9th International Work-Conference on Bioinformatics and Biom...

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9th International Work-conference on Bioinformatics and Biomedical Engineering, IWBBIO 2022

ISBN: (纸本)9783031078019

The proceedings contain 75 papers. The special focus in this conference is on Bioinformatics and Biomedical Engineering. The topics include: Automated TTC Image-Based analysis of Mouse Brain Lesions;PET-Neuroimaging and Neuropsychological Study for Early Cognitive Impairment in Parkinson’s Disease;architecture and Calibration of a Multi-channel Electrical Impedance Myographer;advanced Incremental Attribute Learning Clustering Algorithm for Medical and Healthcare Applications;Assessment of Inflammation in Non-calcified Artery Plaques with Dynamic 18F-FDG-PET/CT: CT Alone, Does-It Detect the Vulnerable Plaque?;Comparative analysis of the Spatial Structure Chloroplasts and Cyanobacteria Photosynthetic Systems I and ii Genes;Unsupervised Classification of Some Bacteria with 16S RNA Genes;modern Approaches to Cancer Treatment;a Service for Flexible Management and analysis of Heterogeneous Clinical data;linear Predictive modeling for Immune Metabolites Related to Other Metabolites;reconfigurable Arduino Shield for Biosignal Acquisition;smart Watch for Smart Health Monitoring: A Literature Review;data Quality Enhancement for Machine Learning on Wearable ECGs;Measurable Difference Between Malignant and Benign Tumor of the Thyroid Gland Recognizable Using Echogenicity Index in Ultrasound B-MODE Imaging: An Experimental Blind Study;initial Prototype of Low-Cost Stool Monitoring System for Early Detection of Diseases;Cerebral Activation in Subjects with Developmental Coordination Disorder: A Pilot Study with PET Imaging;on the Use of Explainable Artificial Intelligence for the Differential Diagnosis of Pigmented Skin Lesions;estimating Frontal Body Landmarks from Thermal Sensors Using Residual Neural Networks;NMF for Quality control of Multi-modal Retinal Images for Diagnosis of Diabetes Mellitus and Diabetic Retinopathy;radiomic-Based Lung Nodule Classification in Low-Dose Computed Tomography;modelling of Arbitrary Shaped Channels and Obstacles by Distance Function;Segmentati

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SARSA $(\lambda)$ Reinforcement Learning Based Path Planning of Unmanned Aerial Vehicles

SARSA $(\lambda)$ Reinforcement Learning Based Path Planning...

引用

data Driven control and Learning Systems (DDCLS)

作者： Huaizhi Dong Dong Zhao Darong Huang Kun Yan Wenjing Ren School of Artificial Intelligence Anhui University Hefei School of Electronic Information Engineering Xi'an Thchnological University Xi'an School of Electrical Engineering and Automation Hefei University of Technology Hefei

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

This paper studies the path planning problem of space Unmanned aerial vehicle model based on SARSA $(\lambda)$ Reinforcement learning method in complex environment. First, we utilize three-dimensional mathematical modeling to describe the positions of drone at each moment and motion trajectories, to facilitate subsequent analysis of state and action spaces. Then, by introducing the concept of eligibility traces into the traditional SARSA reinforcement learning algorithm, we optimize the path planning problem of drones in a three-dimensional environment. Finally, through simulation, it is demonstrated that this method not only accelerates the learning process but also smoothly updates states, thereby enhancing the exploration efficiency of drone path planning in space compared to traditional path planning algorithms.

关键词： Three-dimensional displays Velocity control Reinforcement learning Autonomous aerial vehicles Path planning Mathematical models Trajectory

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6G-enabled Situation-Aware ML-assisted UTM (6G-SAMU)

6G-enabled Situation-Aware ML-assisted UTM (6G-SAMU)

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International Wireless Communications and Mobile Computing conference, IWCMC

作者： Bayrem Zarai Leïla Nasraoui Rihab Boussada Saadi Boudjit COSIM Research Lab Higher School of Communications (SUP’COM) University of Carthage Tunisia National School of Computer Science (ENSI) University of Manouba Tunisia L2TI Institut Galilée University of Sorbonne Paris Nord France

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

In Unmanned Aerial System (UAS) Traffic Management (UTM) environment, small UASs generally fly at lower altitudes than large manned aircraft and their transmission power is very limited. They also fly shorter distances and more densely populated in the airspace than their manned counterparts. Therefore, a finer control and more frequent updates are necessary for their traffic control. To cope with the special requirements in UTM, we here explore and compare the potential of three machine learning models to help safe UAS operation, built on the 3GPP ecosystem model, through awareness about weather conditions. The proposed algorithm operates in two steps, wherein the first step it predicts the wind speed that it exploits during the second step to allow or stop a flight depending on the characteristics of the drone. The prediction process also covers two modes: i) premature 24 -hour prediction for an early decision, and ii) One-hour prediction for a refined final decision. Depending on the weather conditions (stable or frequently changing), the proposed algorithm can use either the premature or the hourly mode.

关键词： 6G mobile communication Wireless communication Time-frequency analysis Wind speed Biological system modeling Atmospheric modeling Traffic control

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Trajectory Planning of Permanent Magnet Synchronous Motor Based on Improved Bidirectional Long Short Term Memory Network

Trajectory Planning of Permanent Magnet Synchronous Motor Ba...

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International conference on Computer and Communications (ICCC)

作者： Meng Chen Hongliang Li Yuanjin Guo Sifang Feng Yun Dong Shenyang Institute of Computing Technology Chinese Academy of Science University of Chinese Academy of Sciences Shenyang China School of Foreign Languages Gannan Normal University Ganzhou China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Because the operation cycle of the CNC system is much longer than the control cycle of the permanent magnet synchronous motor (PMSM), it is necessary to do high-precision interpolation of the rotation Angle of the PMSM rotor on the position instruction sequence given by the CNC system. At the same time, to ensure the smooth transition of PMSM acceleration, this paper proposes a Bi-LSTM based on attention mechanism (Bi-LSTM-Att) interpolation model. The model introduces the attention mechanism optimization interpolation process, and uses the bidirectional long short-term memory network to analyze the time series data from the front and back directions with the global trend. The evaluation indexes of model verification results and simulation results show that Bi-LSTM-Att has smaller interpolation errors, higher interpolation accuracy and more stable control effect than other interpolation models.

关键词： Interpolation Attention mechanisms Accuracy Trajectory planning Simulation Computational modeling Time series analysis Rotors Permanent magnet motors data models

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The Deep Learning Method for Information Extracting about the Components of Patented Devices and their Relationships

The Deep Learning Method for Information Extracting about th...

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International conference on control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Dmitry Korobkin Sergey Vasiliev Sergey Fomenkov Alla Kravets CAD department Volgograd State Technical University Volgograd Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

The study presents an approach to constructing semantic trees based on extracted information about the components of patented devices and their relationships. The object of analysis for data extraction is the abstract of Russian-language patents. Information about the device components can be used in many tasks of analyzing a patent array, such as determining the novelty of an invention, generating a patent application text, but the search for effective approaches to extracting such information is currently ongoing. This article proposes an approach to automating the extraction of morphological features of an invention from patent texts using machine learning methods. The developed system of 3 entities and 5 relations for representing the components of a patented device is shown. The process of forming a training sample consisting of 21,543 entities {SYSTEM - 1961, COMPONENT - 15,028, ATTRIBUTE - 4554} is given. This training sample and the models trained on its basis (ruBert, ruRoberta) were tested on the tasks of healing device components and their relationships.

关键词： Training Patents Technological innovation Semantics Pipelines Feature extraction Transformers Information retrieval Mathematical models data mining

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Visible light backscattering communications in healthcare scenarios: link modeling and performance analysis

Visible light backscattering communications in healthcare sc...

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2022 IEEE International conference on Internet of Things and Intelligence Systems, IoTaIS 2022

作者： Ullah, Muhammad Habib Gelli, Giacinto Verde, Francesco University Federico Ii Department of Electrical Engineering and Information Technology Napoli Italy

ISBN: (数字)9798350396454

ISBN: (纸本)9798350396454

Sensor systems for electronic health (e-Health) applications typically rely on radio-frequency (RF) wireless components, which are bulky and energy-demanding. Moreover, their electromagnetic radiation may cause interference to medical devices and side effects on living organisms. In such applications, an intriguing alternative to RF-based transmission is visible light communications (VLC), which leverage light-emitting diodes (LEDs) for data transmission, in addition to illumination purposes. In many cases, e-Health applications demand bi-directional communication among LEDs and sensor tags. In this paper, we explore the feasibility of potentially using optical backscattering to perform indoors bi-directional VLC for e-Health applications in hospital environments. Specifically, we develop a mathematical model of the visible light backscattering link, which allows one to accurately predict the amount of light required to ensure an acceptable received power. Moreover, we analytically show the impact of the relevant system parameters on the achievable bit-error-rate performance of the information transfer process. Finally, we verify our analytical findings regarding system performance via numerical simulations. © 2022 IEEE.

关键词： Visible light communication

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modeling and Validation of Real Time Kinematic Technique in Autonomous Driving Simulator

Modeling and Validation of Real Time Kinematic Technique in ...

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IEEE conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus)

作者： Kaleev, Dmitrii, V Sergeev, Nikita M. Fedotovskikh, Kirill, V Shiro, Georgiy E. Natl Res Univ Elect Technol Inst Microdevices & Control Syst Moscow Russia

ISBN: (纸本)9781665404761

The ongoing improvement of advanced driving assistance systems (ADAS) is achieved due to the increasing accuracy of sensors of an ego vehicle and due to using vehicle-toeverything (V2X) communication for extra data. In this paper, real time kinematic (RTK) technique that allows to determine high-precision relative position of ego vehicle to other vehicles as well as different infrastructure facilities is presented. The process of developing RTK sensor in autonomous driving simulator CARLA is described. The analysis of the proposed sensor approach, that is based on RTK technique is evaluated in terms of modeling the output data with normal distributed noise. The developed RTK sensor is integrated into an autonomous CARLA simulator. Finally, the simulation results are validated by practical experiment with GNSS equipment and rtklib software.

关键词： ADAS RTK CARLA simulator rtklib

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CT Scan Liver and Liver Tumor 2D Segmentation: A Deep Learning Review and Empirical analysis

CT Scan Liver and Liver Tumor 2D Segmentation: A Deep Learni...

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IEEE conference on Systems, process & control (ICSPC)

作者： Francis Jesmar P. Montalbo Lanie P. Palad Ria L. Castillo Kimberly I. Marasigan Anton Louise P. De Ocampo College of Informatics and Computing Sciences Batangas State University Batangas City Philippines College of Engineering Batangas State University Batangas city Philippines

ISBN: (数字)9798350391398

ISBN: (纸本)9798350391404

Accurate liver tumor segmentation in medical imaging is essential for early diagnosis and improving patient survival, yet it remains a significant challenge. Despite rapid advancements in deep learning (DL), the diverse range of models-convolutional neural networks (CNNs), transformers, and hybrid architectures-has led to fragmented insights regarding their effectiveness in liver tumor segmentation. This review addresses the critical need to systematically evaluate and compare these models, providing valuable guidance for researchers and clinicians in identifying optimal solutions. Our analysis highlights CNNs as more computationally efficient, while transformers demonstrate robustness in performance. Combining both strengths, hybrid models show even more significant potential and balance in cost to performance ratio. Furthermore, CNN-based and hybrid models outperform transformers in data-limited scenarios, particularly when leveraging multiscale feature aggregation. This review charts a path toward advancing liver tumor segmentation in Computed Tomography (CT) scans with cost-efficient, high-performance DL approaches by synthesizing these findings.

关键词： Deep learning Image segmentation Reviews Computational modeling Computed tomography Liver Computer architecture Transformers Convolutional neural networks Tumors

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analysis and Forecasting of Temperature Based on Temporal Fusion Transformer Model: A Case Study of Urumqi

Analysis and Forecasting of Temperature Based on Temporal Fu...

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Artificial Intelligence and Computer Engineering (ICAICE), International conference on

作者： Xinjun Song Haiyang Sun Shiyang Zhan Jin Ran Di Hou School of Traffic and Transportation Engineering Xinjiang University Xinjiang China School of Computer Science and Technology Xinjiang University Xinjiang China Xinjiang Key Laboratory of Green Construction and Smart Traffic Control of Transportation Infrastructure Xinjiang University Urumqi China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

Accurate temperature forecasting is important in many fields such as environmental engineering, disaster prevention and monitoring. This study aims to use the TFT model to predict the daily maximum, minimum and average air temperatures in Urumqi city. It was trained and optimized based on the screened and integrated temperature datasets collected from public databases such as CBE Clima, Weather Spark, and NOAA, and the performance of the TFT model in the temperature data was also investigated. Based on the MAE error criterion and the comparison between the predicted and historical true values of temperature in Urumqi city from May 4, 2024 to May 8, 2024, it is found that the TFT model can predict the short-term temperature accurately with less training set training. It is an important research step in the field of temperature analysis and prediction for finding models with better prediction performance, which will also contribute to the related decision making process.

关键词： Training Temperature distribution Analytical models Accuracy Computational modeling Urban areas Predictive models data models Thin film transistors Forecasting

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