Essays about: "STATE MACHINE"

Showing result 11 - 15 of 763 essays containing the words STATE MACHINE.

  1. 11. Machine Learning for State Estimation in Fighter Aircraft

    University essay from KTH/Optimeringslära och systemteori

    Author : Axel Boivie; [2023]
    Keywords : State estimation; machine learning; fighter aircraft; neural networks; long short- term memory; LSTM; sensor fusion; air data system; ADS.; Tillståndsestimering; maskininlärning; stridsflygplan; neurala nätverk; sensorfusion; luftdatasystem.;

    Abstract : This thesis presents an estimator to assist or replace a fighter aircraft’s air datasystem (ADS). The estimator is based on machine learning and LSTM neuralnetworks and uses the statistical correlation between states to estimate the angleof attack, angle of sideslip and Mach number using only the internal sensorsof the aircraft. READ MORE

  3. 12. Exploring State-of-the-Art Machine Learning Methods for Quantifying Exercise-induced Muscle Fatigue

    University essay from Högskolan i Halmstad/Akademin för informationsteknologi

    Author : Abboud Afram; Danial Sarab Fard Sabet; [2023]
    Keywords : EMG; SEMG; STFT; CWT; SVM; CNN; GAN; DCGAN; BCE; SGD; deep learning; machine learning; muscle fatigue; DCGAN; spectrogram; CNN models; transfers learning; data augmentation; feature extraction;

    Abstract : Muscle fatigue is a severe problem for elite athletes, and this is due to the long resting times, which can vary. Various mechanisms can cause muscle fatigue which signifies that the specific muscle has reached its maximum force and cannot continue the task. READ MORE

  4. 13. Point Cloud Registration using both Machine Learning and Non-learning Methods : with Data from a Photon-counting LIDAR Sensor

    University essay from Linköpings universitet/Datorseende

    Author : Maja Boström; [2023]
    Keywords : Point Cloud Registration; Machine Learning; Photon-counting LIDAR; Iterative Closest Point;

    Abstract : Point Cloud Registration with data measured from a photon-counting LIDAR sensor from a large distance (500 m - 1.5 km) is an expanding field. Data measuredfrom far is sparse and have low detail, which can make the registration processdifficult, and registering this type of data is fairly unexplored. READ MORE

  5. 14. Exploration and Evaluation of RNN Models on Low-Resource Embedded Devices for Human Activity Recognition

    University essay from KTH/Mekatronik och inbyggda styrsystem

    Author : Helgi Hrafn Björnsson; Jón Kaldal; [2023]
    Keywords : Recurrent Neural Networks; Long Short-Term Memory Networks; Embedded Systems; Human Activity Recognition; Edge AI; TensorFlow Lite Micro; Recurrent Neural Networks; Long Short-Term Memory Networks; Innbyggda systyem; Mänsklig aktivitetsigenkänning; Edge AI; TensorFlow Lite Micro;

    Abstract : Human activity data is typically represented as time series data, and RNNs, often with LSTM cells, are commonly used for recognition in this field. However, RNNs and LSTM-RNNs are often too resource-intensive for real-time applications on resource constrained devices, making them unsuitable. READ MORE

  6. 15. Towards gradient faithfulness and beyond

    University essay from Högskolan i Halmstad/Akademin för informationsteknologi

    Author : Vincenzo Buono; Isak Åkesson; [2023]
    Keywords : XAI; Visual Explanations; CAM; Grad-CAM; Expected Grad-CAM; Hyper Expected Grad; Class Activation Maps; Explainable AI; Faithfulness; Neural Network interpretability; Hyper Resolution CAM; Super Resolution CAM; Natural Encoding;

    Abstract : The riveting interplay of industrialization, informalization, and exponential technological growth of recent years has shifted the attention from classical machine learning techniques to more sophisticated deep learning approaches; yet its intrinsic black-box nature has been impeding its widespread adoption in transparency-critical operations. In this rapidly evolving landscape, where the symbiotic relationship between research and practical applications has never been more interwoven, the contribution of this paper is twofold: advancing gradient faithfulness of CAM methods and exploring new frontiers beyond it. READ MORE