Shallow Neural Network for Biometrics from the ECG-WATCH

Applications such as surveillance, banking and healthcare deal with sensitive data whose confidentiality and integrity depends on accurate human recognition. In this sense, the crucial mechanism for performing an effective access control is authentication, which unequivocally yields user identity. In 2018, just in North America, around 445K identity thefts have been denounced. The most adopted strategy for automatic identity recognition uses a secret for encrypting and decrypting the authentication information. This approach works very well until the secret is kept safe. Electrocardiograms (ECGs) can be exploited for biometric purposes because both the physiological and geometrical differences in each human heart correspond to uniqueness in the ECG morphology. Compared with classical biometric techniques, e.g. fingerprints, ECG-based methods can definitely be considered a more reliable and safer way for user authentication due to ECG inherent robustness to circumvention, obfuscation and replay attacks. In this paper, the ECG WATCH, a non-expensive wristwatch for recording ECGs anytime, anywhere, in just 10 s, is proposed for user authentication. The ECG WATCH acquisitions have been used to train a shallow neural network, which has reached a 99% classification accuracy and 100% intruder recognition rate

Leading agritourism facilities along Nearly Zero Energy paths: Proposal of an easy-to-use evaluation method

The tourist sector, despite the present severe constraints imposed by the sanitary emergence, can be considered as an important component of almost all countries' economies. In Italy agriturism, in particular, has been experiences a continuously rising trend in recent years. Clearly, such great interest towards these businesses also calls for a deep attention by an energy point of view, in sight of an energy efficiency improvement, hence a lowering of the pressure that such facilities exert to the natural environment. On the other hand, the European Union has been engaged, for a long time, in awarding the tourist accommodations environmental excellence brands, like the EU Ecolabel. Unfortunately, the achievement of such excellence brands requires the capability of managing complex energy and environmental data, which is often not the prerogative of people running such facilities. In order of contributing to overcome this difficulty, and with the aim of helping the addressing of agritourism towards a Nearly Zero Energy path, we propose here a simple approach that does not require the modelling and simulation of the energy behaviour of an agritourism, being essentially based on the application of easy-to-use ARERA (Italian Regulatory Authority for Energy Networks and Environment) datasheets scheme. On purpose, an application of the method, involving two typical Sicilian agritourism, is presented. The obtained results showed the viability of the proposed methodology, although the need of an update and/or replacement of some technical datasheets arose

The h-EXIN CCA for Bearing Fault Diagnosis

This paper presents the hierarchical EXIN CCA, which represents a novel and reliable approach to complex pattern recognition problems. The methodology is based on the EXIN CCA, which is an extension of the Curvilinear Component Analysis, for data reduction, and neural networks for data classification. The effectiveness of this condition monitoring scheme is verified in a demanding bearing fault diagnostic scenario

Development of sustainable ORC applications in the tertiary sector: a case study in the Mediterranean climate

In recent decades, climate change strong advancement has led many countries, especially the most developed ones, to a greater sense of environmental responsibility. On a global, European and national level, adaptation/mitigation strategies and actions aimed at improving energy-environmental sustainability and resilience in the tertiary sectors have been increasingly intensified. In this sector, therefore, plays a fundamental role the integration/introduction of technologies able to operate an efficient conversion of energy, such as indeed Organic Rankine Cycle (ORC) plant, other than renewable energy sources, in order to reduce both energy consumption and pollutant emissions. Within this scenario, the aim of this work is to investigate the potential application of a cogeneration ORC system powered by solar collector and geothermal sources, by evaluating its energy, environmental and economic advantages and limitations. To this purpose a case study involving the coverage of the energy needs of a hotel located in Catania (Southern Italy) has been simulated and analyzed. The outcomes put in evidence the importance of the operative conditions in optimizing the productivity of an ORC plant, especially when associated with renewable energy sources, although at the moment investment and supply costs are still quite high

Induction Machine Stator Fault Tracking using the Growing Curvilinear Component Analysis

Detection of stator-based faults in Induction Machines (IMs) can be carried out in numerous ways. In particular, the shorted turns in stator windings of IM are among the most common faults in the industry. As a matter of fact, most IMs come with pre-installed current sensors for the purpose of control and protection. At this aim, using only the stator current for fault detection has become a recent trend nowadays as it is much cheaper than installing additional sensors. The three-phase stator current signatures have been used in this study to observe the effect of stator inter-turn fault with respect to the healthy condition of the IM. The pre-processing of the healthy and faulty current signatures has been done via the in-built DSP module of dSPACE after which, these current signatures are passed into the MATLAB® software for further analysis using AI techniques. The authors present a Growing Curvilinear Component Analysis (GCCA) neural network that is capable of detecting and follow the evolution of the stator fault using the stator current signature, making online fault detection possible. For this purpose, a topological manifold analysis is carried out to study the fault evolution, which is a fundamental step for calibrating the GCCA neural network. The effectiveness of the proposed method has been verified experimentally

Estimation of wind velocity over a complex terrain using the Generalized Mapping Regressor

Wind energy evaluation is an important goal in the conversion of energy systems to more environmentally friendly solutions. In this paper, we present a novel approach to wind speed spatial estimation on the isle of Sicily (Italy): an incremental self-organizing neural network (Generalized Mapping Regressor - GMR) is coupled with exploratory data analysis techniques in order to obtain a map of the spatial distribution of the average wind speed over the entire region. First, the topographic surface of the island was modelled using two different neural techniques and by exploiting the information extracted from a digital elevation model of the region. Then, GMR was used for automatic modelling of the terrain roughness. Afterwards, a statistical analysis of the wind data allowed for the estimation of the parameters of the Weibull wind probability distribution function. In the last sections of the paper, the expected values of the Weibull distributions were regionalized using the GMR neural networ

Sensorless Control of Induction Motors by the MSA based MUSIC Technique

This paper proposes a speed sensorless technique for induction motor drives based on the retrieval and tracking of the rotor slot harmonics (RSH). The RSH related to the rotor speed is first extracted from the stator phase current signature by the adoption of two cascaded ADALINEs (ADAptive Linear Element), whose output is the estimated slot harmonic. Then, the frequency of this slot harmonic as well as the speed is estimated by using minor space analysis (MSA) EXIN neural networks, which work on-line to iteratively compute the frequency of the slot harmonics based on MUSIC spectrum estimation theory. Thanks to its sample-based learning and the reduced mean square frequency estimation error, the speed estimation is fast and accurate. The proposed sensorless technique has been experimentally tested on a suitably developed test set-up with a 2-kW induction motor drive. It has been verified that this algorithm can track the rotor speed rapidly and accurately in a very wide speed range, working from rated speed down to 1.3 % of it

Double Channel Neural Non Invasive Blood Pressure Prediction

Cardiovascular Diseases represent the leading cause of deaths in the world. Arterial Blood Pressure (ABP) is an important physiological parameter that should be properly monitored for the purposes of prevention. This work applies the neural network output-error (NNOE) model to ABP forecasting. Three input configurations are proposed based on ECG and PPG for estimating both systolic and diastolic blood pressures. The double channel configuration is the best performing one by means of the mean absolute error w.r.t the corresponding invasive blood pressure signal (IBP); indeed, it is also proven to be compliant with the ANSI/AAMI/ISO 81060-2:2013 regulation for non invasive ABP techniques. Both ECG and PPG correlations to IBP signal are further analyzed using Spearman’s correlation coefficient. Despite it suggests PPG is more closely related to ABP, its regression performance is worse than ECG input configuration one. However, this behavior can be explained looking to human biology and ABP computation, which is based on peaks (systoles) and valleys (diastoles) extraction

Neural Biclustering in Gene Expression Analysis

Clustering in high dimensional spaces is a very difficult task. Dealing with DNA microarrays is even more difficult because gene subsets are coregulated and coexpressed only under specific conditions. Biclusterng addresses the problem of finding such submanifolds by exploiting both gene and condition (tissue) clustering. The paper proposes a self-organizing neural network, GH EXIN, which builds a hierarchical tree by adapting its architecture to data. It is integrated in a framework in which gene and tissue clustering are alternated and controlled by the quality of the bicluster. Examples of the approach and a biological validation of results are also given

Tracking Evolution of Stator-based Fault in Induction Machines using the Growing Curvilinear Component Analysis Neural Network

Stator-based faults are one of the most common faults among induction motors (IMs). The conventional approach to IM control and protection employs current sensors installed on the motor. Recently, most studies have focused on fault detection by means of stator current. This paper presents an application of the Growing Curvilinear Component Analysis (GCCA) neural network aided by the Extended Park Vector Approach (EPVA) for the purpose of transforming the three-phase current signals. The GCCA is a growing neural based technique specifically designed to detect and follow changes in the input distribution, e.g. stator faults. In particular, the GCCA has proven its capability of correctly identifying and tracking stator inter-turn fault in IMs. To this purpose, the three-phase stator currents have been acquired from IMs, which start at healthy operating state and, evolve to different fault severities (up to 10%) under different loading conditions. Data has been transformed using the EPVA and pre-processed to extract statistical time domain features. To calibrate the GCCA neural network, a topological manifold analysis has been carried out to study the input features. The efficacy of the proposed method has been verified experimentally using IM with l.lkW rating and has potential for IMs with different manufacturing conditions