University of Texas at Arlington

About: University of Texas at Arlington is a education organization based out in Arlington, Texas, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 11758 authors who have published 28598 publications receiving 801626 citations. The organization is also known as: UT Arlington & University of Texas-Arlington.

Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines

Abstract: Due to the growing demand on renewable energy, photovoltaic (PV) generation systems have increased considerably in recent years. However, the power output of PV systems is affected by different weather conditions. Accurate forecasting of PV power output is important for system reliability and promoting large-scale PV deployment. This paper proposes algorithms to forecast power output of PV systems based upon weather classification and support vector machines (SVM). In the process, the weather conditions are divided into four types which are clear sky, cloudy day, foggy day, and rainy day. In this paper, a one-day-ahead PV power output forecasting model for a single station is derived based on the weather forecasting data, actual historical power output data, and the principle of SVM. After applying it into a PV station in China (the capability is 20 kW), results show the proposed forecasting model for grid-connected PV systems is effective and promising.

The Constrained Laplacian Rank algorithm for graph-based clustering

Abstract: Graph-based clustering methods perform clustering on a fixed input data graph. If this initial construction is of low quality then the resulting clustering may also be of low quality. Moreover, existing graph-based clustering methods require post-processing on the data graph to extract the clustering indicators. We address both of these drawbacks by allowing the data graph itself to be adjusted as part of the clustering procedure. In particular, our Constrained Laplacian Rank (CLR) method learns a graph with exactly k connected components (where k is the number of clusters). We develop two versions of this method, based upon the L1-norm and the L2-norm, which yield two new graph-based clustering objectives. We derive optimization algorithms to solve these objectives. Experimental results on synthetic datasets and real-world benchmark datasets exhibit the effectiveness of this new graph-based clustering method.

Smart Environments: Technology, Protocols and Applications

Abstract: ContributorsForeword by (Howard E Shrobe)AcknowledgementsPART 1: INTRODUCTION1 Overview (D Cook & S Das)PART 2: TECHNOLOGIES FOR SMART ENVIRONMENTS2 Wireless Sensor Networks (F Lewis)3 Power Line Communication Technologies (H Latchman & A Mundi)4 Wireless Communications and Pervasive Technology (M Conti)5 Middleware (G Youngblood)6 Home Networking and Appliances (D Marples & S Moyer)PART 3: ALGORITHMS AND PROTOCOLS FOR SMART ENVIRONMENTS7 Designing for the Human Experience in Smart Environments (G Abowd & E Mynatt)8 Prediction Algorithms foor Smart Environments (D Cook)9 Location Estimation (Determination and Prediction) Techniques in Smart Environments (A Misra & S Das)10 Automated Decision Making (M Huber)11 Privacy, Security, and Trust Issues in Smart Environments (P Nixon, et al)PART 4: APPLICATIONS12 Lessons from an Adaptive Home (M Mozer)13 Smart Rooms (A Chen, et al)14 Smart Offices (C Gal)15 Perceptual Environments (A Pentland)16 Assistive Environments for Individuals with Special Needs (A Helal, et al)PART 5: CONCLUSIONS17 Ongoing Challenges and Future Directions (S Das & D Cook)Index

Effect of Caloric Restriction or Aerobic Exercise Training on Peak Oxygen Consumption and Quality of Life in Obese Older Patients With Heart Failure With Preserved Ejection Fraction: A Randomized Clinical Trial

Abstract: Importance More than 80% of patients with heart failure with preserved ejection fraction (HFPEF), the most common form of heart failure among older persons, are overweight or obese. Exercise intolerance is the primary symptom of chronic HFPEF and a major determinant of reduced quality of life (QOL). Objective To determine whether caloric restriction (diet) or aerobic exercise training (exercise) improves exercise capacity and QOL in obese older patients with HFPEF. Design, Setting, and Participants Randomized, attention-controlled, 2 × 2 factorial trial conducted from February 2009 through November 2014 in an urban academic medical center. Of 577 initially screened participants, 100 older obese participants (mean [SD]: age, 67 years [5]; body mass index, 39.3 [5.6]) with chronic, stable HFPEF were enrolled (366 excluded by inclusion and exclusion criteria, 31 for other reasons, and 80 declined participation). Interventions Twenty weeks of diet, exercise, or both; attention control consisted of telephone calls every 2 weeks. Main Outcomes and Measures Exercise capacity measured as peak oxygen consumption (Vo 2 , mL/kg/min; co–primary outcome) and QOL measured by the Minnesota Living with Heart Failure (MLHF) Questionnaire (score range: 0–105, higher scores indicate worse heart failure–related QOL; co–primary outcome). Results Of the 100 enrolled participants, 26 participants were randomized to exercise; 24 to diet; 25 to exercise + diet; 25 to control. Of these, 92 participants completed the trial. Exercise attendance was 84% (SD, 14%) and diet adherence was 99% (SD, 1%). By main effects analysis, peak Vo 2 was increased significantly by both interventions: exercise, 1.2 mL/kg body mass/min (95% CI, 0.7 to 1.7), P P 2 (joint effect, 2.5 mL/kg/min). There was no statistically significant change in MLHF total score with exercise and with diet (main effect: exercise, −1 unit [95% CI, −8 to 5], P = .70; diet, −6 units [95% CI, −12 to 1], P = .08). The change in peak Vo 2 was positively correlated with the change in percent lean body mass ( r = 0.32; P = .003) and the change in thigh muscle:intermuscular fat ratio ( r = 0.27; P = .02). There were no study-related serious adverse events. Body weight decreased by 7% (7 kg [SD, 1]) in the diet group, 3% (4 kg [SD, 1]) in the exercise group, 10% (11 kg [SD, 1] in the exercise + diet group, and 1% (1 kg [SD, 1]) in the control group. Conclusions and Relevance Among obese older patients with clinically stable HFPEF, caloric restriction or aerobic exercise training increased peak Vo 2 , and the effects may be additive. Neither intervention had a significant effect on quality of life as measured by the MLHF Questionnaire. Trial Registration clinicaltrials.gov Identifier:NCT00959660