Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

System Identification I

Wireless Sensor Networks in oil and gas industry: Recent advances, taxonomy, requirements, and open challenges

Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same MS, we propose a recursive linear model to detect faults in the system, while using irradiance and temperature on the PV panel as input signals and power as output. The accuracy of the fault detection for a 5 kW power plant used in the test is 93.09%, considering 16 days and around 143 hours of faults in different conditions. Once a fault is detected by this model, a machine-learning-based method classifies each fault in the following cases: short-circuit, open-circuit, partial shadowing, and degradation. Using the same days and faults applied in the detection module, the accuracy of the classification stage is 95.44% for an Artificial Neural Network (ANN) model. By combining detection and classification, the overall accuracy is 92.64%. Such a result represents an original contribution of this work, since other related works do not present the integration of a fault detection and classification approach with an embedded PV plant monitoring system, allowing for the online identification and classification of different PV faults, besides real-time and historical monitoring of electrical and environmental parameters of the plant.

A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants.

The unstructured data impacts 95% of the organizations and costs them millions of dollars annually. If managed well, it can significantly improve business productivity. The traditional information extraction techniques are limited in their functionality, but AI-based techniques can provide a better solution. A thorough investigation of AI-based techniques for automatic information extraction from unstructured documents is missing in the literature. The purpose of this Systematic Literature Review (SLR) is to recognize, and analyze research on the techniques used for automatic information extraction from unstructured documents and to provide directions for future research. The SLR guidelines proposed by Kitchenham and Charters were adhered to conduct a literature search on various databases between 2010 and 2020. We found that: 1. The existing information extraction techniques are template-based or rule-based, 2. The existing methods lack the capability to tackle complex document layouts in real-time situations such as invoices and purchase orders, 3. The datasets available publicly are task-specific and of low quality. Hence, there is a need to develop a new dataset that reflects real-world problems. Our SLR discovered that AI-based approaches have a strong potential to extract useful information from unstructured documents automatically. However, they face certain challenges in processing multiple layouts of the unstructured documents. Our SLR brings out conceptualization of a framework for construction of high-quality unstructured documents dataset with strong data validation techniques for automated information extraction. Our SLR also reveals a need for a close association between the businesses and researchers to handle various challenges of the unstructured data analysis.

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Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

This paper proposes a protocol for the acquisition and processing of biophysical signals in virtual reality applications, particularly in phobia therapy experiments. This protocol aims to ensure that the measurement and processing phases are performed effectively, to obtain clean data that can be used to estimate the users’ anxiety levels. The protocol has been designed after analyzing the experimental data of seven subjects who have been exposed to heights in a virtual reality environment. The subjects’ level of anxiety has been estimated based on the real-time evaluation of a nonlinear function that has as parameters various features extracted from the biophysical signals. The highest classification accuracy was obtained using a combination of seven heart rate and electrodermal activity features in the time domain and frequency domain.

https://www.mdpi.com/1424-8220/20/24/7088/pdf

Integrating Biosignals Measurement in Virtual Reality Environments for Anxiety Detection

Systems with multiple models or multi-controller structure represent one of the successful solutions for the real-time control of the nonlinear or multi-regime processes. The use of these structures imposes solving some specific problems, like best algorithm selection or control algorithm switching. The paper proposes a method for switching the algorithms of the multiple-models structure, based on the principles of manual to automatic bumpless transfer. The applicability of the method is proved using a real-time structure with an RST control algorithm. In the end, its software implementation is also shown.

Switching Solution for Multiple-Models Control Systems

A WSN Based Monitoring System for Oil and Gas Transportation through Pipelines

The restrictions imposed on real-time functioning are sometimes particularly severe and require firm action. At the same time, the design of the control algorithms for perturbations rejection includes a multitude of theoretical and practical solutions that exist in the literature. Here, we can note the methods based on robustification, on algorithms with specific degrees of freedom, etc. The paper presents a method based on the very rapid switching of the control algorithms, proposing at the same time a new switching method. The solutions proposed are tested on an MIMO process in a real time application in the vehicle control area.

Fast Disturbance Rejection in MIMO Process Based on Algorithms Switching

This paper presents the design and the realization of data acquisition system measure, to record and to maximize solar panel power. The special feature of this system is the short sampling time that allows tracking of fast variations in solar irradiance. It was designed to operate autonomously hence; low power consumption and local data storage capabilities were the main requirements that were taken into account during the design stage.

Catalin Petrescu

Papers

Integrating Biosignals Measurement in Virtual Reality Environments for Anxiety Detection

Switching Solution for Multiple-Models Control Systems

A WSN Based Monitoring System for Oil and Gas Transportation through Pipelines

Fast Disturbance Rejection in MIMO Process Based on Algorithms Switching

Data acquisition system for recording of photovoltaic panel power