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arXiv 2025年

作者： Mei, Zihao Li, Jianhao Zhang, Bolin Wang, Chong Guo, Lijun Li, Guoqi Qian, Jiangbo Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Institute of Automation Chinese Academy of Sciences Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology Beijing100045 China

With the rapid growth of dynamic vision sensor (DVS) data, constructing a low-energy, efficient data retrieval system has become an urgent task. Hash learning is one of the most important retrieval technologies which can keep the distance between hash codes consistent with the distance between DVS data. As spiking neural networks (SNNs) can encode information through spikes, they demonstrate great potential in promoting energy efficiency. Based on the binary characteristics of SNNs, we first propose a novel supervised hashing method named Spikinghash with a hierarchical lightweight structure. Spiking WaveMixer (SWM) is deployed in shallow layers, utilizing a multilevel 3D discrete wavelet transform (3D-DWT) to decouple spatiotemporal features into various low-frequency and high-frequency components, and then employing efficient spectral feature fusion. SWM can effectively capture the temporal dependencies and local spatial features. Spiking Self-Attention (SSA) is deployed in deeper layers to further extract global spatiotemporal information. We also design a hash layer utilizing binary characteristic of SNNs, which integrates information over multiple time steps to generate final hash codes. Furthermore, we propose a new dynamic soft similarity loss for SNNs, which utilizes membrane potentials to construct a learnable similarity matrix as soft labels to fully capture the similarity differences between classes and compensate information loss in SNNs, thereby improving retrieval performance. Experiments on multiple datasets demonstrate that Spikinghash can achieve state-of-the-art results with low energy consumption and fewer parameters. Copyright © 2025, The Authors. All rights reserved.

关键词： Discrete wavelet transforms

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Value Stream Mapping for Smart Pharmaceutical Management in a Thai Hospital 6

Value Stream Mapping for Smart Pharmaceutical Management in ...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Boonsothonsatit, Ganda Silapunt, Rardchawadee Vongbunyong, Supachai Kaemarungsi, Kamol Chanpuypetch, Wirachchaya Chonsawat, Nilubon Graduate School of Management and Innovation King Mongkut's University of Technology Thonburi Thailand Electronic and Telecommunication Engineering King Mongkut's University of Technology Thonburi Thailand Hospital Automation Research Center Institute of Field Robotics King Mongkut's University of Technology Thonburi Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Thailand Faculty of Agro-Industry Chiang Mai University Thailand Department of Agricultural Economy and Developement Faculty of Agriculture Chiang Mai University Thailand

The evolution of healthcare systems worldwide necessitates continual improvement in hospital management practices, particularly pharmaceutical management. This paper explores transforming traditional pharmaceutical management systems in large and medium-sized hospitals in Thailand into smart systems using Value Stream Mapping (VSM) as a guiding tool. Traditional systems often face challenges such as inefficiencies and medication errors. VSM, rooted in lean management principles, offers a structured approach to visualize and optimize processes. Additionally, smart technologies like automated inventory systems and electronic health records enhance pharmaceutical management effectiveness. Through a comprehensive research methodology encompassing case studies, data collection, and VSM analysis, this study identifies inefficiencies and proposes evidence-based strategies for improvement. Results from a case study highlight the benefits of VSM implementation in enhancing pharmacy operations, emphasizing improved patient care and operational efficiency. The implementation of a smart medication dispensing system shows promising results in reducing workload and accommodating increased patient arrivals. Continued evaluation and refinement are crucial for maximizing benefits and addressing emerging challenges effectively. © 2025 Elsevier B.V.. All rights reserved.

关键词： Electronic health record

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On Hyper-Stability Theory Based Multivariable Nonlinear Adaptive Control: Experimental Validation on Induction Motors

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IET Electric Power Applications 2025年第1期19卷

作者： Bekhiti, Belkacem Nail, Bachir Tibermacine, Imad Eddine Salim, Ramzi Blida Algeria Faculty of Sciences and Technology University of Ziane Achour Djelfa Algeria Department of Computer Automation and Management Engineering Sapienza University of Rome Rome Italy Laboratoire d’Automatique et Analysis des Systems National Polytechnic School Oran Algeria

This paper presents a novel hyperstability-based adaptive control strategy for induction motors, distinguishing itself from conventional model reference adaptive control (MRAC) approaches by integrating enhanced robustness against parametric uncertainties and external disturbances. Unlike traditional adaptive controllers, the proposed Hyper-stable adaptive controller (H-MRAC) ensures improved transient performance and faster convergence rates, validated through both theoretical analysis and experimental verification. Key innovations include the integration of hyper-stability theory into adaptive control design and a comprehensive evaluation of parameter uncertainties, which significantly improves motor performance in variable conditions. Experimental results demonstrate a 42% reduction in integral squared error (ISE), a 37% improvement in integral absolute error (IAE), a 28.7% improvement in integral time absolute error (ITAE) and a 25% faster convergence compared to standard MRAC. A detailed comparison with passivity-based control strategies shows a 26.5% improvement in steady-state performance and 30% faster transient response. Despite these successes, the paper discusses limitations related to computational complexity, real-time implementation challenges, and the impact of sensor noise on control performance. The potential need for DSPs or FPGA-based solutions is also addressed. Finally, the generalisability of the proposed control method across different motor types and power ratings is considered with future directions for broader validation in diverse industrial scenarios. © 2025 The Author(s). IET Electric Power Applications published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Model reference adaptive control

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TPCap: Unlocking Zero-Shot Image Captioning with Trigger-Augmented and Multi-Modal Purification Modules

arXiv

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arXiv 2025年

作者： Zhang, Ruoyu Wang, Lulu He, Yi Pan, Tongling Yu, Zhengtao Li, Yingna Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650504 China Key Laboratory of Computer Technologies Application Kunming650504 China Hongyun Honghe Group Honghe Cigarette Factory Kunming650202 China

Recent advancements in large language models (LLMs) have significantly enhanced the fluency and logical coherence of image captioning. Retrieval-Augmented Generation (RAG) is widely adopted to incorporate external knowledge into LLMs;however, existing RAG-based methods rely on separate retrieval banks, introducing computational overhead and limiting the utilization of LLMs’ inherent zero-shot capabilities. To address these limitations, we propose TPCap, a novel trigger-augmented and multi-modal purification framework for zero-shot image captioning without external retrieval libraries. TPCap consists of two key components: trigger-augmented (TA) generation and multi-modal purification (MP). The TA module employs a trigger projector with frozen and learnable projections to activate LLMs’ contextual reasoning, enhance visual-textual alignment, and mitigate data bias. The MP module further refines the generated entity-related information by filtering noise and enhancing feature quality, ensuring more precise and factually consistent captions. We evaluate TPCap on COCO, NoCaps, Flickr30k, and WHOOPS datasets. With only 0.82M trainable parameters and training on a single NVIDIA RTX 4090 GPU, TPCap achieves competitive performance comparable to state-of-the-art models. Copyright © 2025, The Authors. All rights reserved.

关键词： Information filtering

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Task Scheduling Mechanism for Crowdsourcing in Mobile Social Networks

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IEEE Transactions on Mobile Computing 2025年

作者： Zhang, Jiale Yi, Longhao Gao, Xiaofeng Bhatti, Shahzad Sarwar Yuan, Ting Chen, Guihai Shanghai Jiao Tong University Department of Computer Science and Engineering China Emerson University Faculty of Computing and Emerging Technologies Multan Pakistan Shanghai Jiao Tong University Department of Automation China

With the popularization of smart phones, mobile crowdsourcing emerged and gained growing attention in the recent years. Mobile users are now able to conduct complex tasks with the communication between each other. In this paper, we study the task scheduling problem in the mobile crowdsourcing systems based on the spontaneously formed mobile social networks (MSNs). We introduce two crowdsourcing task scheduling problems under this system model, with one problem aiming to minimize the total cost of some crowdsourcing tasks and the other focusing on minimizing the final completion time of the tasks belonging to the same project. We introduce a unified framework to solve the problems and propose two approximation algorithms for these two problems in the offline versions respectively and prove Their approximation ratios accordingly. Based on the two algorithms, we further design two online algorithms to deal with the workers' dynamism and also analyze their competitive ratios. Finally, we verify the effectiveness and efficiency of the proposed methods through numerical experiments on real and synthetic datasets. © 2002-2012 IEEE.

关键词： Approximation algorithms

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A Comprehensive Methodology of Field-Oriented Control Design With Parameter Variation Analysis for Interior Permanent Magnet Synchronous Machine Drives

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IEEE Access 2025年 13卷 89524-89541页

作者： Tiago Davi Curi Busarello Abdullah Bubshait Oruganti V. S. R. Varaprasad Abdulhakeem Alsaleem Marcelo Godoy Simões Department of Control Automation and Computation Engineering Federal University of Santa Catarina Florianopolis Brazil Department of Electrical Engineering College of Engineering King Faisal University Al Ahsa Saudi Arabia Smart Transportation Electrification and Energy Research (STEER) Group Department of Electrical Computer and Software Engineering Faculty of Engineering and Applied Science Ontario Tech University Oshawa ON Canada Department of Electrical Engineering College of Engineering Qassim University Buraydah Saudi Arabia School of Technology and Innovations University of Vaasa Vaasa Finland

This paper proposes a comprehensive methodology for Field-Oriented Control (FOC) with parameter variation analysis for Interior Permanent Magnet Synchronous Machines (IPMSM). The modeling approach for an IPMSM is first presented, followed by a step-by-step procedure for designing a vector-controlled strategy. The formulation is based on a dq-rotating reference frame aligned with the rotor shaft position. A key distinction of this methodology from traditional approaches is that the current controller is designed in the time domain based on desired time constants, while the speed control is formulated within a frequency response domain framework. The proposed hybrid approach enables accurate tuning of the Proportional-Integrator (PI) controllers for both current and speed control loops. Additionally, a parameter variation analysis is conducted to enhance the proposed methodology. The validity region for the design procedure is presented, ensuring that for any wide speed variation of the machine, loop gains are properly tuned. One of the main advantages of the proposed methodology is that it provides a fast, reliable, and accurate technique for implementing IPMSM drive systems. Results from a Controller Hardware-in-the-Loop (C-HIL) setup with an external microcontroller are presented. The comprehensive design approach is validated under two different IPMSM parameter sets, demonstrating its effectiveness.

关键词： PI control Stators Tuning Mathematical models Velocity control Rotors Vectors Stator windings Robustness Microcontrollers

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Dual-beam photothermal material trace detection method based on self-mixing interference

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Applied Optics 2025年第16期64卷 4549-4558页

作者： Yan Zhao Yunlong Zhi Hui Zhao Daming Dong Jigang Tong Chunling Tu School of Electrical Engineering and Automation Tianjin University of Technology Tianjin 300384 China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin 300384 China National Engineering Research Center for Intelligent Agricultural Equipment Beijing Academy of Agriculture and Forestry Sciences Beijing 100097 China Department of Computer Systems Engineering Faculty of ICT Tshwane University of Technology Pretoria 0188 South Africa

In this paper, a dual-beam photothermal self-mixing substance trace detection system is proposed. The crystal violet (CV) solution of the sample undergoes a thermal lens effect under the action of pump photoperiod excitation, and the refractive index changes cause the deviation of the probe beam, and the self-mixing interference (SMI) effect occurs when the modulated light returns to the cavity. By combining theoretical and experimental results, the power fluctuation of the SMI signal caused by the thermo-optic effect is linearly related to CV concentration and other parameters. At an excitation power of 24 mW, the detection limit can reach $8.0 \times {10^{- 8}}\;{\rm mol/L}$ . This method does not require labeling or complex CV pretreatment and has high sensitivity and flexibility, providing a guide for CV characterization in biological, environmental, and pharmaceutical research.

关键词： Gaussian beams Laser beams Optical fields Phase shift Refractive index Spatial resolution

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Dynamic behaviors and digital circuit implementation of a Rulkov neuron with a non-polynomial memristor synaptic weight

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The European Physical Journal Special Topics 2025年 1-18页

作者： Tamba, Victor Kamdoum Tofou, Junior Tchiaze Pham, Viet-Thanh Grassi, Giuseppe Department of Telecommunication and Network Engineering IUT-Fotso Victor of Bandjoun University of Dschang Bandjoun Cameroon Research Unit of Automation and Applied Computer Department of Electrical Engineering IUT-FV Bandjoun University of Dschang Bandjoun Cameroon Faculty of Electronics Technology Industrial University of Ho Chi Minh City Ho Chi Minh City Vietnam Department of Engineering for Innovation University of Salento Lecce Italy

Memristors have proven since their discovery to play a very important role as artificial synapses in the implementation of artificial neural networks. Just as biological synapses, memristors can significantly influence the behaviors of the neural network. A non-polynomial memristor is used in this work to investigate the memristive synaptic weight effect on the dynamics of a simplified Rulkov neuron. The model is first introduced from where theoretical analyses show the system’s ability to develop regular and irregular behaviors. Analysis methods such as two parameter charts, bifurcation diagrams and maximum Lyapunov exponents (MLE) are then used to study the dynamics of the proposed Rulkov model. The numerical results show that the memristive synaptic weight Rulkov neuron model can exhibit self-excited and hidden firings. Furthermore, the system can undergo interesting offset boosting dynamics and antimonotonicity features (i.e., bubble bifurcations) for some system parameters. To corroborate the computational findings, the proposed model is implemented on an Arduino Due microcontroller board, from where the experimental results are seen to be in agreement with the obtained numerical results.

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An innovative integrated processing framework for enhanced decoding of Mental-Imagery MEG signals

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Biomedical Signal Processing and Control 2025年 109卷

作者： Wang, Fan Wang, Peng Ding, Peng Gong, Anmin Chen, Yanxiao Xue, Yuhang Fu, Yunfa Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Brain Cognition and Brain-computer Intelligence Integration Group Kunming University of Science and Technology Kunming650500 China School of Information Engineering Chinese People's Armed Police Force Engineering University Xi'an710000 China

Magnetoencephalography (MEG) has emerged as a pivotal tool for developing brain-computer interfaces (BCI) based on mental imagery tasks, owing to its superior spatiotemporal resolution. However, existing decoding strategies for MEG data in mental imagery tasks face two major challenges: (1) inadequate exploitation of the high-dimensional characteristics of MEG signals, and (2) a lack of tailored algorithms for efficient feature extraction and classification. To address these issues, we propose a novel integrated processing framework for enhancing mental imagery-based BCI performance. First, an innovative channel selection method combining correlation coefficient and variance entropy product (CC-VEP) is introduced to identify task-relevant channels while suppressing noise and redundancy. Next, discriminative features are extracted using the DivCSP algorithm with intra-class regularization terms. Subsequently, probabilistic outputs from base classifiers (K-Nearest Neighbors, Random Forests, Support Vector Machines, and Extreme Learning Machines) are fused through a modified multi-criteria decision-based fusion (MCDM-MCF) strategy to generate final class labels. Experimental results on a public MEG dataset demonstrate that the proposed framework achieves a 12.25% improvement in classification accuracy compared to the average accuracy of all base classifiers in traditional methods, and a 7.97% improvement over other existing methods. Moreover, it effectively balances specificity and sensitivity, enhances robustness, and offers a new approach for efficient decoding in mental imagery-based BCI systems. © 2025 Elsevier Ltd

关键词： Support vector machines

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Open and Closed Source Models for LLM-Generated Metaheuristics Solving engineering Optimization Problem 28th

Open and Closed Source Models for LLM-Generated Metaheuris...

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28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Senkerik, Roman Viktorin, Adam Kadavy, Tomas Kovac, Jozef Janku, Peter Pekar, Libor Guzowski, Hubert Smolka, Maciej Byrski, Aleksander Pluhacek, Michal A.I.Lab Faculty of Applied Informatics Tomas Bata University Zlin Czech Republic Department of Automation and Control Engineering Faculty of Applied Informatics Tomas Bata University Zlin Czech Republic Faculty of Computer Science AGH University of Krakow Krakow Poland Center of Excellence in Artificial Intelligence AGH University of Krakow Krakow Poland

ISBN: (纸本)9783031900648

This paper explores the applicability of generative AI (genAI), specifically Large Language Models (LLMs), for the automatic generation and configuration of metaheuristic algorithms to address a real-world engineering problem: the optimal parameter estimation of time-delay systems in interconnected heating-cooling loops. The study introduces a pioneering workflow and iterative architecture with feedback within the emerging field of genAI-driven optimization for real optimization problems, eliminating the need for manually crafted or modified algorithms. This automated system empowers domain experts in engineering to solve complex optimization problems with minimal knowledge of optimization algorithms, lowering the barrier to entry for sophisticated algorithm use. We demonstrate how LLMs can generate effective optimizers under conditions like connstrained optimization problems where the solution lies near the boundaries of the search space. Four state-of-the-art LLMs (closed and open-sourced) have been selected for experiments. These are GPT-4o, GPT-4o mini, Claude Sonnet 3.5 and Llama 3.1. All studied LLMs generated metaheuristics that outperformed the initialization baseline optimization method (Random Search and CMA-ES). Notably, the Claude Sonnet 3.5 model generated a metaheuristic with the best mean results, almost matching the performance of the tuned state-of-the-art DISH algorithm, as an example of adaptive Differential Evolution. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Time of arrival

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