Artificial Intelligence Learning Methods Research Articles

Model Predictive Control Coupled with Artificial Intelligence for Eddy Current Dynamometers

The recent studies on Artificial Intelligence (AI) accompanied by enhanced computing capabilities supports increasing attention into traditional control methods coupled with AI learning methods in an attempt to bringing adaptiveness and fast responding features. The Model Predictive Control (MPC) technique is a widely used, safe and reliable control method based on constraints. On the other hand, the Eddy Current dynamometers are highly nonlinear braking systems whose performance parameters are related to many processes related variables. This study is based on an adaptive model predictive control that utilizes selected AI methods. The presented approach presents an updated the mathematical model of an Eddy Current Dynamometer based on experimentally obtained system operational data. Finally, the comparison of AI methods and related learning performances based on the assessment technique of mean absolute percentage error (MAPE) issues are discussed. The results indicate that Single Hidden Layer Neural Network (SHLNN), General Regression Neural Network (GRNN), Radial Basis Network (RBNN), Neuro Fuzzy Network (ANFIS) coupled MPC have quite satisfying performances. The presented results indicate that, amongst them, GRNN appears to provide the best performance.

IMPROVING HUMAN-ROBOT PHYSICAL INTERACTION COMFORT IN MATERIAL HANDLING TASKS USING A SMART PLATFORM

The use of mobile platforms can help employees automate manual processes and streamline operations to save time and perform their tasks safely and accurately. A power-assisted vehicle to move weight around the place – solution: inexpensive, easy to apply, reliable, safe. It can adjust to various tasks, operators’ gait, loads up to 500 kg. It is a relatively inexpensive, easy-to-apply, reliable, and safe solution for moving weight. The motivation of the study is to increase efficiency and reduce physical strain on the operator in material handling tasks and to promote the implementation of this smart platform. Artificial intelligence learning methods are applied to adapt to individual operator’s experience, resulting in a personalized and more comfortable interaction with the help of Q-learning algorithm with 256 learning outcomes in adjusting controller settings: damping, mass, stiffness.

DGA Method Implementation for Incipient Fault Analysis using Gas Concentrations

Power transformers are essential devices for the durable and reliable performance of an electrical system. the main objective of this study is to analyze three classical diagnosis techniques to identify incipient faults in Transformer oil using Rogers’s Ratio Method, Doernenburg Ratio Method, and ANN which is a type of artificial intelligence learning method. Implementation of the system in MATLAB software for each diagnosis method and compare their accuracy and efficiency and hence design three diagnosis methods of DGA for condition assessment of Power Transformer. And the analysis on the MATLAB software shall be carried so as to detect the best method for detection of a certain type of fault and the best suited method for overall fault analysis for a certain data sets out of the three methods. This technique utilizes the learning capacity of that artificial neural network has been shown to be more efficient in detecting different mistakes. The overall error detection accuracy of such gas neural network study was found to be 73.8 percent.

Bone Age Estimation with X-ray Images Based on EfficientNet Pre-training Model

Human bones have different characteristics in different development stages, so the estimation of bone age can reflect the growth and development level of individuals relatively accurately. Bone age estimation aims to predict the biological age of children, which plays an important role on the diagnosis of some pediatric endocrine diseases. Tradition methods are carried out by doctors, and it is not effective in accuracy and speed. To this end, we proposed a deep-learning based method for bone age estimation. Based on the training set of more than 10000 X-ray images of hand bones from Radiological Society of North America (RSNA), this paper studies the processing, segmentation, feature extraction of X-ray hand bone images by using computer image processing and artificial intelligence learning methods, and uses convolution neural network to process samples and analyze them automatically. The main research work and achievements are as follows: (1) Pre-processing of X-ray hand bone image, unifying the size and cutting, reducing the image area without hand bone; (2) The gray-scale image is transformed into a three-channel image, and pre-processing by EfficientNet of ImageNet. Then convolution neural network is used to learn the features of X-ray hand bone image and evaluate it automatically. Finally, the network is evaluated by the minimum mean square error, so that the minimum mean square error is as close as possible to the minimum value. Through the neural network, the bone age from X-ray hand bone image can be quickly judged, and then it can be applied to clinical research.

Application of BP Dual Network Model Considering Internal and External Factors in Short-Term Load Forecasting

In the reform of electricity sales, the accuracy of short-term load forecasting is very important for power grid operation scheduling, user demand response and pricing mechanism. Based on the theory of internal and external factors, the paper accurately extracted temperature, humidity, entropy of peak load and load volatility as the main influencing factors. Based on the artificial intelligence learning method, it introduced experts’ experience to build a dual neural network short-term load prediction model, simulated load characteristics from multiple angles, and accurately predicted the trend. The simulation results show that the improved short-term load prediction model is more suitable to deal with dynamic problems due to its fast convergence speed and high prediction accuracy.

Forecasting of traffic origin NO and NO 2 concentrations by Support Vector Machines and neural networks using Principal Component Analysis

The main aim of this paper is to predict NO and NO 2 concentrations four days in advance comparing two artificial intelligence learning methods, namely, Multi-Layer Perceptron and Support Vector Machines on two kinds of spatial embedding of the temporal time series. Hourly values of NO and NO 2 concentrations, as well as meteorological variables were recorded in a cross-road monitoring station with heavy traffic in Szeged in order to build a model for predicting NO and NO 2 concentrations several hours in advance. The prediction of NO and NO 2 concentrations was performed partly on the basis of their past values, and partly on the basis of temperature, humidity and wind speed data. Since NO can be predicted more accurately, its values were considered primarily when forecasting NO 2. Time series prediction can be interpreted in a way that is suitable for artificial intelligence learning. Two effective learning methods, namely, Multi-Layer Perceptron and Support Vector Regression are used to provide efficient non-linear models for NO and NO 2 times series predictions. Multi-Layer Perceptron is widely used to predict these time series, but Support Vector Regression has not yet been applied for predicting NO and NO 2 concentrations. Grid search is applied to select the best parameters for the learners. To get rid of the curse of dimensionality of the spatial embedding of the time series Principal Component Analysis is taken to reduce the dimension of the embedded data. Three commonly used linear algorithms were considered as references: one-day persistence, average of several-day persistence and linear regression. Based on the good results of the average of several-day persistence, a prediction scheme was introduced, which forms weighted averages instead of simple ones. The optimization of these weights was performed with linear regression in linear case and with the learning methods mentioned in non-linear case. Concerning the NO predictions, the non-linear learning methods give significantly better predictions than the reference linear methods. In the case of NO 2 the improvement of the prediction is considerable; however, it is less notable than for NO.

The behaviour of the multi-layer perceptron and the support vector regression learning methods in the prediction of NO and NO2 concentrations in Szeged, Hungary

The main aim of this paper is to predict NO and NO2 concentrations 4 days in advance by comparing two artificial intelligence learning methods, namely, multi-layer perceptron and support vector machines, on two kinds of spatial embedding of the temporal time series. Hourly values of NO and NO2 concentrations, as well as meteorological variables were recorded in a cross-road monitoring station with heavy traffic in Szeged, in order to build a model for predicting NO and NO2 concentrations several hours in advance. The prediction of NO and NO2 concentrations was performed partly on the basis of their past values, and partly on the basis of temperature, humidity and wind speed data. Since NO can be predicted more accurately, its values were considered primarily when forecasting NO2. Time series prediction can be interpreted in a way that is suitable for artificial intelligence learning. Two effective learning methods, namely, multi-layer perceptron and support vector regression are used to provide efficient non-linear models for NO and NO2 time series predictions. Multi-layer perceptron is widely used to predict these time series, but support vector regression has not yet been applied for predicting NO and NO2 concentrations. Three commonly used linear algorithms were considered as references: 1-day persistence, average of several day persistence and linear regression. Based on the good results of the average of several day persistence, a prediction scheme was introduced, which forms weighted averages instead of simple ones. The optimization of these weights was performed with linear regression in linear case and with the learning methods mentioned in non-linear case. Concerning the NO predictions, the non-linear learning methods give significantly better predictions than the reference linear methods. In the case of NO2, the improvement of the prediction is considerable, however, it is less notable than for NO.

Linear and Nonlinear Schemes in the Prediction of Urban NO and NO2 Concentrations

ISEE-105 Objective: Because of the harmful effects of the traffic-induced nitric oxide on human health, it is important to have reliable methods enabling the prediction of its concentrations several hours in advance, so that the public authorities could avoid the harmful consequences of severe air pollution episodes. The aim of the paper was to predict NO and NO2 concentrations 4 days in advance comparing 2 artificial intelligence learning methods, namely, multilayer perceptron (MLP) and support vector machines (SVM) on 2 kinds of spatial embedding of the temporal time series. Material and Methods: Hourly values of NO and NO2 concentrations, as well as meteorological variables were recorded in a cross-road monitoring station with heavy traffic in Szeged to build a model for predicting NO and NO2 concentrations several hours in advance. Two effective learning methods, namely, MLP and Support Vector Regression (SVR), were used to provide efficient nonlinear models for NO and NO2 time-series predictions. MLP is widely used to predict these time series, but SVR has not yet been applied for predicting NO and NO2 concentrations. Results: According to the experiments, the applied forecasting techniques can perform well for the prediction of NO and NO2 concentrations. Although MLP improved the results of the best reference algorithms by 5% to 11% for NO prediction, it could not improve those for NO2 prediction. SVR showed 20% to 30% improvement for NO prediction and 2% to 14% for NO2 prediction. Conclusions: Undoubtedly, the application of machine-learning techniques mentioned above can be relatively simple and is worth using. Concerning the NO predictions, the nonlinear learning methods give significantly better predictions than the reference linear methods. In the case of NO2 the improvement of the prediction is considerable; however, it is less notable than for NO.

Forecasting: Adopting the Methodology of Support Vector Machines to Nursing Research

In nursing studies, linear statistical methods are commonly used to analyze data on subjective perceptions. Most of the collected data include participants’ subjective perceptions and are usually not collected in a controlled environment. For linear regression analyses, there is a common understanding that the distributions of some studied variables might not be close to linear in nature. However, when using the support vector machine (SVM), there is no violation concern for the regression assumption of linearity. SVM, as an artificial intelligence learning method, is based on the structural risk minimization principle that relies on the computational learning theory. It is a powerful technique for nonlinear regression or pattern recognition, which can learn complex patterns or trends from input data (independent variables) and create outputs (dependent variables). A trained learning machine can then generate output for unseen data. The quality of SVM was determined by the machine’s training error rate and generation capability. Choosing the approaches of nonlinear regression or pattern recognition is based on the characteristics of the dependent variable (Cristianini & ShaweTaylor 2000; Figure 1). How does SVM differ from the statistical methods? How could nursing researchers adopt SVM to address nursing issues? In this commentary, these two questions are answered. The process of developing SVM and three example studies using SVM are introduced in the following.