IoT-Based-Solar-Panel-Monitoring-System: Beginning of a Final Year Project
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An IoT based solar panel monitoring system uses sensors to collect data on voltage, current, temperature and irradiance. This data is transmitted via a communication network and analyzed and visualized for real-time monitoring and fault detection. The system allows for predictive maintenance to optimize schedules and reduce downtime. Implementing such a system faces challenges regarding data security, power management and energy efficiency.
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IRJET- A Survey on Data Acquisition and Remote Monitoring Display
A STUDY OF IOT BASED REAL-TIME SOLAR POWER REMOTE MONITORING SYSTEM
A STUDY OF IOT BASED REAL-TIME SOLAR POWER REMOTE MONITORING SYSTEM
IRJET- Remotely Monitoring Health of the Solar Power System using Arduino
IRJET- Remotely Monitoring Health of the Solar Power System using Arduino
Design of A REAL TIME Arduino Controlled Wireless Monitoring System for Solar...
Design of A REAL TIME Arduino Controlled Wireless Monitoring System for Solar...
ELEMENTS OF MECHANICAL ENGG. Module 5 MECHATRONICS (1).pptx
ELEMENTS OF MECHANICAL ENGG. Module 5 MECHATRONICS (1).pptx
IoT Based Patient Biomedical Signal Tracking System
IoT Based Patient Biomedical Signal Tracking System
ealth Monitoring System in Emergency Using IoT: A Review
ealth Monitoring System in Emergency Using IoT: A Review
Ima an integrated monitoring architecture with sensor networks
Ima an integrated monitoring architecture with sensor networks
Recently uploaded
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
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
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.
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building
BRAIN TUMOR DETECTION for seminar ppt.pdf
BRAIN TUMOR DETECTION
AND CLASSIFICATION USING
ARTIFICIAL INTELLIGENCE
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NATURAL DEEP EUTECTIC SOLVENTS AS ANTI-FREEZING AGENT
NATURAL DEEP EUTECTIC SOLVENTS AS ANTI-FREEZING AGENT
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
The Python for beginners. This is an advance computer language.
Python language is very important language at this time. we can easily understand this language by these notes.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
Slides for the 4th Presentation on LLM Fine-Tuning with QLoRA Presented by Anant, featuring DataStax Astra
Hematology Analyzer Machine - Complete Blood Count
The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
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Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
NATURAL DEEP EUTECTIC SOLVENTS AS ANTI-FREEZING AGENT
NATURAL DEEP EUTECTIC SOLVENTS AS ANTI-FREEZING AGENT
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
The Python for beginners. This is an advance computer language.
The Python for beginners. This is an advance computer language.
ML Based Model for NIDS MSc Updated Presentation.v2.pptx
ML Based Model for NIDS MSc Updated Presentation.v2.pptx
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
Hematology Analyzer Machine - Complete Blood Count
Hematology Analyzer Machine - Complete Blood Count
Introduction to AI Safety (public presentation).pptx
Introduction to AI Safety (public presentation).pptx
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
IoT-Based-Solar-Panel-Monitoring-System: Beginning of a Final Year Project
- 1. IoT Based Solar Panel Monitoring System for advertisement purpose An introduction to the benefits and importance of an IoT based solar panel monitoring system, its components, functionality and challenges. Contributors: Ms. Sumana Dey, Ms. Chaitaili Kharna, Mr. Sanjeev Kumar Gupta, Mr. Tusar Kumar, Mr. Biplab Hazra Under the Guidance of: Dr. Nirmalya Mallick
- 2. Components of an IoT based solar panel monitoring system Sensors and data collection Various sensors are used to collect data, including voltage, current, temperature and irradiance Communication network Data is transmitted for monitoring Data analysis and visualization Analysis of data, data visualization and real-time reporting
- 3. Visualization Processing Sensing Components of an IoT based solar panel monitoring system
- 4. Functionality of an IoT based solar panel monitoring system Real-time monitoring Real-time performance monitoring of solar panels, such as monitoring the output voltage, current, frequency and temperature, and sending alerts for issues such as reduced efficiency. Fault detection and remote troubleshooting How the system is utilized to identify faults and issues in real-time and provide troubleshooting support remotely without sending an engineer to the site. Predictive maintenance Predictive analysis and machine learning capabilities are implemented into the system to predict maintenance needs, enabling optimization of maintenance schedules, increased overall system lifespan and reduced downtime.
- 5. Challenges and considerations in implementing an IoT based solar panel monitoring system 1 Data security and privacy 2 Power management and energy efficiency
- 6. COMPONENTS Temperature Sensor Light Sensor Voltage Sensor Current Sensor Load ESP32 Microcontroller LCD Display Solar Panel
- 7. Conclusion Recap of Key Points • IoT based solar panel monitoring system components • Functionality • Implementation challenges
- 8. Jayavardhana G.et. Al., “Internet of Things (IoT): A vision, architectural elements, and future directions.” Future generation computer Internet of things (Nov 2016). “http://cerasis.com/2015/10/20/10-things-to-know-about-the internet-of things” References
- 9. Thank You