Brushless motor driver designed for standalone photovoltaic system

To diplomsko delo predstavlja načrtovanje in izvedbo samostojnega sistema za pogon brezkrtačnega motorja s pomočjo fotovoltaičnega modula, in sicer gre za pogon obtočne črpalke za kroženje bazenske vode. Običajno so tako imenovani samostojni fotovoltaični sistemi zgrajeni okrog baterijske banke, ki shranjuje viške energije, kadar je le te preveč, in zagotavlja energijo v času, ko sonca ni. Pri kroženju bazenske vode to ni tako bistveno, baterije pa bi pomenile dodatni strošek, težo in volumen sistema, zato je bil eden glavnih ciljev, da se bateriji izognemo. Ker v tem primeru zalogovnika energije ni, je bilo potrebno zagotoviti, da sistem stalno prilagaja moč delovanja zelo dinamičnim trenutnim razmeram osončenja. Sistem ima zato izvedeno sledenje točki maksimalne moči na fotovoltaičnem modulu [1]. S pomočjo pulzno širinske modulacije (PŠM) [2] prilagaja moč črpalke tako, da je le ta v vsakem trenutku enaka trenutni razpoložljivi moči fotovoltaičnega modula. Na mikrokrmilniku, ki nadzira delovanje celotnega sistema, teče operacijski sistem v realnem času, kar omogoča hiter odziv na spremembe razmer in zanesljivo delovanje. Sistem omogoča tudi priključitev drugih 24 V bremen in delovanje motorja s stalno močjo, nekatere parametre vezja pa je moč nastavljati na vgrajenem prikazovalniku LCD, kjer lahko opazujemo tudi trenutne razmere delovanja sistema.This diploma thesis presents the development of a brushless motor controller, powered by a photovoltaic module, incorporating a maximum power point tracking algorithm. This is essentially an off grid photovoltaic system powering a circulation pomp in a home swimming pool. Off grid photovoltaic systems are normally built around a battery bank that serves as a storage for excess energy during high insolation and supplies loads when solar power is not available. This flexibility is not essential to be used in a swimming pool water circulation system. A battery bank would only present additional cost to the system and require more space for the installation, therefore one of the main goals was to design the system that can operate without the battery. Such a system has to be able to control pump power in real time to constantly track the maximum power point on the photovoltaic module. A microcontroller is used to measure the system\u27s parameters and control the motor, and real time operating system provides fast transient response and high system reliability. The system also allows the supply of other 24V loads and includes a constant power mode. An LCD screen is added where basic measured parameters are displayed

Comparing Solutions under Uncertainty in Multiobjective Optimization

Due to various reasons the solutions in real-world optimization problems cannot always be exactly evaluated but are sometimes represented with approximated values and confidence intervals. In order to address this issue, the comparison of solutions has to be done differently than for exactly evaluated solutions. In this paper, we define new relations under uncertainty between solutions in multiobjective optimization that are represented with approximated values and confidence intervals. The new relations extend the Pareto dominance relations, can handle constraints, and can be used to compare solutions, both with and without the confidence interval. We also show that by including confidence intervals into the comparisons, the possibility of incorrect comparisons, due to inaccurate approximations, is reduced. Without considering confidence intervals, the comparison of inaccurately approximated solutions can result in the promising solutions being rejected and the worse ones preserved. The effect of new relations in the comparison of solutions in a multiobjective optimization algorithm is also demonstrated

Accuracy of cancer diagnosis models inferred by machine learning from gene expression data sets

Using machine learning on gene expression data we can try to predict if tissue is benign or malignant. We have evaluated different machine learning technique on the data that we have obtained from the public data base Gene Expression Omnibus. The algorithms were tested on different data sets to get more reliable results. The methods were scored using AUC measure and statistically compared in a critical distance graph. The results were a bit surprising. We expected that the best method would be support vector machines method, but it was method of random forests. Standard deviation was relatively high so the order of methods could be different on some other data

Accuracy of cancer diagnosis models inferred by machine learning from gene expression data sets

Using machine learning on gene expression data we can try to predict if tissue is benign or malignant. We have evaluated different machine learning technique on the data that we have obtained from the public data base Gene Expression Omnibus. The algorithms were tested on different data sets to get more reliable results. The methods were scored using AUC measure and statistically compared in a critical distance graph. The results were a bit surprising. We expected that the best method would be support vector machines method, but it was method of random forests. Standard deviation was relatively high so the order of methods could be different on some other data

Adult height prediction using the growth curve comparison method.

Understanding the growth pattern is important in view of child and adolescent development. Due to different tempo of growth and timing of adolescent growth spurt, individuals reach their adult height at different ages. Accurate models to assess the growth involve intrusive radiological methods whereas the predictive models based solely on height data are typically limited to percentiles and therefore rather inaccurate, especially during the onset of puberty. There is a need for more accurate non-invasive methods for height prediction that are easily applicable in the fields of sports and physical education, as well as in endocrinology. We developed a novel method, called Growth Curve Comparison (GCC), for height prediction, based on a large cohort of > 16,000 Slovenian schoolchildren followed yearly from ages 8 to 18. We compared the GCC method to the percentile method, linear regressor, decision tree regressor, and extreme gradient boosting. The GCC method outperformed the predictions of other methods over the entire age span both in boys and girls. The method was incorporated into a publicly available web application. We anticipate our method to be applicable also to other models predicting developmental outcomes of children and adolescents, such as for comparison of any developmental curves of anthropometric as well as fitness data. It can serve as a useful tool for assessment, planning, implementation, and monitoring of somatic and motor development of children and youth

Mining telemonitored physiological data and patient-reported outcomes of congestive heart failure patients.

This paper addresses patient-reported outcomes (PROs) and telemonitoring in congestive heart failure (CHF), both increasingly important topics. The interest in CHF trials is shifting from hard end-points such as hospitalization and mortality, to softer end-points such health-related quality of life. However, the relation of these softer end-points to objective parameters is not well studied. Telemonitoring is suitable for collecting both patient-reported outcomes and objective parameters. Most telemonitoring studies, however, do not take full advantage of the available sensor technology and intelligent data analysis. The Chiron clinical observational study was performed among 24 CHF patients (17 men and 7 women, age 62.9 ± 9.4 years, 15 NYHA class II and 9 class III, 10 of ishaemic, aetiology, 6 dilated, 2 valvular, and 6 of multiple aetiologies or cardiomyopathy) in Italy and UK. A large number of physiological and ambient parameters were collected by wearable and other devices, together with PROs describing how well the patients felt, over 1,086 days of observation. The resulting data were mined for relations between the objective parameters and the PROs. The objective parameters (humidity, ambient temperature, blood pressure, SpO2, and sweeting intensity) could predict the PROs with accuracies up to 86% and AUC up to 0.83, making this the first report providing evidence for ambient and physiological parameters to be objectively related to PROs in CHF patients. We also analyzed the relations in the predictive models, gaining some insights into what affects the feeling of health, which was also generally not attempted in previous investigations. The paper strongly points to the possibility of using PROs as primary end-points in future trials

The classification accuracy for each tested data mining algorithm, averaged over all the class definitions and subsets.

<p>The classification accuracy for each tested data mining algorithm, averaged over all the class definitions and subsets.</p

Class definitions with their name, the patients’ labels belonging to the classes bad and good, and the number of instances.

<p>Class definitions with their name, the patients’ labels belonging to the classes bad and good, and the number of instances.</p

Class definitions with number of instances for each.

<p>Class definitions with number of instances for each.</p