AI Seminar – Drone (original) (raw)
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QUIST MIRABLE JETHRO, 2024
ARTIFICIAL INTELLIGENCE (AI) in Action: Empowering Drones. How AI is transforming Drones includes: Autonomous Flight, Enhanced Data Collection. UAVs or Unmanned Aerial Vehicles are aircraft operated without a human pilot on board. They can be remotely controlled or fly automatously using pre-programmed flight plans or more complex dynamic automation systems. Why UAVs? Agility and maneuverability. Cost-effectiveness compared to manned aircraft. Access to hazardous or remote areas. smaller in size and can achieve great speeds. Understanding Artificial Intelligence (AI). AI is Simulation of human intelligence in machines, enabling systems to learn, reason, make decisions. Key Areas for Drones: Machine learning, Algorithms learning from data and Computer vision: Enabling drones to "see" and understand. AI in Action: Empowering Drones. Applications: Search and Rescue. Search and Rescue with AI includes: Rapid coverage of large areas. Thermal imaging for finding people. Facial recognition for missing person identification (consider ethnical implications). Applications: Precision Agriculture. Revolutionizing Agriculture: Crop health monitoring: Identifying diseases and deficiencies. Precision Spraying: Optimize pesticide and fertilizer use. Automated field mapping and yield estimation. Applications: Infrastructure Inspection. Infrastructure Inspection with AI includes: Access to hard-to-reach areas. AI - powered damage detection (cracks, corrosion). Faster and more cost-effective inspections. Applications: Delivery Drones. The Future Delivery: Autonomous deliveries to remote areas. Faster and more efficient delivery items. Considerations: Safety, regulation, public perception. The Future of AI and Drones; Integration with LIDAR, 5G for enhanced capabilities. Expanding to disaster response, environmental monitoring, planetary exploration. Addressing ethical considerations (privacy, security). AI is revolutionizing UAV capabilities. Vast potential for various industries. Exciting future with endless possibilities
Détection & diagnostic de pannes pour les drones utilisant la machine learning
2019
Cette nouvelle ere de petits UAV qui peuplent actuellement l'espace aerien souleve de nombreuses preoccupations en matiere de securite, en raison de l'absence de pilote a bord et de la nature moins precise des capteurs. Cela necessite des approches intelligentes pour faire face aux situations d'urgence qui se produiront inevitablement pour toutes les categories d'operations d'UAV telles que definies par l'AESA (Agence europeenne de la securite aerienne). Les limitations materielles de ces petits vehicules suggerent l'utilisation de la redondance analytique plutot que la pratique habituelle de la redondance materielle dans l'aviation humaine. Au cours de cette etude, des pratiques d'apprentissage automatique sont mises en œuvre afin de diagnostiquer les defaillances d'un petit drone a voilure fixe afin d'eviter le fardeau de la modelisation precise necessaire au diagnostic par le modele. Une methode de classification supervisee, SVM (Suppor...
IRJET- REVIEW ON MACHINE LEARNING
IRJET, 2020
Machine learning is generally a field of computer science which gives the ability to learn without the use of programmer. Machine learning is also said to be artificial intelligence. In this algorithms can be easily understand and need number of raw data to work according to the set of algorithms. It can be easily organized and automatically solve more complex data in the problems. It helps in delivering faster and more accurate results. Some of the programs are based on internet oriented for example Google maps, amazon and other online applications. Mainly machine learning is used in internet of things. There are three different stages in machine learning so that it can execute according to that stages and learns from trainer. There are some of the challenges in Machine learning which can be solved, but few things can't be solved. So machine learning is important in day today's life. In this paper you will come to know about what is machine learning, stages, applications and challenges faced in it.
IJERT-Drone Utterance Cast Analysis using Machine Learning
International Journal of Engineering Research and Technology (IJERT), 2020
https://www.ijert.org/drone-utterance-cast-analysis-using-machine-learning https://www.ijert.org/research/drone-utterance-cast-analysis-using-machine-learning-IJERTCONV8IS08003.pdf Unmanned aerial vehicles (UAVs) networks square measure still untouched and much from analysis field. Security problems square measure the main issues as a result of these networks square measure susceptible to varied attacks which can cause data leak. Cyber Physical Systems (CPS) play a very important role in providing vital services in industries like autonomous vehicle systems, energy, health, producing, etc., by integration computation, physical management, and networking. Most of those systems aren't solely cyber-physical, however additionally operate in an exceedingly safety-critical application wherever a failure or malfunction may lead to injury or perhaps loss of life. An pilotless Aerial System (UAS) meets the wants of a cycle per second and safety-critical system with its dependence on wireless communication, sensors, and algorithms that job synergistically to perform its practicality. Innovation technology has followed the paradigm of enhancing performance as a main priority, with security as either AN afterthought or not thought of in the least, inflicting an absence of security against cyber-attacks in most UAVs. within the past UAVs have costly, heavy, and most typically utilized by the military, however, cost, size, and weight have cut drastically, whereas their capabilities, attributed to technology, have accumulated well.
How to Support the Machine Learning Take-Off: Challenges and Hints for Achieving Intelligent UAVs
2017
Unmanned Aerial Vehicles (UAVs) are getting momentum. A growing number of industries and scientific institutions are focusing on these devices. UAVs can be used for a really wide spectrum of civilian and military applications. Usually these devices run on batteries, thus it is fundamental to efficiently exploit their hardware to reduce their energy footprint. A key aspect in facing the “energy issue” is the exploitation of properly designed solutions in order to target the energy- and hardware-constraints characterising these devices. However, there are not universal approaches easing the implementation of ad-hoc solutions for UAVs; it just depends on the class of problems to be faced. As matter of fact, targeting machine-learning solutions to UAVs could foster the development of a wide range of interesting application. This contribution is aimed at sketching the challenges deriving from the porting of machine-learning solutions, and the associated requirements, to highly distribute...
Eagle View: An Abstract Evaluation of Machine Learning Algorithms based on Data Properties
2021
Data can be generated from almost any type of information. Experimental Data Analysis (EDA) and Feature Engineering for machine learning models necessitate a thorough understanding of the different types of data. Algorithms that interpret and recall future data details use Machine Learning (ML) data. The majority of the data can be found on the internet. In terms of Machine Learning, the majority of the data can be grouped into four categories: numerical data, category data, time-series data, and text. Supervised Learning, a collection of unproven learning algorithms, is the subject of this research.
Machine Learning Algorithms: Optimizing Efficiency in AI Applications
International Journal of Engineering and Management Research , 2024
Machine learning (ML) is an AI technology that creates programs and data models that can perform tasks without being instructed. It has three major types: guided learning, uncontrolled learning, and reinforcement learning. ML is essential for big projects like real-time decision-making systems and self-driving cars, robots, and drones. It improves AI systems by making it easier to create models, work with data, and run algorithms. ML algorithms have different types of learning, require different amounts of data and training times, and can be improved by tuning hyperparameters. Techniques like feature selection, dimensionality reduction, model editing, and compression can improve performance and accuracy in various fields. In the real world, making AI apps more efficient can lead to more options, lower prices, and faster processing. Key techniques like model compression, transfer learning, and edge computing are needed to achieve these goals.
IRJET- A Survey on Various Machine Learning Algorithms
IRJET, 2020
Apparently, we are living in the most defining and developing period of human history. This is the period where computing generation reached from large mainframes to PCs to cloud. But what makes it defining is not what happened, but what is coming our ways in future. There is no doubt that machine learning/ artificial intelligence has rapidly gained more vogue in the previous couple of years. As the hottest mania in the tech industry at present, machine learning extremely powerful to make predictions and calculated suggestions which is generally based on the very large amount of data. This paper tells about how the machine learning algorithms adaptively enhance their performances as the inputs available for learning increases.