Rate of Change Processing of Acoustic Data from a Permanent Monitoring System for Pipe Crack Early Identification: A Case Study

Author(s):  
Mark Stephens ◽  
Chi Zhang ◽  
Martin Lambert
2019 ◽  
Vol 11 (6) ◽  
pp. 1716 ◽  
Author(s):  
Luciano Raso ◽  
Jan Kwakkel ◽  
Jos Timmermans

Climate change raises serious concerns for policymakers that want to ensure the success of long-term policies. To guarantee satisfactory decisions in the face of deep uncertainties, adaptive policy pathways might be used. Adaptive policy pathways are designed to take actions according to how the future will actually unfold. In adaptive pathways, a monitoring system collects the evidence required for activating the next adaptive action. This monitoring system is made of signposts and triggers. Signposts are indicators that track the performance of the pathway. When signposts reach pre-specified trigger values, the next action on the pathway is implemented. The effectiveness of the monitoring system is pivotal to the success of adaptive policy pathways, therefore the decision-makers would like to have sufficient confidence about the future capacity to adapt on time. “On time” means activating the next action on a pathway neither so early that it incurs unnecessary costs, nor so late that it incurs avoidable damages. In this paper, we show how mapping the relations between triggers and the probability of misclassification errors inform the level of confidence that a monitoring system for adaptive policy pathways can provide. Specifically, we present the “trigger-probability” mapping and the “trigger-consequences” mappings. The former mapping displays the interplay between trigger values for a given signpost and the level of confidence regarding whether change occurs and adaptation is needed. The latter mapping displays the interplay between trigger values for a given signpost and the consequences of misclassification errors for both adapting the policy or not. In a case study, we illustrate how these mappings can be used to test the effectiveness of a monitoring system, and how they can be integrated into the process of designing an adaptive policy.


2021 ◽  
Vol 30 (2) ◽  
pp. 116-119
Author(s):  
Makoto Oe ◽  
Kahori Tsuruoka ◽  
Yumiko Ohashi ◽  
Kimie Takehara ◽  
Hiroshi Noguchi ◽  
...  

Objective: Early identification of pre-ulcerative pathology is important to preventing diabetic foot ulcers (DFU), but signs of inflammation are difficult to detect on the feet of patients with diabetic neuropathy due to decreased sensation. However, infrared thermography can objectively identify inflammation. Therefore, a device that allows patients to visualise thermograms of their feet might be an effective way to prevent DFU. We aimed to determine the effects of a novel self-monitoring device to prevent DFU using a thermograph attached to a smartphone. Method: A self-monitoring device comprising a mobile thermograph attached to a smartphone on a selfie stick was created, and its effects in two patients with diabetic neuropathy and foot calluses assessed. Results: For one patient, he understood that walking too much increased the temperature in the skin of his feet (a sign of inflammation). The other patient could not detect high-risk findings, because the temperature of his skin did not increase during the study period. Conclusion: This device might provide self-care incentives to prevent DFU, although some issues, such as the automatic detection of high-risk thermographic changes, need to be improved.


2021 ◽  
Vol 3 (2) ◽  
pp. 11-18
Author(s):  
Taha E. Al-jarakh ◽  
Osama Abbas Hussein ◽  
Alaa Khamees Al-azzawi ◽  
Mahmood Farhan Mosleh

The main challenge of this research is to scale the IoT platform aspects related to exchanging, processing, and archiving messages at the lowest cost compute-wise, through evaluating and selecting the most appropriate techniques that can be used in the design of the environment pollution monitoring system for a case study of Iraq. The entirety of the optimization process aims to provide a nation-wide community-oriented service via the scalable platform. The platform provides an intake for a huge number of sensing nodes. Compute-operations following the form of data analysis, aggregation, sensors’ monitoring for the five air pollutants (SO2, CO, O3, NO2, and PM), in addition to radioactive contamination. Thus system-level performance evaluation takes place on the major compute-intensive operations. Thus, proposals are made to optimize the performance in terms of reducing the scripts execution time and the size of data and messages transmitted and stored in the system.


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