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International Conference of Electrical, Electronic and Networked Energy systems, EENES 2024

作者： Lu, Mengting Xie, Zhengyu Ma, Xiaoning Liu, Min Mao, Junyan School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Institute of Computing Technology China Academy of Railway Sciences Corporation Limited Haidian Beijing100081 China

ISBN: (纸本)9789819620791

With the exponential growth of cross-modal information in railway data-to-service platforms, including accumulated text, images, files, and audiovisual materials, efficiently managing and utilizing these valuable resources has become an important issue for optimizing data organization and improving retrieval efficiency. This paper innovatively proposes a design scheme for an intelligent cross-modal data retrieval system tailored to the railway domain. The system aims to overcome the limitations of traditional single-modal retrieval by integrating multiple data types such as images and files, using names as core indices, to achieve seamless retrieval and integrated presentation of cross-modal information. This system not only accurately retrieves all modal information related to specific individuals, such as academic papers, on-site photos, research reports, and related news links, but also deeply extracts and categorizes key information, such as providing file paths, report titles, and enabling download operations, greatly enhancing the efficiency and precision of railway data utilization and retrieval. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Content based retrieval

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A Comparative Study of Various Clustering Methods on Dengue Microarray Gene Expression Dataset and Pathway Enrichment Analysis

A Comparative Study of Various Clustering Methods on Dengue ...

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International Symposium on Advanced computing and Communication (ISACC)

作者： Epsita Das Piyali Chatterjee Department of Computer Science and Engineering Netaji Subhash Engineering College Kolkata India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Recently, disease progression caused by dengue virus has intensely increased and affected almost 80 countries of WHO. As a result, in the year of 2023, 6.5 million cases of dengue infection followed by 7300 dengue-related deaths have been reported. The global spread of dengue is driven by the lack of vaccines and insufficient vector control efforts. Human and dengue virus interaction affects gene expression value which needs to be investigated for biomarker prediction. Using clustering methods, it is possible to identify genes contributing to a deeper understanding to know how the virus manipulates host cell machinery and gain insight of the same biological pathways or cellular processes. Clustering results may help in identification of biomarkers, uncovering potential therapeutic targets. This study aims to analyze dengue gene expression dataset GSE51808 through various unsupervised clustering algorithms leveraging the identification of new biomarkers through enrichment analysis.

关键词： Drugs Clustering methods Supervised learning Clustering algorithms Vaccines Gene expression Machinery intelligent systems Viruses (medical) Machine vector control

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A Comprehensive Review of Advanced Analytics for Predicting HRQoL in Cancer Survivors Using a Synergistic Approach

A Comprehensive Review of Advanced Analytics for Predicting ...

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intelligent control, computing and Communications (IC3), International Conference on

作者： Vaishnaw Gorakhnath Kale Pravin Ramdas Kshirsagar Bhuvan Unhelkar Prasun Chakrabarti Kamal Upreti Rituraj Jain School of Computer Science Engineering and Applications D. Y. Patil International University Pune MS India Department of Electronics Engineering J.D College of Engg & Mgmt. Nagpur MS India Muma College of Business (Sarasota-Manatee Campus) University of South Florida United States of America Department of Computer Science & Engineering Sir Padampat Singhania University Udaipur Rajasthan India Department of Computer Science Christ University Delhi NCR Ghaziabad India Department of Information Technology Marwadi University Rajkot Gujarat India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

This systematic review explores the role applied and emerging methods including AI, Explainable AI and Quantum machine learning techniques in the prediction of Health-Related Quality of Life (HRQoL) of cancer survivors. It also gives possible benefits and limitation of using the advanced analytics to predict the HRQoL. In all, 141 research papers implemented in the last fifteen years with focus between the years 2008 to 2023 are analyzed. For the convenience, this literature review is divided into four primary categories - (i) Artificial intelligence, (ii) Explainable artificial intelligence, (iii) Quantum machine learning, and (iv) Synergistic integration. The third way the present systematic review paper differs from other papers in the domain is that the paper offers a direction of future research. Furthermore, the hypothetical illustration is provided in order to compare outcomes of the synergistic approach with the existing data. Consequently, this analysis provides beneficial insights for further research and development of the synergistic approach in both research and clinical practice. The assessment shows that there is a continued need for research focusing on improving the quality of life of those that survived cancer.

关键词： Ethics Quantum computing Explainable AI Focusing Teamwork Research and development Faces Cancer Systematic literature review intelligent control

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intelligent Aircraft Antiskid Braking systems – A Review 1st

Intelligent Aircraft Antiskid Braking Systems – A Review

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1st International Conference on intelligent systems in computing and Communications, ISCComm 2023

作者： Sudheendra, P. Bhat, Vinayambika S. Aircraft Research and Design Centre Hindustan Aeronautics Limited Bengaluru India Department of Electronics and Communication Engineering Mangalore Institute of Technology and Engineering Moodabidri India

ISBN: (纸本)9783031756047

This paper presents the evolution of application of Artificial Intelligence (AI)techniques in the design of Aircraft Antiskid Braking systems (AABS). AABS designs that are based on conventional heuristic algorithms fail to generate desired responses when exposed to unpredictable nonlinearities such as actuator and sensor faults and variations in aircraft dynamics. AI techniques such as Artificial Neural Networks (ANN), Fuzzy logic and Machine Learning (ML) can make control systems adapt in response to changes in both internal and external disturbances. This work begins with an introduction on evolution of AABS and limitations of conventional AABS designs. Then an adapted aircraft landing gear and ‘tyre-runway’ dynamic models are presented. This is followed by the review of AABS that are based on the three common AI techniques namely ANN, Fuzzy logic, and ML and intelligent PID controllers. A comparison of the AI based AABS is provided in the concluding section. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Landing gear (aircraft)

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A Method for Abnormal Detection and Poisoned Data Recovery in Clustered Federated Learning 18th

A Method for Abnormal Detection and Poisoned Data Recovery...

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18th International Conference on Wireless Artificial intelligent computing systems and Applications, WASA 2024

作者： Chen, Yingying Shi, Lei Xu, Hao Ye, Junyu Xu, Juan School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei230009 China

ISBN: (纸本)9783031714665

Clustered Federated Learning (CFL), as a type of Federated Learning (FL) paradigm, divides the clients into multiple clusters through the clustering process and trains them within the clusters, thus improving the overall model training accuracy. But the clustering process is also the weak link, which can lead to the failure of the whole model training if it is interfered or destroyed by the attacker. In this paper, we study the attack methods and defense strategies for attackers to implement data poisoning by tampering with client data, resulting in overall clustering failure. Our defense approach is designed to identify anomalous clients and recover their poisoned data. In order to resist the malicious behavior of attackers, we propose a dual-threshold pixel difference and watermark authentication defense method for detecting the presence of anomalous clients. The method can accurately identify the abnormal fluctuations in the data and effectively screen out the abnormal clients. Meanwhile, we propose a self-embedding watermarking defense algorithm based on shuffling idea for recovering poisoned data. Among them, our self-embedding watermarking algorithm not only accurately locates and recovers the tampered region of the image, but also improves the watermark security to prevent attackers from cracking it easily. Simulation results show that our proposed algorithm can accurately identify the abnormal client and recover the data. Even if the client’s data is massively tampered with, we can recover the tampered data images with high quality and ensure that the CFL clustering is not corrupted. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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STMAE: Spatial Temporal Masked Auto-Encoder for Traffic Forecasting 27th

STMAE: Spatial Temporal Masked Auto-Encoder for Traffic For...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Wu, Xing Cai, Chengyou Wang, Xiaoxiao Wang, Jianjia Yao, Junfeng Qian, Quan Song, Jun School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Shanghai Shentong Metro Co. Ltd. Shanghai China School of AI and Advanced Computing XJTLU Entrepreneur College Xi’an Jiaotong-Liverpool University Suzhou China CSSC Seago System Technology Co. Ltd. Shanghai China Department of Geography Hong Kong Baptist University Hong Kong

ISBN: (纸本)9783031781681

The advancement of intelligent transportation systems underscores the importance of data-driven approaches in traffic forecasting, which plays a crucial role in tasks such as traffic signal control and route guidance, among others. However, the inherent uncertainty stemming from regional traffic dynamics, coupled with intricate spatio-temporal correlations, poses formidable challenges to accurate traffic prediction. Moreover, the complexities inherent in sequence forecasting across varying scales further exacerbate the accuracy dilemma. Recognizing the need for integrating information across spatial and temporal dimensions to enhance forecasting precision, a novel solution termed Spatial Temporal Masked Autoencoder (STMAE) is introduced. The STMAE framework addresses these challenges through a two-stage learning process. In the pre-training phase, an autoencoder architecture is employed to extract spatio-temporal features from the data. In the fine-tuning phase, the pre-trained encoder of the STMAE model undergoes further refinement to specifically target traffic forecasting tasks. Extensive evaluations validate the effectiveness of the proposed STMAE model. Notably, STMAE demonstrates competitive performance, achieving 3.32 Vehs MAE for long-term (60 min) traffic forecasting while operating within a reduced computational budget. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Budget control

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A Hybrid Intelligence Model Forecasts the SOC of Electric Vehicle’s Battery 19th

A Hybrid Intelligence Model Forecasts the SOC of Electric ...

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19th International Conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Rubiños, Manuel Arcano-Bea, Paula Timiraos, Míriam Michelena, Álvaro Vega, Rafael Vega Andújar, José Manuel Casteleiro-Roca, José-Luis CTC Department of Industrial Engineering CITIC University of A Coruña EPEF Calle Mendizábal s/n Campus de Esteiro A Coruña Ferrol15403 Spain Department of Electronic Engineering Computer Systems and Automatic University of Huelva Campus de El Carmen Huelva21071 Spain

ISBN: (纸本)9783031750090

Battery technology is advancing due to the demand of safe energy storage in systems such as Electric Vehicles (EVs) and portable electronics systems. Estimating battery conditions and increasing the range of electric vehicles requires specific models and optimize control strategies. Anticipate battery performance is complicated due to several variables, such as temperature, deterioration, and usage models. Knowing battery performance entails an advanced understanding of time series analysis, particularly forecasting. This study proposes the use of Artificial Neural Networks (ANN) to create a Lithium Ion Battery (LIB) prediction model. The experiments were carried out in a 59.2 VDC, 120 Ah LIB, recording the voltage and temperature while simulating the charge and discharge. A hybrid model is developed with internal local models using clustering. The model prognostic the State of Charge (SOC) evolution and reach a Mean Squared Errors with values between 10-3 and 10-4. This research shows that hybrid models are adecuated instrument for predict the battery SOC. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Artificial intelligent Batteries Electric vehicles SOC prediction

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computing Performance Analysis of IEC 61499 Distributed Automation systems

Computing Performance Analysis of IEC 61499 Distributed Auto...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Lyu, Tuojian Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Luleå Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden

ISBN: (纸本)9798331521950

This work analyzes the computing performance of distributed control systems in IEC 61499 on two hardware devices, Dell XPS workstation and Raspi 4B. Based on the test IEC 61499 application, three different system configurations are tested to simulate the distribution options of singleprocess and single-thread, multi-threading, and multi-process on both hardware platforms. Experimental results show that multiprocess configurations yield more consistent execution time due to dedicated resource allocation, while multi-threading, though resource-efficient, can lead to latency variability. These results aim at help in choosing optimal configurations and selecting hardware for performance in distributed automation systems. © 2025 IEEE.

关键词： Parallel processing systems

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Physical mechanisms and integration design of memristors

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Materials Today Nano 2025年 30卷

作者： Wang, Mengna Wang, Kun Sun, Bai Zhou, Guangdong Cao, Zelin Gao, Kaikai Ren, Fenggang Chen, Xiaoliang Li, Xiangming Shao, Jinyou Frontier Institute of Science and Technology and Interdisciplinary Research Center of Frontier Science and Technology Xi'an Jiaotong University Shaanxi Xi'an710049 China Micro-and Nano-technology Research Center State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Shaanxi Xi'an710049 China College of Artificial Intelligence Brain-inspired Computing & Intelligent Control of Chongqing Key Lab Southwest University Chongqing400715 China National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine The First Affiliated Hospital of Xi'an Jiaotong University Shaanxi Xi'an710049 China

Memristors with threshold transition behavior and rich dynamics are ideal candidates for simulating biological pulse neurons and constructing efficient neuromorphic systems. Based on changes in material structure, charge distribution, molecular polarization and other physical states, new integrated memory-computing devices have been constructed and applied to the sensor-memory-computing integrated systems. In this review, we first discuss the classification of memristive materials from the perspective of device structure design and focus on introducing the working mechanisms of valence variation, polarization, and carrier transfer covered internally. Further, the latest progress of memristors with multi-physical mechanisms is comprehensively summarized, including the formation/fracture of conductive filaments, carrier capture/release, polarization/depolarization. In particular, it is discussed in detail the principles and applications of memristors with different working mechanisms for constructing neuromorphic systems. Finally, the potential challenges and opportunities that may exist in the development of memristors in the coming years are discussed in depth. © 2025 Elsevier Ltd

关键词： Molecular structure

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Optimizing the Reuse of Building Demolition Materials for Coastal Dunes Reestablishment 4th

Optimizing the Reuse of Building Demolition Materials for...

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4th International Conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Brunetti, Guglielmo Federico Antonio Bevacqua, Marida Maiolo, Maria Rita Amisano, Maristella Department of Environmental Engineering University of Calabria Rende Italy Department of Business and Legal Sciences University of Calabria Rende Italy

ISBN: (纸本)9783031812439

Effective strategies for designing and managing coastal defenses against erosion require a multidisciplinary approach. From a regulatory perspective, the Italian Dm 152/2022 "End of Waste" governs the reuse of construction and demolition waste (C&D waste) and other inert mineral waste. A noteworthy aspect of this Dm is that it allows such waste to be used for the morphological restoration of eroded coastlines, through beach replenishment and the reestablishment of coastal dunes. This circumstance serves as an opportunity to incorporate porous materials with predetermined granulometric characteristics and values of the main hydraulic parameters, such as hydraulic conductivity. In an attempt to improve the current understanding and shed light on the potentialities of this approach, we utilized the Hydrus-2D software. We investigated whether the implementation of local heterogeneities made by recovered materials with specific grain sizes could effectively regulate the interaction between the sea and groundwater. This could control infiltration, exfiltration, and velocity distribution in a coastal dune, thereby mitigating erosive phenomena. A comparison is made between different scenarios in which heterogeneities have been created inside a dune using highly and lowly conductive materials. We consider cases where the groundwater level is higher than the sea level and vice versa. Results indicate that soil heterogeneity can be used to optimize infiltration adn exfiltration processes. Materials with lower hydraulic conductivity values generally limit velocity fields and exfiltration processes from the dune surface. This could limit erosion, especially in cases where groundwater level is higher than sea water level. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Hydraulic conductivity

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