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Maneuvering Angle Rigid Formations With Global Convergence Guarantees

IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1464-1475页

作者： Liangming Chen Zhiyun Lin Hector Garcia de Marina Zhiyong Sun Mir Feroskhan School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Computer Architecture and Technology and also with CITICUniversity of Granada18014 GranadaSpain Department of Electrical Engineering Eindhoven University of Technology5600 MB Eindhoventhe Netherlands

Angle rigid multi-agent formations can simultaneously undergo translational,rotational,and scaling maneuvering,therefore combining the maneuvering capabilities of both distance and bearing rigid ***,maneuvering angle rigid formations in 2D or 3D with global convergence guarantees is shown to be a challenging problem in the existing literature even when relative position measurements are *** by angle-induced linear equations in 2D triangles and 3D tetrahedra,this paper aims to solve this challenging problem in both 2D and3D under a leader-follower *** the 2D case where the leaders have constant velocities,by using local relative position and velocity measurements,a formation maneuvering law is designed for the followers governed by double-integrator *** the leaders have time-varying velocities,a sliding mode formation maneuvering law is proposed by using the same *** the 3D case,to establish an angle-induced linear equation for each tetrahedron,we assume that all the followers'coordinate frames share a common Z ***,a formation maneuvering law is proposed for the followers to globally maneuver Z-weakly angle rigid formations in *** extension to Lagrangian agent dynamics and the construction of the desired rigid formations by using the minimum number of angle constraints are also *** examples are provided to validate the effectiveness of the proposed algorithms.

关键词： Index Terms—Angle rigid formations formation control formation maneuvering global convergence multi-agent systems

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Machine learning model for predicting long-term energy consumption in buildings

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Discover Internet of Things 2025年第1期5卷 1-24页

作者： Hussien, Aseel Maksoud, Aref Al-Dahhan, Aisha Abdeen, Ahmed Baker, Thar Department of Architectural Engineering University of Sharjah Sharjah United Arab Emirates Department of Computer Science University of Sharjah Sharjah United Arab Emirates Civil Mining Environmental and Architectural Engineering University of Wollongong Wollongong Australia School of Architecture Technology and Engineering University of Brighton Brighton United Kingdom

The rapid growth in the construction sector has led to increased energy consumption and carbon emissions. Calculating energy usage and emissions is essential to energy security and promoting sustainable sector development. Therefore, the study objective is to investigate the utilazation of machine learning algorithm to predict long-term energy consumption in buildings sector, aiming to improve sustainable design and energy optimization, via the implementation of three machine learning models, XGBoost, Support Vector Regression, and Long-Short-Term Memory networks, to predict energy consumption. These models are adept at capturing complex interactions between building characteristics, environmental factors, and energy patterns. Although previous studies have explored various machine learning techniques for energy efficiency, limited research links these models to practical applications in building performance simulation. Furthermore, there is a lack of comparative evaluation of advanced machine learning models such as XGBoost, Support Vector Regression, and Long-Short-Term Memory to predict the energy consumption of building envelopes, particularly in hot climates such as the UAE. This research aims to fill this gap by providing a detailed comparison of these models against alternative approaches mentioned in the literature. The findings position Long-Short-Term Memory as a transformative force in predictive modeling, demonstrating exceptional precision with an R-squared value of 0.993 and a Mean Squared Error of 0.004. In contrast, Support Vector Regression and XGBoost showed limited predictive capabilities, with R-squared values of 0.462 and 0.94, respectively. This study establishes a solid data-driven foundation for architects and engineers to inform decisions on energy-efficient building designs, advocating Long-Short-Term Memory as the superior model for predicting energy performance. © The Author(s) 2025.

关键词： Green buildings

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A Painting Style System using an Improved CNN Algorithm

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IEIE Transactions on Smart Processing and Computing 2022年第5期11卷 332-342页

作者： Zhong, Yuan Huang, Xinyan Department of Architecture Sichuan College of Architectural Technology Chengdu615000 China School of Computer Science and Technology Shandong University of Finance and Economics Ji’nan250014 China

The rapid development of deep learning technology allows ordinary people to create artwork that imitates the style of paintings by famous masters through an algorithm. To create such works with artistic style, this research proposes an artificial neural network algorithm based on an improved convolutional neural network (CNN). First, a fast style-rendering model based on the improved CNN is constructed, and then, a server front end is built with the Bootstrap framework. The server-side back end of the system is built by combining a Python algorithm and a web framework, and finally, a complete model of the front-end and back-end network of the style rendering system is constructed. The model proposed in this paper is compared with two other models to verify its performance. The results show that information entropy of the model constructed is the highest at 5.58, which is higher than information entropy of the other two models. The average gradient value and the peak signal-to-noise ratio under the constructed model are 22.54 and 27.81, respectively, which are also higher than the other two models. Mutual information and the structural similarity index between rendered images and sample images under all three models were compared. Mutual information and structural similarity index of the model constructed by this research are 1.19 and 0.56, respectively, with much larger data sizes than the two comparison models. © 2022 Institute of Electronics and Information Engineers. All rights reserved.

关键词： Convolutional neural networks

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Risk Management for 5G-Enabled Internet of Things by Using Machine Learning: A Survey

Risk Management for 5G-Enabled Internet of Things by Using M...

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2024 Arab ICT Conference, AICTC 2024

作者： Alfaw, Aysha Elmedany, Wael Sharif, Mhd Saeed College of Information Technology University of Bahrain Sakhir Bahrain School of Architecture Computing and Engineering Department Computer Science and Digital Technologies UEL London United Kingdom

ISBN: (纸本)9798350342086

Nowadays, there is a noticeable increase in the development and use of the Internet of Things(loT). With this rapid increase, IoT devices may face several challenges when used in the real world through applications. Using 5G with large numbers of Internet-connected devices of IoT puts many devices at risk and requires risk management. The growing of IoT leads to the growth of cyber-attackers and allow them to expose the vulnerabilities. This paper will present the risk management of 5G-enabled IoT technology and the use of machine learning to mitigate the risk and reduce the attacks on these technologies, and finding solutions through previous researches. This research aims to identify and eliminate potential risks in the IoT based 5G by machine learning. The paper also aims to present a solution to the security problems and risks faced when integrating the fifth-generation network and the Internet of Things. With 5G-enabled IoT, the risk management helps organizations use emerging technologies effectively while mitigating potential underlying risks such as security breaches, and data loss. Authentication,encryption, access control, and communication security are essential for making security. Machine learning algorithms have the potential to remove many obstacles to implementing the security of the Internet of Things, paving the door for the use of sophisticated technology like 5G. With new 5G networks, it is expected that the current IoT will be significantly expanded, which will improve cellular operations and the security of IoT, as well as push the future of the Internet to its edges. Machine learning (ML) creates a secure and intelligent system and provides a robust security mechanism and dynamic for 5G networks. Therefore, this time will also present previous solutions with machine learning against the risks to IoT and 5G. This paper will present a set of previous studies related to insurance of risk management for the 5G-enabled IoT, Which aims to find previ

关键词： Adversarial machine learning

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IoT Based Systems and Services: Recent Security Concerns and Feasible Solutions 1

IoT Based Systems and Services: Recent Security Concerns and...

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1st IEEE Karachi Section Humanitarian technology Conference, Khi-HTC 2024

作者： Attaullah, Hafiz Muhammad Memon, Shahzad Erkan, Ozlem Feyza Khawar, Ruba Mohammad Ali Jinnah University Department of Computer Science Karachi Pakistan Beykoz University Faculty of Engineering and Architecture Department of Computer Engineering Istanbul Turkey NED University of Engineering and Technology Faculty of Information Sciences and Humanities Karachi Pakistan

ISBN: (纸本)9798350373042

The term Internet of Things (IoT) is used to refer as embedded devices or objects with internet access, allowing them to communicate globally, interacting with people and networks. IoT security issues are directly related to the widespread use of its systems. This paper examines the security technologies employed in IoT and their respective features and applications. Additionally, it addresses the various security concerns associated with IoT-based systems and services, highlighting their occurrence within the three-layer framework of IoT. These days, as IoT introduces advanced application architectures that minimizes human intervention by integrating businesses in some way to address digital computer processes by communicating within a given network. Moreover, it will be discussed the key technologies associated with Advanced Encryption Standards (AES) and the changes brought in market value and usage of services that has been taken place b since the IoT incorporated. © 2024 IEEE.

关键词： Network security

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Hybrid Technique-based Optimal Energy Management in Smart Home Appliances

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IEIE Transactions on Smart Processing and Computing 2024年第5期13卷 425-434页

作者： Shirley, C.P. Depaa, R.A.B. Priya, A. Sarala, R. Solanki, Rajdeep Singh Malini, K.V. Venkatakrishna Reddy, Ch. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Civil Engineering Dr. M.G.R Educational and Research Institute Maduravoyal Chennai95 India School of Architecture Koneru Lakshmaiah Educational Foundation Andhra Pradesh Guntur India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai Rameshwaram High Road Viraganoor Madurai625009 India Department of Computer Science Medi-Caps University Indore India Department of Electrical and Electronics Engineering Sri Sairam College of Engineering Anekal India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad75 India

This manuscript presents a hybrid method for optimal energy management in smart home appliances. The proposed approach combines the Ebola Optimization Search Algorithm (EOSA) with the performance of spiking neural networks (SNN). The key objective of the proposed strategy is cost reduction through day-ahead load scheduling while also considering the best demand response (DR), and self-consumption of photovoltaic (PV). The EOSA method manages air conditioning (AC) and ensures thermal comfort. In addition, the SNN method was used to predict the optimal system control signal. Comparative analysis with existing systems, such as PSO, RFA, and BCO, showed that the proposed strategy resulted in 13% cost savings. The proposed system considered parameters, such as solar radiation, occupant presence, and electricity prices. The proposed technique is implemented in the MATLAB software, and the performance is compared with existing approaches. The efficiency of the proposed approach is higher than the existing techniques. © 2024 The Institute of Electronics and Information Engineers.

关键词： Demand response

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A draft drawing structure tool for improve 3D modeling process in student 9

A draft drawing structure tool for improve 3D modeling proce...

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Joint 9th International Conference on Digital Arts, Media and technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2024

作者： Sookkaew, Jirawat Sukmuang, Rachen Changjaikla, Pheeraphon University of Phayao School of Information and Communication Technology Department of Computer Graphic and Multimedia Phayao Thailand Kasem Bundit University School of of Architecture Department of Visual Communication Design Bangkok Thailand

ISBN: (纸本)9798350318241

In this research, the concept of applying the student's original learning based on the course or skills related to the use of hand drawing and sketching was applied by creating a 3D structure drawing and dividing it into 2 groups between Non-Draft Groups and Draft Groups Both groups created 3D models and assessed the structures. Upon examination, it was found that the structural drawing group produced 3D models with higher 3D structural evaluation scores than the other group. To show that the use of the sketch helps the 3D structure to be more complete. © 2024 IEEE.

关键词： Students

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A Novel Hybrid architecture for Superior IoT Threat Detection through Real IoT Environments

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computers, Materials & Continua 2024年第11期81卷 2299-2316页

作者： Bassam Mohammad Elzaghmouri Yosef Hasan Fayez Jbara Said Elaiwat Nisreen Innab Ahmed Abdelgader Fadol Osman Mohammed Awad Mohammed Ataelfadiel Farah H.Zawaideh Mouiad Fadeil Alawneh Asef Al-Khateeb Marwan Abu-Zanona Department of Computer Science Faculty of Computer Science and Information TechnologyJerash UniversityJerash26150Jordan Computer Engineering Department College of Engineering&Information Technology Buraydah CollegesBuraydah51418Saudi Arabia Faculty of Architecture and Design Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityDiriyahRiyadh13713Saudi Arabia Applied College King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Business Intelligence and Data Analysis Faculty of Financial Sciences and BusinessIrbid National UniversityIrbid21110Jordan Faculty of Information Technology Ajloun National UniversityAjlun26810Jordan Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia

As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense *** paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world *** proposed model combines Convolutional Neural Networks(CNN),Bidirectional Long Short-Term Memory(BLSTM),Gated Recurrent Units(GRU),and Attention mechanisms into a cohesive *** integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT *** evaluated our model using the RT-IoT2022 dataset,which includes various devices,standard operations,and simulated *** research’s significance lies in the comprehensive evaluation metrics,including Cohen Kappa and Matthews Correlation Coefficient(MCC),which underscore the model’s reliability and predictive *** model surpassed traditional machine learning algorithms and the state-of-the-art,achieving over 99.6%precision,recall,F1-score,False Positive Rate(FPR),Detection Time,and accuracy,effectively identifying specific threats such as Message Queuing Telemetry Transport(MQTT)Publish,Denial of Service Synchronize network packet crafting tool(DOS SYN Hping),and Network Mapper Operating System Detection(NMAP OS DETECTION).The experimental analysis reveals a significant improvement over existing detection systems,significantly enhancing IoT security *** our experimental analysis,we have demonstrated a remarkable enhancement in comparison to existing detection systems,which significantly strength-ens the security standards of *** model effectively addresses the need for advanced,dependable,and adaptable security solutions,serving as a symbol of the power of deep learning in strengthening IoT ecosystems amidst the constantly evolving cyber threat *** achievemen

关键词： A hybrid deep learning model IoT threat detection real IoT environments cybersecurity attention mechanism

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Detecting Android Malware with Convolutional Neural Networks and Hilbert Space-Filling Curves

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SN computer Science 2024年第7期5卷 810页

作者： Mbungang, Benedict Ngaibe Ali Wacka, Joan Beri Tchakounte, Franklin Polatidis, Nikolaos Nlong II, Jean Michel Tieudjo, Daniel Department of Mathematics and Computer Science University of Ngaoundere Ngaoundere Cameroon Cybersecurity with Computational and Artificial Intelligence Research Group University of Ngaoundere Ngaoundere Cameroon Department of Computer Science University of Buea Buea Cameroon School of Architecture Technology and Engineering University of Brighton Brighton United Kingdom

computer vision techniques have advanced greatly in recent years through deep learning, achieving unprecedented performance. This has motivated applying deep learning to malware detection through image-based approaches to circumvent extensive feature engineering for diverse threats. However, existing work converting Android binaries to rectangular images neglects the intrinsic byte sequence structure, introducing spurious spatial relationships that weaken detection accuracy. To address this, space-filling curves have mapped binaries to images while preserving ordering. This paper proposes a novel method using Hilbert space-filling curves to visualize and classify Android apps. Bytecode is extracted from Dalvik Executable (DEX) files and transformed to grayscale images via Hilbert coding for model training. Additionally, a novel and balanced image dataset is proposed consisting of Hilbert transformations for 4995 benign and 4995 malicious Android apps randomly sampled from the AndroZoo repository. Experiments using this dataset evaluated pre-trained InceptionV3, VGG16, ResNet50 and EfficientNetB0 via transfer learning. A custom Convolutional Neural Network (CNN) was also trained from scratch. InceptionV3 achieved the highest performance at 97.99% accuracy, 98.50% precision, 97.50% recall and 97.99% F1-score. Comparative assessment with previous image-based malware detection research indicates our approach outperforms state-of-the-art approaches. By leveraging Hilbert space-filling curves to map binaries to images while preserving sequential relationships, detection accuracy is improved over methods introducing extraneous spatial representations. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Android Hilbert space-filling curve Image Malware detection

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A Provably Secure Lightweight Key Agreement Protocol for Wireless Body Area Networks in Healthcare System

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IEEE Transactions on Industrial Informatics 2023年第2期19卷 1683-1690页

作者： Zia, Maryam Obaidat, Mohammad S. Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Chaudhry, Shehzad Ashraf Comsats University Islamabad Department of Computer Science Sahiwal57000 Pakistan University of Jordan King Abdullah Ii School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Amity University UP Noida201301 India National Yunlin University of Science and Technology Future Technology Research Center Yunlin64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Abu Dhabi University Department of Computer Science and Information Technology College of Engineering Abu Dhabi United Arab Emirates Nisantasi University Department of Computer Engineering Faculty of Engineering and Architecture Istanbul34398 Turkey

Wireless Body Area Network (WBAN) is a vital application of the Internet of Things (IoT) that plays a significant role in gathering a patient's healthcare information. This collected data helps special professionals like doctors or physicians analyze patients' health status to cure different diseases. However, collecting such information from an insecure channel can be threatening due to the potential security threats. Therefore, it is crucial to secure this sensitive information. This article proposes a secure and lightweight authentication protocol for WBAN. The devised protocol is scalable, secure, and lightweight compared to various relevant competing protocols. The informal security analysis shows that the designed protocol is lightweight, secure, and efficient in resisting various major attacks. The performance analysis demonstrates our protocol's supremacy over various competing protocols in terms of computation and communication costs, inducing efficiency of 20.3% and 12.3%, respectively. Moreover, the practical performance of the designed protocol from the network point of view is measured using the widely recognized NS3 simulation tool. © 2005-2012 IEEE.

关键词： Authentication

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