Forest Safe MVP
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This document describes a forest fire prevention system called Forests Safe. It uses IoT sensors to monitor environmental conditions related to fire risk, including temperature, humidity, rainfall and fuel moisture levels. The sensors transmit data via LoRaWAN to a cloud platform where a risk model calculates the fire risk level in real-time. Risk levels of low, medium, high or very high are pushed to Firebase. The system is intended for small/medium landowners and aims to help prevent fires through early detection without the complexity of a large sensor network. It uses inexpensive, easily deployable hardware and cloud technologies like TheThingsNetwork and AWS to process the risk model.
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This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Understanding Inductive Bias in Machine Learning
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
New techniques for characterising damage in rock slopes.pdf
rock mass characterization and New techniques for characterising damage in rock slopes
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
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%.
International Conference on NLP, Artificial Intelligence, Machine Learning an...
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation
DfMAy 2024 - key insights and contributions
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Modelagem de um CSTR com reação endotermica.pdf
Modelagem em função de transferencia. CSTR não-linear.
A review on techniques and modelling methodologies used for checking electrom...
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
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6th International Conference on Machine Learning & Applications (CMLA 2024)
6th International Conference on Machine Learning & Applications (CMLA 2024)
ML Based Model for NIDS MSc Updated Presentation.v2.pptx
ML Based Model for NIDS MSc Updated Presentation.v2.pptx
New techniques for characterising damage in rock slopes.pdf
New techniques for characterising damage in rock slopes.pdf
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
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
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
International Conference on NLP, Artificial Intelligence, Machine Learning an...
International Conference on NLP, Artificial Intelligence, Machine Learning an...
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...
A review on techniques and modelling methodologies used for checking electrom...
A review on techniques and modelling methodologies used for checking electrom...
Forest Safe MVP
- 1. Internet of things 2018/2019 FORESTS SAFE
- 2. Our team Pietro SpadaccinoLeonardo SalvucciGiorgio De Magistris
- 3. Forests Safe Possibility of fire prevention through a simple and reliable solution It is aimed at small/medium lands owners excluding a large sensor network to limit complexity Source: The operators of the Fire Brigade Detachment of Spoleto (PG)
- 4. Causes The fire ignition triangle describes the elements that MUST be present in order to start a fire: ● Oxygen (air) - to start and sustain combustion. Air supply can be increased by windy conditions. ● Heat - to raise fuel temperatures to their ignition point and to ignite fuels. Common sources of heat are lightning and human activities. ● Fuel - to sustain and/or carry flames. Combustible materials include trees, shrubs, grasses and structures.
- 5. Dead Fuel Moisture Dead Fuel Ignition Point Dead Fuel Moisture Level Air Relative Humidity Precipitations Depends On Depends On
- 6. Dead Fuels Classification According to US National Fire Danger Rating System (NFDRS) dead fuels are classified in four Timelags ● 1 hour → diameter < 0.6 cm (leaves) ● 10 hours → 0.6 < diameter < 2.5 cm (twigs) ● 100 hours → 2.5 < diameter < 8.0 cm (small branches) ● 1000 hours → 8.0 < diameter < 15.0 cm (big branches) A fuel’s time lag is the time necessary for a fuel particle of a particular size to reach 63% of equilibrium between its initial moisture content and its current environment
- 7. KBDIndex Keetch-Byram Drought Index (KBDI) is an indicator adopted in US to measure the millimeters of rain needed to completely saturate the fuels. Applying the four classes in Italy, considering the average seasonal precipitations we obtain: ● KBDIWinter : 0-0.5 mm ● KBDISpring : 0.5-1.0 mm ● KBDISummer : 1.5-2.0 mm ● KBDIAutumn : 1.0-1.5 mm
- 8. Our preventive model t : current time (in hours) MCi : Moisture content (%) of class i timelag : time(h) for class j to reach the equilibrium RH : Relative Humidity of the Air SEASONj : boolean, 1 if we’re in season j, 0 otherwise Mmrain(t): millimeters of rain fallen between t and t-1 w : weight of fuel of class i KBDIj: measure the millimeters of rain needed to completely saturate the fuels INPUT: Air RH, Millimeters of rain OUTPUT: Fire risk (LOW, MEDIUM, HIGH, VERY HIGH) First elaboration: Second elaboration: Midterm result: Fuels RH
- 9. Architecture
- 10. Hardware B-L072Z-LRWAN1 board for LoRa communications plus X-NUCLEO-IKS01A1 expansion shield for temperature and humidity sensors ● Easily deployable in the forest by the user ● Power supply through 3xAAA batteries ● Ping sensor readings every ~5 minutes
- 11. Cloud technologies TheThingsNetwork will function as LoRa backend, providing HTTP integration to an AWS compute unit where the risk model will be run and will push data in real time to Firebase
- 12. Prototype
- 15. Our strengths
- 16. Thank you for the attention! Initial concept presentation https://www.slideshare.net/PietroSpadaccino/forests-safe-project-iot-sapienza-142816402 Team Giorgio De Magistris Pietro SpadaccinoLeonardo Salvucci