Artificial Neural Network Based Prediction of Control Strategies for Multiple Air-Cooling Units in a Raised-floor Data Center

2020 ◽  
Author(s):  
Vibin Shalom Simon ◽  
Ashwin Siddarth ◽  
Dereje Agonafer
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Center ◽  
Control Strategies ◽  
Air Cooling ◽  
Artificial Neural

scholarly journals Experimental implementation of artificial neural network-based active vibration control & chatter suppression

2021 ◽  
Author(s):  
Yong Xia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Control System ◽  
Vibration Control ◽  
Control Strategies ◽  
Active Vibration Control ◽  
Chatter Suppression ◽  
Active Vibration ◽  
Artificial Neural

Vibration control strategies strive to reduce the effect of harmful vibrations such as machining chatter. In general, these strategies are classified as passive or active. While passive vibration control techniques are generally less complex, there is a limit to their effectiveness. Active vibration control strategies, which work by providing an additional energy supply to vibration systems, on the other hand, require more complex algorithms but can be very effective. In this work, a novel artificial neural network-based active vibration control system has been developed. The developed system can detect the sinusoidal vibration component with the highest power and suppress it in one control cycle, and in subsequent cycles, sinusoidal signals with the next highest power will be suppressed. With artificial neural networks trained to cover enough frequency and amplitude ranges, most of the original vibration can be suppressed. The efficiency of the proposed methodology has been verified experimentally in the vibration control of a cantilever beam. Artificial neural networks can be trained automatically for updated time delays in the system when necessary. Experimental results show that the developed active vibration control system is real time, adaptable, robust, effective and easy to be implemented. Finally, an experimental setup of chatter suppression for a lathe has been successfully implemented, and the successful techniques used in the previous artificial neural network-based active vibration control system have been utilized for active chatter suppression in turning.

scholarly journals An Artificial Neural Network Approach to Forecast the Environmental Impact of Data Centers

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 113 ◽  
Author(s):  
Joao Ferreira ◽  
Gustavo Callou ◽  
Albert Josua ◽  
Dietmar Tutsch ◽  
Paulo Maciel
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Co2 Emissions ◽  
Data Center ◽  
Data Centers ◽  
New Technologies ◽  
Operational Costs ◽  
The Us ◽  
Artificial Neural

Due to the high demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the data produced and provide the high availability required. Over the years, this increase in energy consumption has brought about a rise in both the environmental impacts and operational costs. Some companies have adopted the concept of a green data center, which is related to electricity consumption and CO2 emissions, according to the utility power source adopted. In Brazil, almost 70% of electrical power is derived from clean electricity generation, whereas in China 65% of generated electricity comes from coal. In addition, the value per kWh in the US is much lower than in other countries surveyed. In the present work, we conducted an integrated evaluation of costs and CO2 emissions of the electrical infrastructure in data centers, considering the different energy sources adopted by each country. We used a multi-layered artificial neural network, which could forecast consumption over the following months, based on the energy consumption history of the data center. All these features were supported by a tool, the applicability of which was demonstrated through a case study that computed the CO2 emissions and operational costs of a data center using the energy mix adopted in Brazil, China, Germany and the US. China presented the highest CO2 emissions, with 41,445 tons per year in 2014, followed by the US and Germany, with 37,177 and 35,883, respectively. Brazil, with 8459 tons, proved to be the cleanest. Additionally, this study also estimated the operational costs assuming that the same data center consumes energy as if it were in China, Germany and Brazil. China presented the highest kWh/year. Therefore, the best choice according to operational costs, considering the price of energy per kWh, is the US and the worst is China. Considering both operational costs and CO2 emissions, Brazil would be the best option.

scholarly journals Experimental implementation of an artificial neural network-based active vibration control

2021 ◽  
Author(s):  
Yong Xia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vibration Control ◽  
Control Strategies ◽  
Active Vibration Control ◽  
Experimental System ◽  
Experimental Tests ◽  
Development Environment ◽  
Active Vibration ◽  
Artificial Neural

Vibration control strategies strive to reduce the effect of harmful vibrations on machinery and people. In general, these strategies are classified as passive or active. While passive vibration control techniques are generally less complex, there is a limit to their effectiveness. Active vibration control strategies, on the other hand, require more complex algorithms but can be very effective. In this current work, a novel active vibration control experimental system, including the hardware setup and software development environment, has been successfully implemented. A static artificial neural network-based active vibration control system has been designed and tested based on the experimental system. The artificial neural network is trained to model the plant using a backpropagation algorithm. After training, the network model is used as part of a feedforward controller. the efficiency of this controller is shown through experimental tests.

scholarly journals Application of Artificial Neural Network for the Optimum Control of HVAC Systems in Double-Skinned Office Buildings

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4754 ◽  
Author(s):  
Byeongmo Seo ◽  
Yeo Beom Yoon ◽  
Jung Hyun Mun ◽  
Soolyeon Cho
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Savings ◽  
Control Strategies ◽  
Time Lag ◽  
Hvac System ◽  
Humid Climate ◽  
Air Supply ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Double Skin Façade (DSF) systems have become an alternative to the environmental and energy savings issues. DSF offers thermal buffer areas that can provide benefits to the conditioned spaces in the form of improved comforts and energy savings. There are many studies conducted to resolve issues about the heat captured inside DSF. Various window control strategies and algorithms were introduced to minimize the heat gain of DSF in summer. However, the thermal condition of the DSF causes a time lag between the response time of the Heating, Ventilation, and Air-Conditioning (HVAC) system and cooling loads of zones. This results in more cooling energy supply or sometimes less than required, making the conditioned zones either too cold or warm. It is necessary to operate the HVAC system in consideration of all conditions, i.e., DSF internal conditions and indoor environment, as well as proper DSF window controls. This paper proposes an optimal air supply control for a DSF office building located in a hot and humid climate. An Artificial Neural Network (ANN)-based control was developed and tested for its effectiveness. Results show a 10.5% cooling energy reduction from the DSF building compared to the non-DSF building with the same HVAC control. Additionally, 4.5% more savings were observed when using the ANN-based control.

Artificial Neural Network Model Analysis Based on the Rules that unless Overtaking otherwise Driving on the Right

2014 ◽  
Vol 926-930 ◽  
pp. 1696-1699
Author(s):  
Jia Huan Lu
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Artificial Neural Network ◽  
Control Strategies ◽  
Short Circuit ◽  
Driving Behavior ◽  
Real Problem ◽  
Bp Artificial Neural Network ◽  
Artificial Neural ◽  
The Mathematical Model

In this article, I analyzing the real problem and abstract the mathematical model to save it step by step. First I consider the mathematical model underlying one lane, in the hypothesis of even flow and single kind, I analyze the relationships between flow, velocity and density; I figure out the linear relationship of velocity and density, including both high-density and low-density conditions; the parabolic relationship flow and density follow so that I gain the best density when flow gets its maximum. Then I extend it to two-lane ( slow lane and fast lane that is equal to the overtaking lane and normal lane ) model: I expand and modify the above relationship in changing system to include the problem of road congestion, as well as the changing lane condition and the density affection from its downstream neighbor . Besides, three scenarios were discussed to control the situation of changing lanes. After this, I use a safety multi-lane highway overtaking control model based on BP artificial neural network to reduce the complexity of problem solving, and discuss the algorithm of the BP artificial neural network, and give examples and preliminary results. Short circuit in a multi-vehicle lane driving, driving behavior mainly consists of acceleration, deceleration and lane changing , the reality of traffic environment, the driver's decision-making are affected by many factors, such as driving rules , vehicle types and other basic physical attributes , characters and vehicle drivers’ plans , driving behavior control strategies and so on. I focus on the main factors to form of some the rules of changing lanes.

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