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A Novel UAV View Planning Strategy for High Quality 3D Reconstruction of Irregular-Shaped Architecture 20th

A Novel UAV View Planning Strategy for High Quality 3D Recon...

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20th international conference on computing in Civil and Building Engineering, ICCCBE 2024

作者： Liang, Zhenyu Liang, Zhaolun Wang, Mingzhu Yang, Liu Cheng, Jack C. P. Department of Civil and Environmental Engineering Hong Kong University of Science and Technology China School of Architecture Building and Civil Engineering Loughborough University Loughborough United Kingdom

ISBN: (纸本)9783031842078

UAV-based 3D reconstruction has proven to be an effective technique for assessing the current state of architectures during the maintenance stage. Many buildings are designed with irregular shapes to fulfill aesthetic requirements. These buildings have lots of occlusion regions and edge regions, which makes it difficult for most commercially available flight planners to perform high-quality 3D reconstruction. To address this problem, we propose a novel UAV viewpoint generation strategy of 3D flight planning. Firstly, we detect the occlusion regions and edge regions through mesh analysis, devoid of any dependence on prior knowledge or deep-learning techniques. This makes our algorithm applicable to various scenes. Besides, we formulate the viewpoints optimization problem based on triangles in the mesh model with the consideration of two regions and MVS influence factors. Then we propose the SSA-variant algorithm to directly optimize the viewpoints’ location and orientation within a continuous flyable space. We validate our view planning strategy by assessing both the optimization convergence and reconstruction quality in a synthetic scene. The result shows that our SSA-variant algorithm performs higher convergency speed and accuracy than other optimization algorithms, such as GA and PSO. Compared to off-the-shelf flight planners and state-of-art 3D UAV path planning methods, images captured by our strategy can generate finer 3D reconstruction model for irregular-shaped architecture. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mesh generation

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Extragradient Method For Monotone Variational Inequality in CAT(0) Spaces 4th

Extragradient Method For Monotone Variational Inequality in...

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4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Salisu, Sani Sriwongsa, Songpon Kumam, Poom 126 Pracha-Uthit Road Bang Mod Thung Khru Bangkok10140 Thailand Department of Mathematics Faculty of Natural and Applied Sciences Sule Lamido University Kafin Hausa Jigawa Nigeria

ISBN: (纸本)9783031812439

The problem of finding a solution to a variational inequality problem, which involves monotone mappings on certain CAT(0) spaces, is analyzed and solved by extending the well-known extragradient scheme to these spaces. For a monotone mapping, the sequence generated by the scheme is shown to converge to a solution of the problem. The ambient spaces provide tools that allow non-convex (or constrained) problems in Euclidean spaces to be translated into geodesic convex (or unconstrained) problems. To illustrate the proposed approach and the scheme’s implementation, examples are provided. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： CAT(0) space convergence Extragradient method monotone mapping Variational inequality

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Design of Smart Farm Service Architecture Based-On Microservice

Design of Smart Farm Service Architecture Based-On Microserv...

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IEEE international conference on Consumer Electronics (ICCE)

作者： Hye-Been Nam Sunghyun Yoon Juyoung Park Dong Sig Shin Sang-Hwan Lee Seok-Joo Koh Computer Science and Engineering Kyungpook National University Daegu Republic of Korea Protocol Engineering Center Electronics & Telecommunications Research Institute Daejeon Republic of Korea Convergence Technology Research Center Korea Institute of Machinery & Materials Daegu Republic of Korea Artificial Intelligence Data Research Team Korea Institute of Science and Technology Information Daejeon Republic of Korea

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

With the rapid advancements in IoT, big data, and AI technologies, smart farm and agriculture have emerged as a key solution for improving productivity and maintaining optimal crop growth environments, leading to various ongoing studies. As part of this research, this paper proposes a microservice-based smart agriculture framework that integrates cloud and edge computing. The framework consists of cloud, edge, controller, and device entities, each playing a crucial role in ensuring efficient environmental control and real-time decision-making for crop growth. By incorporating edge computing, the proposed service architecture mitigates issues related to network latency and failures, thereby enhancing system stability and responsiveness. Moreover, the service management module, present in both cloud and edge entities, enables flexible service scalability and version control while ensuring seamless operation even during network outages. Through the implementation of a testbed, we validated the system's operational procedures and demonstrated that the edge-enabled architecture reduces response times by approximately 40% in high-latency network conditions. This study highlights the potential of edge computing in smart agriculture and lays the groundwork for future research focusing on comprehensive performance evaluations and high-availability service monitoring in various environments.

关键词： Smart agriculture Performance evaluation Cloud computing Scalability Microservice architectures Crops Computer architecture Real-time systems Time factors Edge computing

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Machine Learning-Enhanced Dual Axis Solar Tracking with Weather Monitoring for Rural Electrification

Machine Learning-Enhanced Dual Axis Solar Tracking with Weat...

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Advanced computing Technologies (ICoACT), international conference on

作者： Logesh S Jeevashree J Arun Kumar P Jeevitha Kandasamy Jaisiva S Harish R Electrical and Electronics Engineering Sri Krishna College of Technology Coimbatore Tamilnadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The proposed dual-axis solar tracking system integrates real-time weather monitoring and machine learning to enhance solar energy efficiency. Machine learning algorithms analyze historical weather data alongside real-time sensor readings, which is crucial for optimizing solar energy capture, as demonstrated in various studies on solar tracking systems. The integration of a mobile application interface allows users to access real-time data and receive alerts, facilitating immediate adjustments to the system settings. This aligns with advancements in remote monitoring technologies, such as those used in microalgal productivity prediction, which leverage real-time sensor data for operational forecasting. Furthermore, the use of the Blynk IoT platform for remote control enhances user engagement and system responsiveness, reflecting a growing trend in smart energy management solutions. Overall, this project exemplifies the convergence of renewable energy technology, machine learning, and IoT, promising improved operational reliability and energy efficiency.

关键词： Renewable energy sources Machine learning Solar energy Solar tracking Market research Real-time systems Energy efficiency Mobile applications Remote monitoring Meteorology

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A Multi-Optimizer Metaheuristic Approach for Solving Constrained Patch Scheduling Problem

A Multi-Optimizer Metaheuristic Approach for Solving Constra...

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Computational Intelligence Approaches and Applications (ICCIAA), international conference on

作者： Louay Karadsheh Faten Hamad Hussam Fakhouri Faculty of Information Technology University of Petra Amman Jordan Sultan Qaboos University Sultanate of Oman The University of Jordan Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Timely security patches are crucial in reducing vulnerabilities across computing infrastructures, yet excessive downtime or violations of concurrency constraints can adversely affect service availability and overall performance. In this work, we optimize the problem of constrained patch scheduling using a multi-optimizer metaheuristic approach. We first model patch scheduling as an optimization problem aiming to minimize both total downtime and vulnerability exposure while also respecting concurrency limits, which restrict the number of machines simultaneously offline. The objective function incorporates a penalty factor to discourage over-concurrent patching. We compare eight modern metaheuristic algorithms with variable patch durations. Extensive experiments are conducted, analyzing best objective scores, downtime, exposure, concurrency penalty, computational time, and convergence trends. **Our findings indicate that Equilibrium Optimizer (EO) achieves the lowest final cost (6.5351), followed closely by Multi-Verse Optimizer (MVO) (6.8295). Whereas Grey Wolf Optimizer (GWO) and Moth Flame Optimization (MFO), yield moderate results, while Whale Optimization Algorithm (WOA) exhibits the highest cost (10.3944). These outcomes underscore the impact of concurrency-limited patching constraints on performance and emphasize the benefits of selecting optimizers that effectively balance exploration and exploitation for scheduling in security-critical environments.

关键词： Concurrent computing Costs Processor scheduling Metaheuristics Fires Market research Linear programming Whale optimization algorithms Security convergence

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K-means Clustering Hybridized with Improved Chaos Based Particle Swarm Optimization for High Dimensional Problem

K-means Clustering Hybridized with Improved Chaos Based Part...

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Disruptive Technologies (ICDT), international conference on

作者： Afroj Alam Asad Mohammed Khan B Pakruddin J Riyazulla Rahaman Shaik Salma Begum K.O. Mohammed Aarif Department of CSE Presidency University Bengaluru India School of Electronics Engineering-SENSE Vellore Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

PSO algorithms are gaining considerable attention because of its involvement in swarm evolutionary computing. However, there are still issues in achieving a balance between exploration and exploitation. This work presents a hybridized technique that combines K-means and a revolutionary PSO-based algorithm to tackle these problems. The research introduces Chaos Adaptive Particle Swarm Optimization (CAPSO), which utilizes chaos theory to dynamically modify important parameters and a regulating factor. This approach improves the adaptability of the algorithm and prevents premature convergence of the PSO algorithm. Subsequently, the integration of K-means clustering with CAPSO is employed to expedite convergence and employ chaotic search for enhancing the Optimal Point Globally in High dimensional data. The efficiency of KCAPSO in solving difficult multidimensional problems has been confirmed by experimental findings. KCAPSO exhibits significant advancements compared to traditional K-means, rendering them highly helpful for utilization in diverse optimization endeavors.

关键词： Chaos Resistance Heuristic algorithms Simulation Clustering algorithms Rendering (computer graphics) Stability analysis Particle swarm optimization Optimization convergence

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Advancing the Metaverse: The convergence of Digital Twins, AI, and Emerging Technologies

Advancing the Metaverse: The Convergence of Digital Twins, A...

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Advanced computing Technologies (ICoACT), international conference on

作者： Preetish Kakkar Shiwam Mittal Sethuraman Ulaganathan Tamilarasan Kannadasan Sai Likhith Kanuparthi

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The development of artificial intelligence (AI) has led to technical breakthroughs in various fields. AI has now improved services in smart cities, healthcare, agriculture, and education. It would not have been possible to achieve these technological achievements without quick, safe, and reliable communication channels. For realistic services, the conventional processing, interaction, and storage methods available cannot maintain and provide the elevated level of flexibility and user experience desired. Virtual Reality (VR) and Augmented Reality (AR) technology is used to create the metaverse, an immersive three-dimensional (3D) virtual world that combines reality and fantasy. Such an ecosystem is still in the early stages of development and will need much research to reach its full potential. Some of the most important issues needed to materialize the metaverse are covered in this paper. To sustain continuous end-to-end metaverse services, a framework combining digital twins (DT) with other cutting-edge technologies, including blockchain, artificial intelligence, and sixth-generation (6G) communication networks is needed. All the criteria required for an integrated DT-enabled metaverse framework are also described in this paper in detail, which offers a complete outlook on these developing fields.

关键词： 6G mobile communication Metaverse Avatars Scalability Ecosystems Real-time systems Blockchains Digital twins Artificial intelligence Edge computing

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An Architectural Framework in Support of the Quantum Internet

An Architectural Framework in Support of the Quantum Interne...

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Quantum Communications, Networking, and computing (QCNC), international conference on

作者： Diego R. López Blanca Lopez Vicente Martin Telefónica Madrid Spain Telematic Engineering Department IMDEA Networks Universidad Carlos III de Madrid Madrid Spain Universidad Politecnica de Madrid Madrid Spain

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

While quantum communications technology evolves, the promise of a Quantum Internet becomes closer and different proposals to build it have been already proposed, addressing different aspects of what such a global network would imply. We believe a consistent reference architecture model for the Quantum Internet is required to progress in its evolution, addressing the different dimensions such a network has to consider, including the interaction with the "classical" Internet. Such a reference architecture would provide a framework for the integration of the protocols applicable to it, and enable the advance of the applications based on it. As a reference model, it should allow the proposition and analysis of different approaches and solutions, providing a framework to reason about these solutions and their interplay to achieve a global Quantum-enabled open *** a model has to satisfy three essential requirements: agility, enabling it to adapt to the evolution of quantum communications base technologies, sustainability, with open availability in technological and economical terms, and pliability, being able to integrate with the operations and management procedures in current networks. This paper describes the reference architecture framework currently being discussed in the QIRG (Quantum Internet Research Group) of IRTF, and intended to provide a conceptual common framework for the integration of the different technology proposals to build the Quantum Internet infrastructure and its convergence with the current Internet. The framework is based on the already extensive experience in the deployment and operation of QKD infrastructures and on recent initiatives focused on the integration of networks and services.

关键词： Analytical models Adaptation models Quantum computing Protocols Computer architecture Network architecture Internet Proposals Sustainable development Quantum communication

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Plant Leaf Disease Detection Using Extended CNN with Parrot Optimizer for Sustainable Farming

Plant Leaf Disease Detection Using Extended CNN with Parrot ...

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Advanced computing Technologies (ICoACT), international conference on

作者： R Chithra H Sandeep E Nivass E Selvaprasanth Department of Computer Science and Engineering K. S. Rangasamy College of Technology Tiruchengode India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

Sustainable farming requires early and accurate detection of plant diseases, which is critical for crop health and yield. In this article, we present an extended Convolutional Neural Network (CNN) model combined with the Parrot optimizer for successful detection and classification of plant leaf diseases. The enhanced CNN architecture is designed to capture detailed leaf patterns and characteristics, increasing its detection accuracy across various disease types. The Parrot optimizer, known for its faster convergence and stability, fine-tunes the network's settings, reducing computational complexity and training times. Experimental findings show that our methodology significantly outperforms standard optimization methods in disease categorization, achieving an accuracy of 95.2% after class balancing, compared to 88.5% before. Precision and recall also improved, with values reaching 94.5% and 93.8%, respectively. This model can be a valuable tool for farmers, promoting sustainable agriculture by reducing pesticide use and improving crop management.

关键词： Training Plant diseases Accuracy Crops Optimization methods Pesticides Stability analysis Convolutional neural networks Standards Farming

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An Enhanced Evolutionary Algorithm for Optimized Real-Time Scheduling in Cloud computing

An Enhanced Evolutionary Algorithm for Optimized Real-Time S...

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Computational Intelligence Approaches and Applications (ICCIAA), international conference on

作者： Asia Bataineh Hamzah Alqudah Mohammad A. Alsharaiah Waleed Awwad Computer Science Department Faculty of Information Technology University of Petra (UoP) Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology American University of Madaba (AUM) Amman Jordan Information Technology Department King Abdullah II School for Information Technology The University of Jordan (JU) Amman Jordan Department of Computer Science College of Science Northern Border University (NBU) Arar KSA

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The exponential growth of cloud computing applications has posed significant challenges in achieving efficient resource management, reduced processing time, high through-put, and minimized energy consumption. Despite advancements, existing evolutionary algorithms often suffer from premature convergence, inefficient exploration-exploitation balance, and high energy overhead, particularly under heavy workloads. This paper proposes an enhanced hybrid Whale Optimization Algorithm (WOA) integrated with the Lévy Flight Mechanism for optimized resource management in cloud computing environments to address these challenges. The primary motivation for this work stems from the limitations observed in state-of-the-art algorithms. The research aims to bridge this gap by designing an evolutionary-based approach that improves resource utilization, reduces task execution delays, and optimizes energy consumption under varying workloads. The proposed model employs the WOA, enhanced with the Lévy Flight Mechanism, to ensure a more effective balance between exploration and exploitation during optimization. Extensive simulations were conducted on workloads ranging from 100 to 700 tasks, comparing the proposed method against leading algorithms. The results demonstrate that the proposed algorithm consistently outperforms the state-of-the-art techniques. Specifically, it achieves higher resource utilization (1.1 vs. 0.9), lower processing time, improved throughput (up to 500), and reduced energy consumption (50 joules compared to 70+ joules) under heavy workloads. These findings validate the enhanced WOA's effectiveness in addressing critical cloud computing optimization problems. The proposed approach provides a robust solution for dynamic resource allocation, ensuring energy efficiency and performance scalability, making it suitable for modern cloud-based environments with increasing computational demands.

关键词： Cloud computing Energy consumption Processor scheduling Scalability Evolutionary computation Throughput Whale optimization algorithms Real-time systems Resource management Optimization

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