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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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Tri-Level Linear Programming Model for Automatic Load Shedding Using Spectral Clustering

Tri-Level Linear Programming Model for Automatic Load Sheddi...

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Innovative Smart Grid Technologies (ISGT)

作者： Mario D. Baquedano-Aguilar Arturo Bretas Sean Meyn Nader Aljohani Department of Electrical & Computer Engineering University of Florida Gainesville FL USA Distributed Systems Group Pacific Northwest National Laboratory Richland WA USA G2Elab Grenoble INP CNRS Université Grenoble Alpes Grenoble France Department of Electrical Engineering College of Engineering Taibah University Medina Saudi Arabia

Traditional load shedding schemes can be inadequate in grids with high renewable penetration, leading to unstable events and unnecessary grid islanding. Although for both manual and automatic operating modes load shedding areas have been predefined by grid operators, they have remained fixed, and may be sub-optimal due to dynamic operating conditions. In this work, a distributed tri-level linear programming model for automatic load shedding to avoid system islanding is presented. Preventing islanding is preferred because it reduces the need for additional load shedding besides the disconnection of transmission lines between islands. This is crucial as maintaining the local generation-demand balance is necessary to preserve frequency stability. Furthermore, uneven distribution of generation resources among islands can lead to increased load shedding, causing economic and reliability challenges. This issue is further compounded in modern power systems heavily dependent on non-dispatchable resources like wind and solar. The upper-level model uses complex power flow measurements to determine the system areas to shed load depending on actual operating conditions using a spectral clustering approach. The mid-level model estimates the area system state, while the lower-level model determines the locations and load values to be shed. The solution is practical and promising for real-world applications.

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Towards Enhanced Verification in Process Mining: A Ctmc-Based Probabilistic Model Checking Approach

SSRN

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SSRN 2024年

作者： Mangi, Fawad Ali Su, Guoxin Zhang, Minjie Liu, Li School of Computing and Information Technology University of Wollongong Wollongong2522 Australia Department of Computer Systems Engineering Mehran University of Engineering and Technology Sindh Jamshoro76062 Pakistan School of Big Data and Software Engineering Chongqing University Chongqing400030 China

Process mining is a rapidly growing research field that uses data science techniques to analyse business processes and extract insights about their performance, efficiency, and compliance. The process mining approach begins with process discovery, which involves creating process models based on event logs;and depending on the type of models discovered, the correctness of models needs to be guaranteed. Verification is paramount to ensure the accuracy and reliability of discovered process models in process mining, the current research lacks to model the stochastic nature of process mining models. This paper introduces a novel technique for verification of process mining models, leveraging continuous time Markov chain (CTMC) model of probabilistic model checking technique. The proposed method aims to address the inherent uncertainties and dynamic nature of real-world processes, providing a more robust and comprehensive verification framework compared to traditional methods. The technique employs CTMC to represent the stochastic behavior of processes, allowing for the incorporation of time and probability in the analysis of process models. This approach enables the evaluation of process models under various conditions and scenarios, considering the probabilistic aspects and temporal characteristics inherent in business processes. The paper presents a detailed methodology of the proposed technique, illustrating its application through a case study of loan application process performed in a financial institution, and empirical evaluations. © 2024, The Authors. All rights reserved.

关键词： Model checking

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Smart Pilot Decontamination Strategy for High and Low Contaminated Users in Massive MIMO-5G Network 16th

Smart Pilot Decontamination Strategy for High and Low Contam...

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16th International Conference on Information Technology and Applications, ICITA 2022

作者： Khan, Khalid Banoori, Farhad Adnan, Muhammad Zahoor, Rizwan Khan, Tarique Obite, Felix William, Nobel John Ahmad, Arshad Qayum, Fawad Nazir, Shah Beijing University of Posts and Telecommunications Beijing China Guangzhou China Institute of Software Systems Engineering Johannes Kepler University Linz Austria Linz Austria Department of Computer Science University of Swabi Swabi Pakistan University of Campania Luigi Vanvitelli Caserta Italy University of Politechnico Delle Marche Ancona Italy Department of Computer Science IT University of Malakand Totakan Pakistan Department of Physics Ahmadu Bello University Zaria Nigeria University of Valencia Valencia Spain

ISBN: (纸本)9789811993305

Massive multiple-input multiple-output (m-MIMO) is recognized as large-scale MIMO (LS-MIMO) and is a crucial podium for fifth-generation communication networks (5G) to reinforce spectral efficiency (SE) and capacity of the system. Pilot contamination (PC) arises because the non-orthogonal pilot sequences transmitted by users in a cell similar to the nearby cells is one of the blockages in m-MIMO to avail the essential goals. In the current paper, we intended an intelligent pilot assignment policy to substantially extenuate the potency of PC in the multi-cells m-MIMO system. Based on the intended strategy, the entire users are divided based on their large-scale fading into two groups, i.e., High contaminate K H and low contaminate K L users. Likewise, K H is assigned by orthogonal pilots due to high inter-cell-interference (ICI) and to extenuate the extreme PC, whereas non-orthogonal sets of pilots are dedicated to K L. Afterward, severe PC among K H almost vanished, whereas a slight problem of PC still exists among K L, which deteriorates the system efficiency. Thus, a competent approach based on hyper-graph-coloring-based pilot allocation (HCPA) is projected to overwhelm the PC of K L. However, the simulation effects expose that the proposed strategy extremely stimulates both attainable sum-rate and signal-to-interference-plus-noise-ratios (SINRs). Furthermore, the proposed strategy is extremely efficient to suppress PC and optimize the entire system efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： MIMO systems

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Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center

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Journal of computer Science & Technology 2023年第4期38卷 773-792页

作者：田文洪徐敏贤周光耀吴逵须成忠 Rajkumar Buyya School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu 610054China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Department of Computer Science University of VictoriaVictoriaBCV8W 3P6Canada State Key Laboratory of Internet of Things for Smart City University of MacaoMacao 999078China School of Computing and Information Systems University of MelbourneMelbourne 3010Australia

Load balancing is vital for the efficient and long-term operation of cloud data *** virtualization,post(reactive)migration of virtual machines(VMs)after allocation is the traditional way for load balancing and consoli***,it is not easy for reactive migration to obtain predefined load balance objectives and it may interrupt services and bring ***,we provide a new approach,called Prepartition,for load *** partitions a VM request into a few sub-requests sequentially with start time,end time and capacity demands,and treats each sub-request as a regular VM *** this way,it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal,which supports the resource allocation in a fine-grained *** with real-world trace and synthetic data show that our proposed approach with offline version(PrepartitionOff)scheduling has 10%–20%better performance than the existing load balancing baselines under several metrics,including average utilization,imbalance degree,makespan and Capacity_*** also extend Prepartition to online load *** results show that our proposed approach also outperforms state-of-the-art online algorithms.

关键词： cloud computing physical machine(PM) virtual machine(VM) reservation load balancing Prepartition

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ML-Based Service Type Priority Decision Method Using Ambient Information for 5GB

ML-Based Service Type Priority Decision Method Using Ambient...

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International Conference on Smart Communications and Networking (SmartNets)

作者： Umut Altunan Halenur Sazak Ahmet Yazar Department of Electrical and Electronics Engineering Eskisehir Osmangazi University Eskisehir Turkey Department of Computer Engineering Eskisehir Osmangazi University Eskisehir Turkey Department of Information Systems Engineering Sakarya University Sakarya Turkey Department of Software Engineering Eskisehir Osmangazi University Eskisehir Turkey

5G communications systems offer several types of service groups include enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). The ability to meet various mobile communications requirements of different users is ensured by these service groups. In this paper, a novel service type priority decision method is proposed for 5G and beyond (5GB) systems to determine the most needed service group under a specific region. This approach is useful for the resource allocation planning and optimization through a coverage region. The proposed method is based on the machine learning (ML) usage with several ambient information. Instance-based and model-based techniques are compared for ML. Also, the ensemble methods are tested on the generated synthetic dataset.

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Recursive Least-Square-Based Parameter Estimation for Dynamic State Estimation in Power Grids

Recursive Least-Square-Based Parameter Estimation for Dynami...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Shahin Riahinia Amir Ameli Mohsen Ghafouri Abdulsalam Yassine Department of Electrical and Computer Engineering Lakehead University Thunder Bay ON Canada Concordia Institute for Information Systems Engineering (CIISE) Concordia University Montreal QC Canada Department of Software Engineering Lakehead University Thunder Bay ON Canada

Nowadays, Dynamic State Estimation (DSE) has become integral to power system control and real-time contingency analysis. The efficacy of DSE in wide-area power systems is contingent upon several factors, most notably the accuracy of information regarding the system topology, generation, and load. Given that these parameters can vary dynamically, it becomes imperative to accurately estimate ongoing changes; since misestimations can compromise control and protective actions within power grids. This paper introduces a Recursive Least Squares (RLS) approach, which is grounded on the inverse power flow problem, to effectively estimate the reduced admittance matrix (Y-bus) for DSE applications based on the measurements received from Phasor Measurement Units (PMUs). The proposed RLS estimation technique offers reliable estimates of system parameters despite their dynamic behavior (both smooth and sudden changes) in the presence of measurement noise. The efficacy of the proposed method is validated on the IEEE 14-bus test system using diverse DSE scenarios.

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SCTransNet: Spatial-channel Cross Transformer Network for Infrared Small Target Detection

arXiv

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arXiv 2024年

作者： Yuan, Shuai Qin, Hanlin Yan, Xiang Akhtar, Naveed Mian, Ajmal The School of Optoelectronic Engineering Xidian University Xi’an710071 China The School of Computing and Information Systems Faculty of Engineering and IT The University of Melbourne ParkvilleVIC3052 Australia The Department of Computer Science and Software Engineering The University of Western Australia Perth6009 Australia

This is the pre-acceptance version, to read the final version please go to IEEE TRANSACTION ON GEOSCIENCE AND REMOTE SENSING on IEEE Xplore. Infrared small target detection (IRSTD) has recently benefitted greatly from U-shaped neural models. However, largely overlooking effective global information modeling, existing techniques struggle when the target has high similarities with the background. We present a Spatial-channel Cross Transformer Network (SCTransNet) that leverages spatial-channel cross transformer blocks (SCTBs) on top of long-range skip connections to address the aforementioned challenge. In the proposed SCTBs, the outputs of all encoders are interacted with cross transformer to generate mixed features, which are redistributed to all decoders to effectively reinforce semantic differences between the target and clutter at full levels. Specifically, SCTB contains the following two key elements: (a) spatial-embedded single-head channel-cross attention (SSCA) for exchanging local spatial features and full-level global channel information to eliminate ambiguity among the encoders and facilitate high-level semantic associations of the images, and (b) a complementary feed-forward network (CFN) for enhancing the feature discriminability via a multi-scale strategy and cross-spatial-channel information interaction to promote beneficial information transfer. Our SCTransNet effectively encodes the semantic differences between targets and backgrounds to boost its internal representation for detecting small infrared targets accurately. Extensive experiments on three public datasets, NUDT-SIRST, NUAA-SIRST, and IRSTD-1K, demonstrate that the proposed SCTransNet outperforms existing IRSTD methods. Our code will be made public at https://***/xdFai/SCTransNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Remote sensing

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On the Performance of RIS-enabled NOMA for Aerial Networks

On the Performance of RIS-enabled NOMA for Aerial Networks

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IEEE Conference on Wireless Communications and Networking

作者： Lina Bariah Fouzi Boukhalfa Wael Jaafar Sami Muhaidat Halim Yanikomeroglu Technology Innovation Institute Masdar City Abu Dhabi UAE Department of Software and Information Technology Engineering École de Technologie Supérieure (ÉTS) Montreal QC Canada Department of Electrical Engineering and Computer Science Center for Cyber-Physical Systems Khalifa University Abu Dhabi UAE Department of Systems and Computer Engineering Carleton University ON Canada

In this paper, we investigate the performance of reconfigurable intelligent surface (RIS)-assisted aerial communications, where a ground base station (GBS) communicates with distant terrestrial and/or aerial users through the assistance of a RIS-equipped unmanned aerial vehicle (RIS-UAV). The GBS uses the non-orthogonal multiple access (NOMA) scheme to transmit its signal, which is directed to the users via the RIS-UAV. First, the end-to-end channel is characterized, by considering the shadowed Rician fading, then the outage probability performance metric is derived for the underlying system model. Through numerical results, we demonstrate the impact of several system parameters on the performance of NOMA users. Specifically, we found that RIS elements need to be carefully allocated among different NOMA users, according to their channel conditions, in order to achieve the needed quality of service.

关键词： Measurement NOMA Base stations Rician channels Quality of service Probability Power system reliability

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PBF-AMP-Onto: an ontology for powder bed fusion additive manufacturing processes 1

PBF-AMP-Onto: an ontology for powder bed fusion additive man...

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1st International Workshop on Semantic Materials Science: Harnessing the Power of Semantic Web Technologies in Materials Science, SeMatS 2024

作者： Pour, Mina Abd Nikooie Tarafder, Prithwish Wiberg, Anton Li, Huanyu Moverare, Johan Lambrix, Patrick Department of Computer and Information Science Linköping University Linköping Sweden Swedish e-Science Research Centre Linköping University Linköping Sweden Department of Management and Engineering Linköping University Linköping Sweden Wallenberg Initiative Materials Science for Sustainability Linköping University Linköping Sweden Wallenberg AI Autonomous Systems and Software Program Linköping University Linköping Sweden

Additive manufacturing is an innovative production approach aimed at creating products that traditional techniques cannot produce with the desired quality and requirements. Throughout the additive manufacturing process, data is either used (such as materials properties, printer characteristics and settings) or generated (such as monitoring data during printing, slicing strategies setting parameters). However, managing such data with complex relationships remains a significant challenge in both research and industry in the additive manufacturing field. To address this issue, we developed a modular ontology that can be used as the basis for a framework that supports decision-making systems, facilitate semanticsaware data management, and enhance the understanding and optimization of additive manufacturing processes. In this paper we focus on one of the state-of-the-art additive manufacturing approaches, i.e., powder bed fusion. To show the use and the feasibility of our approach, we created a knowledge graph for an actual additive manufacturing experiment based on our ontology, and show how queries relevant to domain experts can be answered using this knowledge graph. © 2024 CEUR-WS. All rights reserved.

关键词： Ontology

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