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Green hydrogen as a sustainable operations strategy: A socio-economic perspective

Sustainable Operations and computers 2025年第1期6卷 1-14页

作者： Bonesso, Francesco D'Adamo, Idiano Gastaldi, Massimo Giannini, Marco Sapienza University of Rome Rome Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy Department of Industrial and Information Engineering and Economics University of L'Aquila L'Aquila Italy

Hydrogen is an energy carrier that can support the development of sustainable and flexible energy systems. However, decarbonization can occur when green sources are used for energy production and appropriate water use is manifested. This work aims to propose a socio-economic analysis of hydrogen production from an integrated wind and electrolysis plant in southern Italy. The estimated production amounts to about 1.8 million kg and the LCOH is calculated to be 3.60 €/kg in the base scenario. Analyses of the alternative scenarios allow us to observe that with a high probability the value ranges between 3.20–4.00 €/kg and that the capacity factor is the factor that most affects the economic results. Social analysis, conducted through an online survey, shows a strong knowledge gap as only 27.5 % claim to know the difference between green and grey hydrogen. There is a slight propensity to install systems near their homes, but this tends to increase due to increased knowledge on the topic. Respondents state sustainable behaviours, and this study suggests that these aspects should also be transformed into the energy choices that are implemented every day. The study suggests information to policy-makers, businesses and citizens as it outlines that green hydrogen is an operations strategy that moves toward sustainable development. © 2024 The Author(s)

关键词： Green development

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Predicting Traffic Flow Using Dynamic Spatial-Temporal Graph Convolution Networks

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computers, Materials & Continua 2024年第3期78卷 4343-4361页

作者： Yunchang Liu Fei Wan Chengwu Liang School of Computer and Data Science Henan University of Urban ConstructionPingdingshan467036China School of Electrical and Control Engineering Henan University of Urban ConstructionPingdingshan467036China

Traffic flow prediction plays a key role in the construction of intelligent transportation ***,due to its complex spatio-temporal dependence and its uncertainty,the research becomes very *** of the existing studies are based on graph neural networks that model traffic flow graphs and try to use fixed graph structure to deal with the relationship between ***,due to the time-varying spatial correlation of the traffic network,there is no fixed node relationship,and these methods cannot effectively integrate the temporal and spatial *** paper proposes a novel temporal-spatial dynamic graph convolutional network(TSADGCN).The dynamic time warping algorithm(DTW)is introduced to calculate the similarity of traffic flow sequence among network nodes in the time dimension,and the spatiotemporal graph of traffic flow is constructed to capture the spatiotemporal characteristics and dependencies of traffic *** combining graph attention network and time attention network,a spatiotemporal convolution block is constructed to capture spatiotemporal characteristics of traffic *** on open data sets PEMSD4 and PEMSD8 show that TSADGCN has higher prediction accuracy than well-known traffic flow prediction algorithms.

关键词： Intelligent transportation graph convolutional network traffic flow DTW algorithm attention mechanism

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Anti-Windup for Internal Model control With Asymmetric Input Saturation

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IEEE control Systems Letters 2025年 9卷 126-131页

作者： Adegbege, Ambrose A. Owolabi, Opeyemi S. Ogunba, Kolawole S. Jubril, Abimbola M. The College of New Jersey Laboratory for Embedded Control and Optimization Department of Electrical and Computer Engineering EwingNJ08628 United States Obafemi Awolowo University Department of Electronic and Electrical Engineering Ife220282 Nigeria

In this letter, we introduce a novel anti-windup design approach for internal model control (IMC) that addresses the issue of asymmetric input saturation. To enhance closed-loop performance during periods of saturation, we utilize information about the asymmetrical limits of input saturation in the anti-windup design. This is achieved through a combination of static matrix scaling, a constant offset term, and a generalized sector condition that guarantees both global and regional stability of the closed loop. Our approach generalizes existing techniques for incorporating directionality compensation into anti-windup designs and can be interpreted as a search over a class of left-coprime factorization of the linear internal model controller. We demonstrate the effectiveness of this design approach on a well-known and widely used anti-windup example. © 2017 IEEE.

关键词： Matrix factorization

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Vaccination, Booster Doses and Social Constraints: A Steady State and an Optimal Transient Approaches to Epidemics Containment

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SN computer Science 2024年第1期5卷 125页

作者： Di Giamberardino, Paolo Iacoviello, Daniela Department of Computer Control and Management Engineering Sapienza University of Rome via Ariosto 25 Rome 00185 Italy

The problem of the definition of control actions to contain epidemic diseases is crucial in case of high infectivity, dangerous or fatal consequences, large inhabited areas involved. Unfortunately, during the last three years, the COVID-19 pandemic has represented a critical situation all over the world. On the basis of the experiences for known diseases and the literature on the epidemic modeling, various strategies have been proposed and applied in different countries, someone using all the possible efforts, some others just maintaining the global health compact within acceptable levels. The effectiveness of the approaches has been always measured on the basis of the reproduction number R_t , which is intrinsically a steady-state evaluation, since it does not takes into account the control variations. In the present paper, with reference to a mathematical model which takes into account the different level of vaccination in the population, an optimal condition based approach is adopted to define the actions of intervention, bringing to a switching optimal control scheme based on the time by time evolution of the disease. The two approaches are developed and compared, showing that the use of partial information can bring to counter-intuitive situations and supporting the necessity of a feedback action to better adapt the containment measure to the situation. Numerical simulations are performed to better show the claimed results. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Booster doses COVID-19 Epidemic modeling Optimal control Social behavior Vaccination

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QTFN:A General End-to-End Time-Frequency Network to Reveal the Time-Varying Signatures of the Time Series

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Big Data Mining and Analytics 2024年第3期7卷 905-919页

作者： Tao Chen Yang Jiao Lei Xie Hongye Su State Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou 310027China Shenzhen Key Laboratory of Intelligent Bioinformatics and College of Computer Science and Control EngineeringShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Nonstationary time series are ubiquitous in almost all natural and engineering *** the time-varying signatures from nonstationary time series is still a challenging problem for data *** Time-Frequency Distribution(TFD)provides a powerful tool to analyze these ***,they suffer from Cross-Term(CT)issues that impair the readability of ***,to achieve high-resolution and CT-free TFDs,an end-to-end architecture termed Quadratic TF-Net(QTFN)is proposed in this *** by classic TFD theory,the design of this deep learning architecture is heuristic,which firstly generates various basis functions through ***,more comprehensive TF features can be extracted by these basis ***,to balance the results of various basis functions adaptively,the Efficient Channel Attention(ECA)block is also embedded into ***,a new structure called Muti-scale Residual Encoder-Decoder(MRED)is also proposed to improve the learning ability of the model by highly integrating the multi-scale learning and encoder-decoder ***,although the model is only trained by synthetic signals,both synthetic and real-world signals are tested to validate the generalization capability and superiority of the proposed QTFN.

关键词： Time-Frequency Analysis(TFA) Multi-scale Residual Encoder-Decoder(MRED) quadratic Time-Frequency Distribution(TFD)

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Assembling Solar Panels by Dual Robot Arms Towards Full Autonomous Lunar Base Construction

Assembling Solar Panels by Dual Robot Arms Towards Full Auto...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Nunziante, Luca Uno, Kentaro Diaz, Gustavo H. Santra, Shreya De Luca, Alessandro Yoshida, Kazuya Sapienza University of Rome Department of Computer Control and Management Engineering Italy Department of Aerospace Engineering Sendai980-8579 Japan

ISBN: (纸本)9798331531614

Since the successful Apollo program, humanity is once again aiming to return to the Moon for scientific discovery, resource mining, and inhabitation. Upcoming decades focus on building a lunar outpost, with robotic systems playing a crucial role to safely and efficiently establish essential infrastructure such as solar power generating towers. Similar to the construction of the International Space Station (ISS), shipping necessary components via modules and assembling them in situ should be a practical scenario. In this context, this paper focuses on the integration of vision, control, and hardware systems within an autonomous sequence for a dual-arm robot system. We explore a perception and control pipeline specifically designed for assembling solar panel modules, one of the benchmark tasks. Ad hoc hardware was designed and tested in real-world experiments. A mock-up of modular solar panels and active-passive connectors are employed, with the control of this grappling fixture integrated into the proposed pipeline. The successful implementation of our method demonstrates that the two robot manipulators can effectively connect arbitrarily placed panels, highlighting the seamless integration of vision, control, and hardware systems in complex space applications. © 2025 IEEE.

关键词： Space applications

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Exploiting Machine Learning and LSTM for Human Activity Recognition: Using Physiological and Biological Sensor Data from Actigraph 25

Exploiting Machine Learning and LSTM for Human Activity Reco...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Oyeleye, Matthew Chen, Tianhua Su, Pan Antoniou, Grigoris School Of Computing And Engineering University Of Huddersfield Department Of Computer Science United Kingdom School Of Control And Computer Engineering North China Electric Power University China

ISBN: (纸本)9798350340266

Human activity recognition involves identifying the daily living activities of an individual through the utilization of sensor attributes and intelligent learning algorithms. The identification of intricate human activities proves to be a labo-rious task, given the inherent difficulty of capturing long-term dependencies and extracting efficient features from unprocessed sensor data. For this purpose, this study aims at recognizing and classifying human activities using physiological and biological sensor data generated by Actigraph, as they can accurately measure moderate-to-vigorous intensity physical which is mostly affected by body composition and also better suited for self-monitoring. We examined the effectiveness of these features by applying prevalent machine learning classifiers and long short-term memory (LSTM) networks on recently publicly available data, which includes accelerometer and heart rate recordings. The results from our experiments showed that LSTM models performed better than conventional ML classifiers with the best result achieving an accuracy of 86.5%. The findings also confirms the significance of the heart rate in accurately classifying and identification of human activity more. © 2024 IEEE.

关键词： Long short-term memory

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An Attack on a Proposed Construction of Small-State Stream Ciphers and Proposals for New Constructions (Nov, bxac165, 2022)

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computer JOURNAL 2024年第2期67卷 806-806页

作者： Amin-Ghafari, Vahid Attari, Mahmoud Ahmadian School of Information and Control Engineering China University of Mining and Technology Jiangsu 221116 P. R. China Electrical and Computer Engineering School K. N. Toosi University of Technology Tehran *** Iran

Small-state stream ciphers (SSCs) idea is based on using key bits not only in the initialization but also continuously in the keystream generation phase. A time-memory-data tradeoff (TMDTO) distinguishing attack was successfully applied against all SSCs in 2017 by Hamann et al. They suggested using not only key bits but also initial value (IV) bits continuously in the keystream generation phase to strengthen SSCs against TMDTO attacks. Then, Hamann and Krause proposed a construction based on using only IV bits continuously in the packet mode. They suggested an instantiation of an SSC and claimed that it is resistant to TMDTO attacks. We point out that accessing IV bits imposes an overhead on cryptosystems that might be unacceptable in some applications. More importantly, we show that the proposed SSC remains vulnerable to TMDTO attacks 1. To resolve this security threat, the current paper proposes constructions based on storing key or IV bits that are the first to provide full security against TMDTO attacks. Five constructions are proposed for different applications by considering efficiency. Designers can obtain each construction’s minimum volatile state length according to the desirable keystream, key and IV lengths.

关键词： cryptography distinguishing attack IoT security lightweight encryption stream cipher time-memory-data tradeoff attack

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Hybrid Optimization Algorithm for Handwritten Document Enhancement

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computers, Materials & Continua 2024年第3期78卷 3763-3786页

作者： Shu-Chuan Chu Xiaomeng Yang Li Zhang Václav Snášel Jeng-Shyang Pan College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China Faculty of Electrical Engineering and Computer Science VŠB-TU OstravaOstrava70080Czech Republic Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

关键词： Metaheuristic algorithm gannet optimization algorithm hybrid algorithm handwritten document enhancement

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SEFormer:A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis

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computers, Materials & Continua 2025年第1期82卷 1417-1437页

作者： Hongxing Wang Xilai Ju Hua Zhu Huafeng Li State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing University of Aeronautics and AstronauticsNanjing210016China School of Computer Science and Engineering Nanyang Technological UniversitySingapore639798Singapore

Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain ***,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis ***,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault ***,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative *** complexity results in high computational costs and limited industrial *** tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault ***,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration ***,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global ***,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and *** study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.

关键词： CNN-Transformer separable multiscale depthwise convolution efficient self-attention fault diagnosis

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