A case study in using two-level control stores

1987 ◽  
Author(s):  
Onat Menzilcioglu
Keyword(s):  
Level Control

scholarly journals IoT-based sound-level control for audio amplifiers: mosques as a case study

2022 ◽  
Vol 12 (1) ◽  
pp. 859
Author(s):  
Naeem Al-Oudat
Keyword(s):  
Internet Of Things ◽  
Sound Level ◽  
Uneven Distribution ◽  
Level Control ◽  
Audio Amplifiers ◽  
Audio Amplifier ◽  
Sound Levels

<p><span>When using audio-amplifiers in the open, uneven distribution of sound makes people unpleasant because it is loud or unheared. This unfortunate situation arises because audio-amplifiers volumes are set according to the guess of sound technicians. Mosques, as an example, are distributed inside wide areas and fire Azan five times a day. Due to the relatively long distances between them, speed and direction of the wind impose setting sound levels prior to each Azan such that all the area is covered and the overlap is minimized. In this paper, we propose a system based on internet of things (IoT) model to control the sound level of each mosque in the community. An IoT device (one in a mosque) sets the level of sound fired by the audio-amplifier. To do that, a synchronized series of tones is fired from each node. Once a node hears these tones, the process of sound level control starts to indicate the distances to heared nodes. As the approximate distances between nodes are known, each node can calculate its suitable sound level. Results showed that the proposed system is effective in setting sound levels for mosques audio amplifiers.</span></p>

scholarly journals Type-2 fuzzy controller’s performance index. Case study: tank level control

2020 ◽  
Vol 1704 ◽  
pp. 012016
Author(s):  
J E Araujo ◽  
J L Diaz Rodriguez ◽  
O M Duque ◽  
A Pardo García
Keyword(s):  
Performance Index ◽  
Index Case ◽  
Level Control ◽  
Tank Level

A case study in using two-level control stores

1988 ◽  
Vol 19 (3) ◽  
pp. 46-48 ◽  
Author(s):  
Onat Menzilcioglu
Keyword(s):  
Level Control

scholarly journals Improved day ahead heating demand forecasting by online correction methods

2019 ◽  
Author(s):  
Felix Bünning ◽  
Andrew Bollinger ◽  
Philipp Heer ◽  
Roy S. Smith ◽  
John Lygeros
Keyword(s):  
Machine Learning ◽  
Error Correction ◽  
Energy Demand ◽  
Demand Forecasting ◽  
Coefficient Of Determination ◽  
Average Error ◽  
Small Scale ◽  
Level Control ◽  
Box Models

To reduce the heating and cooling energy demand of buildings and districts novel control strategies are constantly being developed that require information on the future demand of the controlled entity. Demand forecasting is commonly done with deterministic white box models or fitted grey-box models, however, recently more and more data and machine learning based approaches are being developed. All approaches have weaknesses: white-box models require major modelling effort, grey-box approaches are limited by their model or parameter complexity and machine learning is dependent on hyperparameters, some of which are randomly chosen, and therefore considered unreliable. Here we develop a forecasting approach based on Artificial Neural Networks (ANN) and introduce error correction methods based on online learning and the learned autocorrelation of the forecasting error. We compare the approach to other regression based and grey-box methods in a real case study of a small-scale district energy system with mixed use and unknown lower-level control. We show that the proposed method outperforms the other forecasting methods in terms of average error and coefficient of determination. We further demonstrate that in our case study the error correction methods significantly reduce variance in ANN performance created by randomly initialized parameters in the networks.

Hydropower reservoir level control: A case study

Automatica ◽  
1993 ◽  
Vol 29 (5) ◽  
pp. 1203-1214 ◽  
Author(s):  
A.H. Glattfelder ◽  
L. Huser
Keyword(s):  
Level Control ◽  
Reservoir Level ◽  
Hydropower Reservoir
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