ElasticSDK: A Monitoring Software Development Kit for enabling Data-driven Management and Control in 5G

2020 ◽  
Author(s):  
Xenofon Vasilakos ◽  
Berkay Koksal ◽  
Dwi Hartati Izaldi ◽  
Navid Nikaein ◽  
Robert Schmidt ◽  
...  
Keyword(s):  
Software Development ◽  
Data Driven ◽  
Software Development Kit ◽  
And Control ◽  
Monitoring Software

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

A Data-Driven Electric Water Heater Scheduling and Control System

2021 ◽  
pp. 110924
Author(s):  
Gulai Shen ◽  
Zachary E. Lee ◽  
Ali Amadeh ◽  
K. Max Zhang
Keyword(s):  
Control System ◽  
Data Driven ◽  
Water Heater ◽  
And Control

Research on MES Domain Analysis Driven by Ontology

2010 ◽  
Vol 20-23 ◽  
pp. 1084-1090 ◽  
Author(s):  
Wen Long
Keyword(s):  
Control System ◽  
Software Development ◽  
Method Development ◽  
Domain Analysis ◽  
Manufacturing Execution System ◽  
Development Method ◽  
And Performance ◽  
And Control ◽  
Plan Management ◽  
Manufacturing Execution

Manufacturing Execution System (MES) links plan management and workshop control in an enterprise, which is an integrative management and control system of workshop production oriented to manufacturing process. To overcome the difficulties of traditional software development method, development of MES based on component is adopted to prompt development efficiency and performance of MES, which can be more reconstructing, reuse, expansion and integration, and MES domain analysis driven by ontology is investigated in detail. MES domain analysis driven by ontology is feasible and efficient through developing a pharmaceutics MES which applied in a pharmaceutics manufacturing factory.

From a Scrum Reference Ontology to the Integration of Applications for Data-Driven Software Development

2021 ◽  
pp. 106570
Author(s):  
Paulo Sérgio Santos Júnior ◽  
Monalessa Perini Barcellos ◽  
Ricardo de Almeida Falbo ◽  
João Paulo A. Almeida
Keyword(s):  
Software Development ◽  
Data Driven ◽  
Reference Ontology

Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Nurali Virani ◽  
Devesh K. Jha ◽  
Zhenyuan Yuan ◽  
Ishana Shekhawat ◽  
Asok Ray
Keyword(s):  
Particle Filtering ◽  
Dynamic Models ◽  
Training Data ◽  
Data Driven ◽  
Conditional Density ◽  
Bayesian Filtering ◽  
State Dependent ◽  
State Evolution ◽  
And Control ◽  
Nonparametric Kernel

This paper addresses the problem of learning dynamic models of hybrid systems from demonstrations and then the problem of imitation of those demonstrations by using Bayesian filtering. A linear programming-based approach is used to develop nonparametric kernel-based conditional density estimation technique to infer accurate and concise dynamic models of system evolution from data. The training data for these models have been acquired from demonstrations by teleoperation. The trained data-driven models for mode-dependent state evolution and state-dependent mode evolution are then used online for imitation of demonstrated tasks via particle filtering. The results of simulation and experimental validation with a hexapod robot are reported to establish generalization of the proposed learning and control algorithms.

Attitude Determination And Control System Flight Software Development And Design

2021 ◽  
Author(s):  
William Travis ◽  
Michael W.R. Alger ◽  
Ijaz Qureshi ◽  
Emily Shepherdson ◽  
Anton de Ruiter
Keyword(s):  
Control System ◽  
Software Development ◽  
Attitude Determination ◽  
And Control
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