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Digital Selves Based Intelligent Construction Framework

International Journal of Crowd Science 2024年第4期8卷 184-194页

作者： Liu, Xiaomeng Sun, Hongbo Lin, Jiarui School of Computer and Control Engineering Yantai University Yantai264005 China Department of Civil Engineering Tsinghua University Beijing100084 China

Construction is the pillar industry of Chinese national economy, but the profit rate has continued to decline in recent years. The conventional job-centric construction information system cannot meet the requirements for safety, quality, and efficiency. In order to solve the above problems, a digital selves based intelligent construction framework has been proposed which is centred on workers, equipment, and sites. It changes the conventional job-centered construction information system and realizes efficient coordination of information among various departments and stages. Applying the digital selves based intelligent construction framework to the case has been proved to be a good solution to the problem of information sharing. Meanwhile, the authenticity and reliability of the information have been ensured. The system has improved management efficiency and information systematization for all participants. It has enabled companies and workers to have a more comprehensive understanding of themselves and to make timely and effective strategies against hazards. In addition, the system has effectively raised the level of digitalization, automation, and intelligence of construction, making governance more accurate and more effective. © The author(s) 2024.

关键词： Efficiency

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Federated Learning With Meta-Layers Training for Privacy-Preserving in Vehicular Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 621-629页

作者： Shen, Xiaoyang Li, Haibin Li, Yaqian Zhang, Wenming Alenazi, Mohammed J.F. Agarwal, Kadambri Yanshan University College of Electrical Engineering Hebei Qinhuangdao066000 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Hebei Qinhuangdao066000 China King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia ABES Engineering College Department of Computer Science and Engineering Ghaziabad201009 India

Vehicular consumer electronics, such as autonomous vehicles (AVs), need collecting large amounts of private user information, which face the risk of privacy leakage. To protect the privacy of consumers, researchers have proposed to apply federated learning (FL) to privacy-preserving vehicular consumer electronics, that is, leveraging FL for collaborative training of a decision model without exchanging the sensitive information generated by AVs. However, FL generally needs to exchange the gradients of client models periodically with the central server, which can be attacked by adversaries to infer user information. Thereby, it may still face the risk of privacy leakage. To solve that challenge, we put forward a novel FL framework, called FL with Meta-Layers Training (FL-MLT). Instead of exchanging the gradients of the client models, it exchanges, meta-layers with the central servers. Since meta-layers are only a slice of the client models, exchanging them helps protect privacy. On the other hand, they contain meta-knowledge to help the FL training process. In the experiments, we conduct extensive visual classification simulation to evaluate FL-MLT, and the experimental results demonstrate the superior performance of FL-MLT. © 1975-2011 IEEE.

关键词： Federated learning

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Switching event-triggered self-adjusting prescribed performance force control of electrohydraulic load simulator

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ISA Transactions 2025年 162卷 217-226页

作者： Li, Jiafeng Ji, Ruihang Yan, Hao Shan, Jiefeng Chen, Zanwei School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore

This paper addresses the problem of switching event-triggered self-adjusting prescribed performance force control of electro-hydraulic load simulator (EHLS). A reduced-order fuzzy speed state observer for speed estimation is designed and a self-adjusting performance boundary is devised to mitigate the commutation jitter problem. The switching event-triggered mechanism dynamically adjusts the triggering conditions to optimize resource utilization. To reduce the complexity of the traditional inverse stepping control, the dynamic surface technique is adopted. The results demonstrate that the proposed control method guarantees the boundedness of the closed-loop system states and confines the tracking error to a small range near the equilibrium point within a finite time horizon. Finally, the effectiveness of the proposed method is demonstrated through experimentation. © 2025 International Society of Automation

关键词： Closed loop control systems

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Nanogenerator-based bidirectional pressure sensor array and its demonstration in underwater invasive species detection

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Nano Research 2023年第9期16卷 11822-11831页

作者： Yunqi Cao Hongyang Shi Xiaobo Tan Nelson Sepúlveda State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Department of Electrical and Computer Engineering Michigan State UniversityEast LansingMichigan 48824USA

Lakes areas,which cause catastrophic damages in both commercial fishery and ecological ***,current assessment strategies may pose challenges for lake-wide abundance estimation and non-target anadromous species ***,we demonstrate an efficacious species-specific non-destructive sensing system based on porous ferroelectret nanogenerator for in-situ monitoring of lamprey spawning migration using their unique suction *** show that the porous structure enables a redistribution of surface charges under bidirectional deformations,which allows the detection of both positive and negative *** quasi-piezoelectric effect is further validated by quantitative analysis in a wide pressure range of−50 to 60 kPa,providing detailed insights into transduction working *** reliable lamprey detection,a 4×4-pixel sensor array is developed and integrated with a complementary metal-oxide-semiconductor(CMOS)based signal processing array thus constituting a sensing panel capable of recording oral suction patterns in an underwater environment.

关键词： bidirectional pressure sensing nanogenerators self-powered sensor array ferroelectret invasive species detection

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ELCD-NSC2: a novel early lung cancer detection and non-small cell classification framework

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Neural Computing and Applications 2024年第24期36卷 15149-15164页

作者： Helaly, Hadeer A. Badawy, Mahmoud El-Gendy, Eman M. Haikal, Amira Y. Electrical Engineering Department Faculty of Engineering Damietta University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Computer Science and Informatics Taibah University Medina Saudi Arabia

The survival rate of lung cancer relies significantly on how far the disease has spread when it is detected, how it reacts to the treatment, the patient’s overall health, and other factors. Therefore, the earlier the lung cancer diagnosis, the higher the survival rate. For radiologists, recognizing malignant lung nodules from computed tomography (CT) scans is a challenging and time-consuming process. As a result, computer-aided diagnosis (CAD) systems have been suggested to alleviate these burdens. Deep-learning approaches have demonstrated remarkable results in recent years, surpassing traditional methods in different fields. Researchers are currently experimenting with several deep-learning strategies to increase the effectiveness of CAD systems in lung cancer detection with CT. This work proposes a deep-learning framework for detecting and diagnosing lung cancer. The proposed framework used recent deep-learning techniques in all its layers. The autoencoder technique structure is tuned and used in the preprocessing stage to denoise and reconstruct the medical lung cancer dataset. Besides, it depends on the transfer learning pre-trained models to make multi-classification among different lung cancer cases such as benign, adenocarcinoma, and squamous cell carcinoma. The proposed model provides high performance while recognizing and differentiating between two types of datasets, including biopsy and CT scans. The Cancer Imaging Archive and Kaggle datasets are utilized to train and test the proposed model. The empirical results show that the proposed framework performs well according to various performance metrics. According to accuracy, precision, recall, F1-score, and AUC metrics, it achieves 99.60, 99.61, 99.62, 99.70, and 99.75%, respectively. Also, it depicts 0.0028, 0.0026, and 0.0507 in mean absolute error, mean squared error, and root mean square error metrics. Furthermore, it helps physicians effectively diagnose lung cancer in its early stages and allows spe

关键词： Image classification

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An enhanced neuro-adaptive PID sliding mode control for robot manipulators:promoting sustainable automation

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Neural Computing and Applications 2025年第9期37卷 6877-6898页

作者： Elmogy, Ahmed Alhemaly, Nagah El-Ghaish, Hany Elawady, Wael Computer Engineering Deptartment Prince Sattam Ibn Abdelaziz University Alkharj16273 Saudi Arabia Computers & Control Engineering Department Tanta University Tanta31527 Egypt

The control of robot manipulators presents significant challenges, primarily due to their complex, nonlinear dynamics. Another major difficulty arises from environmental and operational disturbances. Numerous control frameworks and approaches have been proposed in the literature to address these issues. However, many of these approaches are discontinuous or non-adaptive, which negatively affects their performance and makes them unsuitable for real-time applications. This paper proposes a novel integrated control scheme that combines adaptive control with an improved continuous second-order sliding mode control (CSOS), utilizing generalized artificial neural networks (GANN) for enhanced performance in robot manipulators. The proposed control method consists of two key components: An adaptive proportional-integral-derivative (PID) controller and an adaptive CSOS-based control module (CSOSSD-APID), designed to deliver superior transient and steady-state performance. In this approach, the adaptive CSOS benefits from GANN’s strong noise handling capabilities and its ability to estimate uncertainties effectively. This integration significantly enhances the robustness of robot manipulator control across various tracking tasks, using a single pre-trained GANN model with fine-tuned weights tailored to each task. Numerical simulations demonstrate the effectiveness and versatility of the proposed control scheme, particularly in managing highly time-varying trajectories while contributing to more sustainable and efficient automation practices. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Adaptive control systems

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Prototype-Based Interpretable Graph Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1486-1495页

作者： Ragno, Alessio Rosa, Biagio La Capobianco, Roberto Sapienza University of Rome Department of Computer Control and Management Engineering Rome00185 Italy Sony AI Zurich8952 Switzerland

Graph neural networks have proved to be a key tool for dealing with many problems and domains, such as chemistry, natural language processing, and social networks. While the structure of the layers is simple, it is difficult to identify the patterns learned by the graph neural network. Several works propose post hoc methods to explain graph predictions, but few of them try to generate interpretable models. Conversely, the topic of the interpretable models is highly investigated in image recognition. Given the similarity between image and graph domains, we analyze the adaptability of prototype-based neural networks for graph and node classification. In particular, we investigate the use of two interpretable networks, ProtoPNet and TesNet, in the graph domain. We show that the adapted networks manage to reach better or higher accuracy scores than their respective black-box models and comparable performances with state-of-the-art self-explainable models. Showing how to extract ProtoPNet and TesNet explanations on graph neural networks, we further study how to obtain global and local explanations for the trained models. We then evaluate the explanations of the interpretable models by comparing them with post hoc approaches and self-explainable models. Our findings show that the application of TesNet and ProtoPNet to the graph domain produces qualitative predictions while improving their reliability and transparency. © 2020 IEEE.

关键词： computer architecture

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A Real-Time Machine Learning Based Solution for Privacy Enforcement in Video Recordings and Live Streaming 9

A Real-Time Machine Learning Based Solution for Privacy Enfo...

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9th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2024

作者： Conforti, Pietro Manganelli Emanuele, Matteo Mandelli, Lorenzo Department of Computer Control and Management Engineering Sapienza University of Rome Italy

These past years the world had to deal with a whole new situation brought by Covid-19. Everyone’s routine changed and we started passing way more time than before on virtual meeting, virtual chats and similar. With this, many privacy problems arised from all the video data generated by a single user. Google and Zoom introduced the possibility to blur out the background while using a front face camera, but this did not solve many privacy concerns ranging from showing people in videos without their permission, to the leaking of sensible data and information from videos uploaded online. We propose a solution build over the use of computer vision techniques like image segmentation and classification for context recognition for a privacy enforcement solution capable of fitting the user’s personal need, blurring out selectively specific objects from a video based on the user’s preferences for each room in which they are. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Differential privacy

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Comparative Analysis of Wearable and computer Vision-Based Methods for Human Torso Posture Estimation 9

Comparative Analysis of Wearable and Computer Vision-Based M...

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9th International Conference on Mathematics and computers in Sciences and Industry, MCSI 2024

作者： Muntean, Emanuel Leba, Monica University of Petrosani Department of System Control and Computer Engineering Petrosani Romania

ISBN: (纸本)9798350377224

This paper conducts a comparative analysis of human torso posture estimation methodologies, focusing on an inertial measurement unit (IMU) sensor coupled with an Arduino UNO as a wearable approach, and Kinect V2, utilizing OpenCV for posture analysis. The core objective of this study is to ascertain which method yields greater precision in the estimation of human torso posture. The IMU sensor, characterized by its wearability, provides the distinct advantage of unobtrusiveness and the capability to record motion across diverse environments. Conversely, Kinect V2 leverages computer vision techniques to derive posture estimations from visual data in real-time. Through comprehensive experimentation, this research evaluates the accuracy of both methodologies by juxtaposing their posture estimation outcomes. The findings of this investigation aim to significantly contribute to the enhancement of human posture estimation systems, with wide-ranging implications for health, sports, and ergonomics. © 2024 IEEE.

关键词： Ergonomics

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DanteLLM: Let's Push Italian LLM Research Forward! 30

DanteLLM: Let's Push Italian LLM Research Forward!

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Bacciu, Andrea Campagnano, Cesare Trappolini, Giovanni Silvestri, Fabrizio Department of Computer Control and Management Engineering Sapienza University of Rome Italy

ISBN: (纸本)9782493814104

In recent years, the dominance of Large Language Models (LLMs) in the English language has become evident. However, there remains a pronounced gap in resources and evaluation tools tailored for non-English languages, underscoring a significant disparity in the global AI landscape. This paper seeks to bridge this gap, specifically focusing on the Italian linguistic context. We introduce a novel benchmark, and an open LLM Leaderboard, designed to evaluate LLMs' performance in Italian, providing a rigorous framework for comparative analysis. In our assessment of currently available models, we highlight their respective strengths and limitations against this standard. Crucially, we propose "DanteLLM", a state-of-the-art LLM dedicated to Italian. Our empirical evaluations underscore Dante's superiority, as it emerges as the most performant model on our benchmark, with improvements by up to 6 points. This research not only marks a significant stride in Italian-centric natural language processing but also offers a blueprint for the development and evaluation of LLMs in other languages, championing a more inclusive AI paradigm. Our code at: https://***/RSTLess-research/DanteLLM. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Benchmarking

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