scholarly journals On-Line Impact Load Identification

2013 ◽  
Vol 20 (1) ◽  
pp. 123-141 ◽  
Author(s):  
Krzysztof Sekuła ◽  
Cezary Graczykowski ◽  
Jan Holnicki-Szulc
Keyword(s):  
Real Time ◽  
Impact Load ◽  
Research Area ◽  
Impact Loads ◽  
Load Identification ◽  
Line Identification ◽  
On Line ◽  
Smart Technologies ◽  
Measured Response ◽  
Impact Absorption

The so-called AdaptiveImpact Absorption(AIA) is a research area ofsafety engineeringdevoted to problems of shock absorption in various unpredictable scenarios of collisions. It makes use ofsmart technologies(systems equipped with sensors, controllable dissipaters and specialised tools for signal processing). Examples of engineering applications for AIA systems are protective road barriers, automotive bumpers or adaptive landing gears. One of the most challenging problems for AIA systems is on-line identification of impact loads, which is crucial for introducing the optimum real-time strategy of adaptive impact absorption. This paper presents the concept of animpactometerand develops the methodology able to perform real-time impact load identification. Considered dynamic excitation is generated by a massM1impacting with initial velocityV0. An analytical formulation of the problem, supported with numerical simulations and experimental verifications is presented. Two identification algorithms based on measured response of the impacted structure are proposed and discussed. Finally, a concept of the AIA device utilizing the idea ofimpactometeris briefly presented.

scholarly journals Automatic Implementation of a Self-Adaption Non-Intrusive Load Monitoring Method Based on the Convolutional Neural Network

Processes ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 704 ◽  
Author(s):  
Xin Wu ◽  
Dian Jiao ◽  
Yu Du
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Load Identification ◽  
Neural Network Models ◽  
Monitoring Method ◽  
Line Load ◽  
The Real ◽  
Load Monitoring ◽  
On Line

Non-intrusive load monitoring (NILM) is an effective way to achieve demand-side measurement and energy efficiency optimization. This paper studies a method of non-intrusive on-line load monitoring under a high-frequency mode of electric data acquisition, which enables the NILM to be automated and in real-time, including the short-term construction of a dynamic signature library and continuous on-line load identification. Firstly, in the short initial operation phase, load separation and category determination are carried out to construct the load waveform library of the monitoring user. Then, the continuous load monitoring phase begins. Based on the data of each user’s signature library, the decomposition waveforms are classified by convolutional neural network models that are constructed to be suitable for each signature library in order to realize load identification. The real-time power consumption status of the load can be obtained continuously. In this paper, the electricity data of actual users are collected and used to perform the experiments, which show that the proposed method can construct the load signature library adaptively for different users. Meanwhile, the classification of the convolutional neural network model based on a library constructed in actual operation ensures the real-time and accuracy of load monitoring.

scholarly journals Real Time FFT Based On-line Identification of Power System Oscillation Modes

2000 ◽  
Vol 120 (2) ◽  
pp. 134-140 ◽  
Author(s):  
Naoto Suzuki ◽  
Takashi Hiyama ◽  
Takahiko Funakoshi
Keyword(s):  
Power System ◽  
Real Time ◽  
Line Identification ◽  
Oscillation Modes ◽  
On Line

Multiestimation Scheme for Identification and Adaptive Control of Nonlinear Laboratory Model DTS200

2015 ◽  
Vol 816 ◽  
pp. 451-460
Author(s):  
Petr Navrátil ◽  
Ján Ivanka
Keyword(s):  
Adaptive Control ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Real Time ◽  
Controller Synthesis ◽  
Laboratory Model ◽  
Controlled System ◽  
Line Identification ◽  
On Line ◽  
And Control

This paper is focused on usability of multiestimation scheme approach in the area of identification and control of nonlinear systems. A multiestimation scheme is introduced and subsequently used for the adaptive control of real-time nonlinear system. The multiestimation scheme integrates on-line identification of suitable models of a controlled system and a controller synthesis on base of the identified model. The real-time testing has been carried out by the control of nonlinear laboratory model of interconnected tanks (DTS200 by Amira company).

Adaptive Signal Analysis and Interpretation for Real-time Intelligent Patient Monitoring

1994 ◽  
Vol 33 (01) ◽  
pp. 60-63 ◽  
Author(s):  
E. J. Manders ◽  
D. P. Lindstrom ◽  
B. M. Dawant
Keyword(s):  
Computer Architecture ◽  
Real Time ◽  
Data Abstraction ◽  
Monitoring Strategy ◽  
Intelligent Monitoring ◽  
Patient Status ◽  
On Line ◽  
Main Components ◽  
And Control ◽  
Adaptive Signal

Abstract:On-line intelligent monitoring, diagnosis, and control of dynamic systems such as patients in intensive care units necessitates the context-dependent acquisition, processing, analysis, and interpretation of large amounts of possibly noisy and incomplete data. The dynamic nature of the process also requires a continuous evaluation and adaptation of the monitoring strategy to respond to changes both in the monitored patient and in the monitoring equipment. Moreover, real-time constraints may imply data losses, the importance of which has to be minimized. This paper presents a computer architecture designed to accomplish these tasks. Its main components are a model and a data abstraction module. The model provides the system with a monitoring context related to the patient status. The data abstraction module relies on that information to adapt the monitoring strategy and provide the model with the necessary information. This paper focuses on the data abstraction module and its interaction with the model.

Nitrogen Removal in a Real-Time Controlled Sequencing Batch Membrane Bioreactor

2010 ◽  
Vol 5 (3) ◽  
Author(s):  
Cheng-Nan Chang ◽  
Li-Ling Lee ◽  
Han-Hsien Huang ◽  
Ying-Chih Chiu
Keyword(s):  
Real Time ◽  
Nitrogen Removal ◽  
Membrane Bioreactor ◽  
Synthetic Wastewater ◽  
Real Time Control ◽  
Time Control ◽  
Cake Layer ◽  
On Line ◽  
Solid Liquid ◽  
Solid Liquid Separation

The performance of a real-time controlled Sequencing Batch Membrane Bioreactor (SBMBR) for removing organic matter and nitrogen from synthetic wastewater has been investigated in this study under two specific ammonia loadings of 0.0086 and 0.0045g NH4+-N gVSS−1 day−1. Laboratory results indicate that both COD and DOC removal are greater than 97.5% (w/w) but the major benefit of using membrane for solid-liquid separation is that the effluent can be decanted through the membrane while aeration is continued during the draw stage. With a continued aeration, the sludge cake layer is prevented from forming thus alleviating the membrane clogging problem in addition to significant nitrification activities observed in the draw stage. With adequate aeration in the oxic stage, the nitrogen removal efficiency exceeding 99% can be achieved with the SBMBR system. Furthermore, the SBMBR system has also been used to study the occurrence of ammonia valley and nitrate knee that can be used for real-time control of the biological process. Under appropriate ammonia loading rates, applicable ammonia valley and nitrate knee are detected. The real-time control of the SBMBR can be performed based on on-line ORP and pH measurements.

Modifications of rainfall runoff and decision finding models for on-line simulation in real time control

1999 ◽  
Vol 39 (9) ◽  
pp. 201-207
Author(s):  
Andreas Cassar ◽  
Hans-Reinhard Verworn
Keyword(s):  
Real Time ◽  
Urban Drainage ◽  
Rain Event ◽  
Rainfall Runoff ◽  
Real Time Control ◽  
Design Storm ◽  
Time Control ◽  
Timing Data ◽  
On Line ◽  
Rainfall Runoff Model

Most of the existing rainfall runoff models for urban drainage systems have been designed for off-line calculations. With a design storm or a historical rain event and the model system the rainfall runoff processes are simulated, the faster the better. Since very recently, hydrodynamic models have been considered to be much too slow for real time applications. However, with the computing power of today - and even more so of tomorrow - very complex and detailed models may be run on-line and in real time. While the algorithms basically remain the same as for off-line simulations, problems concerning timing, data management and inter process communication have to be identified and solved. This paper describes the upgrading of the existing hydrodynamic rainfall runoff model HYSTEM/EXTRAN and the decision finding model INTL for real time performance, their implementation on a network of UNIX stations and the experiences from running them within an urban drainage real time control project. The main focus is not on what the models do but how they are put into action and made to run smoothly embedded in all the processes necessary in operational real time control.

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