Deep neural networks have outstanding performance as data-driven solutions for hyperspectral image classification for ground object detection. Among these, convolutional models outperform other ...strategies for their back-propagated local-filtering mechanisms. However, convolutional models lack the support of intuitive details learning and squeezed weighted distribution. These solutions have constraints of exploring only within convolution space. To alleviate these issues, in this paper, a multi-domain kernel network has been proposed leveraging a dynamic attention mechanism in order to harvest spectral-spatial domain information from multiple receptive regions. The Dynamic Kernel Network uses various kernel strategies to gather maximum spectral-spatial features for high-performance classification. Experimental results on the real hyperspectral images explicate the validity of the proposed dynamic kernel network. The proposed framework outperforms the base methods and attained 97.225%, 99.385% & 99.922% overall accuracy (OA) on Indian Pines (IP), University of Pavia (UP) & Salinas Valley (SV) datasets, respectively. Also, the ablative analysis on spatial windows, spectral bands, computation time and training samples proves the robustness of the spectral-spatial kernel learning strategy.
This review provides a comprehensive overview of the state-of-the-art methods of graph-based networks from a deep learning perspective. Graph networks provide a generalized form to exploit ...non-euclidean space data. A graph can be visualized as an aggregation of nodes and edges without having any order. Data-driven architecture tends to follow a fixed neural network trying to find the pattern in feature space. These strategies have successfully been applied to many applications for euclidean space data. Since graph data in a non-euclidean space does not follow any kind of order, these solutions can be applied to exploit the node relationships. Graph Neural Networks (GNNs) solve this problem by exploiting the relationships among graph data. Recent developments in computational hardware and optimization allow graph networks possible to learn the complex graph relationships. Graph networks are therefore being actively used to solve many problems including protein interface, classification, and learning representations of fingerprints. To encapsulate the importance of graph models, in this paper, we formulate a systematic categorization of GNN models according to their applications from theory to real-life problems and provide a direction of the future scope for the applications of graph models as well as highlight the limitations of existing graph networks.
Wind energy is a promising scheme in the power generation sector due to pollution-free power production and wind resources’ sufficiency worldwide. Installing wind turbines in all the possible extents ...can mitigate the rising energy demand. Built-up areas possess high potential for wind energy, including the rooftop of high-rise buildings, railway track, the region between or around multistoried buildings, and city roads. Harnessing wind energy from these areas is quite challenging since it has dramatic nature and turbulence for higher roughness on urban surfaces. This review paper endeavors to highlight the present status of urban wind farm technology and its commercial and environmental aspects. Observations and upcoming research trends have been presented based on up-to-the-minute information. It is concluded that further investigation of wind mapping and the suitable design of turbines is essential to make the urban wind farm a reliable and feasible option for decentralized power generation.
•The urban wind farm can be an attractive auxiliary option to produce electricity.•Precise wind mapping is the key to get the best result from this type of farm.•The selection of the proper wind turbine is also necessary.•Currently, vertical axis turbines dominate the horizontal ones for turbulent wind.
The major hindrances in the energy system are ecological consciousness, lack of clean and sustainable energy management, insufficient energy distribution–transmission–optimization, expensive power ...transfer costs, and increased customer knowledge of energy charges. Thus why, universal access to the grid with high cybersecurity, and reliability is needed to solve all these challenges. The digital twin concept turns a new dimension of technology into the world. Electric Digital Twin grid can perform online analysis of the grid in real-time and integrates all the past and present data and express the current grid status to the producers and consumers and also predicts the future grid status. Thus, the power grid transmission loss and location of the overheated line and power connection missing can be detected in addition decision-making and self-healing can possible. The future prediction saves the power grid from small to long accidents such as power outages and even blackout problems. The whole consumers and nation feel relief from these types of accidents and saves from large economic and business loss. The blockchain-enabled digital twin grid provides high security for the grid from cyberattacks. The paper conveys the framework of the electric digital twin grid and the concept of the DT grid processing and the way of serving the producer, prosumers, consumers even the whole nation in infrastructure, education, research, economic, business, and political development.
The most well-known research on driverless vehicles at the moment is connected autonomous vehicles (CAVs), which reflects the future path for the self driving field. The development of connected ...autonomous vehicles (CAVs) is not only increasing logistics operations, but it is also opening up new possibilities for the industry's sustainable growth. In this review, we will explore the cloud-controlled wireless network-based model of cyber physical aspect of the autonomous vehicle, which is coupled with unmanned aerial vehicles (UAVs). Additionally, this model is Internet of Things (IoT) managed and AI-based, with a blockchain-based security mechanism. Additionally, we'll focus on lateral control in autonomous driving, particularly the lane change maneuver, taking social behavior into account. Here, we briefly reviewed Vehicle-to-Everything (V2X) communication, which is carried out by on-board sensors and connected wireless medium that enhance the lane departure processes while retaining human driver behavior relying on obstacle avoidance.
A distance metric known as non-Euclidean distance deviates from the laws of Euclidean geometry, which is the geometry that governs most physical spaces. It is utilized when Euclidean distance is ...inappropriate, for as when dealing with curved surfaces or spaces with complex topologies. The ability to apply deep learning techniques to non-Euclidean domains including graphs, manifolds, and point clouds is made possible by non-Euclidean deep learning. The use of non-Euclidean deep learning is rapidly expanding to study real-world datasets that are intrinsically non-Euclidean. Over the years, numerous novel techniques have been introduced, each with its benefits and drawbacks. This paper provides a categorized archive of non-Euclidean approaches used in computer vision up to this point. It starts by outlining the context, pertinent information, and the development of the field’s history. Modern state-of-the-art methods have been described briefly and categorized by application fields. It also highlights the model’s shortcomings in tables and graphs and shows different real-world applicability. Overall, this work contributes to a collective information and performance comparison that will help enhance non-Euclidean deep-learning research and development in the future.
Meteorological changes urge engineering communities to look for sustainable and clean energy technologies to keep the environment safe by reducing CO
2
emissions. The structure of these technologies ...relies on the deep integration of advanced data-driven techniques which can ensure efficient energy generation, transmission, and distribution. After conducting thorough research for more than a decade, the concept of the smart grid (SG) has emerged, and its practice around the world paves the ways for efficient use of reliable energy technology. However, many developing features evoke keen interest and their improvements can be regarded as the next-generation smart grid (NGSG). Also, to deal with the non-linearity and uncertainty, the emergence of data-driven NGSG technology can become a great initiative to reduce the diverse impact of non-linearity. This paper exhibits the conceptual framework of NGSG by enabling some intelligent technical features to ensure its reliable operation, including intelligent control, agent-based energy conversion, edge computing for energy management, internet of things (IoT) enabled inverter, agent-oriented demand side management, etc. Also, a study on the development of data-driven NGSG is discussed to facilitate the use of emerging data-driven techniques (DDTs) for the sustainable operation of the SG. The prospects of DDTs in the NGSG and their adaptation challenges in real-time are also explored in this paper from various points of view including engineering, technology, et al. Finally, the trends of DDTs towards securing sustainable and clean energy evolution from the NGSG technology in order to keep the environment safe is also studied, while some major future issues are highlighted. This paper can offer extended support for engineers and researchers in the context of data-driven technology and the SG.
In today’s world, it is anticipated that DTw and IoRT will transform the idea of digital healthcare and advancement of it what was previously unknown. DTw is (physical or virtual) machines or ...computer-based models capable of mimicking or “twinning” the lives of actual entities such as objects, humans, or human-related features. Since the advent of digital and smart healthcare, the world has swiftly integrated several technologies in healthcare to support improved patient service, extend life expectancy, and lower healthcare expenses.DTw and IoRT are promising technologies and great improvements in this field. In recent years, the concepts of IoRT and DTw have captured the interest of the surgical sector. The flawless integration of data between a physical and virtual machine in either direction is the best way to conduct smart surgery. Artificial intelligence, nanotechnology, various kinds of robotic equipment, and the Internet of Things (IoT) are our pillars of completing the task. Using the DTw technological framework and IoRT infrastructure, this review paper proposes a comparative overview of traditional and IoRT collaborated DTwT and executes an adaptive perspective, opportunities, and challenges in the healthcare sector, namely surgical advances. This is an important addition to digital surgeries and to improve healthcare operations. Our literature review has sorted them according to research areas: the technological innovation, the prospective and opportunities we have, problems facing the surgical industry today and in the future, and offers an evaluation of the enabling technologies for the betterment of employing DTw and IoRT for the developing and secure surgical sector for both patients and doctors.
•The importance of Industrial autonomous machining and the economic impact are surveyed.•Based on the dimensional defects, the types of grinding operations are suggested.•A list of control algorithms ...with improvements is systematically reviewed (Especially for the grinding and other machining operations).•The significance of process manufacturing and SMART machining are summarized.•Proposed several methods for future improvement of Autonomous manufacturing research.
Autonomous Grinding (AG) is a unique kind of manufacturing process which needs extra operative care, for both robot and environment. Due to the rapid growth of research on AG, a detailed analysis on autonomous grinding and the comparative analysis with other machining processes are gathered and listed (like calibration, contact state estimation) based on operating algorithms. This paper has come to the conclusion that there are still some spaces for certain techniques and planning for future growth in the domain of industrial grinding areas such as green manufacturing (GM) methodology for better safety of the environment and better planning for waste material disposal techniques which were not reviewed before in the autonomous grinding machining sectors. At the same time, because of the motion of the rotating motors, the effects of vibration are investigated carefully and how to minimize the various errors with the help of intelligent monitoring with SMART sensors are also observed. Finally, this standpoint survey provides a rigorous outlook of the various algorithms used in the last several years in autonomous grinding manufacturing which will help the Robot industry like factory 5.0 adopt GM practices.
Technological advancements in smart grid energy systems (SGESs) are introducing sustainable frameworks to meet the demand for the fourth industrial energy revolution. These frameworks are planned to ...be used in the forthcoming future to maintain the energy network operation with optimization, energy trading, grid automation, and so on. Blockchain (BCn), developing after passing a diverse period of the research journey, comes to the mind of researchers and its integration in SGES paves the way to reach the goal of energy demand. However, still of interest is ongoing in the improvement of BCn features which can be regarded as the next-generation blockchain framework. This paper exhibits the technical framework of the next-generation BCn framework and explores its benefits and challenges in performing the emerging aspects of SGES. This framework enables some advanced features for the sustainable operation of SGES like smart metering, peer-to-peer (P2P) energy trading, self-operation, and transparency. The technical explanation of this BCn technology established on essential features and requisites is also presented in this paper from various points of view which include smart mechanism, intelligent storage system, and interoperability. We also highlight the recent progress and limitations of the current BCn framework in SGES. Finally, some challenges towards integrating the next-generation BCn technology in SGES are reported. This work can provide extended support for the practitioner and researcher in the context of BCn technology and SGES.
Display omitted
•Review the development aspect of the existing BCn framework in the field of SGES.•Emerging features of the next-generation BCn framework are discussed.•Review the current trends of BCn framework in the real-time energy sector.•Challenges and benefits of the next-generation BCn framework in SGES are discussed.
PDF
