A Methodology for Real-Time Data Verification exploiting Deep Learning and Model Checking

The traditional real-time data scheduling method ignores the optimization process of job data that leads to delayed delivery, high inventory cost, and low utilization rate of equipment. This paper proposes a novel real-time data scheduling method based on deep learning and an improved fuzzy algorithm for flexible operations in the papermaking workshop. The algorithm is divided into three parts: the first part describes the flexible job shop scheduling problem; the second part constructs the fuzzy scheduling model of flexible job data in papermaking workshop; and finally the third part uses a genetic algorithm to obtain the optimal solution of fuzzy scheduling of flexible job data in papermaking workshop. The results show that the optimal solution is obtained in 48 seconds at the 23rd attempt (iteration) under the application of the proposed method. This result is much better than the three traditional scheduling methods with which we compared our results. Hence, this paper improves the work efficiency and quality of papermaking workshop and reduces the operating cost of the papermaking enterprise.

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Real-Time Data Filling and Automatic Retrieval Algorithm of Road Traffic Based on Deep-Learning Method

Symmetry ◽

10.3390/sym13010001 ◽

2020 ◽

Vol 13 (1) ◽

pp. 1

Author(s):

Jie Zhu ◽

Weixiang Xu

Keyword(s):

Deep Learning ◽

Real Time ◽

Road Traffic ◽

Retrieval Algorithm ◽

Target Area ◽

Learning Method ◽

Time Data ◽

The Real ◽

Automatic Retrieval ◽

Real Time Data

In order to enhance the real-time and retrieval performance of road traffic data filling, a real-time data filling and automatic retrieval algorithm based on the deep-learning method is proposed. In image detection, the depth representation is extracted according to the detection target area of a general object. The local invariant feature is extracted to describe local attributes in the region, and it is fused with depth representation to complete the real-time data filling of road traffic. According to the results of the database enhancement, the retrieval results of the deep representation level are reordered. In the index stage, unsupervised feature updating is realized by neighborhood information to improve the performance of a feature retrieval. The experimental results show that the proposed method has high recall and precision, a short retrieval time and a low running cost.

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Scalable and Reliable Deep Learning Model to handle real-time Streaming Data

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c6272.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 3840-3844 ◽

Cited By ~ 1

Keyword(s):

Deep Learning ◽

Real Time ◽

Data Streams ◽

Learning Algorithm ◽

Streaming Data ◽

Experimental Result ◽

Time Data ◽

Data Set ◽

Public Data ◽

Real Time Data

We have real-time data everywhere and every day. Most of the data comes from IoT sensors, data from GPS positions, web transactions and social media updates. Real time data is typically generated in a continuous fashion. Such real-time data are called Data streams. Data streams are transient and there is very little time to process each item in the stream. It is a great challenge to do analytics on rapidly flowing high velocity data. Another issue is the percentage of incoming data that is considered for analytics. Higher the percentage greater would be the accuracy. Considering these two issues, the proposed work is intended to find a better solution by gaining insight on real-time streaming data with minimum response time and greater accuracy. This paper combines the two technology giants TensorFlow and Apache Kafka. is used to handle the real-time streaming data since TensorFlow supports analytics support with deep learning algorithms. The Training and Testing is done on Uber connected vehicle public data set RideAustin. The experimental result of RideAustin shows the predicted failure under each type of vehicle parameter. The comparative analysis showed 16% improvement over the traditional Machine Learning algorithm.

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Real Time Data Verification of Load and Pressure Testing Using von Mises Yield Criterion for Thick Walled Tubular Products

International Journal of Mechanical Engineering and Robotics Research ◽

10.18178/ijmerr.8.2.167-172 ◽

2019 ◽

pp. 167-172

Author(s):

Donnie Curington

Keyword(s):

Real Time ◽

Yield Criterion ◽

Time Data ◽

Data Verification ◽

Pressure Testing ◽

Real Time Data ◽

Von Mises Yield Criterion ◽

Von Mises

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Critical utility of e-solutions in risk based monitoring

International Journal of Clinical Trials ◽

10.18203/2349-3259.ijct20163957 ◽

2016 ◽

Vol 3 (4) ◽

pp. 199

Author(s):

Pranjal R. Chaturvedi

Keyword(s):

Clinical Trial ◽

Real Time ◽

Clinical Practices ◽

Time Data ◽

Data Verification ◽

Source Data ◽

Real Time Data ◽

Source Data Verification

<p class="abstract"><span lang="EN-IN">As the clinical trial industry is evolving from a traditional 100% SDV (source data verification) approach to reduced SDV & Centralized Monitoring by applying a risk based monitoring (RBM) approach, hence forth leveraging of e-Solutions and real time data integrations would be critical in RBM. Technological considerations are critical to collect data real time to assess, monitor and mitigate risk in compliance with Good Clinical Practices. This article will examine the importance of e-Solutions and real time analytics in alignment with various systems for developing an effective RBM strategy</span>.</p>

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