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2023 International Conference on ENTERprise Information Systems, CENTERIS 2023 - International Conference on Project MANagement, ProjMAN 2023 - International Conference on health and Social Care Information Systems and Technologies, HCist 2023

作者： Elgendy, Nada Paivarinta, Tero Elragal, Ahmed Hannula, Karoliina Puolitaival, Kaisa M3S Faculty of Information Technology and Electrical Engineering University of Oulu Finland Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden Antell Oulu Finland

Human-machine collaboration has potentially led to higher quality and more informed data-driven decisions. However, evaluating these decisions is necessary to measure the benefits, as well as enable experiential learning and posterior rationalization of the results and consequences. Nevertheless, the multiplicity of human-machine collaboration modes, as well as the multi-faceted nature of data-driven decisions complicates evaluation, and evaluation solutions are lacking both in research and in practice. This is further reflected in the complexity of incorporating evaluation in the design of such data-driven decision making systems, since developers are left without theoretically grounded and practically feasible principles to guide implementation. In this paper, we propose a set of five design principles, explicated from theory and practice, for systems implementing data-driven decision evaluation as the output of design science research cycles. The design principles are: 1) multi-faceted evaluation criteria, 2) unified viewpoint, 3) collaborative rationality, 4) processual ex-post evaluation, and 5) adaptive feedback and learning loops. They are further contextualized in the case of AI-enabled menu design at Antell, an innovative pioneer in the restaurant business in Finland, and consequently evaluated by the development managers of the project. Accordingly, the design principles contribute to the knowledge base on metahuman systems and data-driven decision evaluation, by concretizing existing normative concepts into prescriptive knowledge, also guiding future research and generalizing towards a design theory. Furthermore, they provide implementable statements for designing and developing such systems in practice and can be used as a checklist to compare and evaluate existing systems. © 2024 The Author(s).

关键词： Man machine systems

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Triboelectric-electromagnetic hybrid generator with swing-blade structures for effectively harvesting distributed wind energy in urban environments

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Nano Research 2023年第9期16卷 11621-11629页

作者： Yangyang Yan Jinzhi Zhu Yuejun Zhang Zhaohui Wang Lin Jiang Zhong Lin Wang Jianyang Zhu Tinghai Cheng Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and TechnologyWuhan 430081China Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of SciencesBeijing 101400China Georgia Institute of Technology AtlantaGA 30332-0245USA Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China

The wind energy in cities cannot be exploited effectively because natural wind is unstable and ***,a triboelectricelectromagnetic hybrid generator with swing-blade structures(SBS-TEHG)was designed to effectively harvest intermittent and continuous wind energy in an urban ***,the spring structure and base were considered to realize the maximum output performance of triboelectric ***,the computational fluid dynamics method was applied to optimize the structure of the SBS-TEHG to improve its aerodynamic *** starting wind speed of the SBS-TEHG was 2 m/s,and its energy conversion efficiency was 9.04%,159%higher than that of the SBS-TEHG without guide plates at 4 m/*** results demonstrated that the SBS-TEHG lit 105 light-emitting diodes(LEDs)under the intermittent-wind harvesting mode at a wind frequency of 1 Hz when the single swing blade operated,while a wireless PM_(2.5)&PM_(10)sensor was powered by the SBS-TEHG after a period of operation under the continuous-wind harvesting *** findings of this study provide a novel solution for lowspeed wind energy harvesting in cities and demonstrate the potential of SBS-TEHG as a distributed energy source.

关键词： triboelectric nanogenerator swing-blade structure low-speed wind energy computational fluid dynamics distributed energy source

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Pedestrian Crossing Intention Prediction Using Transformer-Based Feature Fusion 10

Pedestrian Crossing Intention Prediction Using Transformer-B...

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10th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2024

作者： Salehi, Yasaman Ezoji, Mehdi Mohammadpourmir, Farzam Babol Noshirvani University of Technology Faculty of Electrical and Computer Engineering Babol Iran Electrical Power and Computer Science and Research Branch Islamic Azad University Faculty of Mechanics Tehran Iran

ISBN: (纸本)9798331532543

Pedestrian intention prediction can be used in Advanced Driver Assistance Systems to prevent pedestrian-vehicle collision in case of driver distractions. The use of these tools will reduce pedestrian fatalities in traffic accidents. In addition, predicting pedestrians' intention and avoiding collisions are parts of the functions of autonomous vehicles. Improving these models will lead to safer vehicles and higher levels of automation. In most studies, inspired from human behavior, sequences of spatial features, e.g. pedestrian's location, are considered as inputs. For more accurate prediction, temporal features are also extracted from these sequences of spatial features. We have realized that it is important to represent key objects to ensure model's perception of the scene. In this work, a spatio-temporal approach has been studied that predicts pedestrians' intention using spatial features of cropped images. We use the same strategy as our reference model for temporal features and feature fusion. We have modified the masking model of the reference model to reduce inference time, while we improve the performance. With inference time of 58 ms, we achieved accuracy, AUC and F1-Score of 0.90, 0.87, and 0.75 on JAAD dataset, respectively. © 2024 IEEE.

关键词： Advanced driver assistance systems

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Enhanced Self-Deployment in IoT Sensor Networks through Leveraging 3D-Voronoi Diagrams with an Advanced Genetic Algorithm

Enhanced Self-Deployment in IoT Sensor Networks through Leve...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Andreas, Andreou Mavromoustakis, Constandinos X. Markakis, Evangelos Bourdena, Athina Mastorakis, George University of Nicosia Department of Computer Science Nicosia Cyprus Hellenic Mediterranean University Department of Electrical and Computer Engineering Crete Greece Hellenic Mediterranean University Department of Management Science and Technology Crete Greece

ISBN: (纸本)9798350351255

Smart spaces integrate advanced technologies like the Internet of Things (IoT), Machine Learning, and Artificial Intelligence (AI) to enhance automation and control within various environments. Effective deployment of IoT nodes is crucial for maximizing coverage, minimizing costs, and ensuring network stability in these spaces. This paper presents a novel approach combining 3D Voronoi diagrams with a modified Genetic Algorithm (GA) to optimize IoT node placement in three-dimensional environments. The proposed method starts with node placement using a homogeneous Poisson Point Process (PPP) and partitions the space into Voronoi cells, followed by iterative adjustments using the modified GA. The method achieves a 15% improvement in coverage ratio, a 10% reduction in deployment effort, and a 20% increase in network stability compared to existing algorithms, with results statistically significant at 5%. Moreover, optimising sensor placements indirectly enhances network security by reducing redundant data paths and strengthening network resilience. This study provides a scalable, efficient solution for IoT network deployment in complex environments, addressing key challenges in smart space optimization and paving the way for more secure and robust IoT infrastructures. © 2024 IEEE.

关键词： Genetic algorithms

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A Highly Selective Dual Bandpass Filter using Couple Line Resonator for Modern Wireless Communication Systems 4

A Highly Selective Dual Bandpass Filter using Couple Line Re...

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4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Mushtaq, Bilal Rehman, Muhammad Abdul Hussain, Aqib Abbass, Muhammad Jamshed Riphah International University Department of Electrical Engineering Islamabad Pakistan Wroclaw University of Science and Technology Department of Computer Engineering Poland Wroclaw University of Science and Technolog Department of Electrical Engineering Poland

ISBN: (纸本)9798350335316

In this paper, a miniaturized dual-band bandpass filter (DB-BPF) based on a dual-path stub-loaded resonator is presented. A transversal filtering design is introduced in the proposed DB-BPF, which provides two signal propagation paths from input to output. In total, six transmission poles and four transmission zeros are obtained, which improves overall filter selectivity. The fractional bandwidth (FBW) of dual bands with center frequencies of 2.24 and 7.35 GHz is 36.4% and 31.2%, respectively. The physical size of a fabricated filter is (0.43 × 0.17)λg, where λg is the guided wavelength at 2.24 GHz. The DB-BPF prototype is fabricated using a Roger Duroid RT/5880 substrate to further validate the result. The full-wave EM simulation and measured results show good agreement. The proposed dual-band bandpass is applicable to a wide range of modern communication technologies, including Wi-Fi, satellite, cellular, radar, and broadcast communication. © 2023 IEEE.

关键词： Bandpass filters

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Unveiling the Power of Sparse Neural Networks for Feature Selection 27

Unveiling the Power of Sparse Neural Networks for Feature Se...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Atashgahi, Zahra Liu, Tennison Pechenizkiy, Mykola Veldhuis, Raymond Mocanu, Decebal Constantin van der Schaar, Mihaela Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Computer Science University of Luxembourg Luxembourg

ISBN: (纸本)9781643685489

Sparse Neural Networks (SNNs) have emerged as powerful tools for efficient feature selection. Leveraging the dynamic sparse training (DST) algorithms within SNNs has demonstrated promising feature selection capabilities while drastically reducing computational overheads. Despite these advancements, several critical aspects remain insufficiently explored for feature selection. Questions persist regarding the choice of the DST algorithm for network training, the choice of metric for ranking features/neurons, and the comparative performance of these methods across diverse datasets when compared to dense networks. This paper addresses these gaps by presenting a comprehensive systematic analysis of feature selection with sparse neural networks. Moreover, we introduce a novel metric considering sparse neural network characteristics, which is designed to quantify feature importance within the context of SNNs. Our findings show that feature selection with SNNs trained with DST algorithms can achieve, on average, more than 50% memory and 55% FLOPs reduction compared to the dense networks, while outperforming them in terms of the quality of the selected features. © 2024 The Authors.

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Multiple detectors-based Bayesian inference and statistics analysis for incipient fault detection

Multiple detectors-based Bayesian inference and statistics a...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Wang, Ruixue Zhao, Wenqing Cheng, Chao Zou, Xiaoyu Ji, Hongquan Shandong University of Science and Technology College of Electrical Engineering and Automation Qingdao China Changchun University of Technology College of Computer Science and Engineering Changchun China China University of Mining and Technology School of Mechanical and Electrical Engineering Xuzhou China

ISBN: (纸本)9798350337754

To establish a secure and dependable operational setting for practical industrial processes, it is crucial to detect incipient faults promptly and accurately. In this work, a novel data-driven process monitoring approach is presented for incipient fault detection. First, inspired by the ensemble learning algorithm, multiple Mahalanobis distance (MD) detectors are trained, and corresponding detection indices for each sample are calculated. Second, with Bayesian inference, all the obtained indices are converted to probabilities for samples being faulty. Furthermore, the statistics of probabilities are analyzed by implementing sliding time window technique, ultimately achieving incipient fault detection. Finally, the effectiveness of the proposed strategy is demonstrated by a numerical example and the continuous stirred tank reactor benchmark process. The proposed approach inherits the advantages of ensemble learning, so it has the capability to improve the stability of detection performance. Meanwhile, due to the utilization of Bayesian inference, together with the sliding time window technique, it can better differentiate normal and faulty samples and improve the sensitivity to incipient faults. © 2023 IEEE.

关键词： Fault detection

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A novel threshold changeable secret sharing scheme

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Frontiers of computer science 2022年第1期16卷 127-133页

作者： Lein HARN Chingfang HSU Zhe XIA Department of Computer Science Electrical Engineering University of Missouri-Kansas CityKansas CityMO64110USA Computer School Central China Normal UniversityWuhan430079China Department of Computer Science Wuhan University of TechnologyWuhan430071China

A(t,n)threshold secret sharing scheme is a fundamental tool in many security applications such as cloud computing and multiparty *** conventional threshold secret sharing schemes,like Shamir’s scheme based on a univariate polynomial,additional communication key share scheme is needed for shareholders to protect the secrecy of their shares if secret reconstruction is performed over a *** the secret reconstruction,the threshold changeable secret sharing(TCSS)allows the threshold to be a dynamic value so that if some shares have been compromised in a given time,it needs more shares to reconstruct the ***,a new secret sharing scheme based on a bivariate polynomial is proposed in which shares generated initially by a dealer can be used not only to reconstruct the secret but also to protect the secrecy of shares when the secret reconstruction is performed over a *** this paper,we further extend this scheme to enable it to be a TCSS without any *** proposed TCSS is dealer-free and *** generated by a dealer in our scheme can serve for three purposes,(a)to reconstruct a secret;(b)to protect the secrecy of shares if secret reconstruction is performed over a network;and(c)to enable the threshold changeable property.

关键词： cryptography 94A60 authentication and secret sharing 94A62

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An adaptive data hiding for AMBTC compressed images with recoverability using bound shifting technique

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Multimedia Tools and Applications 2023年第10期82卷 15593-15612页

作者： Zhou, Xiaoyu Yang, Guangsong Hong, Wien Chen, Kia Sheng Chen, Tung-Shou School of Ocean Information Engineering Jimei University Xiamen361021 China Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung City404 Taiwan School of Electrical and Computer Engineering Nanfang College of Guangzhou Guangzhou510970 China

This paper proposes an efficient data hiding method for absolute moment block truncation coding (AMBTC) images with the recoverability of compressed code. The existing methods sacrifice some embedding capacity and image quality for recovering the AMBTC compressed code. To remedy these problems, this paper divides the quantized differences into four intervals to embed data bits with different lengths. Therefore, the embedding capacity is improved. Moreover, our method uses the bound shifting technique (BST) to provide a significant improvement in image quality. The experimental results show that the payload of our method increases by 19.14% and the image quality is also enhanced by 8.02% compared with prior works for the test Lena image. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Image quality

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Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

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computers, Materials & Continua 2023年第4期75卷 183-196页

作者： Samah Alshathri Ezz El-Din Hemdan Walid El-Shafai Amged Sayed Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportSmart Village CampusEgypt

In recent years, Digital Twin (DT) has gained significant interestfrom academia and industry due to the advanced in information technology,communication systems, Artificial Intelligence (AI), Cloud Computing (CC),and Industrial Internet of Things (IIoT). The main concept of the DT isto provide a comprehensive tangible, and operational explanation of anyelement, asset, or system. However, it is an extremely dynamic taxonomydeveloping in complexity during the life cycle that produces a massive amountof engendered data and information. Likewise, with the development of AI,digital twins can be redefined and could be a crucial approach to aid theInternet of Things (IoT)-based DT applications for transferring the data andvalue onto the Internet with better decision-making. Therefore, this paperintroduces an efficient DT-based fault diagnosis model based on machinelearning (ML) tools. In this framework, the DT model of the machine isconstructed by creating the simulation model. In the proposed framework,the Genetic algorithm (GA) is used for the optimization task to improvethe classification accuracy. Furthermore, we evaluate the proposed faultdiagnosis framework using performance metrics such as precision, accuracy,F-measure, and recall. The proposed framework is comprehensively examinedusing the triplex pump fault diagnosis. The experimental results demonstratedthat the hybrid GA-ML method gives outstanding results compared to MLmethods like LogisticRegression (LR), Na飗e Bayes (NB), and SupportVectorMachine (SVM). The suggested framework achieves the highest accuracyof 95% for the employed hybrid GA-SVM. The proposed framework willeffectively help industrial operators make an appropriate decision concerningthe fault analysis for IIoT applications in the context of Industry 4.0.

关键词： Automated fault diagnosis control system ML AI CC IIoT digital twins genetic algorithm GA-ML technique

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