scholarly journals A web-based, real-time quality control and progress monitoring tool for multicenter environmental health surveys

2019 ◽  
Vol 4 (3) ◽  
pp. 81
Author(s):  
Tiantian Li ◽  
Yi Zhang ◽  
Jianlong Fang ◽  
Peng Du ◽  
Jiaonan Wang ◽  
...  
Keyword(s):  
Quality Control ◽  
Environmental Health ◽  
Real Time ◽  
Health Surveys ◽  
Progress Monitoring ◽  
Monitoring Tool ◽  
Web Based

scholarly journals Internet of Things (IoT) for digital concrete quality control (DCQC): A conceptual framework

2019 ◽  
Author(s):  
Arka Ghosh ◽  
M. Reza Hosseini ◽  
Riyadh Al-Ameri ◽  
Gintaris Kaklauskas ◽  
Bahareh Nikmehr
Keyword(s):  
Quality Control ◽  
Internet Of Things ◽  
Conceptual Framework ◽  
Real Time ◽  
Progress Monitoring ◽  
Predictive Analytics ◽  
Time Data ◽  
Ongoing Research ◽  
Concrete Members ◽  
Concrete Quality

Concreting is generally a manual, labour intensive and time-consuming process, putting additional burden on constrained resources. Current practices of concreting are wasteful, non-sustainable and end products usually lack proper quality conformance. This paper, as the first outcome of an ongoing research project, proposes concrete as an area ripe for being disrupted by new technological developments and the wave of automation. It puts forward arguments to show that The Internet of Things (IoT), as an emerging concept, has the potential to revolutionize concreting operations, resulting in substantial time savings, confidence in its durability and enhanced quality conformance. A conceptual framework for a digital concrete quality control (DCQC) drawing upon IoT is outlined; DCQC facilitates automated lifecycle monitoring of concrete, controlled by real-time monitoring of parameters like surface humidity, temperature variance, moisture content, vibration level, and crack occurrence and propagation of concrete members through embedded sensors. Drawing upon an analytical approach, discussions provide evidence for the advantages of adopting DCQC. The proposed system is of particular appeal for practitioners, as a workable solution for reducing water, energy consumption and required man-hours for concreting procedures, as well as, providing an interface for access to real-time data, site progress monitoring, benchmarking, and predictive analytics purposes.

scholarly journals ORBIT-RT: A Real-Time, Open Platform for Knowledge-Based Quality Control of Radiotherapy Treatment Planning

2021 ◽  
pp. 134-142 ◽  
Author(s):  
Brent M. Covele ◽  
Kartikeya S. Puri ◽  
Karoline Kallis ◽  
James D. Murphy ◽  
Kevin L. Moore
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Treatment Planning ◽  
Treatment Plan ◽  
Estimation Methods ◽  
Web Based ◽  
Knowledge Based ◽  
Validation Set ◽  
Control Feedback ◽  
Dicom Rt

PURPOSE Access to knowledge-based treatment plan quality control has been hindered by the complexity of developing models and integration with different treatment planning systems (TPS). Online Real-time Benchmarking Information Technology for RadioTherapy (ORBIT-RT) provides a free, web-based platform for knowledge-based dose estimation that can be used by clinicians worldwide to benchmark the quality of their radiotherapy plans. MATERIALS AND METHODS The ORBIT-RT platform was developed to satisfy four primary design criteria: web-based access, TPS independence, Health Insurance Portability and Accountability Act compliance, and autonomous operation. ORBIT-RT uses a cloud-based server to automatically anonymize a user's Digital Imaging and Communications in Medicine for RadioTherapy (DICOM-RT) file before upload and processing of the case. From there, ORBIT-RT uses established knowledge-based dose-volume histogram (DVH) estimation methods to autonomously create DVH estimations for the uploaded DICOM-RT. ORBIT-RT performance was evaluated with an independent validation set of 45 volumetric modulated arc therapy prostate plans with two key metrics: (i) accuracy of the DVH estimations, as quantified by their error, DVHclinical − DVHprediction and (ii) time to process and display the DVH estimations on the ORBIT-RT platform. RESULTS ORBIT-RT organ DVH predictions show < 1% bias and 3% error uncertainty at doses > 80% of prescription for the prostate validation set. The ORBIT-RT extensions require 3.0 seconds per organ to analyze. The DICOM upload, data transfer, and DVH output display extend the entire system workflow to 2.5-3 minutes. CONCLUSION ORBIT-RT demonstrated fast and fully autonomous knowledge-based feedback on a web-based platform that takes only anonymized DICOM-RT as input. The ORBIT-RT system can be used for real-time quality control feedback that provides users with objective comparisons for final plan DVHs.

Development and Application of Quality Control System for Highway Subgrade Construction

2019 ◽  
Vol 2 (5) ◽  
Author(s):  
Tong Wang
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Detection Method ◽  
System Development ◽  
Effective Control ◽  
Construction Process ◽  
Development Mode ◽  
The Road ◽  
Whole Process

The compaction quality of the subgrade is directly related to the service life of the road. Effective control of the subgrade construction process is the key to ensuring the compaction quality of the subgrade. Therefore, real-time, comprehensive, rapid and accurate prediction of construction compaction quality through informatization detection method is an important guarantee for speeding up construction progress and ensuring subgrade compaction quality. Based on the function of the system, this paper puts forward the principle of system development and the development mode used in system development, and displays the development system in real-time to achieve the whole process control of subgrade construction quality.

scholarly journals Deep-Framework: A Distributed, Scalable, and Edge-Oriented Framework for Real-Time Analysis of Video Streams

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4045
Author(s):  
Alessandro Sassu ◽  
Jose Francisco Saenz-Cogollo ◽  
Maurizio Agelli
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Data ◽  
Video Analytics ◽  
Web Based ◽  
Real Time Analysis ◽  
Open Source Framework ◽  
Cluster Configuration ◽  
Time Requirements ◽  
High Level

Edge computing is the best approach for meeting the exponential demand and the real-time requirements of many video analytics applications. Since most of the recent advances regarding the extraction of information from images and video rely on computation heavy deep learning algorithms, there is a growing need for solutions that allow the deployment and use of new models on scalable and flexible edge architectures. In this work, we present Deep-Framework, a novel open source framework for developing edge-oriented real-time video analytics applications based on deep learning. Deep-Framework has a scalable multi-stream architecture based on Docker and abstracts away from the user the complexity of cluster configuration, orchestration of services, and GPU resources allocation. It provides Python interfaces for integrating deep learning models developed with the most popular frameworks and also provides high-level APIs based on standard HTTP and WebRTC interfaces for consuming the extracted video data on clients running on browsers or any other web-based platform.

Sign in / Sign up

Export Citation Format

Share Document