Altamont Pass Commuter Study: A Longitudinal Analysis of Perceptions and Behavior Change

The Altamont Pass commuter survey study examines commuters’ perceptions and behaviors towards public transportation during 2019-2020. Results are compared with surveys conducted in 2000 and 2006 to investigate whether there have been any longitudinal changes in the perceptions and behaviors of Altamont Pass commuters over the twenty-year interval. As the previous surveys do, this study focuses on the same three counties, namely, San Joaquin, Stanislaus, and Merced that comprise the Northern San Joaquin Valley (NSJV). When compared with the previous surveys, these findings reveal some significant differences of responses to most questions, and minor differences of responses to other questions, prompting several important conclusions

Data Mining Models for Tackling High Dimensional Datasets and Outliers

High dimensional data and the presence of outliers in data each pose a serious challenge in supervised learning. Datasets with significantly larger number of features compared to samples arise in various areas, including business analytics and biomedical applications. Such datasets pose a serious challenge to standard statistical methods and render many existing classification techniques impractical. The generalization ability of many classification algorithms is compromised due to the so-called curse of dimensionality. A new binary classification method called constrained subspace classifier (CSC) is proposed for such high dimensional datasets. CSC improves on an earlier proposed classification method called local subspace classifier (LSC) by accounting for the relative angle between subspaces while approximating the classes with individual subspaces. CSC is formulated as an optimization problem and can be solved by an efficient alternating optimization technique. Classification performance is tested in publicly available datasets. The improvement in classification accuracy over LSC shows the importance of considering the relative angle between the subspaces while approximating the classes. Additionally, CSC appears to be a robust classifier, compared to traditional two step methods that perform feature selection and classification in two distinct steps. Outliers can be present in real world datasets due to noise or measurement errors. The presence of outliers can affect the training phase of machine learning algorithms, leading to over-fitting which results in poor generalization ability. A new regression method called relaxed support vector regression (RSVR) is proposed for such datasets. RSVR is based on the concept of constraint relaxation which leads to increased robustness in datasets with outliers. RSVR is formulated using both linear and quadratic loss functions. Numerical experiments on benchmark datasets and computational comparisons with other popular regression methods depict the behavior of our proposed method. RSVR achieves better overall performance than support vector regression (SVR) in measures such as RMSE and R2 adj while being on par with other state-of-the-art regression methods such as robust regression (RR). Additionally, RSVR provides robustness for higher dimensional datasets which is a limitation of RR, the robust equivalent of ordinary least squares regression. Moreover, RSVR can be used on datasets that contain varying levels of noise. Lastly, we present a new novelty detection model called relaxed one-class support vector machines (ROSVMs) that deals with the problem of one-class classification in the presence of outliers

Low-Cost Data Acquisition System for Solar Thermal Collectors

Solar thermal collectors are among the most popular renewable energy research subjects. Automatic Data Acquisition Systems (ADAS) have greatly facilitated their experimental testing, but their high cost is a drawback. In this paper, we present the design and testing of a decentralized, low-cost alternative ADAS based on the ESP32 microcontroller and on open-source software. The proposed system can be used for the experimental characterization of water (or air) operated solar thermal collectors in accordance with the ISO 9806:2017 requirements, but it is also compatible with sensors with lower specifications. We present also its performance results when applied for the testing of a solar thermal collector

Low-Cost Data Acquisition System for Solar Thermal Collectors

Solar thermal collectors are among the most popular renewable energy research subjects. Automatic Data Acquisition Systems (ADAS) have greatly facilitated their experimental testing, but their high cost is a drawback. In this paper, we present the design and testing of a decentralized, low-cost alternative ADAS based on the ESP32 microcontroller and on open-source software. The proposed system can be used for the experimental characterization of water (or air) operated solar thermal collectors in accordance with the ISO 9806:2017 requirements, but it is also compatible with sensors with lower specifications. We present also its performance results when applied for the testing of a solar thermal collector

Συγκεντρωτικά ηλιακά θερμικά συστήματα: πειραματική και υπολογιστική μελέτη καινοτόμων διατάξεων για εφαρμογές σε μέσες και ενδιάμεσες θερμοκρασίες

Concentrating solar thermal systems are widely used to convert solar energy for space heating and refrigeration, solar water heating, as well as industrial process heat. The state-of-the-art methods for solar process heat production use different concepts among which tracking Fresnel mirrors or parabolic troughs. However, very few commercially available devices operating at intermediate to medium temperatures (100−250 °C) exist. In order to fill this gap, an innovative concentrating solar thermal system is proposed and studied. The proposed device is able to concentrate the solar radiation to the receiver using a micro-mirror array and a flat receiver. A prototype is designed, constructed and tested outdoors. The findings of the theoretical study through numerical simulations and the experimental results are presented and discussed in the present work.Τα συγϰεντρωτιϰά ϑερμιϰά ηλιαϰά συστήματα χρησιμοποιούνται ευρέως για τη μετατροπή ηλιαϰής ενέργειας σε ϑερμότητα για ϑέρμανση ϰαι ψύξη χώρων, ζεστό νερό χρήσης ϰαι ϑερμότητα για βιομηχανιϰές διεργασίες. Στις σύγχρονες τεχνιϰές μετατροπής της ηλιαϰής σε ϑερμιϰή ενέργεια περιλαμβάνονται τα συστήματα παραϰολούϑησης του ήλιου με παραβολιϰά ϰάτοπτρα ή ϰάτοπτρα Φρενέλ.΄Ομως, υπάρχουν ελάχιστες συσϰευές στο εμπόριο με ενδιάμεση ή μέση ϑερμοϰρασία λειτουργίας (100 − 250 °C). Προϰειμένου να ϰαλυφϑεί αυτό το ϰενό στην αγορά, προτείνεται ϰαι μελετάται ένα ϰαινοτόμο συγϰεντρωτιϰό ϑερμιϰό ηλιαϰό σύστημα. Χαραϰτηριστιϰό της προτεινόμενης συσϰευής είναι η συγϰέντρωση της ηλιαϰής αϰτινοβολίας σε επίπεδο αποδέϰτη μέσω ενός πεδίου ϰαϑρεπτών. Η πρωτότυπη διάταξη σχεδιάστηϰε, ϰατασϰευάστηϰε ϰαι δοϰιμάστηϰε σε πραγματιϰές συνϑήϰες. Στην παρούσα εργασία παρουσιάζονται τα ευρήματα της ϑεωρητιϰής μελέτης μέσω αριϑμητιϰών προσομοιώσεων ϰαϑώς ϰαι τα αποτελέσματα της πειραματιϰής μελέτης της εν λόγω διάταξης

Sparse Proximal Support Vector Machines For Feature Selection In High Dimensional Datasets

Classification of High Dimension Low Sample Size (HDLSS) datasets is a challenging task in supervised learning. Such datasets are prevalent in various areas including biomedical applications and business analytics. In this paper, a new embedded feature selection method for HDLSS datasets is introduced by incorporating sparsity in Proximal Support Vector Machines (PSVMs). Our method, called Sparse Proximal Support Vector Machines (sPSVMs), learns a sparse representation of PSVMs by first casting it as an equivalent least squares problem and then introducing the l1-norm for sparsity. An efficient algorithm based on alternating optimization techniques is proposed. sPSVMs removes more than 98% of features in many high dimensional datasets without compromising on generalization performance. Stability in the feature selection process of sPSVMs is also studied and compared with other univariate filter techniques. Additionally, sPSVMs offers the advantage of interpreting the selected features in the context of the classes by inducing class-specific local sparsity instead of global sparsity like other embedded methods. sPSVMs appears to be robust with respect to data dimensionality. Moreover, sPSVMs is able to perform feature selection and classification in one step, eliminating the need for dimensionality reduction on the data. To that end, sPSVMs can be used for preprocessing free classification tasks

Soil-Compressibility Prediction Models Using Machine Learning

The magnitude of the overall settlement depends on several variables such as the compression index, Cc, and recompression index, Cr, which are determined by a consolidation test; however, the test is time consuming and labor intensive. Correlations have been developed to approximate these compressibility indexes. In this study, a data driven approach has been employed to estimate Cc and Cr. Support vector machines classification is used to determine the number of distinct models to be developed. Classification accuracy is used for detecting the existence of separability between different soil classes that in turn is indicative of the number of models needed. The statistical models are built through a forward selection stepwise regression procedure. Seven variables were used, including the moisture content (w), initial void ratio (eo), dry unit weight (γdry), wet unit weight (γwet), automatic hammer SPT blow count (N), overburden stress (σ), and fines content (-200). The results confirm the need for separate models for three out of four soil types, these being coarse grained, fine grained, and organic peat. The models for each classification have varying degrees of accuracy. The model for the fine grained classification performs on par with existing correlations, with respect to Cc, whereas the models for coarse grained and organic peat classifications perform considerably better than that of existing correlations. The models generated also incorporate several factors not utilized in correlations from previous literature. These factors include the fines content (-200), automatic hammer blow count (N), and the interactions between the wet and dry density (γwet and γdry)

Air-based photovoltaic thermal collectors: theoretical and experimental analysis of a novel low-cost prototype

In this paper the performance of an innovative low-cost flat-plate PhotoVoltaic Thermal (PVT) collector prototype is investigated. By such hybrid solar panel both electricity and thermal energy can be obtained. As commonly known, the electrical efficiency of PV cells dramatically drops as the operating temperature rises. In order to improve the PV cells efficiency avoiding the overheating, a low-cost heat extraction system was made. Specifically, the cooling system (located under the PV panel, Figure 1a) is composed of two black galvanized steel sheets, properly shaped in order to form seven air channels (Figure 1b). On the top of the structure seven fans were installed in parallel to draw air through suitable air channels. The obtained air thermal energy can be exploited for different building uses. The prototype was built and tested under different operating conditions at the University of Patras (Greece). Then, in order to carry out a deep investigation on the system, a suitable dynamic simulation model was developed for assessing the energy, economic and environmental performance analysis of the system for different weather conditions and building uses. Here, hourly weather data (TMY, IWEC, etc.) can be processed (solar radiation, air temperature and humidity, wind, etc.). The model, implemented in MatLab environment, was validated vs. the above mentioned collected experimental data and a good agreement between the simulation results and the measurements was achieved. In addition, in order to optimize the system design, a specific tool for the system parametric analysis was developed and added to the simulation code. By such tool the effects on the collector performance of the variation of different design and operating parameters (i.e. air channel depth, air mass flow rate, fans speed, etc.) can be carried out. Finally, in order to investigate the convenience of the presented prototype and the potentiality of the developed simulation tool, a suitable case study is discussed. Here, the PVT collector prototype is coupled to a heat pump for building space heating. Specifically, the air heated in the seven collector channels is supplied to the evaporator of the heat pump, increasing its energy performance and decreasing its operating costs. An office building in three different European weather zones was investigated. Useful design criteria and interesting energy and economic results were obtained

Constrained Subspace Classifier For High Dimensional Datasets

Datasets with significantly larger number of features, compared to samples, pose a serious challenge in supervised learning. Such datasets arise in various areas including business analytics. In this paper, a new binary classification method called constrained subspace classifier (CSC) is proposed for such high dimensional datasets. CSC improves on an earlier proposed classification method called local subspace classifier (LSC) by accounting for the relative angle between subspaces while approximating the classes with individual subspaces. CSC is formulated as an optimization problem and can be solved by an efficient alternating optimization technique. Classification performance is tested in publicly available datasets. The improvement in classification accuracy over LSC shows the importance of considering the relative angle between the subspaces while approximating the classes. Additionally, CSC appears to be a robust classifier, compared to traditional two step methods that perform feature selection and classification in two distinct steps