scholarly journals Approach to Define the Reliability of Safety-Related Machine Learning Based Functions in Highly Automated Driving

2021 ◽  
Author(s):  
Pengyu Si ◽  
Ossmane Krini ◽  
Nadine Müller ◽  
Aymen Ouertani
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Pedestrian Detection ◽  
Quality Criteria ◽  
Automated Driving ◽  
Safety Requirements ◽  
Functional Reliability ◽  
Current Standards ◽  
Highly Automated Driving

Current standards cannot cover the safety requirements of machine learning based functions used in highly automated driving. Because of the opacity of neural networks, some self-driving functions cannot be developed following the V-model. These functions require the expansion of the standards. This paper focuses on this gap and defines functional reliability for such functions to help the future standards control the quality of machine learning based functions. As an example, reliability functions for pedestrian detection are built. Since the quality criteria in computer vision do not consider safety, new approaches for expression and evaluation of this reliability are designed.

Machine Learning in Gifted Education: A Demonstration Using Neural Networks

2019 ◽  
Vol 63 (4) ◽  
pp. 243-252 ◽  
Author(s):  
Jaret Hodges ◽  
Soumya Mohan
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Supervised Learning ◽  
Language Processing ◽  
Gifted Education ◽  
Learning Algorithms ◽  
Simulated Data ◽  
Machine Learning Algorithms ◽  
Automated Driving ◽  
The Neural Network

Machine learning algorithms are used in language processing, automated driving, and for prediction. Though the theory of machine learning has existed since the 1950s, it was not until the advent of advanced computing that their potential has begun to be realized. Gifted education is a field where machine learning has yet to be utilized, even though one of the underlying problems of gifted education is classification, which is an area where learning algorithms have become exceptionally accurate. We provide a brief overview of machine learning with a focus on neural networks and supervised learning, followed by a demonstration using simulated data and neural networks for classification issues with a practical explanation of the mechanics of the neural network and associated R code. Implications for gifted education are then discussed. Finally, the limitations of supervised learning are discussed. Code used in this article can be found at https://osf.io/4pa3b/

scholarly journals ALS POINT CLOUD CLASSIFICATION USING POINTNET++ AND KPCONV WITH PRIOR KNOWLEDGE

2021 ◽  
Vol XLVI-4/W4-2021 ◽  
pp. 91-96
Author(s):  
M. Kada ◽  
D. Kuramin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Prior Knowledge ◽  
Laser Scanning ◽  
Point Clouds ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Software Applications ◽  
Point Cloud Classification

Abstract. In the practical and professional work of classifying airborne laser scanning (ALS) point clouds, there are nowadays numerous methods and software applications available that are able to separate the points into a few basic categories and do so with a known and consistent quality. Further refinement of the classes then requires either manual or semi-automatic work, or the use of supervised machine learning algorithms. In using supervised machine learning, e.g. Deep Learning neural networks, however, there is a significant chance that they will not maintain the approved quality of an existing classification. In this study, we therefore evaluate the application of two neural networks, PointNet++ and KPConv, and propose to integrate prior knowledge from a pre-existing classification in the form of height above ground and an encoding of the already available labels as additional per-point input features. Our experiments show that such an approach can improve the quality of the 3D classification results by 6% to 10% in mean intersection over union (mIoU) depending on the respective network, but it also cannot completely avoid the aforementioned problems.

scholarly journals A dynamic discarding technique to increase speed and preserve accuracy for YOLOv3

2021 ◽  
Author(s):  
Ignacio Martinez-Alpiste ◽  
Gelayol Golcarenarenji ◽  
Qi Wang ◽  
Jose Maria Alcaraz-Calero
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Processing Speed ◽  
State Of The Art ◽  
Autonomous Vehicle ◽  
Pedestrian Detection ◽  
Training Process ◽  
Acceleration Technique ◽  
Machine Learning Model

AbstractThis paper proposes an acceleration technique to minimise the unnecessary operations on a state-of-the-art machine learning model and thus to improve the processing speed while maintaining the accuracy. After the study of the main bottlenecks that negatively affect the performance of convolutional neural networks, this paper designs and implements a discarding technique for YOLOv3-based algorithms to increase the speed and maintain accuracy. After applying the discarding technique, YOLOv3 can achieve a 22% of improvement in terms of speed. Moreover, the results of this new discarding technique were tested on Tiny-YOLOv3 with three output layers on an autonomous vehicle for pedestrian detection and it achieved an improvement of 48.7% in speed. The dynamic discarding technique just needs one training process to create the model and thus execute the approach, which preserves accuracy. The improved detector based on the discarding technique is able to readily alert the operator of the autonomous vehicle to take the emergency brake of the vehicle in order to avoid collision and consequently save lives.

Application of Artificial Neural Networks for Processing and Interpretation of Data from a Scanning Magnetic Introscope

2021 ◽  
Author(s):  
Victor Evgenevich Kosarev ◽  
Ekaterina Anatolevna Yachmeneva ◽  
Aleksandr Vladimirovich Starovoyto ◽  
Dmitrii Ivanovich Kirgizov ◽  
Rustem Ramilevich Mukhamadiev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Machine Learning Algorithms ◽  
Test Machine ◽  
Subjective Factor ◽  
Mathematical Algorithms ◽  
Speed Up ◽  
Artificial Neural

Summary This paper presents the efficiency of using artificial neural networks for solving problems of processing and interpreting geophysical data obtained by scanning magnetic introscopy. Neural networks of various architectures have been implemented to solve the problems of processing primary material, searching for well structure objects,identifying casing defects. The analysis of the capabilities of neural networks in comparison with mathematical algorithms is carried out. To test machine learning algorithms and mathematical algorithms for processing, visualizing and storing the results, a software shell was created in which all tasks are solved using a set of tools. It was found that the use of artificial neural networks can significantly speed up the process of data processing and interpretation, as well as improve the quality of the results in comparison with individual mathematical algorithms. Nevertheless, the use of mathematical algorithms in solving some problems gives consistently better results. In particular, the problematic aspects were identified at the stage of interpretation when identifying defects. This is due to the presence of conventions in the isolation of defects by the operator at the stage of preparing data for training neural networks, which is a subjective factor and requires a deeper study.

scholarly journals Kpejigaou: An Indigenous, High-Protein, Low-Fat, Cowpea-Based Griddled Food Proposed for Coastal West Africa

2008 ◽  
Vol 29 (4) ◽  
pp. 329-333 ◽  
Author(s):  
Eric Oscar Amonsou ◽  
Esther Sakyi-Dawson ◽  
Firibu Kwesi Saalia ◽  
Paul Houssou
Keyword(s):  
Fast Food ◽  
Quality Index ◽  
Protein A ◽  
Quality Criteria ◽  
Low Fat ◽  
Important Quality ◽  
Traditional Processing ◽  
Nutritional Potential ◽  
Current Standards

Background Griddled cowpea paste foods have high nutritional potential because they are low in fat but high in protein. A good understanding of process and product characteristics of kpejigaou is necessary to improve its quality and enhance acceptability. Objective To describe the product, evaluate critical variables in traditional processing, and determine consumer quality criteria and preferences for kpejigaou. Methods A survey of kpejigaou processing was carried out among processors and regular consumers of kpejigaou. Results Kpejigaou is flat and circular in shape, with uniform thickness and porous structure. The production process of kpejigaou was found to be simple and rapid, but the quality of the finished product varied among processors and among batches. Critical processing variables affecting quality were dehulling of the cowpeas, type of griddling equipment, and griddling temperature. Texture (sponginess) is the most important quality index that determines the preference and acceptability of kpejigaou by consumers. Conclusions Traditionally processed kpejigaou does not meet current standards for high-quality foods. This study provides the basis for efforts to standardize the kpejigaou process to ensure consistent product quality and enhance the acceptability of kpejigaou among consumers. Kpejigaou has a potential for success if marketed as a low-fat, nutritious fast food.

scholarly journals The phonetic value of the Proto-Indo-European laryngeals

2021 ◽  
pp. 1-59
Author(s):  
Frederik Hartmann
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
New Method ◽  
Phonetic Features ◽  
Sound Features ◽  
Phonetic Value

Abstract Discussion of the exact phonetic value of the so-called ‘laryngeals’ in Proto-Indo-European has been ongoing ever since their discovery, and no uniform consensus has yet been reached. This paper aims at introducing a new method to determine the quality of the laryngeals that differs substantially from traditional techniques previously applied to this problem, by making use of deep neural networks as part of the larger field of machine learning algorithms. Phonetic environment data serves as the basis for training the networks, enabling the algorithm to determine sound features solely by their immediate phonetic neighbors. It proves possible to assess the phonetic features of the laryngeals computationally and to propose a quantitatively founded interpretation.

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