Cloud platforms for remote monitoring system: a comparative case study

Hao, Yuqiuge; Helo, Petri; Gunasekaran, Angappa

doi:10.1080/09537287.2019.1631459

Cited by 22 publications

(6 citation statements)

References 42 publications

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“…e remote surveillance system receives, processes, and stores the data collected by the drone. Furthermore, the center of the grounds includes deep learning-based detection and alert activation capabilities [29]. At the ground monitoring console, the employees can view the acquired forest photos in real-time.…”

Section: Remote Monitoring Systemmentioning

confidence: 99%

An Enhanced Drone Technology for Detecting the Human Object in the Dense Areas Using a Deep Learning Model

Refaai

Rinku

Thamarai³

et al. 2022

Advances in Materials Science and Engineering

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During the recent decade, emerging technological and dramatic uses for drones were devised and accomplished, including rescue operations, monitoring, vehicle tracking, forest fire monitoring, and environmental monitoring, among others. Wildfires are one of the most significant environmental threats to wild areas and forest management. Traditional firefighting methods, which rely on ground operation inspections, have major limits and may threaten firefighters’ lives. As a result, remote sensing techniques, particularly UAV-based remotely sensed techniques, are currently among the most sought-after wildfire-fighting approaches. Current improvements in drone technology have resulted in significant breakthroughs that allow drones to perform a wide range of more sophisticated jobs. Rescue operations and forest monitoring, for example, demand a large security camera, making the drone a perfect tool for executing intricate responsibilities. Meanwhile, growing movement of the deep learning techniques in computer vision offers an interesting perspective into the project’s objective. They were used to identify forest fires in their beginning stages before they become out of control. This research describes a methodology for recognizing the presence of humans in a forest setting utilizing a deep learning framework and a human object detection method. The goal of identifying human presence in forestry areas is to prevent illicit forestry operations like illegal access into forbidden areas and illegal logging. In recent years, a lot of interest in automated wildfire identification utilizes UAV-based visual information and various deep learning techniques. This study focused on detecting wildfires at the beginning stages in forest and wilderness areas, utilizing deep learning-based computer vision algorithms that control and then mitigate massive damages to human life and forest management.

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Section: Remote Monitoring Systemmentioning

confidence: 99%

An Enhanced Drone Technology for Detecting the Human Object in the Dense Areas Using a Deep Learning Model

Refaai

Rinku

Thamarai³

et al. 2022

Advances in Materials Science and Engineering

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Section: Analysis Of Resultsmentioning

confidence: 99%

“…In this way, the system contributes to an efficient performance measurement by its connection to the cloud [44]. Studies have reported that these applications have brought competitive advantages through technologies from Industry 4.0 supported by data-based KPIs [63]. This combination of IIoT and the Cloud is fundamental for the digital transformation of data for performance measurement in manufacturing management [25,57] The reports observed in the research on how performance measurement is being treated in the context of Industry 4.0 identified that small and medium industries are constituted by systems with low adoption of technologies, consisting of legacy systems (machines and equipment), which do not have minimal connectivity capability with Industry 4.0 technologies.…”

Section: Model For Measuring Equipment Performancementioning

confidence: 99%

Performance measurement based on machines data: Systematic literature review

Martins

Deschamps

Detro

et al. 2022

IET Collab Intel Manufact

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Industry 4.0 driven by the internet of things (IoT) is changing the way of producing and has been offering smart manufacturing systems with support technologies for the digital transformation of manufacturing plants seeking improvements in productivity, in control over the process, and customisation of production, among others. Due to these technological developments, small and medium-sized industries have been identified as a weak link in adapting their processes and resources, where they are usually the biggest victims in the transition to industry 4.0. The evidence points out that the excess data inserted in the databases of the manufacturing system of the industries influences the decision-making process of managers, making the process more complex and dynamic. This research focuses on a systematic literature review to assess how data-based performance measurements for machines are being handled in the context of industry 4.0. The methodological approach follows the application of the PROKNOW-C (Knowledge Development Process-Constructivist) method used to build a Bibliographic Portfolio in a structured way in line with the research theme. The results presented in the Bibliometric Analysis enabled the construction of a performance measurement model based on the sources of the researched articles.

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“…A number of literatures indicate that managing the integration of operating systems and servers can make a significant contribution to operational efficiency and system response speed [33]. By understanding the close relationship between OS and Server, companies can optimize the performance of their systems, reduce application deployment time, and increase response capacity to rapidly changing business demands [34].…”

Section: Iaic Transactions On Sustainable Digital Innovation (Itsdi)mentioning

confidence: 99%

Operating System and Server Integration for Business Effectiveness

Apriani,

Afrijaldi,

Auliya

et al. 2024

itsdi

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Management Information Systems (MIS) have a crucial role in supporting the effectiveness of a company or agency. A strong MIS ensures optimal internal planning and control, involving people, documents, technology and procedures. The importance of MIS demands synergistic integration between the operating system and servers to manage hardware resources and store information efficiently. This research aims to explore the factors that influence SIM in the context of operating system and server integration. The focus is on understanding the impact of this integration on business effectiveness, particularly in terms of planning, control and data storage. Research reveals that the integration of operating systems and servers positively contributes to increasing SIM effectiveness. Harmonious cooperation between the operating system and servers encourages optimization of hardware resource management and information storage, supporting more efficient internal planning and control. These findings provide a new view regarding the importance of operating system and server integration in optimizing SIM. The implementation of this integration is expected to improve business performance through more accurate planning, tight control and efficient data storage. The novelty of this research lies in the integrative approach to operating systems and servers in the context of SIM. A deeper understanding of these relationships can provide practical guidance for companies to improve the effectiveness of their information systems.

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Cloud platforms for remote monitoring system: a comparative case study

Cited by 22 publications

References 42 publications

An Enhanced Drone Technology for Detecting the Human Object in the Dense Areas Using a Deep Learning Model

An Enhanced Drone Technology for Detecting the Human Object in the Dense Areas Using a Deep Learning Model

Performance measurement based on machines data: Systematic literature review

Operating System and Server Integration for Business Effectiveness

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