Application of artificial neural networks to the real-time operation of conducting polymer sensors: a pattern recognition approach

1996 ◽  
Vol 82 (1) ◽  
pp. 27-33 ◽  
Author(s):  
Afshad Talaie ◽  
Jose A. Romagnoli
2010 ◽  
Vol 143-144 ◽  
pp. 576-579 ◽  
Author(s):  
Shu Xian Zhu ◽  
Bang Fu Wang ◽  
Xue Li Zhu ◽  
Sheng Hui Guo

PLC dynamically adjusts power voltage by controlling switches of transformers with different combinations, which is based on the theory of 8421, and makes voltage changes in a very small range. By using this system, energy is saved, and the damage of illumination equipment from voltage instability is greatly reduced, and the life of lamps is effectively extended. Meanwhile, by using the touch screen, this system not only can realize the real time parameter display, but also achieve the real time operation on the panel.


Author(s):  
Eva Volna ◽  
Martin Kotyrba

The chapter is focused on an analysis and pattern recognition in time series, which are fractal in nature. Our goal is to find and recognize important Elliott wave patterns which repeatedly appear in the market history for the purpose of prediction of subsequent trader's action. The pattern recognition approach is based on neural networks. Artificial neural networks are suitable for pattern recognition in time series mainly because of learning only from examples. This chapter introduces a methodology that allows analysis of Elliot wave's patterns in time series for the purpose of a trend prediction. The functionality of the proposed methodology was validated in experimental simulations, for whose implementation was designed and created an application environment. In conclusion, all results were evaluated and compared with each other. This chapter is composed only from our published works that present our proposed methodology. We see the main contribution of this chapter in its range, which allows us to present all our published works concerning our proposed methodology together.


Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 655
Author(s):  
Jürgen Marchgraber ◽  
Wolfgang Gawlik

Battery Energy Storage Systems (BESS) based on Li-Ion technology are considered to be one of the providers of services in the future power system. Although prices for Li-Ion batteries are falling continuously, it is still difficult to achieve profitability from a single service today. Multi-use operation of BESS in order to reach a so-called “value-stacking” of services therefore is a hotly debated topic in literature, since such an operation holds the potential to increase profitability dramatically. The multi-use operation of a BESS can be divided into two parts: the operational planning phase and the real-time operation. While the operational planning phase has been examined in many studies, there seems to be a lack of discussion for the real-time operation. This paper therefore tries to address the topic of the real-time operation in more detail. For this reason, this paper discusses concepts for implementing a real-time multi-use operation and introduces the novel concept of dynamic prioritization, which allows resolving conflicts of services. Besides the ability to cope with abnormal grid conditions, this concept also holds potential for a better utilization of resources during normal grid conditions. A mathematical framework is used to describe several services and their interaction, taking into account the concept of dynamic prioritization. Several applications are presented in order to demonstrate the behavior of the concept during normal and abnormal grid conditions. These applications are simulated in Matlab/Simulink for specific events and in the form of long-time simulations.


2021 ◽  
Author(s):  
Yu Fan ◽  
Jianhua Guo ◽  
Quan Cao ◽  
JingLun Ma ◽  
Jun Zhu ◽  
...  

Abstract Nowadays oil & gas industry is receiving a bulk of data than ever before from its onsite wells where may hundred miles away from operator's headquarter, which benefits us monitoring and analyzing those digital fortune in a data hub, saving a lot of expenditure and improving the efficiency compared to old-fashioned approach which requires senior engineers with rich experience working on wellsite. In this way, the oil & gas operators save money tremendously on human cost under the booming of drilling operations. While, could we do more to dig out further values from those data? Make our operations less dependable on limited resources, the senior drilling engineers, especially when the oil and gas industry face the chasm of human resources sustainability after the hit of downturn, also make the plain real-time data more intuitive and self-explanatory to the operation decision makers in an unprecedented way. What's more, could we make our drilling activities more visible and interactive? This paper is going to introduce using augmented reality technology to create an intuitive platform to integrate and present real-time operation parameters and data. Like any revolutionary method or technology, it could improve the industry efficiency in a non-negligible way, help us manage massive real-time data more effectively and efficiently. The 3D holographic projection presents dynamic models or systems based on the data stream and graphic algorithm, which evolves our industry from 2D world to 3D world, combining the reality environment with the digital world, creating a digital reflection of the real wellsite, bottom hole assembly (BHA), well trajectories, lithological layers, etc. Thanks to the visualization technologies and augmented reality, we can create a digital twin of physic world for those engineers, technicians, managers using holographic method to interact with, scale up and down, analyzing in a better awareness. In this paper, we will describe a digital drilling wellsite which is established on operator's Real Time Operation Center (RTOC)office to monitor and analyze live field operations, the operator could have an overview of their on-site operations, tracking the equipment performance, engineering parameters and downhole status to enhance the understanding and interaction with the on-going field operations. The wellbore trajectory model gives the team a superior knowledge by combining the engineering data or geological data. Not only help well placement in desired reservoir but also improve the anti-collision concept in direction drilling. This model is extreme meaningful when engineers need a discussion to optimize or change their drilling plan as it is 3D visible and able to interact with. We will continues digging out further more value of the real-time data collected from wellsite to educate us find the cost-saving ways which improve our performance and eliminate the complicated conditions that normally resulted in Non production time (NPT) event. For our oil & gas industry, we are just start to have a more adventure and prosperous journey in digitalizing transforming.


Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1626 ◽  
Author(s):  
Aida Jabbari ◽  
Deg-Hyo Bae

Hydrometeorological forecasts provide future flooding estimates to reduce damages. Despite the advances and progresses in Numerical Weather Prediction (NWP) models, they are still subject to many uncertainties, which cause significant errors forecasting precipitation. Statistical postprocessing techniques can improve forecast skills by reducing the systematic biases in NWP models. Artificial Neural Networks (ANNs) can model complex relationships between input and output data. The application of ANN in water-related research is widely studied; however, there is a lack of studies quantifying the improvement of coupled hydrometeorological model accuracy that use ANN for bias correction of real-time rainfall forecasts. The aim of this study is to evaluate the real-time bias correction of precipitation data, and from a hydrometeorological point of view, an assessment of hydrological model improvements in real-time flood forecasting for the Imjin River (South and North Korea) is performed. The comparison of the forecasted rainfall before and after the bias correction indicated a significant improvement in the statistical error measurement and a decrease in the underestimation of WRF model. The error was reduced remarkably over the Imjin catchment for the accumulated Mean Areal Precipitation (MAP). The performance of the real-time flood forecast improved using the ANN bias correction method.


Sign in / Sign up

Export Citation Format

Share Document