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HBase is the top option for storing huge data. HBase has been selected for several purposes, including its scalability, efficiency, strong consistency support, and the capacity to support a broad range of data models.
Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. It is capable of adopting self-defined pseudolabels as supervision and use the learned representations for several downstream tasks.
Electrical power system stability is of upmost importance for a secure and reliable supply of electrical energy to residential, commercial and industrial premises. Voltage stability of microgrids, as new components of a power system, is an emerging research area within the concept of power system stability. The main purpose of developing microgrids is to facilitate the integration of renewable energy sources into the power grid. Renewable energy sources are normally connected to the grid via power electronic inverters. As various types of renewable energy sources are increasingly connected to the electrical power grid, power systems of the near future will have more inverter-based generators (IBGs) instead of synchronous generators (SGs). Since IBGs have significant differences in their characteristics compared to SGs , particularly with regard to their inertia and capability to provide reactive power, their impacts on the system dynamics are different compared to SGs. A comprehensive review on voltage stability of power systems with the inclusion of inverter-based generators is presented.
Additive manufacturing with an emphasis on 3D printing has recently become popular due to its exceptional advantages over conventional manufacturing processes. However, 3D printing process parameters are challenging to optimize, as they influence the properties and usage time of printed parts. Therefore, it is a complex task to develop a correlation between process parameters and printed parts’ properties via traditional optimization methods. A machine-learning technique was recently validated to carry out intricate pattern identification and develop a deterministic relationship, eliminating the need to develop and solve physical models. In machine learning, artificial neural network (ANN) is the most widely utilized model, owing to its capability to solve large datasets and strong computational supremacy.
Chronic diseases are growing exponentially. Today, there are over 900 million individuals suffering with some chronic diseases around the world. For this reason, e-health systems are being developed to design a better quality of life for patients. For instance, we have systems for detection of epilepsy, monitoring of vital signs, control of diabetes, among others. Deep learning has being an important technique embedded in these systems to predict and sort the data without the need of a 24-hour monitoring specialist. However, by combining e-health systems and deep learning techniques also brings several challenges that need to be overcome.
LPWAN stands for Low Power Wide Area Network; LPWAN provides long-distance communication for rural and urban areas to support IIoT devices considered by a ten-year provision time to acclimate IIoT applications with higher extensibility, availability of intelligent monitoring infrastructure for a small portion of data exchanges. LoRa is favorable to use with smart sensing applications working IIoT non-authored spectrum. NBIoT is suitable for supporting agriculture and environmental data collection and observations, industrial data tracking and monitoring, inventory tracking, smart billing, and smart buildings, smart metering, and smart cities. Machine-to-machine (M2M) communication uses the Bluetooth Low Energy (BLE) technique for the data communication, the other IIoT applications used in healthcare, smart agriculture, intelligent home, smart vehicles, smart city, smart gadgets, and industries use the cognitive LPWAN, LoRA, Sigfox. There is a need to mix most LPWAN technologies in heterogeneous IIoT applications to provide more efficient and convenient intelligent services. In heterogeneous IIoT applications, there a need to mix most LPWAN technologies to provide more efficient and convenient intelligent services. This will be deployed by cognitive LPWAN
Financial Technology (Fintech) is a new financial industry that applies technology to improve financial activities. Moreover, according to Leong and Sung (2018), Fintech can also be considered as “any innovative ideas that improve financial service processes by proposing technology solutions according to different business situations" .
This the article presents a comprehensive, contemporary review of the latest subsystems, architectures and integrated technologies of MIMO wireless signals backhauling using optical fibre or fibre access networks, such as passive optical networks (PONs).
For ensuring the safety and reliability of high-speed trains, fault diagnosis (FD) technique plays an important role. Benefiting from the rapid developments of artificial intelligence, intelligent FD (IFD) strategies have obtained much attention in the field of academics and applications, where the qualitative approach is an important branch.
The Industrial Internet of Things (IIoT) has the potential to improve the production and business processes by enabling the extraction of valuable information from industrial processes. The mining industry, however, is rather traditional and somewhat slow to change due to infrastructural limitations in communication, data management, storage, and exchange of information. Most research efforts so far on applying IIoT in the mining industry focus on specific concerns such as ventilation monitoring, accident analysis, fleet and personnel management, tailing dam monitoring, and pre-alarm system while an overall IIoT architecture suitable for the general conditions in the mining industry is still missing.