Magnetic Angular Rate and Gravity Sensor Based Supervised Learning for Positioning Tasks

This paper deals with sensor fusion of magnetic, angular rate and gravity sensor (MARG). The main contribution of this paper is the sensor fusion performed by supervised learning, which means parallel processing of the different kinds of measured data and estimating the position in periodic and non-periodic cases. During the learning phase, the position estimated by sensor fusion is compared with position data of a motion capture system. The main challenge is avoiding the error caused by the implicit integral calculation of MARG. There are several filter based signal processing methods for disturbance and noise estimation, which are calculated for each sensor separately. These classical methods can be used for disturbance and noise reduction and extracting hidden information from it as well. This paper examines the different types of noises and proposes a machine learning-based method for calculation of position and orientation directly from nine separate sensors. This method includes the disturbance and noise reduction in addition to sensor fusion. The proposed method was validated by experiments which provided promising results on periodic and translational motion as well

A Study on Human Face Expressions using Convolutional Neural Networks and Generative Adversarial Networks

Human beings express themselves via words, signs, gestures, and facial emotions. Previous research using pre-trained convolutional models had been done by freezing the entire network and running the models without the use of any image processing techniques. In this research, we attempt to enhance the accuracy of many deep CNN architectures like ResNet and Senet, using a variety of different image processing techniques like Image Data Generator, Histogram Equalization, and UnSharpMask. We used FER 2013, which is a dataset containing multiple classes of images. While working on these models, we decided to take things to the next level, and we attempted to make changes to the models themselves to improve their accuracy. While working on this research, we were introduced to another concept in Deep Learning known as Generative Adversarial Networks, which are also known as GANs. They are generative deep learning models which are based on deep CNN models, and they comprise two CNN models - a Generator and a Discriminator. The primary task of the former is to generate random noises in the form of images and passes them to the latter. The Discriminator compares the noise with the input image and accepts/rejects it, based on the similarity. Over the years, there have been various distinguished architectures of GANs namely CycleGAN, StyleGAN, etc. which have allowed us to create sophisticated architectures to not only generate the same image as the original input but also to make changes to them and generate different images. For example, CycleGAN allows us to change the season of scenery from Summer to Winter or change the emotion in the face of a person from happy to sad. Though these sophisticated models are good, we are working with an architecture that has two deep neural networks, which essentially creates problems with hyperparameter tuning and overfitting

Neural network-enhanced fault diagnosis of robot joints

Industrial robots play an indispensable role in flexible production lines, and the faults caused by degradation of equipment, motors, mechanical system joints, and even task diversity affect the efficiency of production lines and product quality. Aiming to achieve high-precision multiple size of fault diagnosis of robotic arms, this study presents a back propagation (BP) multiclassification neural network-based method for robotic arm fault diagnosis by taking feature fusion of position, attitude and acceleration of UR10 robotic arm end-effector to establish the database for neural network training. The new algorithm achieves an accuracy above 95% for fault diagnosis of each joint, and a diagnostic accuracy of up to 0.1 degree. It should be noted that the fault diagnosis algorithm can detect faults effectively in time, while avoiding complex mathematical operations

Synthesizing Cyber Intrusion Alerts using Generative Adversarial Networks

Cyber attacks infiltrating enterprise computer networks continue to grow in number, severity, and complexity as our reliance on such networks grows. Despite this, proactive cyber security remains an open challenge as cyber alert data is often not available for study. Furthermore, the data that is available is stochastically distributed, imbalanced, lacks homogeneity, and relies on complex interactions with latent aspects of the network structure. Currently, there is no commonly accepted way to model and generate synthetic alert data for further study; there are also no metrics to quantify the fidelity of synthetically generated alerts or identify critical attributes within the data. This work proposes solutions to both the modeling of cyber alerts and how to score the fidelity of such models. Generative Adversarial Networks are employed to generate cyber alert data taken from two collegiate penetration testing competitions. A list of criteria defining desirable attributes for cyber alert data metrics is provided. Several statistical and information-theoretic metrics, such as histogram intersection and conditional entropy, meet these criteria and are used for analysis. Using these metrics, critical relationships of synthetically generated alerts may be identified and compared to data from the ground truth distribution. Finally, through these metrics, we show that adding a mutual information constraint to the model’s generation increases the quality of outputs and successfully captures alerts that occur with low probability

Human face detection techniques: A comprehensive review and future research directions

Face detection which is an effortless task for humans are complex to perform on machines. Recent veer proliferation of computational resources are paving the way for a frantic advancement of face detection technology. Many astutely developed algorithms have been proposed to detect faces. However, there is a little heed paid in making a comprehensive survey of the available algorithms. This paper aims at providing fourfold discussions on face detection algorithms. At first, we explore a wide variety of available face detection algorithms in five steps including history, working procedure, advantages, limitations, and use in other fields alongside face detection. Secondly, we include a comparative evaluation among different algorithms in each single method. Thirdly, we provide detailed comparisons among the algorithms epitomized to have an all inclusive outlook. Lastly, we conclude this study with several promising research directions to pursue. Earlier survey papers on face detection algorithms are limited to just technical details and popularly used algorithms. In our study, however, we cover detailed technical explanations of face detection algorithms and various recent sub-branches of neural network. We present detailed comparisons among the algorithms in all-inclusive and also under sub-branches. We provide strengths and limitations of these algorithms and a novel literature survey including their use besides face detection

Use of Quality Management Methods and Tools - a Systematic Review of the Literature

Bakalářská práce se skládá ze dvou částí: teoretické a praktické. V teoretické části práce popisujeme a charakterizujeme metody a nástroje managementu kvality. V praktické části jsme se zaměřili na shromáždění a analýzu publikací zabývajících se možnostmi využívání metod a nástrojů managementu kvality v různých ekonomických a sociálních oblastech. Pro rychlejší vyhledávání sledovaných publikací jsme využili dvě databáze: (www.webofscience.com) a IEE Xplore (https://ieeexplore.ieee.org).The Bachelor thesis consists of two parts: theoretical and practical. In the theoretical part of the work, we describe and characterize the methods and tools of quality management. In the practical part, we focused on gathering and analysing publications dealing with the possibilities of using quality management methods and tools in various economic and social areas. We used two databases for faster searches of monitored publications: (www.webofscience.com) and IEE Xplore(https://ieeexplore.ieee.org).639 - Katedra managementu kvalitydobř