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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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A Data-Driven Adaptive Control Approach for Enhancing the Dynamic Response of VSGs in Varying Grid Conditions

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IEEE Transactions on Power Delivery 2025年第3期40卷 1421-1433页

作者： Fahad, Shah She, Buxin Yin, Junjie Li, Fangxing Cui, Hantao Bo, Rui Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Senior Power Systems Engineer ETAP Irvine CA USA Optimization & Control Group Parcific Northwest National Laboratory Richland USA Department of Electrical Engineering and Computer Science University of Tennessee at Knoxville Knoxville TN USA Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA

Conventionally, a virtual synchronous generator (VSG) is designed for islanded mode (IM) operation to meet specific operational requirements such as the rate of change of frequency (RoCoF). However, the operation of VSG designed for IM may not meet the operational and control criteria in grid connected mode (GCM) when the grid conditions vary. In addition, conventional VSG control technology does not consider the influence of the presynchronization scheme when connected to a weak grid, which degrades the RoCoF in IM. To overcome the aforementioned challenges, the proposed study presents a twin-delayed deep deterministic policy gradient (TD3) algorithm to improve the seamless transition performance of VSG from IM to GCM and vice versa. In the first step, the VSG-based power system model is used as a foundation for problem formulation of the proposed TD3 algorithm. Secondly, a reward function is designed according to the performance requirements, i.e., frequency and RoCoF requirements, of the VSG in order to guide the training of the agent in varying load, power reference, and grid conditions. Finally, the superiority of the proposed algorithm over existing methods is validated in MATLAB/SIMULINK and RTDS based real-time simulation environment. © 1986-2012 IEEE.

关键词： Deep reinforcement learning

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Parameter Tuning in Automatic Detection Algorithm for Detecting Artificial Brain Shift 24

Parameter Tuning in Automatic Detection Algorithm for Detect...

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6th International Conference on Intelligent Medicine and Image Processing, IMIP 2024

作者： Watanabe, Kaoru Noborio, Hiroshi Koeda, Masanao Department of Computer Science Osaka Electro-Communication University Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University Japan

ISBN: (纸本)9798400710032

In neurosurgery to remove brain tumors, DICOM data, a medical imaging standard, is generated preoperatively using CT and MRI. This data is used for surgical planning. However, brain deformation problems, known as brain shift, occur during surgery, resulting in discrepancies from the preoperative surgical plan. Deviations from the surgical plan due to brain shift are a life-threatening problem as they reduce the success rate of surgery. Brain shifts have not yet been elucidated, and DICOM data acquired from MRIs and CTs for surgical planning remain stored and archived in hospital information centers. Here, it is planned to extract the physical parameters of the brain model from a large amount of brain deformation data using pre- and post-operative brain DICOM pairs, which are stored in large quantities in hospitals and discarded after a particular time. This study investigates the optimization of parameters in the brain shift detection algorithm. © 2024 ACM.

关键词： Digital Imaging and Communications in Medicine (DICOM)

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FedRec:Trusted rank-based recommender scheme for service provisioning in federated cloud environment

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Digital Communications and Networks 2023年第1期9卷 33-46页

作者： Ashwin Verma Pronaya Bhattacharya Umesh Bodkhe Deepti Saraswat Sudeep Tanwar Kapal Dev Department of Computer Science and Engineering Institute of TechnologyNirma UniversityAhmedabadGujarat382481India Department of Institute of Intelligent Systems University of JohannesburgPO Box 524South Africa

The emergence of on-demand service provisioning by Federated Cloud Providers(FCPs)to Cloud Users(CU)has fuelled significant innovations in cloud provisioning *** to the massive traffic,massive CU resource requests are sent to FCPs,and appropriate service recommendations are sent by ***,the FourthGeneration(4G)-Long Term Evolution(LTE)network faces bottlenecks that affect end-user throughput and ***,the data is exchanged among heterogeneous stakeholders,and thus trust is a prime *** address these limitations,the paper proposes a Blockchain(BC)-leveraged rank-based recommender scheme,FedRec,to expedite secure and trusted Cloud Service Provisioning(CSP)to the CU through the FCP at the backdrop of base 5G communication *** scheme operates in three *** the first phase,a BCintegrated request-response broker model is formulated between the CU,Cloud Brokers(BR),and the FCP,where a CU service request is forwarded through the BR to different *** service requests,Anything-as-aService(XaaS)is supported by 5G-enhanced Mobile Broadband(eMBB)*** the next phase,a weighted matching recommender model is proposed at the FCP sites based on a novel Ranking-Based Recommender(RBR)model based on the CU *** the final phase,based on the matching recommendations between the CU and the FCP,Smart Contracts(SC)are executed,and resource provisioning data is stored in the Interplanetary File systems(IPFS)that expedite the block *** proposed scheme FedRec is compared in terms of SC evaluation and formal *** simulation,FedRec achieves a reduction of 27.55%in chain storage and a transaction throughput of 43.5074 Mbps at 150 *** the IPFS,we have achieved a bandwidth improvement of 17.91%.In the RBR models,the maximum obtained hit ratio is 0.9314 at 200 million CU requests,showing an improvement of 1.2%in average servicing latency over non-RBR models and a maximization trade-off of QoE index of 2.76

关键词： Blockchain 5G-enhanced mobile broadband Federated clouds Rank-based recommender model Smart contracts

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APFormer: Anti-Phishing Transformer for Website-Phishing Detection Via Joint Feature Learning

APFormer: Anti-Phishing Transformer for Website-Phishing Det...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Memon, Anam Manjotho, Ali Asghar National University of Modern Languages Department of Computer Science Hyderabad Pakistan Mehran University of Engineering and Technology Department of Computer Systems Engineering Jamshoro Pakistan

ISBN: (纸本)9798350349719

Phishing is a cybercrime activity where the perpe-Trator tricks users into believing that a fraudulent website is authentic, revealing sensitive information. Recently, the frequency of phishing attacks has increased, targeting both individuals and corporate sectors, thereby rendering traditional methods of detecting phishing websites less effective. These methods often produce high false positive rates due to the complexities involved in creating a strong correlation between brand identity and website features. Furthermore, sophisticated attacks, such as DOM element manipulation tricks, evade the existing methods and convince the user with visual and behavioural similarity. We propose a transformer-based approach for website phishing detection using joint feature learning. The approach jointly learns textual, visual, and heuristic multi-modal features to establish a stronger connection between brand identity and the webpage. The proposed model comprises a feature extraction module and a transformer network. The feature extraction module extracts textual, visual, and heuristic features from the DOM tree, while the transformer module classifies the input page as legitimate or phishing by learning the feature mapping to brand identity. Our method outperforms current state-of-The-Art methods on benchmark datasets by achieving a 3.5% increase in recall and a 5.9% reduction in false positive rate. © 2024 IEEE.

关键词： Websites

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Smart Monitoring and Irrigation Regulation via IoT and Cloud 8

Smart Monitoring and Irrigation Regulation via IoT and Cloud

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sumalatha, Konatham Harshdeep, Kumar Vellore Institute of Technology School of Computer Science & Engineering Department of Database Systems Vellore India Vellore Institute of Technology Computer Science and Engineering Vellore India

ISBN: (纸本)9798350376425

The increasing demand for agricultural produce and the strain on global water resources highlight the need for innovative solutions to improve water efficiency in farming. This work introduces an IoT-powered Smart Irrigation System designed to optimize water usage, ensuring sustainable crop cultivation. Traditional irrigation methods often lead to inefficiencies, wasting water and increasing costs, as they fail to consider varying soil moisture and environmental conditions across fields. This can result in suboptimal crop growth and resource depletion. To overcome these challenges, the proposed system integrates IoT technology with smart irrigation practices. Sensors deployed in the field collect real-time data on soil moisture, temperature, humidity, and light intensity, which a central controller then processes. Using cloud-based analytics, the controller adjusts irrigation schedules and water distribution to match current conditions precisely. The system's primary goal is to create an eco-friendly, cost-effective solution that reduces water waste, lowers operational costs, and minimizes environmental impact. Scalable and adaptable to various crops and environments, this smart irrigation system promises to improve crop yields, promote sustainable agriculture, and address water scarcity challenges globally. © 2024 IEEE.

关键词： Cloud Computing Real-Time Data Analytics Smart Irrigation Soil Moisture Sensors

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EDSA-Ensemble: an Event Detection Sentiment Analysis Ensemble Architecture

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IEEE Transactions on Affective Computing 2024年第2期16卷 1-18页

作者： PETRESCU, Alexandru TRUICA, Ciprian-Octavian APOSTOL, Elena-Simona PASCHKE, Adrian Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology Politehnica Bucharest Bucharest Romania Fraunhofer Institute for Open Communication Systems Berlin Germany

As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around sharing and discussing current events. Within these communities, users are enabled to share their opinions about each event. Using Sentiment Analysis to understand the polarity of each message belonging to an event, as well as the entire event, can help to better understand the general and individual feelings of significant trends and the dynamics on online social networks. In this context, we propose a new ensemble architecture, EDSAEnsemble (Event Detection Sentiment Analysis Ensemble), that uses Event Detection and Sentiment Analysis to improve the detection of the polarity for current events from Social Media. For Event Detection, we use techniques based on Information Diffusion taking into account both the time span and the topics. To detect the polarity of each event, we preprocess the text and employ several Machine and Deep Learning models to create an ensemble model. The preprocessing step includes several word representation models: raw frequency, TFIDF, Word2Vec, and Transformers. The proposed EDSA-Ensemble architecture improves the event sentiment classification over the individual Machine and Deep Learning models. Authors

关键词： Sentiment analysis

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New improved model for joint segmentation and registration of multi-modality images: application to medical images

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Journal of Intelligent and Fuzzy systems 2024年第4期46卷 8755-8770页

作者： Badshah, Noor Begum, Nasra Rada, Lavdie Ashfaq, Muniba Atta, Hadia Department of Basic Sciences and Islamiat University of Engineering and Technology Peshawar Pakistan Faculty of Engineering and Natural Science Bahcesehir University Istanbul Turkey Department of Computer Systems Engineering University of Engineering and Technology Peshawar Pakistan Department of Mathematics Islamia College Peshawar Pakistan

Joint segmentation and registration of images is a focused area of research nowadays. Jointly segmenting and registering noisy images and images having weak boundaries/intensity inhomogeneity is a challenging task. In medical image processing, joint segmentation and registration are essential methods that aid in distinguishing structures and aligning images for precise diagnosis and therapy. However, these methods encounter challenges, such as computational complexity and sensitivity to variations in image quality, which may reduce their effectiveness in real-world applications. Another major issue is still attaining effective joint segmentation and registration in the presence of artifacts or anatomical deformations. In this paper, a new nonparametric joint model is proposed for the segmentation and registration of multi-modality images having weak boundaries/noise. For segmentation purposes, the model will be utilizing local binary fitting data term and for registration, it is utilizing conditional mutual information. For regularization of the model, we are using linear curvature. The new proposed model is more efficient to segmenting and registering multi-modality images having intensity inhomogeneity, noise and/or weak boundaries. The proposed model is also tested on the images obtained from the freely available CHOAS dataset and compare the results of the proposed model with the other existing models using statistical measures such as the Jaccard similarity index, relative reduction, Dice similarity coefficient and Hausdorff distance. It can be seen that the proposed model outperforms the other existing models in terms of quantitatively and qualitatively. © 2024 - IOS Press. All rights reserved.

关键词： Image segmentation

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Resource Based Automatic Calibration System (RBACS) Using Kubernetes Framework

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Intelligent Automation & Soft Computing 2023年第1期35卷 1165-1179页

作者： Tahir Alyas Nadia Tabassum Muhammad Waseem Iqbal Abdullah S.Alshahrani Ahmed Alghamdi Syed Khuram Shahzad Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science&IT Virtual University of PakistanLahore54000Pakistan Department of Software Engineering The Superior UniversityLahore54000Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of Jeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan

Kubernetes,a container orchestrator for cloud-deployed applications,allows the application provider to scale automatically to match thefluctuating intensity of processing *** cluster technology is used to encapsulate,isolate,and deploy applications,addressing the issue of low system reliability due to interlocking ***-based platforms usually entail users define application resource supplies for eco container *** is a constant problem of over-service in data centers for cloud service *** operating costs and incompetent resource utilization can occur in a waste of *** revolutionized the orchestration of the container in the cloud-native *** can adaptively manage resources and schedule containers,which provide real-time status of the cluster at runtime without the user’s *** clusters face unpredictable traffic,and the cluster performs manual expansion configuration by the *** to operational delays,the system will become unstable,and the service will be *** work proposed an RBACS that vigorously amended the distribution of containers operating in the entire Kubernetes *** allocation pattern is analyzed with the Kubernetes *** estimate the overall cost of RBACS,we use several scientific benchmarks comparing the accomplishment of container to remote node migration and on-site *** experiments ran on the simulations to show the method’s effectiveness yielded high precision in the real-time deployment of resources in eco *** to the default baseline,Kubernetes results in much fewer dropped requests with only slightly more supplied resources.

关键词： Docker container virtualization cloud resource kubernetes

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Decision making framework for heterogeneous QoS information: an application to cloud service selection

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2915-2934页

作者： Tiwari, Rohit Kumar Kumar, Rakesh Baranwal, Gaurav Buyya, Rajkumar Department of Computer Science and Engineering Madan Mohan Malaviya University of Technology Gorakhpur273010 India Department of Computer Science BHU Varanasi India Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems University of Melbourne Melbourne Australia

In recent times, appropriate decision-making in challenging and critical situations has been very well supported by multicriteria decision-making (MCDM) methods. The technique for order of preference by similarity to ideal solution (TOPSIS) is the most widely used MCDM method for solving decision problems. However, it restricts decision-makers to use only one type of Quality of Service (QoS) information, and it suffers from the rank reversal problem. Restriction to only one type of QoS makes the decision problems more challenging, as it restricts the decision-makers freedom. Further, the rank reversal problem makes the decision result unreliable. To address these issues of TOPSIS, we have proposed a reliable rank reversal robust modular TOPSIS (RMo-TOPSIS). RMo-TOPSIS allows crisp, interval, fuzzy, intuitionistic and neutrosophic fuzzy QoS metrics. It does not suffer from the rank reversal problem. Cloud computing provides computing services on-demand basis without involving maintenance by its users. The availability of many cloud service providers and their services makes cloud service selection a challenging problem. To validate RMo-TOPSIS, we select the cloud service selection consisting of different types of QoS metrics as an application. Experiments on cloud service selection show consistency and accuracy in results obtained by RMo-TOPSIS and its robustness against rank reversal. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Decision making

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