The preservation of construction heritage faces increasing challenges associated with aging infrastructure, environmental degradation, and limited conservation resources. This study proposes a structured Digital Guardian Framework integrating artificial intelligence (AI), robotics, and digital twin technologies for intelligent heritage conservation. Unlike conventional review-based discussions, this research introduces a methodological framework that combines AI-driven predictive analytics, robotic inspection and intervention systems, and digital monitoring environments to support decision-making in heritage preservation. The framework is validated through analytical evaluation and application to the Sultan Abdul Samad Building in Malaysia, demonstrating how automated inspection, risk prediction, and non-invasive restoration strategies can enhance conservation efficiency while maintaining architectural authenticity. Quantitative performance indicators suggest improvements in inspection accuracy, risk reduction, and lifecycle sustainability compared with traditional conservation approaches. The study contributes a scalable interdisciplinary model bridging robotics engineering and heritage conservation, supporting future smart preservation ecosystems. (c) 2022 The Author. Published by The Society of Artificial Life and Robotics. This is an open access article distributed under the CC BY-NC 4.0 license

Adaptive control has emerged as a key approach in flight system design, enabling robust performance and reliability in the presence of parametric uncertainties and external disturbances. In this article, a radial basis function (RBF) neural network-based adaptive sliding mode control (NNASMC) strategy is developed for the attitude and altitude dynamics of a quadcopter. Adaptive laws based on neural networks are formulated to achieve online estimation of system parameters, and Lyapunov theory is employed to rigorously prove closed-loop stability. The effectiveness of the proposed control scheme is validated through numerical simulations under parameter uncertainties and external disturbances, and its performance is benchmarked against the conventional sliding mode control (SMC) approach. The results demonstrate the superior performance of the proposed NNASMC approach, yielding significant reductions in rise time ($t_{r}$), settling time ($t_{s}$), percentage overshoot (% OS), and integral absolute error (IAE).

This paper presents a flexible Domain-Adaptation architecture that integrates the classifier of real and sketch images. This framework proves useful in robotic-based automated image pre-processing for face recognition, forensic identification, and other cross-domain visual tasks.

Intrusion Detection Systems (IDSs) play a crucial role in addressing the constantly rising, dynamic, and high-speed network cyber threats. Traditional signature-based systems are generally ineffective at detecting zero-day or low-frequency attacks. This research aims to enhance real-time intrusion detection by designing an optimized hybrid model that incorporates information gain, autoencoder-based feature reduction, and a gradient boosting ensemble classifier. The method employs a two-step feature selection process, first utilizing information gain to select discriminative features, followed by the application of autoencoder-based dimension reduction. The resulting features are used to train the ensemble of XGBoost, LightGBM, and CatBoost classifiers. Experiments were conducted on the CICIDS2018 dataset, which has over 1 million network traffic samples. All the ensemble classifiers demonstrated excellent detection performance, with ROC-AUC values exceeding 0.90 for all three. 99

The modern manufacturing industry increasingly demands cost-effective and efficient rapid prototyping solutions, especially vital for start-up businesses, small-scale research laboratories, and R&D departments. Traditional prototyping methods often fall short by failing to provide actionable data early in the development process, necessitating significant upfront financial investments without assurance of feasibility. High costs associated with industrial equipment, skilled labor, raw materials, and components further impede smaller enterprises, especially when supply chain disruptions exacerbate these financial and logistical challenges. To address these barriers, this study proposes an innovative approach utilizing a fully 3D-printed, six-axis robotic arm integrated with a similar 3D-printed conveyor system, simulating a complete, scaled-down manufacturing operation. This integrated system is designed for both autonomous and manual control, facilitating diverse prototyping scenarios, specifically emulating a packaging and sorting environment. The conveyor system manages the inflow of stocked pallets and the removal of empty ones, while the robotic arm autonomously organizes and sorts products, replicating realistic customer order assembly processes. This approach offers a scalable and cost-effective prototyping framework, enabling comprehensive validation of the entire system rather than isolated component testing, thereby significantly reducing risks and providing valuable insights into potential improvements before committing to fullscale production. Beyond cost and schedule benefits, the platform functions as an instructional testbed for training, debugging, and iterative algorithm development, creating a lowrisk pathway to mature designs prior to industrial deployment.

Unmanned Aerial Vehicles (UAVs) have revolutionized data acquisition across various domains, presenting immense potential for image processing and semantic segmentation. This literature review encompasses a thorough exploration of advancements, techniques, challenges, and datasets pertaining to UAV image semantic segmentation. It begins by introducing the fundamental concepts of UAVs, highlighting their pivotal role in capturing high-resolution imagery that serves diverse applications. The integration of deep learning algorithms with UAVs is emphasized, unlocking new horizons in autonomous flight, security, and environmental monitoring. Delving into the core principles of semantic segmentation, the review elucidates the critical task of classifying every pixel in an image. Convolutional Neural Networks (CNNs) are presented as the cornerstone technology, tracing their evolution from traditional CNNs to the highly adaptable Fully Convolutional Networks (FCNs). A substantial portion of the review is dedicated to FCNs, underscoring their ability to process images of varying dimensions while maintaining spatial coherence in the output. Their pivotal role in semantic segmentation, encompassing both classification and localization, is articulated. The subsequent sections delve into a comprehensive survey of state-of-the-art models, including SegNet, PSPNet, DeepLabNet, EfficientNet, DenseNet-C, and LinkNet. Each model's unique strengths and applications contribute to the evolving landscape of semantic segmentation tasks. The versatility of the U-Net architecture takes center stage in the latter parts of the review. Its fundamental structure is elucidated, followed by a comprehensive examination of its manifold adaptations—3D-U-Net, ResU-Net, U-Net++, Adversarial U-Net, Cascaded U-Net, and Improved U-Net 3+. These modifications address intrinsic challenges such as limited receptive fields and class imbalances, propelling U-Net to the forefront of image segmentation techniques. The subsequent sections pivot toward the application of U-Net in UAV image segmentation, illustrating its efficacy in diverse tasks, including land cover and crop classification. Nevertheless, persisting challenges, such as the scarcity of annotated datasets and the need for model generalization across varied environmental conditions, remain key areas of concern. The review culminates by underlining the significance of large, authentic datasets and data augmentation techniques. Furthermore, a brief exploration of publicly available UAV image datasets is presented, enhancing our understanding of the resources accessible for training and evaluating models. This comprehensive literature review encapsulates the dynamism of UAV image processing and semantic segmentation, illuminating recent developments and avenues for future research in this burgeoning field.

This paper has investigated the application of the definite time over-current (DTOC) which reacts to protect the breaker from damage during the occurrence of over-current in the transmission lines. After a distance relay, this kind of over-current relay is utilized as backup protection. The overcurrent relay will provide a signal after a predetermined amount of time delay, and the breaker will trip if the distance relay does not detect a line failure. As a result, this over-current relay functions with a time delay that is just slightly longer than the combined working times of the distance relay and the breaker. This DTOC is tested for various types of faults which are 3- phase fault occurring at load 1, 3-phase fault occurring at load 2, a 3-phase fault occurring before primary protection, and the behaviour of voltage and current with a failed primary protection. All the results will be obtained using the MATLAB/Simulink software package.

Encapsulates a comprehensive investigation into the symbiotic relationship between Unmanned Aerial Vehicles (UAVs) and data mining, as encapsulated in the discourse titled "Drones and Data."This exploration delves into the multifaceted applications and transformative impact of UAV technology within the data mining landscape.The examination begins by elucidating the pivotal role of UAVs, highlighting their mobility, accessibility, and capability to collect data from challenging or remote environments.As technology evolves, UAVs have emerged as versatile platforms that redefine the data is collected and analysed across diverse sectors.The narrative unfolds through distinct dimensions, encompassing precision agriculture, environmental monitoring, infrastructure inspection, mining and exploration, disaster response, and urban planning.The technological transitions facilitated by UAVs, emphasizing the integration of machine learning algorithms, cloud-based data processing, and the collaborative synergy between stakeholders.These advancements position UAVs as transformative tools that not only enhance the efficiency of information acquisition but also open avenues for innovative solutions and insights.Therefore, this study a glimpse into the intricate web of applications and technological advancements at the intersection of UAVs and data mining.It serves as a scholarly guide, navigating the reader through the evolving landscape of "Drones and Data," UAVs play a central role in unlocking unprecedented insights and efficiencies, reshaping the future of data mining.