scholarly journals High Three-Dimensional Detection Accuracy in Piezoelectric-Based Touch Panel in Interactive Displays by Optimized Artificial Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 753 ◽  
Author(s):  
Shuo Gao ◽  
Yanning Dai ◽  
Vasileios Kitsos ◽  
Bo Wan ◽  
Xiaolei Qu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Cost ◽  
Machine Learning Algorithms ◽  
Smart Device ◽  
Detection Accuracy ◽  
Force Level ◽  
High Detection ◽  
Artificial Neural ◽  
Interactive Displays

High detection accuracy in piezoelectric-based force sensing in interactive displays has gained global attention. To achieve this, artificial neural networks (ANN)—successful and widely used machine learning algorithms—have been demonstrated to be potentially powerful tools, providing acceptable location detection accuracy of 95.2% and force level recognition of 93.3% in a previous study. While these values might be acceptable for conventional operations, e.g., opening a folder, they must be boosted for applications where intensive operations are performed. Furthermore, the relatively high computational cost reported prevents the popularity of ANN-based techniques in conventional artificial intelligence (AI) chip-free end-terminals. In this article, an ANN is designed and optimized for piezoelectric-based touch panels in interactive displays for the first time. The presented technique experimentally allows a conventional smart device to work smoothly with a high detection accuracy of above 97% for both location and force level detection with a low computational cost, thereby advancing the user experience, and serviced by piezoelectric-based touch interfaces in displays.

MODELING OF NEURAL PREDICTIVE CONTROL OF IRRIGATION MACHINES

2021 ◽  
pp. 14-22
Author(s):  
G. N. KAMYSHOVA ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Predictive Control ◽  
Intelligent Control ◽  
Machine Learning Algorithms ◽  
Intelligent Control Systems ◽  
Artificial Neural ◽  
The One ◽  
Neural Predictive Control

The purpose of the study is to develop new scientific approaches to improve the efficiency of irrigation machines. Modern digital technologies allow the collection of data, their analysis and operational management of equipment and technological processes, often in real time. All this allows, on the one hand, applying new approaches to modeling technical systems and processes (the so-called “data-driven models”), on the other hand, it requires the development of fundamentally new models, which will be based on the methods of artificial intelligence (artificial neural networks, fuzzy logic, machine learning algorithms and etc.).The analysis of the tracks and the actual speeds of the irrigation machines in real time showed their significant deviations in the range from the specified speed, which leads to a deterioration in the irrigation parameters. We have developed an irrigation machine’s control model based on predictive control approaches and the theory of artificial neural networks. Application of the model makes it possible to implement control algorithms with predicting the response of the irrigation machine to the control signal. A diagram of an algorithm for constructing predictive control, a structure of a neuroregulator and tools for its synthesis using modern software are proposed. The versatility of the model makes it possible to use it both to improve the efficiency of management of existing irrigation machines and to develop new ones with integrated intelligent control systems.

Convolutional Neural Network

2022 ◽  
pp. 1559-1575
Author(s):  
Mário Pereira Véstias
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Model ◽  
Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

scholarly journals Artificial Neural Networks for Automated Cell Quantification in Lensless LED Imaging Systems

Proceedings ◽  
2018 ◽  
Vol 2 (13) ◽  
pp. 989 ◽  
Author(s):  
Agus Budi Dharmawan ◽  
Gregor Scholz ◽  
Shinta Mariana ◽  
Philipp Hörmann ◽  
Igi Ardiyanto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Artificial Neural Networks ◽  
Light Emitting Diode ◽  
Principal Component ◽  
Component Analysis ◽  
Detection Accuracy ◽  
Cell Detection ◽  
Artificial Neural ◽  
Feature Values

Cell registration by artificial neural networks (ANNs) in combination with principal component analysis (PCA) has been demonstrated for cell images acquired by light emitting diode (LED)-based compact holographic microscopy. In this approach, principal component analysis was used to find the feature values from cells and background, which would be subsequently employed as neural inputs into the artificial neural networks. Image datasets were acquired from multiple cell cultures using a lensless microscope, where the reference data was generated by a manually analyzed recording. To evaluate the developed automatic cell counter, the trained system was assessed on different data sets to detect immortalized mouse astrocytes, exhibiting a detection accuracy of ~81% compared with manual analysis. The results show that the feature values from principal component analysis and feature learning by artificial neural networks are able to provide an automatic approach on the cell detection and registration in lensless holographic imaging.

scholarly journals Modeling Semiarid River–Aquifer Systems with Bayesian Networks and Artificial Neural Networks

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 107
Author(s):  
Ana D. Maldonado ◽  
María Morales ◽  
Francisco Navarro ◽  
Francisco Sánchez-Martos ◽  
Pedro A. Aguilera
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Bayesian Networks ◽  
Structure Learning ◽  
Computational Cost ◽  
Predictive Performance ◽  
Water Infiltration ◽  
Groundwater Temperature ◽  
Aquifer Systems ◽  
Artificial Neural

In semiarid areas, precipitations usually appear in the form of big and brief floods, which affect the aquifer through water infiltration, causing groundwater temperature changes. These changes may have an impact on the physical, chemical and biological processes of the aquifer and, thus, modeling the groundwater temperature variations associated with stormy precipitation episodes is essential, especially since this kind of precipitation is becoming increasingly frequent in semiarid regions. In this paper, we compare the predictive performance of two popular tools in statistics and machine learning, namely Bayesian networks (BNs) and artificial neural networks (ANNs), in modeling groundwater temperature variation associated with precipitation events. More specifically, we trained a total of 2145 ANNs with different node configurations, from one to five layers. On the other hand, we trained three different BNs using different structure learning algorithms. We conclude that, while both tools are equivalent in terms of accuracy for predicting groundwater temperature drops, the computational cost associated with the estimation of Bayesian networks is significantly lower, and the resulting BN models are more versatile and allow a more detailed analysis.

scholarly journals Solar Photovoltaic Power Forecasting in Jordan using Artificial Neural Networks

2018 ◽  
Vol 8 (1) ◽  
pp. 497 ◽  
Author(s):  
Mohammad H. Alomari ◽  
Jehad Adeeb ◽  
Ola Younis
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power Systems ◽  
Output Power ◽  
Solar Irradiance ◽  
Machine Learning Algorithms ◽  
Solar Photovoltaic ◽  
Solar Pv ◽  
Intensive Training ◽  
Artificial Neural

In this paper, Artificial Neural Networks (ANNs) are used to study the correlations between solar irradiance and solar photovoltaic (PV) output power which can be used for the development of a real-time prediction model to predict the next day produced power. Solar irradiance records were measured by ASU weather station located on the campus of Applied Science Private University (ASU), Amman, Jordan and the solar PV power outputs were extracted from the installed 264KWp power plant at the university. Intensive training experiments were carried out on 19249 records of data to find the optimum NN configurations and the testing results show excellent overall performance in the prediction of next 24 hours output power in KW reaching a Root Mean Square Error (RMSE) value of 0.0721. This research shows that machine learning algorithms hold some promise for the prediction of power production based on various weather conditions and measures which help in the management of energy flows and the optimisation of integrating PV plants into power systems.

scholarly journals Provision of Data to Use in Artificial Intelligence Algorithms for Single Room Heating

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 523
Author(s):  
Kristin Majetta ◽  
Christoph Clauß ◽  
Christoph Nytsch-Geusen
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Learning Algorithms ◽  
Parameter Variation ◽  
Machine Learning Algorithms ◽  
Data Basis ◽  
Single Room ◽  
Artificial Neural

This paper describes a way to generate a great amount of data and to use it to find a relation between a room controller and a certain room. Therefore, simulation scenarios are defined and developed that contain different room, location, usage and controller models. With parameter variation and optimization of the corresponding controller parameters a data basis is created with about 5300 entries. On the basis of this data, machine learning algorithms like artificial neural networks can be used to investigate the relation between rooms and their best suited controllers.

Artificial neural networks applied for solidified soils data prediction: a bibliometric and systematic review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vinicius Luiz Pacheco ◽  
Lucimara Bragagnolo ◽  
Antonio Thomé
Keyword(s):  
Systematic Review ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
State Of The Art ◽  
Computational Cost ◽  
Soil Improvement ◽  
The State ◽  
Data Set ◽  
Content Type ◽  
Artificial Neural

Purpose The purpose of this article is to analyze the state-of-the art in a systematic way, identifying the main research groups and their related topics. The types of studies found are fundamental for understanding the application of artificial neural networks (ANNs) in cemented soils and the potential for using the technique, as well as the feasibility of extrapolation to new geotechnical or civil and environmental engineering segments. Design/methodology/approach This work is characterized as being bibliometric and systematic research of an exploratory perspective of state-of-the-art. It also persuades the qualitative and quantitative data analysis of cemented soil improvement, biocemented or microbially induced calcite precipitation (MICP) soil improvement by prediction/modeling by ANN. This study sought to compile and study the state of the art of the topic which possibilities to have a critical view about the theme. To do so, two main databases were analyzed: Scopus and Web of Science. Systematic review techniques, as well as bibliometric indicators, were implemented. Findings This paper connected the network between the achievements of the researches and illustrated the main application of ANNs in soil improvement prediction, specifically on cemented-based soils and biocemented soils (e.g. MICP technique). Also, as a bibliometric and systematic review, this work could achieve the key points in the absence of researches involving soil-ANN, and it provided the understanding of the lack of exploratory studies to be approached in the near future. Research limitations/implications Because of the research topic the article suggested other applications of ANNs in geotechnical engineering, such as other tests not related to geomechanical resistance such as unconfined compression test test and triaxial test. Practical implications This article systematically and critically presents some interesting points in the direction of future research, such as the non-approach to the use of ANNs in biocementation processes, such as MICP. Social implications Regarding the social environment, the paper brings approaches on methods that somehow mitigate the computational use, or elements necessary for geotechnical improvement of the soil, thereby optimizing the same consequently. Originality/value Neural networks have been studied for a long time in engineering, but the current computational power has increased the implementation for several engineering applications. Besides that, soil cementation is a widespread technique and its prediction modes often require high computational strength, such parameters can be mitigated with the use of ANNs, because artificial intelligence seeks learning from the implementation of the data set, reducing computational cost and increasing accuracy.

Predicting Ship Maintenance and Repair Labor with Artificial Neural Networks

2021 ◽  
pp. 1-10
Author(s):  
Pierre-Andre M. Fruytier ◽  
Arun Kr Arun Kr Dev
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Analytics ◽  
Machine Learning Algorithms ◽  
Data Set ◽  
Repair Work ◽  
Maintenance And Repair ◽  
Independent Variables ◽  
Ship Maintenance ◽  
Artificial Neural

Ship maintenance and repair work cost estimation is often regarded as an “Art,” which may contribute to the financial success or distress of a shipyard. Regarded as experts by senior management, estimators are among the most valued resources, and nonetheless, human. Over time, estimators learn from mistakes, and get better with tenure at sharpening assessments. When estimators retire without having groomed an apprentice, shipyards may be at risk of losing a lot of know-how, all at once. These shipyards may well find very costly to experience, for a while, estimating skills stepping back on the learning curve. Yet, even shipyards relying on less advanced information technology may have unwittingly accumulated a lot of valuable data relevant to ship maintenance and repair works. These shipyards may overlook how easily accessible knowledge can be turned into a competitive advantage through predictive analytics. Not only can this data be literally mined, but machine learning algorithms, such as Artificial Neural Networks (ANN), can now process it for a speedy and preliminary estimate through faster and cheaper computing power. To be clear, the purpose is not to replace the human estimator but to help the expert quickly assess, when times are busy, whether to bid or not on a specific project opportunity. In the absence of The Master Estimator, an Apprentice may also look for a quick and cheap sanity check of the prepared estimate before submitting a bid. The study carried out in this article is based on all ship maintenance and repair data recorded at a single North American shipyard over the last 19 years since the current information systems were implemented. This raw data extract with all directly paid hours logged daily by workers on 1277 ship maintenance and repair projects was screened through advanced data cleansing. To enrich the cleansed data tables, additional independent variables were subsequently collected internally and externally to develop a training–testing data set. The final 657 projects represent 136 vessels regrouped in eight types, for which 28 other independent variables were all made available for training up to testing simple ANN models. The scope of this article is limited to the estimation of the direct labor required to complete ship maintenance and repair projects on a specific type of vessels for which workforce planning and tactical pricing was deemed the most relevant to keep the business afloat.

Multi-Objective Optimization of Parameters for Milling Using Evolutionary Algorithms and Artificial Neural Networks

2019 ◽  
Author(s):  
Amit Banerjee ◽  
Issam Abu-Mahfouz ◽  
AHM Esfakur Rahman
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Evolutionary Algorithms ◽  
Cost Function ◽  
Machine Learning Algorithms ◽  
Machining Parameters ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Artificial Neural

Abstract Model-based design of manufacturing processes have been gaining popularity since the advent of machine learning algorithms such as evolutionary algorithms and artificial neural networks (ANN). The problem of selecting the best machining parameters can be cast an optimization problem given a cost function and by utilizing an input-output connectionist framework using as ANNs. In this paper, we present a comparison of various evolutionary algorithms for parameter optimization of an end-milling operation based on a well-known cost function from literature. We propose a modification to the cost function for milling and include an additional objective of minimizing surface roughness and by using NSGA-II, a multi-objective optimization algorithm. We also present comparison of several population-based evolutionary search algorithms such as variants of particle swarm optimization, differential evolution and NSGA-II.

scholarly journals Technical note: Application of artificial neural networks in groundwater table forecasting – a case study in a Singapore swamp forest

2016 ◽  
Vol 20 (4) ◽  
pp. 1405-1412 ◽  
Author(s):  
Yabin Sun ◽  
Dadiyorto Wendi ◽  
Dong Eon Kim ◽  
Shie-Yui Liong
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Numerical Models ◽  
Groundwater Resources ◽  
Computational Cost ◽  
Groundwater Table ◽  
Physical Parameters ◽  
Swamp Forest ◽  
Freshwater Swamp ◽  
Artificial Neural

Abstract. Accurate prediction of groundwater table is important for the efficient management of groundwater resources. Despite being the most widely used tools for depicting the hydrological regime, numerical models suffer from formidable constraints, such as extensive data demanding, high computational cost, and inevitable parameter uncertainty. Artificial neural networks (ANNs), in contrast, can make predictions on the basis of more easily accessible variables, rather than requiring explicit characterization of the physical systems and prior knowledge of the physical parameters. This study applies ANN to predict the groundwater table in a freshwater swamp forest of Singapore. The inputs to the network are solely the surrounding reservoir levels and rainfall. The results reveal that ANN is able to produce an accurate forecast with a leading time of 1 day, whereas the performance decreases when leading time increases to 3 and 7 days.

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