Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The organization of behavior: A neuropsychological theory

http://ieeexplore.ieee.org/iel5/5610941/5624686/05624745.pdf

Power electronics

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

Linear system identification using the TLS EXIN neuron

The GH-EXIN neural network for hierarchical clustering

Mostly the faults in electrical machines are related with the bearings. Thus, a reliable bearing condition monitoring scheme able to detect either local or distributed defects are mandatory to avoid a breakdown in the machine. So far, the research has been carried out mainly in the detection of local faults, such as balls and raceways faults, but surface roughness is not so reported. This paper deals with a novel and reliable scheme capable to detect any fault that may occur in a bearing, based on EXIN Curvilinear Component Analysis, CCA, and Neural Network. The EXIN CCA, which is an improvement of the Curvilinear Component Analysis, has been conceived for data visualization, interpretation and classification for real time industrial applications. The effectiveness of this condition monitoring scheme has been verified by experimental results obtained from different operation conditions.

A novel condition monitoring scheme for bearing faults based on Curvilinear Component Analysis and hierarchical neural networks

Continuous vital signal monitoring is becoming more relevant in preventing diseases that afflict a large part of the world’s population; for this reason, healthcare equipment should be easy to wear and simple to use. Non-intrusive and non-invasive detection methods are a basic requirement for wearable medical devices, especially when these are used in sports applications or by the elderly for self-monitoring. Arterial blood pressure (ABP) is an essential physiological parameter for health monitoring. Most blood pressure measurement devices determine the systolic and diastolic arterial blood pressure through the inflation and the deflation of a cuff. This technique is uncomfortable for the user and may result in anxiety, and consequently affect the blood pressure and its measurement. The purpose of this paper is the continuous measurement of the ABP through a cuffless, non-intrusive approach. The approach of this paper is based on deep learning techniques where several neural networks are used to infer ABP, starting from photoplethysmogram (PPG) and electrocardiogram (ECG) signals. The ABP was predicted first by utilizing only PPG and then by using both PPG and ECG. Convolutional neural networks (ResNet and WaveNet) and recurrent neural networks (LSTM) were compared and analyzed for the regression task. Results show that the use of the ECG has resulted in improved performance for every proposed configuration. The best performing configuration was obtained with a ResNet followed by three LSTM layers: this led to a mean absolute error (MAE) of 4.118 mmHg on and 2.228 mmHg on systolic and diastolic blood pressures, respectively. The results comply with the American National Standards of the Association for the Advancement of Medical Instrumentation. ECG, PPG, and ABP measurements were extracted from the MIMIC database, which contains clinical signal data reflecting real measurements. The results were validated on a custom dataset created at Neuronica Lab, Politecnico di Torino.

/pdf/a-comparison-of-deep-learning-techniques-for-arterial-blood-eghwj9m5v5.pdf

A Comparison of Deep Learning Techniques for Arterial Blood Pressure Prediction

This paper proposes a technological solution with a scroll compressor driven by a high performance induction machine drive for the air management of PEMFC (proton exchange membrane fuel cells) for automotive applications. The torque-speed characteristics of a real scroll compressor have been measured and then emulated by a brushless IMPM (internal mounted permanent magnets) machine controlled in torque. This emulated scroll compressor has been driven by a field oriented controlled (FOC) induction motor drive. The whole car model as well as the PEMFC have been implemented by software on the same DSP which implements the drive control algorithm; in this way the hardware-in-the-loop structure has been employed for emulating the behaviour of a real car including the electrical supply of the PEMFC. The whole system has been tested with a classic European driving cycle. Two experimental rigs have been setup, one for characterizing the scroll compressor and the other for emulating the entire vehicle. The experimental results have shown that the speed reference profile, obtained during a urban driving cycle, is followed correctly and that the tank-to-wheel efficiency remains at quite high values confirming the goodness of the proposed technological solution. The hardware-in-the-loop approach of the experimental rig allows tests to be performed to verify the operation in steady and transient states with low cost and reduced employment of the Fuel Cell stack, with resulting increased durability.

Giansalvo Cirrincione

Papers

Linear system identification using the TLS EXIN neuron

The GH-EXIN neural network for hierarchical clustering

A novel condition monitoring scheme for bearing faults based on Curvilinear Component Analysis and hierarchical neural networks

A Comparison of Deep Learning Techniques for Arterial Blood Pressure Prediction

A Scroll Compressor with a High Performance Induction Motor Drive for the Air Management of a PEMFC System for Automotive Applications