3kw hybrid model
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This document provides specifications for a 3KW hybrid inverter system including electrical connections, input voltage, wire gauges, circuit protection ratings, solar panel specifications of 250W polycrystalline panels with maximum power voltage and current of 30.5V and 8.2A respectively, and four 12V, 150Ah lead-acid batteries. It was prepared by Muhammad Asfandyar Ali Khan, a management trainee.
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![N AC
IN N AC
OUT PV-PV+ BAT-BAT+
~ AC
INPUT:230VAC
4mm^2
4mm^2
N
L
Changeover
4mm^2
N
G
L
PV-
PV+
4mm^2
4mm^2
10A
AC
6A
AC
63A
DC
63A
DC
L(IN) L(out)
N N
12V,150AH 12V,150AH 12V,150AH 12V,150AH
Spectifications of Inverter
3KW Hybrid Inverter(Infini)
Max Voltage[VOC]: 500V DC
Max Charging Current: 25A
Solar Panels
250W Poly
Voc: 37.2,Isc: 8.72A
Vmp: 30.5v, Imp: 8.20A
Batteries
12V,200A
Prepared By:
Muhammad Asfandyar Ali Khan
Management Trainee](https://image.slidesharecdn.com/3kwhybrid-model-170209124335/85/3kw-hybrid-model-1-320.jpg)
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Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
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model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
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