scholarly journals Robust Wireless Communication for Small Exploration Rovers Equipped with Multiple Antennas by Estimating Attitudes of Rovers in Several Experimental Environments

2017 ◽  
Vol 29 (5) ◽  
pp. 864-876 ◽  
Author(s):  
Masahiko Mikawa ◽  
Keyword(s):  
Estimation Method ◽  
Attitude Estimation ◽  
The Body ◽  
Training Data ◽  
Large Area ◽  
Data Set ◽  
Wireless Mesh ◽  
Surface Exploration ◽  
The Face ◽  
Micro Gravity

We are developing a robotic system for an asteroid surface exploration. The system consists ofmultiplesmall size rovers, that communicate with each other over a wireless network. Since the rovers configure over a wireless mesh sensor network on an asteroid, it is possible to explore a large area on the asteroid effectively. The rovers will be equipped with a hopping mechanism for transportation, which is suitable for exploration in a micro-gravity environment like a small asteroid’s surface. However, it is difficult to control the rover’s attitude during the landing. Therefore, a cube-shaped rover was designed. As every face has two antennas respectively, the rover has a total of twelve antennas. Furthermore, as the body shape and the antenna arrangements are symmetric, irrespective of the face on top, a reliable communication state among the rovers can be established by selecting the proper antennas on the top face. Therefore, it is important to estimate which face of the rover is on top. This paper presents an attitude estimation method based on the received signal strength indicators (RSSIs) obtained when the twelve antennas communicate among each other. Since the RSSI values change depending on an attitude of the rover and the surrounding environment, a significantly large number of RSSIs were collected as a training data set in different kinds of environments similar to an asteroid; consequently, a classifier for estimating the rover attitude was trained from the data set. A few of the experimental results establish the validity and effectiveness of the proposed exploration system and attitude estimation method.

Interlaced Attitude Estimation for Spacecraft Using Vector Observations

2021 ◽  
Vol 01 (03) ◽  
Author(s):  
Lubin Chang
Keyword(s):  
Prior Information ◽  
State Of The Art ◽  
Estimation Method ◽  
Attitude Estimation ◽  
The Body ◽  
Body Frame ◽  
Initial Value ◽  
Initial State ◽  
Initial Errors ◽  
Initial Attitude

This paper proposes an interlaced attitude estimation method for spacecraft using vector observations, which can simultaneously estimate the constant attitude at the very start and the attitude of the body frame relative to its initial state. The arbitrary initial attitude, described by constant attitude at the very start, is determined using quaternion estimator which requires no prior information. The multiplicative extended Kalman filter (EKF) is competent for estimating the attitude of the body frame relative to its initial state since the initial value of this attitude is exactly known. The simulation results show that the proposed algorithms could achieve better performance compared with the state-of-the-art algorithms even with extreme large initial errors. Meanwhile, the computational burden is also much less than that of the advanced nonlinear attitude estimators.

scholarly journals Real Time Detection and Identification of Human Emotions through Live Streaming

2019 ◽  
Vol 8 (4) ◽  
pp. 12842-12845
Keyword(s):  
Skin Color ◽  
Communication Complexity ◽  
Opinion Mining ◽  
Training Data ◽  
Live Streaming ◽  
Data Set ◽  
Detection And Identification ◽  
Challenging Tasks ◽  
The Face ◽  
Video Input

Automating the analysis of facial expressions of individuals is one of the challenging tasks in opinion mining. In this work, the proposed technique for identifying the face of an individual and the emotions, if present from a live camera. Expression detection is one of the sub-areas of computer visions which is capable of finding a person from a digital image and identify the facial expression which are the key factors of nonverbal communication. Complexity involves mainly in two cases viz., 1)if more than one emotions coexist on a face. 2) expressing same emotion between individuals is not exactly same. Our aim was to make the processes automatic by identify the expressions of people in a live video. In this system OpenCV library containing face recognizer module for detecting the face and for training the model. It was able to identify the seven different expressions with 75-85% accuracy. The expressions identified are happy, sadness, disgust, fear, anger, surprise and neutral. The this an image frame from is captured from the video, locate the face in it and then test it against the training data for predicting the emotion and update the result. This process is continued till the video input exists. On top of this the data set for training should be in such a way that , it prediction should be independent of age, gender, skin color orientation of the human face in the video and also the lamination around the subject of reference

scholarly journals LiDAR DNN based self-attitude estimation with learning landscape regularities

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ryota Ozaki ◽  
Naoya Sugiura ◽  
Yoji Kuroda
Keyword(s):  
Data Augmentation ◽  
Estimation Method ◽  
Time Estimation ◽  
Attitude Estimation ◽  
Training Data ◽  
Fine Tuning ◽  
Depth Image ◽  
Neural Network Learning ◽  
Synthetic Datasets ◽  
Gravity Direction

AbstractThis paper presents an EKF (extended Kalman filter) based self-attitude estimation method with a LiDAR DNN (deep neural network) learning landscape regularities. The proposed DNN infers the gravity direction from LiDAR data. The point cloud obtained with the LiDAR is transformed to a depth image to be input to the network. It is pre-trained with large synthetic datasets. They are collected in a flight simulator because various gravity vectors can be easily obtained, although this study focuses not only on UAVs. Fine-tuning with datasets collected with real sensors is done after the pre-training. Data augmentation is processed during the training in order to provide higher general versatility. The proposed method integrates angular rates from a gyroscope and the DNN outputs in an EKF. Static validations are performed to show the DNN can infer the gravity direction. Dynamic validations are performed to show the DNN can be used in real-time estimation. Some conventional methods are implemented for comparison.

scholarly journals A NOVEL APPROACH FOR FINDING DIABETIC MELLITUS USING ENSEMBLE MODEL FOR AN OPTIMIZED CLASSIFICATION

2017 ◽  
Vol 10 (9) ◽  
pp. 15 ◽  
Author(s):  
Sekar Kr ◽  
Kamaladevi M ◽  
Sethuraman J ◽  
Ravichandran Ks
Keyword(s):  
Ensemble Classifier ◽  
The Body ◽  
Training Data ◽  
Diabetic Mellitus ◽  
Data Set ◽  
Medical Practitioners ◽  
Novel Approach ◽  
High Care ◽  
Control Disease ◽  
The University

  Diabetic mellitus is a chronic disease caused by hyperglycemia which should be treated with high care and medications. The objective of this work is to identify and classify the severity of the diabetic disease using the training data set. This is caused due to the defect in insulin secretion that may affect several organs in the body. Blood pressure and diabetic mellitus are the common twin diseases occurred in about 69.2 million people living in India around 8.7% of the population as per the data resealed in the year 2015. Correct diet, regular exercise will control disease to a great extent. In this research paper the applied methodology is a concurrent classifier for the diabetic mellitus and the results are analyzed with the supervised learning. From the University of California and Irvine repository related attributes for the diabetic mellitus are carefully measured through the ensemble classifier and the results are categorized in the dataset. This work results that boosting can be made to the dataset for obtaining accurate results and classifications. In the conclusion, ensemble methodology is the well proven methodology from the year 1993. For forecasting in “N” number of domains, so for the ensemble classifier produces 93% of the accurate results are made. An audit can be made on the results and suggestions are given to the patients for taking medications with the help of medical practitioners.

scholarly journals Optimizing Few-Shot Learning based on Variational Autoencoders

2021 ◽  
Author(s):  
Ruoqi Wei ◽  
Ausif Mahmood
Keyword(s):  
Face Recognition ◽  
Data Augmentation ◽  
Training Data ◽  
The Novel ◽  
Limited Data ◽  
Data Set ◽  
Learning Tasks ◽  
The Face ◽  
Face Generation ◽  
Generative Approach

Despite the importance of few-shot learning, the lack of labeled training data in the real world, makes it extremely challenging for existing machine learning methods as this limited data set does not represent the data variance well. In this research, we suggest employing a generative approach using variational autoencoders (VAEs), which can be used specifically to optimize few-shot learning tasks by generating new samples with more intra-class variations. The purpose of our research is to increase the size of the training data set using various methods to improve the accuracy and robustness of the few-shot face recognition. Specifically, we employ the VAE generator to increase the size of the training data set, including the basic and the novel sets while utilizing transfer learning as the backend. Based on extensive experimental research, we analyze various data augmentation methods to observe how each method affects the accuracy of face recognition. We conclude that the face generation method we proposed can effectively improve the recognition accuracy rate to 96.47% using both the base and the novel sets.

scholarly journals Semi-Supervised Learning to Perceive Children's Affective States in a Tablet Tutor

2020 ◽  
Vol 34 (09) ◽  
pp. 13350-13357
Author(s):  
Mansi Agarwal ◽  
Jack Mostow
Keyword(s):  
Supervised Learning ◽  
Affective State ◽  
Training Data ◽  
Affective States ◽  
Data Set ◽  
Tutoring Systems ◽  
Screen Capture ◽  
Student Affect ◽  
The Face ◽  
Authentic Data

Like good human tutors, intelligent tutoring systems should detect and respond to students' affective states. However, accuracy in detecting affective states automatically has been limited by the time and expense of manually labeling training data for supervised learning. To combat this limitation, we use semi-supervised learning to train an affective state detector on a sparsely labeled, culturally novel, authentic data set in the form of screen capture videos from a Swahili literacy and numeracy tablet tutor in Tanzania that shows the face of the child using it. We achieved 88% leave-1-child-out cross-validated accuracy in distinguishing pleasant, unpleasant, and neutral affective states, compared to only 61% for the best supervised learning method we tested. This work contributes toward using automated affect detection both off-line to improve the design of intelligent tutors, and at runtime to respond to student affect based on input from a user-facing tablet camera or webcam.

Uncertainty quantification for a sparse machine learning (ML) data set in non-destructive testing in civil engineering (NDT-CE) 

2021 ◽  
Author(s):  
Christoph Völker ◽  
Sabine Kruschwitz ◽  
Philipp Benner
Keyword(s):  
Performance Metrics ◽  
Corrosion Damage ◽  
Training Data ◽  
Large Area ◽  
Destructive Testing ◽  
Cell Potential ◽  
Data Set ◽  
Potential Mapping ◽  
Ground Penetrating ◽  
Non Destructive

<p>ML has been successfully applied to solve many NDT-CE tasks. This is usually demonstrated with performance metrics that evaluate the model as a whole based on a given set of data. However, since in most cases the creation of reference data is extremely expensive, the data used is generally much sparser than in other areas, such as e-commerce. As a result, performance indicators often do not reflect the practical applicability of the ML model. Estimates that quantify transferability from one case to another are necessary to meet this challenge and pave the way for real world applications.</p><p>In this contribution we invetigate the uncertainty of ML in new NDT-CE scenarios. For this purpose, we have extended an existing training data set for the classification of corrosion damage by a new case study. Our data set includes half-cell potential mapping and ground-penetrating radar measurements. The measurements were performed on large-area concrete samples with built-in chloride-induced corrosion of reinforcement. The experiment simulated the entire life cycle of chloride induced exposed concrete components in the laboratory. The unique ability to monitor deterioration and initiate targeted corrosion initiation allowed the data to be labelled - which is crucial to ML. To investigate transferability, we extend our data by including new design features of the test specimen and environmental conditions. This allows to express the change of these features in new scenarios as uncertainties using statistical methods. We compare different sampling and statistical distribution-based approaches and show how these methods can be used to close knowledge gaps of ML models in NDT.</p>

Questions on the role of biofilms for the adaptation of microorganisms to unfavorable environmental factors by the example of P. aeruginosa

2020 ◽  
Vol 99 (4) ◽  
pp. 379-383
Author(s):  
Vasily N. Afonyushkin ◽  
N. A. Donchenko ◽  
Ju. N. Kozlova ◽  
N. A. Davidova ◽  
V. Yu. Koptev ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Environmental Factors ◽  
Barrier Function ◽  
Infectious Agent ◽  
Antagonistic Activity ◽  
The Body ◽  
Main Function ◽  
Pathogenic Potential ◽  
The Face

Pseudomonas aeruginosa is a widely represented species of bacteria possessing of a pathogenic potential. This infectious agent is causing wound infections, fibrotic cystitis, fibrosing pneumonia, bacterial sepsis, etc. The microorganism is highly resistant to antiseptics, disinfectants, immune system responses of the body. The responses of a quorum sense of this kind of bacteria ensure the inclusion of many pathogenicity factors. The analysis of the scientific literature made it possible to formulate four questions concerning the role of biofilms for the adaptation of P. aeruginosa to adverse environmental factors: Is another person appears to be predominantly of a source an etiological agent or the source of P. aeruginosa infection in the environment? Does the formation of biofilms influence on the antibiotic resistance? How the antagonistic activity of microorganisms is realized in biofilm form? What is the main function of biofilms in the functioning of bacteria? A hypothesis has been put forward the effect of biofilms on the increase of antibiotic resistance of bacteria and, in particular, P. aeruginosa to be secondary in charcter. It is more likely a biofilmboth to fulfill the function of storing nutrients and provide topical competition in the face of food scarcity. In connection with the incompatibility of the molecular radii of most antibiotics and pores in biofilm, biofilm is doubtful to be capable of performing a barrier function for protecting against antibiotics. However, with respect to antibodies and immunocompetent cells, the barrier function is beyond doubt. The biofilm is more likely to fulfill the function of storing nutrients and providing topical competition in conditions of scarcity of food resources.

A driver drowsiness and distraction warning system based on raspberry Pi 3 Kit

2019 ◽  
Vol 70 (3) ◽  
pp. 184-192
Author(s):  
Toan Dao Thanh ◽  
Vo Thien Linh
Keyword(s):  
Processing System ◽  
Warning System ◽  
Raspberry Pi ◽  
Data Set ◽  
Lower Lip ◽  
Driver Drowsiness ◽  
Image Sensing ◽  
The Face ◽  
The Common ◽  
University Laboratory

In this article, a system to detect driver drowsiness and distraction based on image sensing technique is created. With a camera used to observe the face of driver, the image processing system embedded in the Raspberry Pi 3 Kit will generate a warning sound when the driver shows drowsiness based on the eye-closed state or a yawn. To detect the closed eye state, we use the ratio of the distance between the eyelids and the ratio of the distance between the upper lip and the lower lip when yawning. A trained data set to extract 68 facial features and “frontal face detectors” in Dlib are utilized to determine the eyes and mouth positions needed to carry out identification. Experimental data from the tests of the system on Vietnamese volunteers in our University laboratory show that the system can detect at realtime the common driver states of “Normal”, “Close eyes”, “Yawn” or “Distraction”

Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks

2019 ◽  
Vol 12 (2) ◽  
pp. 120-127 ◽  
Author(s):  
Wael Farag
Keyword(s):  
Gradient Descent ◽  
Autonomous Driving ◽  
Driving Behavior ◽  
Training Data ◽  
Stochastic Gradient Descent ◽  
Data Set ◽  
Safe Driving ◽  
Processing Pipeline ◽  
Self Driving Cars ◽  
And Training

Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.

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