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Dynamics of Systems, Mechanisms and Machines (Dynamics)

作者： Ivan A. Ershov Sergey A. Gladyshev Sergey V. Bykov Faculty of Automation and Computer Engineering Novosibirsk State Technical University Novosibirsk Russia

ISBN: (数字)9798331509019

ISBN: (纸本)9798331509026

Uninterruptible power supply devices are widely used in protection facilities. It is necessary to ensure stable operation without power supply during design or redevelopment of the system. At the same time, there is no generally accepted algorithm for calculating battery parameters that are optimal in terms of the ratio of cost and discharge time. This problem is usually solved by using additional batteries, which increases the cost of the system. We suggest using the results of battery tests to optimize their amount in an uninterruptible power supply device. A test bench has been developed that allows you to adjust the load current. Three 12V rechargeable batteries with a Capacity of 12, 17 and 26 Ah were tested. The results showed that when the load current is increased by 20% and 30%, the battery life with Capacity 12 Ah is reduced by 50%. Batteries with a Capacity of 17 Ah and 26 Ah are reduced by 20% and 10%, respectively. It is concluded that when using batteries with a Capacity of 17 and 26 Ah, it is possible to increase the load current. The results obtained make it possible to more accurately calculate the required battery capacity for an uninterruptible power supply device and optimize their amount.

关键词： Lead acid batteries Costs Power supplies Optimization methods Lead Discharges (electric) Energy efficiency Batteries Protection Uninterruptible power systems

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Observer based Descriptor Approach for Model Reference Tracking Control of Quadrotor 6

Observer based Descriptor Approach for Model Reference Track...

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6th International Conference on Control, Robotics and Informatics, ICCRI 2023

作者： Bezzaoucha Rebai, Souad EIGSI- La Rochelle Department of Electrical Computer Engineering and Automation 26 Rue François de Vaux de Foletier La Rochelle17041 France

ISBN: (纸本)9798350323283

In this paper, an algorithm based on a polytopic - fuzzy-TS Takagi-Sugeno PDC (Parrallel Distributed Control) control to ensure the stability and model reference tracking of a quadrotor is considered. After giving the nonlinear model of the UAV, its representation by a polytopic model is first discussed. Next, an observer and PDC controller are synthesized which guarantee desired control performances (stability and L2 reference tracking performance). The given observer and controller gains are designed using numerical tools (Linear Matrix Inequalities-LMI). The simulation results show effectiveness and robustness of the proposed method. © 2023 IEEE

关键词： Unmanned aerial vehicles (UAV)

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Development of a Software System Based on Microservice Architecture for Flaw Detection of Industrial Products

Development of a Software System Based on Microservice Archi...

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Informatics, Electronics, and Radio engineering (PIERE), International Conference Problems of

作者： Alexander V. Peshkov Faculty of Automation and Computer Engineering Novosibirsk State Technical University Novosibirsk Russia

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

This paper presents the results of developing a prototype software system for detecting fine defects in industrial products. The core of the computing unit contains a previously developed computational-heuristic algorithm that allows detecting thin defects in the product under study based on a priori information about the reference sample of this product. The developed software system is designed on the basis of a microservice architecture, which allows dividing a monolithic application into independently operating modules. The paper also presents an algorithm for generating cracks in the object model, which allows creating the necessary set of test data close to real ones. The presented results demonstrate a significant increase in system performance compared to the previous implementation due to the use of a multi-threaded approach. The developed prototype of the system can be further developed and used to solve real problems in the field of industrial flaw detection.

关键词： Heuristic algorithms System performance Software algorithms Fault tolerant systems Microservice architectures Prototypes computer architecture Software systems Software reliability Informatics

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Enhanced Brain Tumor Detection Using Transfer Learning with Fine-Tuned Efficientnet-B3 Blocks and Graph-Based Spatial Attention Capsule Networks 21

Enhanced Brain Tumor Detection Using Transfer Learning with ...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Aggrey, Esther Stacy E.B Zhen, Qin Kodjiku, Seth Larweh Fiasem, Delali Linda Sey, Collins Ejiyi, Chukwuebuka Joseph Ntia, Nsikakabasi Williamson School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Technology Zhejiang Gongshang University Hangzhou China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China

ISBN: (纸本)9798331519254

Brain tumors are critical medical conditions that require timely and accurate diagnosis to improve survival outcomes. This research proposes a cutting-edge framework for brain tumor classification, combining fine-tuned EfficientNet-B3 blocks with graph-based spatial attention capsule networks. By addressing challenges such as limited data availability, overfitting, and the complex nature of tumor structures, the framework employs partial transfer learning, advanced feature extraction, and attention mechanisms to effectively model spatial relationships in MRI images. Evaluated on a dataset of 3,064 contrast-enhanced brain MRI images, the model demonstrated exceptional performance, achieving 99.85% classification accuracy and notable improvements in precision, recall, and F1 scores for glioma, meningioma, and pituitary tumours. The use of advanced data augmentation techniques significantly improved the model's ability to generalize, while its computational efficiency makes it well-suited for clinical diagnostic applications. © 2024 IEEE.

关键词： Dynamic contrast enhanced MRI

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Deep Interactive Image Semantic and Instance Segmentation 12th

Deep Interactive Image Semantic and Instance Segmentation

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12th International Conference on Image and Graphics, ICIG 2023

作者： Wu, Xuzijing Yang, Pengfei Shao, Wenteng Wang, Tao Sun, Quansen School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Automation Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9783031463075

Existing deep interactive segmentation methods only focus on the extraction of specific instance objects. When the target of interest to users is a category, existing methods have to perform more interactions. We take advantages of the relevance between semantic and instance segmentations, and give two definitions based on the interactive mode, i.e. interactive semantic segmentation and interactive instance segmentation, to distinguish the actual requirements of instance objects and semantic categories that users are interested in. Then, a dual-branch-based full convolutional network model is proposed to integrate the above two tasks (similar to a multi-task learning), in which the relevance between the interactive semantic and instance segmentations can be jointly explored along with a simultaneous output of potential interested semantic categories and instance objects. Vast experimental results on GrabCut, Berkeley, SBD and DAVIS datasets verified the effectiveness of the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Semantics

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Journal of computer Science & Technology 2022年第1期37卷 231-251页

作者： Jian-Zhe Zhao Xing-Wei Wang Ke-Ming Mao Chen-Xi Huang Yu-Kai Su Yu-Chen Li Software College Northeastern UniversityShenyang 110169China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China College of Computer Science and Engineering Northeastern UniversityShenyang 110819China

Differential privacy(DP)is widely employed for the private data release in the single-party *** utility could be degraded with noise generated by ubiquitous data correlation,and it is often addressed by sensitivity reduction with correlation ***,increasing multiparty data release applications present new challenges for existing *** this paper,we propose a novel correlated differential privacy of the multiparty data release(MP-CRDP).It effectively reduces the merged dataset's dimensionality and correlated sensitivity in two steps to optimize the *** also propose a multiparty correlation analysis *** on the prior knowledge of multiparty data,a more reasonable and rigorous standard is designed to measure the correlated degree,reducing correlated sensitivity,and thus improve the data ***,by adding noise to the weights of machine learning algorithms and query noise to the release data,MP-CRDP provides the release technology for both low-noise private data and private machine learning *** experiments demonstrate the effectiveness and practicability of the proposed method on the utilized Adult and Breast Cancer datasets.

关键词： correlated differential privacy multiparty data release machine learning

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DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 879-893页

作者： Shuaiqi Liu Siqi Wang Chaolei Sun Bing Li Shuihua Wang Fei Li College of Electronic and Information Engineering Hebei UniversityBaodingChina Machine Vision Technology Innovation Center of Hebei Province BaodingChina The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science and Technology Henan Polytechnic UniversityJiaozuoChina

Diagnosing individuals with autism spectrum disorder(ASD)accurately faces great chal-lenges in clinical practice,primarily due to the data's high heterogeneity and limited sample *** tackle this issue,the authors constructed a deep graph convolutional network(GCN)based on variable multi‐graph and multimodal data(VMM‐DGCN)for ASD ***,the functional connectivity matrix was constructed to extract primary ***,the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel ***,the authors brought the non‐imaging in-formation into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between *** extracting the deeper features of population graphs using the deep GCN(DeepGCN),the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD *** proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I(ABIDE I)dataset,achieving an accuracy of 91.62%and an area under the curve value of 95.74%.These results demon-strated its outstanding performance compared to other ASD diagnostic algorithms.

关键词： machine learning medical image processing medical signal processing

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Modification of the Prony Method for Pulse Signal Modeling

Modification of the Prony Method for Pulse Signal Modeling

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Alexey B. Gavrilov Arina I. Bakulina Faculty of Automation and Computer Engineering Novosibirsk State Technical University Novosibirsk Russia

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

This paper considers a modification of the Prony parametric approximation method for modeling impulse signals. It is assumed that the experimentally measured signal is represented as a time series. The method is based on the construction of a model of the experimental signal under study as a sum of damped sinusoids. The problem, which does not allow approximating impulse signals with the required accuracy, is revealed. On the basis of the analysis of the classical formulation of the method, its modification is proposed, which allows to solve this problem. Mathematical substantiation of the modified method is presented. The possibility of interference filtering on the basis of analyzing the spectral structure of the signal model is substantiated. The possibility of identification of dynamic characteristics of elements of automatic control systems in analytical form using the developed approach is investigated. The results of modeling a test signal in the form of a rectangular pulse are given. The directions of development of the modified method are discussed, as well as possible areas of practical application.

关键词： Analytical models Accuracy Time series analysis Noise Process control Materials reliability Time measurement

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Causality-Aware Multi-Graph Convolutional Networks With Critical Node Dynamics for Electric Vehicle Charging Station Load Forecasting

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IEEE Transactions on Smart Grid 2025年第4期16卷 3210-3225页

作者： Huang, Yaohui Wu, Senzhen Wang, Zhijin Liu, Xiufeng Li, Chendan Hu, Yue Central South University School of Automation Changsha410083 China Jimei University College of Computer Engineering Xiamen361021 China Technical University of Denmark Department of Technology Management and Economics 2800 Denmark University of Genoa DITEN Genova Italy

Accurately forecasting the load of electric vehicle charging stations (EVCSs) is crucial for optimizing grid operations and facilitating EV integration, yet existing methods struggle to capture the intricate spatio-temporal dependencies and the impact of influential EVCSs within charging networks. To address this, we propose a novel framework, Causality-Aware Dynamic Multi-Graph Convolutional Network (CADGN), a multi-graph convolutional network that integrates causal inference and critical node modeling. It consists of two core modules: the Causality-Aware Graph Learning Module (CAGLM) uncovers and represents causal relationships between EVCSs, while the Critical Relationship Graph Learning Module (CRGLM) dynamically models the evolving connections among critical EVCS nodes. Temporal patterns extracted from these modules are then fused to generate accurate load predictions. Extensive experiments using real-world datasets of hourly charging data from multiple cities demonstrate CADGN’s superiority over state-of-the-art EVCS load forecasting models, particularly for short-term and mid-term horizons. Notably, our model achieves an average 4.7% reduction in Mean Absolute Error (MAE) compared to Graph WaveNet across all datasets and prediction horizons, highlighting the practical benefits of considering both causal and critical relationships for enhanced grid operations and EV integration. These results emphasize the importance of incorporating causality and the identification of critical relationships in the EVCS load forecast to achieve higher accuracy. © 2010-2012 IEEE.

关键词： Electric load forecasting

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Compressed Air Energy Storage in Wind Solar Complementary Systems 2

Compressed Air Energy Storage in Wind Solar Complementary Sy...

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2nd International Conference on Clean Energy Storage and Power engineering, CESPE 2023

作者： Wang, Yanan Lei, Zhen Wu, Jiekang School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China School of Automation Guangdong University of Technology Guangdong Guangzhou China

ISBN: (纸本)9798350395495

Renewable energy resources are abundant and developing rapidly in the power industry. This article establishes a wind-solar energy storage hybrid power generation system and analyzes the coordinated operation of energy systems in multiple scenarios. In a multi-scenario energy environment, the hybrid wind-solar energy storage system, driven by wind and solar energy, uses compressed air as energy storage equipment and a cold water tank as an intermediate regulating element, which can absorb heat and improve compressor efficiency. Considering system operation and equipment constraints, a multi-objective optimization model was established to reasonably calculate and allocate wind energy on compressed trains and solar energy on extended trains, in order to achieve the goal of minimizing system costs and maximizing profits. The simulation results indicate that this analysis has inspiration in practical engineering applications and can provide reference value for the coordinated operation of multi-energy power supply systems. © 2023 IEEE.

关键词： Multiobjective optimization

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