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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Xinyu Fan Galaktion V. Shvedov Chuqiao Cai Electrical Power Systems Department National Research University “MPEI” Moscow Russian Federation Information Processing and Management Systems Department Faculty of Computer Science and Control Systems Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This paper proposes a CNN-GRU-Attention model for short-term charging load prediction at electric vehicle (EV) charging stations. The model leverages the strengths of Convolutional Neural Networks (CNN) for feature extraction and dimensionality reduction, while Gated Recurrent Units (GRU) enhance temporal sequence learning. The attention mechanism assigns dynamic weights to the extracted features, improving the prediction accuracy. Hyperparameters are optimized using the Sparrow Search Algorithm (SSA), and an improved version, ISSA, further enhances model performance. Experimental results demonstrate that the CNN-GRU-Attention model outperforms baseline models, including CNN, GRU, LSTM (Long Short-Term Memory), and BiLSTM (Bidirectional Long Short-Term Memory), with higher prediction accuracy and fewer trainable parameters. The model's predictive capabilities enable optimization of power supply strategies in smart grids. However, it faces challenges in long-term load prediction and computational resource demands during hyperparameter optimization.

关键词： Adaptation models Accuracy Computational modeling Predictive models Hyperparameter optimization Feature extraction Smart grids Convolutional neural networks Optimization Load modeling

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An Approach to Digital Transformation of the Registration Expertise of Devices using Blockchain Technologies

An Approach to Digital Transformation of the Registration Ex...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Maria V. Yakovleva Artem I. Antonov “Management” department Bauman Moscow State Technical University Moscow Russian Federation “Information processing and control systems” department Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

In the article, the authors conducted an extensive study of the current situation with the use of digital transformation tools in the life cycle management system of medical devices based on the blockchain. The analysis showed that blockchain technology is being actively implemented in two directions: in the field of supply chain management of medicines, products, equipment and in the field of ensuring the security of medical data exchange in the digital space. It is concluded that at the moment an ideal blockchain-based system has not yet been formed to solve the problem of supervision of the supply chain of medical devices. In this study, attention is paid to the development of recommendations for organizing the possibility of submitting documents for the registration dossier of medical devices to the state body by using the possibilities of electronic document management and blockchain technology.

关键词： Medical devices Costs Supply chain management Digital transformation Supply chains Process control Blockchains Safety Certification Systematic literature review

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UW-DNeRF: Deformable Soft Tissue Reconstruction with Uncertainty-Guided Depth Supervision and Local information Integration

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Shan, Jiwei Zhang, Zixin Li, Hao Hsieh, Cheng-Tai Li, Yirui Wu, Wenhua Wang, Hesheng School of Electronic Information and Electrical Engineering of Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University School of Mechanical Engineering Shanghai200240 China Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China

Reconstructing deformable soft tissues from endoscopic videos is a critical yet challenging task. Leveraging depth priors, deformable implicit neural representations have seen significant advancements in this field. However, depth priors from pre-Trained depth estimation models are often coarse, and inaccurate depth supervision can severely impair the performance of these neural networks. Moreover, existing methods overlook local similarities in input sequences, which restricts their effectiveness in capturing local details and tissue deformations. In this paper, we introduce UW-DNeRF, a novel approach utilizing neural radiance fields for high-quality reconstruction of deformable tissues. We propose an uncertainty-guided depth supervision strategy to mitigate the impact of inaccurate depth information. This strategy relaxes hard depth constraints and unlocks the potential of implicit neural representations. In addition, we design a local window-based information sharing scheme. This scheme employs local window and keyframe deformation networks to construct deformations with local awareness and enhances the model's ability to capture fine details. We demonstrate the superiority of our method over state-of-The-Art approaches on synthetic and in-vivo endoscopic datasets. Code is available at: https://***/IRMVLab/UW-DNeRF. © 2025 IEEE.

关键词： Endoscopy

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S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

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REDUNDANT RESIDUE POLYNOMIAL CODES

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information AND control 1968年第6期13卷 597-&页

作者： BOSSEN, DC YAU, SS Department of Electrical Engineering and Information-Processing and Control Systems Laboratory Northwestern University Evanston Illinois 60201 USA

In this paper, a new class of error-correcting linear block codes using symbols from GF(2 m ) is presented. These codes are not cyclic codes, but posses instead a unique algebraic structure. It is shown that they are instantaneously decodable with a modest amount of hardware consisting almost entirely of mod 2 adders for correcting burst errors. Furthermore, their efficiency compares favorably with the Varsharmov-Gilbert bound for both random errors over GF(2 m ) and burst errors over GF(2).

关键词： information theory

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BAYESIAN APPROACH TO OPTIMIZATION OF ADAPTIVE systems

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IEEE TRANSACTIONS ON systems SCIENCE AND CYBERNETICS 1967年第2期SSC3卷 77-&页

作者： LIN, TT YAU, SS Information-Processing and Control Systems Laboratory and the Department of Electrical Engineering Northwestern University Evanston IL USA

This paper describes how an adaptive system can adapt itself to optimize its performance under the influence of uncertain environment. At each stage of adaptation, the uncertain environment, which is represented by a random vector with an unknown statistical property, is estimated by Bayesian approach from its past outcomes up to the latest one. This approach is investigated in general so that the probability distribution of the future outcomes of the random vector is not restricted to any particular one. For most of the adaptive systems, these probability distributions are assumed to be the same. However, in the case of signal adaptation, it is shown that the results as well as the execution of the optimization technique are alike whether or not the probability distributions of the forthcoming outcomes of the random vector are the same.

关键词： Bayesian methods Adaptive systems Probability distribution control systems Temperature sensors Humidity

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A NONBULK ADDITION TECHNIQUE FOR ASSOCIATIVE PROCESSORS

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IEEE TRANSACTIONS ON ELECTRONIC COMPUTERS 1966年第6期EC15卷 938-+页

作者： YAU, SS YANG, CC Information-Processing and Control Systems Laboratory and Department of Electrical Engineering Northwestern University Evanston IL USA Program of Information Science and the Department of Electrical Engineering Washington State University Pullman USA Information-processing and Control Systems Laboratory and Department of Electrical Engineering Northwestern University Evanston IL USA

A process is described for continuing adaptation, after hyperplanes that separate pattern classes in pattern space have been found, in order to increase the distance between sample patterns and hyperplanes.

关键词： Boolean functions Logic Network synthesis Testing Silicon carbide Sufficient conditions Arithmetic Software performance Associative memory Hardware

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Real-time tracking of fast-moving object in occlusion scene

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Journal of systems Engineering and Electronics 2024年第3期35卷 741-752页

作者： LI Yuran LI Yichen ZHANG Monan YU Wenbin GUAN Xinping Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Systems Control and Information Processing Ministry of Education of ChinaShanghai 200240China

Tracking the fast-moving object in occlusion situations is an important research topic in computer vision. Despite numerous notable contributions have been made in this field,few of them simultaneously incorporate both object's extrinsic features and intrinsic motion patterns into their methodologies,thereby restricting the potential for tracking accuracy improvement. In this paper, on the basis of efficient convolution operators(ECO) model, a speed-accuracy-balanced model is put forward. This model uses the simple correlation filter to track the object in real-time, and adopts the sophisticated deep-learning neural network to extract high-level features to train a more complex filter correcting the tracking mistakes, when the tracking state is judged to be poor. Furthermore, in the context of scenarios involving regular fast-moving, a motion model based on Kalman filter is designed which greatly promotes the tracking stability, because this motion model could predict the object's future location from its previous movement pattern. Additionally,instead of periodically updating our tracking model and training samples, a constrained condition for updating is proposed,which effectively mitigates contamination to the tracker from the background and undesirable samples avoiding model degradation when occlusion happens. From comprehensive experiments, our tracking model obtains better performance than ECO on object tracking benchmark 2015(OTB100), and improves the area under curve(AUC) by about 8% and 32% compared with ECO, in the scenarios of fast-moving and occlusion on our own collected dataset.

关键词： speed-accuracy balanced motion modeling constrained updater

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PATTERN RECOGNITION BY USING AN ASSOCIATIVE MEMORY

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IEEE TRANSACTIONS ON ELECTRONIC COMPUTERS 1966年第6期EC15卷 944-+页

The purpose of this paper is to present a simple template-matching pattern recognition technique by using any general-purpose associative memory. The input patterns for recognition may have wide variations, provided that the distinct features of individual pattern classes can be extracted. Each pattern class is allowed to have deviations in size, style, orientation, etc. within certain limits. This pattern recognition technique is extremely efficient in handwritten character recognition, which is used for illustration in this paper. Because each input pattern is processed with all the pattern classes simultaneously, the speed of this pattern recognition technique is very high. It is found that most input patterns are recognized within first comparison process and no input patterns require more than two comparison processes for their recognition.

关键词： Pattern recognition Associative memory Character recognition Pattern classification Feature extraction Convergence Machine learning Piecewise linear techniques Parallel processing Random number generation

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Development of a switching circuit for the operation of a multichannel system in reception and emission modes

Development of a switching circuit for the operation of a mu...

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2021 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2021

作者： Shlaev, Victor I. Bilchuk, Maria V. Tyasto, Sergey A. Information Systems Sub-Department MSUT «STANKIN» Moscow Russia Automated Information Processing and Control Systems Sub-Department MSUT «STANKIN» Moscow Russia

ISBN: (纸本)9781728145877

A comparative analysis of measuring devices for the recording data process automation from the multichannel electrical signals system in reception and emission modes is carried out. The process of developing a general circuit for switching channels based on the multiplexor circuit is described. The process of developing a circuit for switching channels in a multichannel system and the choice of the emission and reception modes are described. The process of developing a circuit for switching channels with additional power amplification and control system signals amplification modules is described. The process of developing a switching circuit for an electrical signals multichannel system based on eight-channel relay modules is described. The implementation of the selected circuit based on eight-channel relay boards is presented. The choice of the most optimal switching scheme of a multichannel system of electrical signals in the emission and reception modes for use with the L-Card E-502 measuring module has been carried out. An example of using the developed switching scheme for a multichannel system of electrical signals based on eight-channel relay modules is presented. © 2021 IEEE.

关键词： Switching

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