scholarly journals Capacitive Load-Based Smart OTF for High Power Rated SPV Module

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 788
Author(s):  
Javed Sayyad ◽  
Paresh Nasikkar ◽  
Abhaya Pal Singh ◽  
Stepan Ozana
Keyword(s):  
Energy Demand ◽  
Cost Effective ◽  
Renewable Resource ◽  
Test Facility ◽  
Raspberry Pi ◽  
Resistive Load ◽  
Data Logging ◽  
Performance Characterization ◽  
Capacitive Load

Solar energy is the most promising renewable resource with an unbounded energy source, capable of meeting all human energy requirements. Solar Photovoltaic (SPV) is an effective approach to convert sunlight into electricity, and it has a promising future with consistently rising energy demand. In this work, we propose a smart solution of outdoor performance characterization of the SPV module utilizing a robust, lightweight, portable, and economical Outdoor Test Facility (OTF) with the Internet of Things (IoT) capability. This approach is focused on the capacitive load-based method, which offers improved accuracy and cost-effective data logging using Raspberry Pi and enables the OTF to sweep during the characterization of the SPV module automatically. A demonstration using an experimental setup is also provided in the paper to validate the proposed OTF. This paper further discusses the advantages of using the capacitive load approach over the resistive load approach. IoT’s inherent benefits empower the proposed OTF method on the backgrounds of real-time tracking, data acquisition, and analysis for outdoor output performance characterization by capturing Current–Voltage (I–V) and Power–Voltage (P–V) curves of the SPV module.

PERFORMANCE CHARACTERIZATION AND CFD ANALYSIS OF AN AXIAL BLOOD PUMP

2005 ◽  
Vol 05 (01) ◽  
pp. 151-163 ◽  
Author(s):  
W. K. CHAN ◽  
Y. W. WONG ◽  
S. Y. KOH ◽  
V. CHONG
Keyword(s):  
Flow Separation ◽  
Cost Effective ◽  
Pressure Head ◽  
Blood Pump ◽  
Cfd Modeling ◽  
Performance Characterization ◽  
Cardiac Assist Devices ◽  
And Performance ◽  
Experimental Trials

This paper describes the performance characterization of an axial blood pump that is developed in our laboratory. Using computational fluid dynamics (CFD), regions of flow separation and high shear stress were identified since they are of concern in the development of cardiac assist devices. CFD is an efficient and cost effective tool in assisting the designer to reduce the number of experimental trials needed. Preliminary CFD studies showed the existence of substantial backflow in the impeller passage. The impeller geometry was improved using CFD modeling. Regions of flow separation were eliminated while regions of scalar stress of up to 150 Pa were observed near to the impeller tip. The final prototype can deliver a flow rate of 5 L/min at a pressure head of 14 kPa when operating at a speed of 10,000 rpm. The model was fabricated using rapid prototyping techniques and performance characterization of the pump has demonstrated that the CFD prediction of the pump performance curve and the pressure developed along the impeller agrees reasonably well with experimental results.

scholarly journals Affordable Remote Monitoring of Plant Growth and Facilities using Raspberry Pi Computers

2019 ◽  
Author(s):  
Brandin Grindstaff ◽  
Makenzie E. Mabry ◽  
Paul D. Blischak ◽  
Micheal Quinn ◽  
J. Chris Pires
Keyword(s):  
Remote Monitoring ◽  
Data Transfer ◽  
Low Cost ◽  
Cost Effective ◽  
Environmental Data ◽  
Raspberry Pi ◽  
List Type ◽  
Environmental Sensors ◽  
Data Logging ◽  
The Stability

ABSTRACTPremise of the study: Environmentally controlled facilities, such as growth chambers, are essential tools for experimental research. Automated remote monitoring of such facilities with low-cost hardware can greatly improve both the reproducibility and the accurate maintenance of their conditions.Methods and Results: Using a Raspberry Pi computer, open-source software, environmental sensors, and a camera, we developed a cost-effective system for monitoring growth chamber conditions, which we have called ‘GMpi.’ Coupled with our software, GMpi_Pack, our setup automates sensor readings, photography, alerts when conditions fall out of range, and data transfer to cloud storage services.Conclusions: The GMpi offers low-cost access to environmental data logging, improving reproducibility of experiments, as well as reinforcing the stability of controlled environmental facilities. The device is also flexible and scalable, allowing customization and expansion to include other features such as machine vision.

A Cost-Effective SDR Platform for Performance Characterization of RFID Tags

2012 ◽  
Vol 61 (4) ◽  
pp. 903-911 ◽  
Author(s):  
Luca Catarinucci ◽  
Danilo De Donno ◽  
Riccardo Colella ◽  
Fabio Ricciato ◽  
Luciano Tarricone
Keyword(s):  
Cost Effective ◽  
Rfid Tags ◽  
Performance Characterization

Monitoring of Petroleum-Related Environmental Contamination Using Fluorescence Fingerprinting

2006 ◽  
Vol 1 (2) ◽  
Author(s):  
P. Literathy ◽  
M. Quinn
Keyword(s):  
Environmental Contamination ◽  
Statistical Evaluation ◽  
Oil Pollution ◽  
Emission Spectra ◽  
Cost Effective ◽  
Petroleum Products ◽  
Danube River ◽  
Aromatic Components ◽  
Analytical Approaches

Petroleum and its refined products are considered the most complex contaminants frequently impacting the environment in significant quantities. They have heterogeneous chemical composition and alterations occur during environmental weathering. No single analytical method exists to characterize the petroleum-related environmental contamination. For monitoring, the analytical approaches include gravimetric, spectrometric and chromatographic methods having significant differences in their selectivity, sensitivity and cost-effectiveness. Recording fluorescence fingerprints of the cyclohexane extracts of the water, suspended solids, sediment or soil samples and applying appropriate statistical evaluation (e.g. by correlating the concatenated emission spectra of the fingerprints of the samples with arbitrary standards (e.g. petroleum products)), provides a powerful, cost-effective analytical tool for characterization of the type of oil pollution and detecting the most harmful aromatic components of the petroleum contaminated matrix. For monitoring purposes, the level of the contamination can be expressed as the equivalent concentration of an appropriate characteristic standard, based on the fluorescence intensities at the relevant characteristic wavelengths. These procedures are demonstrated in the monitoring of petroleum-related pollution in the water and suspended sediment in the Danube river basin

Green Synthesis and Spectroscopic Studies of Ag-rGO Nanocomposites for Highly Selective Mercury (II) Sensing

2018 ◽  
Vol 9 (1) ◽  
pp. 101-108 ◽  
Author(s):  
Shubhangi J. Mane-Gavade ◽  
Sandip R. Sabale ◽  
Xiao-Ying Yu ◽  
Gurunath H. Nikam ◽  
Bhaskar V. Tamhankar
Keyword(s):  
Green Synthesis ◽  
Color Change ◽  
Limit Of Detection ◽  
Aqueous Media ◽  
Suitable Condition ◽  
Cost Effective ◽  
Spectroscopic Studies ◽  
Calibration Plot ◽  
First Time

Introduction: Herein we report the green synthesis and characterization of silverreduced graphene oxide nanocomposites (Ag-rGO) using Acacia nilotica gum for the first time. Experimental: We demonstrate the Hg2+ ions sensing ability of the Ag-rGO nanocomposites form aqueous medium. The developed colorimetric sensor method is simple, fast and selective for the detection of Hg2+ ions in aqueous media in presence of other associated ions. A significant color change was noticed with naked eye upon Hg2+ addition. The color change was not observed for cations including Sr2+, Ni2+, Cd2+, Pb2+, Mg2+, Ca2+, Fe2+, Ba2+ and Mn2+indicating that only Hg2+ shows a strong interaction with Ag-rGO nanocomposites. Under the most suitable condition, the calibration plot (A0-A) against concentration of Hg2+ was linear in the range of 0.1-1.0 ppm with a correlation coefficient (R2) value 0.9998. Results & Conclusion The concentration of Hg2+ was quantitatively determined with the Limit of Detection (LOD) of 0.85 ppm. Also, this method shows excellent selectivity towards Hg2+ over nine other cations tested. Moreover, the method offers a new cost effective, rapid and simple approach for the detection of Hg2+ in water samples.

scholarly journals Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4649
Author(s):  
İsmail Hakkı ÇAVDAR ◽  
Vahit FERYAD
Keyword(s):  
Smart Grids ◽  
Energy Demand ◽  
Optimization Technique ◽  
Cost Effective ◽  
Optimization Method ◽  
Long Term Memory ◽  
Successful Implementation ◽  
Efficient Design ◽  
Energy Disaggregation ◽  
Load Monitoring

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training.

scholarly journals Semi-automated regional classification of the style of activity of slow rock-slope deformations using PS InSAR and SqueeSAR velocity data

Landslides ◽  
2021 ◽  
Author(s):  
Chiara Crippa ◽  
Elena Valbuzzi ◽  
Paolo Frattini ◽  
Giovanni B. Crosta ◽  
Margherita C. Spreafico ◽  
...  
Keyword(s):  
Rock Slope ◽  
Progressive Failure ◽  
Principal Component ◽  
Cost Effective ◽  
Displacement Rate ◽  
Machine Learning Classification ◽  
Management Perspective ◽  
Alpine Environments

AbstractLarge slow rock-slope deformations, including deep-seated gravitational slope deformations and large landslides, are widespread in alpine environments. They develop over thousands of years by progressive failure, resulting in slow movements that impact infrastructures and can eventually evolve into catastrophic rockslides. A robust characterization of their style of activity is thus required in a risk management perspective. We combine an original inventory of slow rock-slope deformations with different PS-InSAR and SqueeSAR datasets to develop a novel, semi-automated approach to characterize and classify 208 slow rock-slope deformations in Lombardia (Italian Central Alps) based on their displacement rate, kinematics, heterogeneity and morphometric expression. Through a peak analysis of displacement rate distributions, we characterize the segmentation of mapped landslides and highlight the occurrence of nested sectors with differential activity and displacement rates. Combining 2D decomposition of InSAR velocity vectors and machine learning classification, we develop an automatic approach to characterize the kinematics of each landslide. Then, we sequentially combine principal component and K-medoids cluster analyses to identify groups of slow rock-slope deformations with consistent styles of activity. Our methodology is readily applicable to different landslide datasets and provides an objective and cost-effective support to land planning and the prioritization of local-scale studies aimed at granting safety and infrastructure integrity.

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