Development of Failure Detection System for Network Control using Collective Intelligence of Social Networking Service in Large-Scale Disasters

2016 ◽  
Author(s):  
Chihiro Maru ◽  
Miki Enoki ◽  
Akihiro Nakao ◽  
Shu Yamamoto ◽  
Saneyasu Yamaguchi ◽  
...  
Keyword(s):  
Social Networking ◽  
Large Scale ◽  
Collective Intelligence ◽  
Detection System ◽  
Failure Detection ◽  
Network Control ◽  
Social Networking Service

scholarly journals Perceptions Related to Nursing and Nursing Staff in Long-Term Care Settings during the COVID-19 Pandemic Era: Using Social Networking Service

2021 ◽  
Vol 18 (14) ◽  
pp. 7398
Author(s):  
Juhhyun Shin ◽  
Sunok Jung ◽  
Hyeonyoung Park ◽  
Yaena Lee ◽  
Yukyeong Son
Keyword(s):  
Social Networking ◽  
Nursing Staff ◽  
Large Scale ◽  
Long Term Care ◽  
Social Networking Service ◽  
Term Care ◽  
Wide Range ◽  
Role Of Nursing

Purpose: The purpose of this study was to investigate what opinions and perceptions people have about nursing and the role of nursing staff in nursing homes (NHs) on Social Networking Service (SNS) by analyzing large-scale data through social big-data analysis. Methods: This study investigated changes in perception related to nursing and nursing staff in NHs during the COVID-19 pandemic era using target channels (blogs, cafes, Instagram, communities, Twitter, etc.). Data were collected on the channel from 12 September 2019 to 11 September 2020, 6 months before and after 12 March 2020 when the COVID-19 pandemic was declared. Selected keywords included “nursing,” “nurse,” and “nursing staff,” and included words were “long-term care settings,” “geriatric hospital,” and “nursing home.” Text mining, opinion mining, and social network analysis were conducted. Results: After the COVID-19 pandemic, the frequency of keywords increased about 1.5 times compared to before. In March 2020 when the COVID-19 pandemic was declared, the negative phrase “be infected” ranked number one, resulting in a sharp 8% rise in the percentage of negative words in that month. The related words that have risen in rank significantly, or were newly ranked in the Top 30 after the pandemic, were related with COVID-19. Conclusion: The public began to realize the role of nursing staff in the prevention and management of mass infection in NHs and the importance of nursing staff after the pandemic. Further studies should examine the perceptions of those who have received nursing services and include a wide range of foreign channels.

A Failure Detection System for Large Scale Distributed Systems

2011 ◽  
Vol 2 (3) ◽  
pp. 64-87 ◽  
Author(s):  
Andrei Lavinia ◽  
Ciprian Dobre ◽  
Florin Pop ◽  
Valentin Cristea
Keyword(s):  
Distributed Systems ◽  
Large Scale ◽  
Detection System ◽  
Failure Detection ◽  
Difficult Problem ◽  
Distributed Environment ◽  
Dynamic Configuration ◽  
Fundamental Building Block ◽  
Heavy Loads ◽  
Traffic Optimization

Failure detection is a fundamental building block for ensuring fault tolerance in large scale distributed systems. It is also a difficult problem. Resources under heavy loads can be mistaken as being failed. The failure of a network link can be detected by the lack of a response, but this also occurs when a computational resource fails. Although progress has been made, no existing approach provides a system that covers all essential aspects related to a distributed environment. This paper presents a failure detection system based on adaptive, decentralized failure detectors. The system is developed as an independent substrate, working asynchronously and independent of the application flow. It uses a hierarchical protocol, creating a clustering mechanism that ensures a dynamic configuration and traffic optimization. It also uses a gossip strategy for failure detection at local levels to minimize detection time and remove wrong suspicions. Results show that the system scales with the number of monitored resources, while still considering the QoS requirements of both applications and resources.

A Failure Detection System for Large Scale Distributed Systems

2012 ◽  
pp. 127-151
Author(s):  
Andrei Lavinia ◽  
Ciprian Dobre ◽  
Florin Pop ◽  
Valentin Cristea
Keyword(s):  
Distributed Systems ◽  
Large Scale ◽  
Detection System ◽  
Failure Detection ◽  
Difficult Problem ◽  
Distributed Environment ◽  
Dynamic Configuration ◽  
Fundamental Building Block ◽  
Heavy Loads ◽  
Traffic Optimization

Failure detection is a fundamental building block for ensuring fault tolerance in large scale distributed systems. It is also a difficult problem. Resources under heavy loads can be mistaken as being failed. The failure of a network link can be detected by the lack of a response, but this also occurs when a computational resource fails. Although progress has been made, no existing approach provides a system that covers all essential aspects related to a distributed environment. This paper presents a failure detection system based on adaptive, decentralized failure detectors. The system is developed as an independent substrate, working asynchronously and independent of the application flow. It uses a hierarchical protocol, creating a clustering mechanism that ensures a dynamic configuration and traffic optimization. It also uses a gossip strategy for failure detection at local levels to minimize detection time and remove wrong suspicions. Results show that the system scales with the number of monitored resources, while still considering the QoS requirements of both applications and resources.

scholarly journals A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1261
Author(s):  
Christopher Gradwohl ◽  
Vesna Dimitrievska ◽  
Federico Pittino ◽  
Wolfgang Muehleisen ◽  
András Montvay ◽  
...  
Keyword(s):  
Power Plants ◽  
Large Scale ◽  
Failure Detection ◽  
Energy Yield ◽  
Combined Approach ◽  
Levelized Cost Of Electricity ◽  
Term Operation ◽  
Model Based

Photovoltaic (PV) technology allows large-scale investments in a renewable power-generating system at a competitive levelized cost of electricity (LCOE) and with a low environmental impact. Large-scale PV installations operate in a highly competitive market environment where even small performance losses have a high impact on profit margins. Therefore, operation at maximum performance is the key for long-term profitability. This can be achieved by advanced performance monitoring and instant or gradual failure detection methodologies. We present in this paper a combined approach on model-based fault detection by means of physical and statistical models and failure diagnosis based on physics of failure. Both approaches contribute to optimized PV plant operation and maintenance based on typically available supervisory control and data acquisition (SCADA) data. The failure detection and diagnosis capabilities were demonstrated in a case study based on six years of SCADA data from a PV plant in Slovenia. In this case study, underperforming values of the inverters of the PV plant were reliably detected and possible root causes were identified. Our work has led us to conclude that the combined approach can contribute to an efficient and long-term operation of photovoltaic power plants with a maximum energy yield and can be applied to the monitoring of photovoltaic plants.

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