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A critical review of the integration of renewable energy sources with various technologies

Protection and Control of Modern Power Systems 2021年第1期6卷 37-54页

作者： Erdiwansyah Mahidin H.Husin Nasaruddin M.Zaki Muhibbuddin Doctoral Program School of EngineeringUniversitas Syiah KualaBanda Aceh23111Indonesia Faculty of Engineering Universitas Serambi MekkahBanda Aceh23245Indonesia Department of Chemical Engineering Universitas Syiah KualaBanda Aceh23111Indonesia Department of Electrical and Computer Engineering Universitas Syiah KualaBanda Aceh23111Indonesia Department of Mechanical and Industrial Engineering Universitas Syiah KualaBanda Aceh23111Indonesia

Wind power, solar power and water power are technologies that can be used as the main sources of renewable energy so that the target of decarbonisation in the energy sector can be achieved. However, when compared with conventional power plants, they have a significant difference. The share of renewable energy has made a difference and posed various challenges, especially in the power generation system. The reliability of the power system can achieve the decarbonization target but this objective often collides with several challenges and failures, such that they make achievement of the target very vulnerable, Even so, the challenges and technological solutions are still very rarely discussed in the literature. This study carried out specific investigations on various technological solutions and challenges, especially in the power system domain. The results of the review of the solution matrix and the interrelated technological challenges are the most important parts to be developed in the future. Developing a matrix with various renewable technology solutions can help solve RE challenges. The potential of the developed technological solutions is expected to be able to help and prioritize them especially cost-effective energy. In addition, technology solutions that are identified in groups can help reduce certain challenges. The categories developed in this study are used to assist in determining the specific needs and increasing transparency of the renewable energy integration process in the future.

关键词： Integration RE Energy source Technology system energy Power system Variable RE

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Outage Performance Analysis of Terahertz Band Drone-to-Ground Communications

Outage Performance Analysis of Terahertz Band Drone-to-Groun...

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Communications and Networking (BalkanCom), International Balkan Conference on

作者： Akhtar Saeed Ozgur Gurbuz Fatih Alagoz Department of Electrical Engineering DHA Suffa University Karachi Pakistan Electronics Engineering Program Sabanci University Istanbul Turkiye Department of Computer Engineering Bogazici University Istanbul Turkiye

Recent studies have shown the potential of em-ploying the Terahertz (THz) band for 6G non-terrestrial communications including drones. This paper assesses the outage performance of the THz band Drone-to-Ground (D2G) vertical communications up to 100 m altitude at four practically feasible THz transmission windows (TWs): f 1 = 0.140 THz; [0.110-0.170] THz, f 2 = 0.300 THz; [0.275-0325] THz, f 3 = 0.850 THz; [0.790-0.910] THz, and f 4 = 0.935 THz; [0.930-03940] THz. We consider vertical D2G communication, where realistic THz-based absorption gains of the four THz TWs at the drone altitudes are obtained via the International Telecommunications Union (ITU) model using the Standard Weather Profile. Additionally, multipath (MP) fading and beam misalignment (BM) fading are incorporated into the overall channel gain for assessing the outage performance. Numerical results reveal that under severe BM fading and Rayleigh fading, THz band D2G communication employing 60 dBi total antenna gains at $f_{1},\ f_{2},\ f_{3}$, and f 4 can be reliably established up to the drone altitudes: 76.9 m, 59.2m, 34.4 m, and 43.2 m, and up to 85.1 m, 60.3m, 36.9m, 45.5m under the severe BM fading and the presence of a strong Line-of-Sight component, truly showcasing the achievable altitude and range limits for the THz band D2G communications among 6G non-terrestrial cognitive networks.

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Reverse engineering of Mobile Banking Applications

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computer Systems Science & engineering 2021年第9期38卷 265-278页

作者： Syeda Warda Asher Sadeeq Jan George Tsaramirsis Fazal Qudus Khan Abdullah Khalil Muhammad Obaidullah National Center for Cyber Security Department of Computer Science&Information TechnologyUniversity of Engineering&TechnologyPeshawar25120Pakistan Higher Colleges of Technology Abu Dhabi Women’s CollegeAbu DhabiUAE Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Software Engineering University of Engineering&TechnologyMardan23200Pakistan

software reverse engineering is the process of analyzing a software system to extract the design and implementation *** engineering provides the source code of an application,the insight view of the architecture and the third-party *** a security perspective,it is mostly used for finding vulnerabilities and attacking or cracking an *** process is carried out either by obtaining the code in plaintext or reading it through the binaries or ***,reverse engineering is widely used for mobile applications and is considered a security *** Open Web Application Security Project(OWASP),a leading security research forum,has included reverse engineering in its top 10 list of mobile application *** applications are used in many sectors,e.g.,banking,education,*** particular,the banking applications are critical in terms of security as they are used for financial transactions.A security breach of such applications can result in huge financial losses for the customers as well as the *** exist various tools for reverse engineering of mobile applications,however,they have deficiencies,e.g.,complex configurations,lack of detailed analysis *** this research work,we perform an analysis of the available tools for reverse engineering of mobile *** dataset consists of the mobile banking applications of the banks providing services in *** results indicate that none of the existing tools can carry out the complete reverse engineering process as a standalone *** addition,we observe significant differences in terms of the execution time and the number of files generated by each tool for the same file.

关键词： Reverse engineering mobile banking applications security analysis

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Editorial:Special Topic on Federated Learning for IoT and Edge Computing

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ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.

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S3GCL: spectral, swift, spatial graph contrastive learning 24

S3GCL: spectral, swift, spatial graph contrastive learning

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Proceedings of the 41st International Conference on Machine Learning

作者： Guancheng Wan Yijun Tian Wenke Huang Nitesh V Chawla Mang Ye National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Department of Computer Science University of Notre Dame National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China and Taikang Center for Life and Medical Sciences Wuhan University Wuhan China

Graph Contrastive Learning (GCL) has emerged as a highly effective self-supervised approach in graph representation learning. However, prevailing GCL methods confront two primary challenges: 1) They predominantly operate under homophily assumptions, focusing on low-frequency signals in node features while neglecting heterophilic edges that connect nodes with dissimilar features. 2) Their reliance on neighborhood aggregation for inference leads to scalability challenges and hinders deployment in real-time applications. In this paper, we introduce S3GCL, an innovative framework designed to tackle these challenges. Inspired by spectral GNNs, we initially demonstrate the correlation between frequency and homophily levels. Then, we propose a novel cosine-parameterized Chebyshev polynomial as low/high-pass filters to generate biased graph views. To resolve the inference dilemma, we incorporate an MLP encoder and enhance its awareness of graph context by introducing structurally and semantically neighboring nodes as positive pairs in the spatial domain. Finally, we formulate a cross-pass GCL objective between fullpass MLP and biased-pass GNN filtered features, eliminating the need for augmentation. Extensive experiments on real-world tasks validate S3GCL proficiency in generalization to diverse homophily levels and its superior inference efficiency.

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Cement Strength Prediction using Regression Techniques

Cement Strength Prediction using Regression Techniques

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Ritu Dewan Rohith Polishetty Bhagwati Sharan Mayank Kumar Goyal Amadou Mouctar Balde Ameen Mohamad Ahmad Greynoon Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India Sr. Software Engineer Migo India Limited Hyderabad India Department of Computer Science and Engineering School of Engineering and Sciences SRM University-AP Amaravati India Department of Computer Science & Engineering School of Engineering & Technology Sharda University Greater Noida India Department of Computer Science & Applications School of Engineering & Technology Sharda University Greater Noida India

One of the primary structural materials in the development business is concrete. Compressive strength is the most frequently utilized metric when assessing the strength and nature of cement. This paper clarifies how to make use of technical regression (RT) methodology to decide the legitimate dose of the concrete mixture. Water, Cement, Fly Ash, Blast Furnace, Superplasticizer, Coarse, and Fine Aggregate, were used as input parameters, on the other hand, compressive strength was used as an output parameter. The produced models were evaluated using statistical parameters: Linear Regression, Decision Tree Regression, and Random Forest Regression. In contrast with other applicable models, it was found that the Gaussian process regression model based on the function of the nucleus of radial bases delivered relatively better results. The recommended models should get a good deal on saving money on materials, labor, and time while also improving accuracy. The proposed concrete should be more durable and accordingly more cost-effective.

关键词： Computational modeling Roads Stacking Prediction algorithms Concrete Real-time systems Task analysis

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Multimodal Data Fusion Based on Generative Adversarial Network in University Teaching Evaluation

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第2期10卷 845-858页

作者： Liu, Min Teng, Hua Zhang, Hong Li, Hao School of Computer Science and Software Engineering Southwest Petroleum University Nanchong637001 China Department of Computer China West Normal University Nanchong637009 China Faculty of Computer and Information Science SouthWest University Chongqing400700 China Krirk University Bangkok10220 Thailand

With the advancement of educational evaluation reform and the popularization of higher education, university teaching quality evaluation has been a research hotspot in the field of educational evaluation. In response to the issue of multimodal heterogene-ity gap in current university education evaluation data, which makes it tough to integrate evaluation data, this article suggests a Multimodal Data Fusion (MDF) method for university teaching evaluation relied on Generative Adversarial Network (GAN). Firstly, the MDF model is constructed from two aspects: establishing similarity network and designing regularization terms. By designing sparse regularization terms, the feature selection of multimodal samples is carried out to address the issue of large deviation of fusion data. Secondly, BERT pre-training model is adopted to extract the modal embedding features of teaching evaluation text, and Bi-LSTM is adopted to extract the audio and visual modal embedding features of teaching evaluation. Then, on the ground of the constructed MDF model, combined with the GAN and autoencoder, a multi-modal adversarial autoencoder learning model is established to reduce the heterogeneous gap between modes and form a unified representation. At last, the self-attention pretest network is established to achieve the automatic classification of multi-modal university teaching evaluation data. The experimental outcome implies that the accuracy, F1 value and determination coefficient R2 of the suggested method are 92.18%, 93.72% and 90.5%, respectively, which are superior to the comparison method, showing good classification performance. © 2025, J. Network Intell. All rights reserved.

关键词： Image fusion

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MOT: A Multi-Omics Transformer for Multiclass Classification Tumour Types Predictions

MOT: A Multi-Omics Transformer for Multiclass Classification...

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16th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2023

作者： Abiodoun Osseni, Mazid Tossou, Prudencio Laviolette, FrancOis Corbeil, Jacques GRAAL Institute Intelligence and Data Department of Computer Science and Software Engineering Université Laval QuebecQC Canada Valence AI Discovery MontréalQC Canada Department of Molecular Medicine Université Laval QuebecQC Canada

Motivation: Breakthroughs in high-throughput technologies and machine learning methods have enabled the shift towards multi-omics modelling as the preferred means to understand the mechanisms underlying biological processes. Machine learning enables and improves complex disease prognosis in clinical settings. However, most multi-omic studies primarily use transcriptomics and epigenomics due to their over-representation in databases and their early technical maturity compared to others omics. For complex phenotypes and echanisms, not leveraging all the omics despite their varying degree of availability can lead to a failure to understand the underlying biological mechanisms and leads to less robust classifications and predictions. Results: We proposed MOT (Multi-Omic Transformer), a deep learning based model using the transformer architecture, that discriminates complex phenotypes (herein cancer types) based on five omics data types: transcriptomics (mRNA and miRNA), epigenomics (DNA methylation), copy number variations (CNVs), and proteomics. This model achieves an F1-score of 98:37% among 33 tumour types on a test set without missing omics views and an F1-score of 96:74% on a test set with missing omics views. It also identifies the required omic type for the best prediction for each phenotype and therefore could guide clinical decision-making when acquiring data to confirm a diagnostic. The newly introduced model can integrate and analyze five or more omics data types even with missing omics views and can also identify the essential omics data for the tumour multiclass classification tasks. It confirms the importance of each omic view. Combined, omics views allow a better differentiation rate between most cancer diseases. Our study emphasized the importance of multi-omic1 data to obtain a better multiclass cancer classification. Availability and implementation: MOT source code is available at predictions. © 2023 by SCITEPRESS – Science and Technology Publications,

关键词： Diseases

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Contextual Defeasible Reasoning Framework for Heterogeneous Systems 9th

Contextual Defeasible Reasoning Framework for Heterogeneous ...

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9th EAI International Conference on Context-Aware Systems and Applications, ICCASA 2020 and 6th EAI International Conference on Nature of Computation and Communication, ICTCC 2020

作者： Akhtar, Salwa Muhammad Ul Haque, Hafiz Mahfooz Department of Computer Science University of Lahore Lahore Pakistan Department of Software Engineering University of Lahore Lahore Pakistan

ISBN: (纸本)9783030671006

This paper presents a contextual defeasible reasoning based multi-agent formalism to model heterogeneous systems using the notion of a multi-context system. This framework relies on the semantic knowledge sources which allow us to model context-aware non-monotonic reasoning agents to infer the desired goals using the extracted rules from the ontologies and handles inconsistencies using conflicting contextual information. We illustrate the validity and correctness of the proposed formalism using a simple case study of a smart healthcare system with the prototypal implementation of the system. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Knowledge representation

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Multi-Unit Floor Plan Recognition and Reconstruction Using Improved Semantic Segmentation of Raster-Wise Floor Plans

arXiv

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arXiv 2024年

作者： Kratochvila, Lukas de Jong, Gijs Arkesteijn, Monique Bilík, Šimon Zemčík, Tomáš Horak, Karel Rellermeyer, Jan S. Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science TU Delft Delft Netherlands Department of Management in the Built Environment Faculty of Architecture and the Built Environment TU Delft Delft Netherlands Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Dependable and Scalable Software Systems Institute of Systems Engineering Faculty of Electrical Engineering and Computer Science Leibniz University Hannover Hannover Germany

Digital twins have a major potential to form a significant part of urban management in emergency planning, as they allow more efficient designing of the escape routes, better orientation in exceptional situations, and faster rescue intervention. Nevertheless, creating the twins still remains a largely manual effort, due to a lack of 3D-representations, which are available only in limited amounts for some new buildings. Thus, in this paper we aim to synthesize 3D information from commonly available 2D architectural floor plans. We propose two novel pixel-wise segmentation methods based on the MDA-Unet and MACU-Net architectures with improved skip connections, an attention mechanism, and a training objective together with a reconstruction part of the pipeline, which vectorizes the segmented plans to create a 3D model. The proposed methods are compared with two other state-of-the-art techniques and several benchmark datasets. On the commonly used CubiCasa benchmark dataset, our methods have achieved the mean F1 score of 0.86 over five examined classes, outperforming the other pixel-wise approaches tested. We have also made our code publicly available to support research in the field. © 2024, CC BY.

关键词： Semantic Segmentation

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