Research on the Control Method of Unmanned Helicopter Under the Background of Artificial Intelligence

2021 ◽  
Author(s):  
Wenze Li ◽  
Xuanzi Zhang ◽  
Bo Huang ◽  
Yi Chen ◽  
Ruiqi Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Reference System ◽  
Control Method ◽  
Performance Ratio ◽  
Cost Ratio ◽  
Complex Data ◽  
Unmanned Helicopter ◽  
Research Activities ◽  
Wide Range

A synopsis of a multidisciplinary research initiative focused on critical strategies for the genuine independent flight of tiny, vertical flying take-off (VTOL) unmanned aerial vehicles (UAVs). The research activities are the flight testbed, a simulation and test environment, and integrated components for onboard navigation, perception, design, and control. The necessity to create an unmanned helicopter system in different new civil applications cannot be overlooked. A highly reliable model may be used in the design, analysis, and implementation. The helicopter is fitted with a reference system for flight test data measurement and recording attitude heading reference system (AHRS) and the accompanying data storage modules. Recently, artificial intelligence-based deep learning (DL) has demonstrated excellent outcomes for a wide range of robotic activities in the areas of perception, planning, location, and management. Its remarkable skills to learn from complex data obtained in actual surroundings make it appropriate for many autonomous robotic applications. At the same time, UHS is currently widely utilized in various civil tasks in security, cinematography, disaster assistance, package delivery, or warehouse management (Unmanned Helicopter System). This paper conducted detailed work on current applications and the most significant advances and their performance and limits for the DL-UHS method. Furthermore, the essential strategies for deep learning are explained in depth — finally, discussing the principal hurdles of applying deep learning for UHS solutions. The proposed DL-UHS enhance outcome to evaluate the control strategies for the unmanned helicopter to achieve the low signal to noise error ratio of 31.3%, the error rate of 33.6%, the high-performance ratio of 91.4%, enhance accurate path planning 97.5%, prediction ratio of 96.3%, less trajectory cost ratio of 17.8% and increased safety tracking rate 93.6% when compared to other popular methods.

scholarly journals Developing Deep Learning Applications for Life Science and Pharma Industry

Drug Research ◽  
2018 ◽  
Vol 68 (06) ◽  
pp. 305-310 ◽  
Author(s):  
Daniel Siegismund ◽  
Vasily Tolkachev ◽  
Stephan Heyse ◽  
Beate Sick ◽  
Oliver Duerr ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Life Science ◽  
Digital Pathology ◽  
Cost Effective ◽  
Automated Analysis ◽  
Complex Data ◽  
Research Activities ◽  
Human Labor ◽  
Effective Development

AbstractDeep Learning has boosted artificial intelligence over the past 5 years and is seen now as one of the major technological innovation areas, predicted to replace lots of repetitive, but complex tasks of human labor within the next decade. It is also expected to be ‘game changing’ for research activities in pharma and life sciences, where large sets of similar yet complex data samples are systematically analyzed. Deep learning is currently conquering formerly expert domains especially in areas requiring perception, previously not amenable to standard machine learning. A typical example is the automated analysis of images which are typically produced en-masse in many domains, e. g., in high-content screening or digital pathology. Deep learning enables to create competitive applications in so-far defined core domains of ‘human intelligence’. Applications of artificial intelligence have been enabled in recent years by (i) the massive availability of data samples, collected in pharma driven drug programs (=‘big data’) as well as (ii) deep learning algorithmic advancements and (iii) increase in compute power. Such applications are based on software frameworks with specific strengths and weaknesses. Here, we introduce typical applications and underlying frameworks for deep learning with a set of practical criteria for developing production ready solutions in life science and pharma research. Based on our own experience in successfully developing deep learning applications we provide suggestions and a baseline for selecting the most suited frameworks for a future-proof and cost-effective development.

scholarly journals Deep Learning in Retinal Image Segmentation and Feature Extraction: A Review

2021 ◽  
Vol 17 (14) ◽  
pp. 103-118
Author(s):  
Mohammed Enamul Hoque ◽  
Kuryati Kipli
Keyword(s):  
Artificial Intelligence ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Deep Learning ◽  
Cardiovascular Diseases ◽  
Retinal Image ◽  
Feature Recognition ◽  
Image Features ◽  
Complex Data ◽  
Retinal Image Segmentation

Image recognition and understanding is considered as a remarkable subfield of Artificial Intelligence (AI). In practice, retinal image data have high dimensionality leading to enormous size data. As the morphological retinal image datasets can be analyzed in an expansive and non-invasive way, AI more precisely Deep Learning (DL) methods are facilitating in developing intelligent retinal image analysis tools. The most recently developed DL technique, Convolutional Neural Network (CNN) showed remarkable efficiency in identifying, localizing, and quantifying the complex and hierarchical image features that are responsible for severe cardiovascular diseases. Different deep layered CNN architectures such as LeeNet, AlexNet, and ResNet have been developed exploiting CNN morphology. This wide variety of CNN structures can iteratively learn complex data structures of different datasets through supervised or unsupervised learning and perform exquisite analysis for feature recognition independently to diagnose threatening cardiovascular diseases. In modern ophthalmic practice, DL based automated methods are being used in retinopathy screening, grading, identifying, and quantifying the pathological features to employ further therapeutic approaches and offering a wide potentiality to get rid of ophthalmic system complexity. In this review, the recent advances of DL technologies in retinal image segmentation and feature extraction are extensively discussed. To accomplish this study the pertinent materials were extracted from different publicly available databases and online sources deploying the relevant keywords that includes retinal imaging, artificial intelligence, deep learning and retinal database. For the associated publications the reference lists of selected articles were further investigated.

scholarly journals Autism Spectrum Disorder Classification Using Deep Learning

2021 ◽  
Vol 17 (08) ◽  
pp. 103
Author(s):  
Abdulrazak Yahya Saleh ◽  
Lim Huey Chern
Keyword(s):  
Artificial Intelligence ◽  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Image Data ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Wide Range

<p class="0abstract">The goal of this paper is to evaluate the deep learning algorithm for people placed in the Autism Spectrum Disorder (ASD) classification. ASD is a developmental disability that causes the affected people to have significant communication, social, and behavioural challenges. People with autism are saddled with communication problems, difficulties in social interaction and displaying repetitive behaviours. Several methods have been used to classify the ASD from non-ASD people. However, there is a need to explore more algorithms that can yield better classification performance. Recently, deep learning methods have significantly sharpened the cutting edge of learning algorithms in a wide range of artificial intelligence tasks. These artificial intelligence tasks refer to object detection, speech recognition, and machine translation. In this research, the convolutional neural network (CNN) is employed. This algorithm is used to find processes that can classify ASD with a higher level of accuracy. The image data is pre-processed; the CNN algorithm is then applied to classify the ASD and non-ASD, and the steps of implementing the CNN algorithm are clearly stated. Finally, the effectiveness of the algorithm is evaluated based on the accuracy performance. The support vector machine (SVM) is utilised for the purpose of comparison. The CNN algorithm produces better results with an accuracy of 97.07%, compared with the SVM algorithm. In the future, different types of deep learning algorithms need to be applied, and different datasets can be tested with different hyper-parameters to produce more accurate ASD classifications.</p>

scholarly journals ARTIFICIAL INTELLIGENCE AND NEXT GENERATION PATHOLOGY: TOWARDS PERSONALIZED MEDICINE

2021 ◽  
Vol 65 (2) ◽  
Author(s):  
Oleksandr Dudin ◽  
◽  
Ozar Mintser ◽  
Oksana Sulaieva ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Personalized Medicine ◽  
Digital Pathology ◽  
Automated Image Analysis ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Wide Range

Introduction. Over the past few decades, thanks to advances in algorithm development, the introduction of available computing power, and the management of large data sets, machine learning methods have become active in various fields of life. Among them, deep learning possesses a special place, which is used in many spheres of health care and is an integral part and prerequisite for the development of digital pathology. Objectives. The purpose of the review was to gather the data on existing image analysis technologies and machine learning tools developed for the whole-slide digital images in pathology. Methods: Analysis of the literature on machine learning methods used in pathology, staps of automated image analysis, types of neural networks, their application and capabilities in digital pathology was performed. Results. To date, a wide range of deep learning strategies have been developed, which are actively used in digital pathology, and demonstrated excellent diagnostic accuracy. In addition to diagnostic solutions, the integration of artificial intelligence into the practice of pathomorphological laboratory provides new tools for assessing the prognosis and prediction of sensitivity to different treatments. Conclusions: The synergy of artificial intelligence and digital pathology is a key tool to improve the accuracy of diagnostics, prognostication and personalized medicine facilitation

The organization of effective multi-user operation of hybrid computing systems

2019 ◽  
Author(s):  
С.И. Смагин ◽  
А.А. Сорокин ◽  
С.И. Мальковский ◽  
С.П. Королёв ◽  
О.А. Лукьянова ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Parallel Programming ◽  
Computer Applications ◽  
Computing Systems ◽  
Scheduling System ◽  
Hybrid Computing ◽  
Wide Range ◽  
Virtualization Technology

Исследуются вопросы организации многопользовательской работы гибридных вычислительных систем. На примере кластера Центра коллективного пользования Центр данных ДВО РАН, построенного на архитектуре OpenPOWER, рассмотрены особенности функционирования систем подобного класса и предложены решения для организации их работы. С использованием механизма виртуальных узлов проведена адаптация системы диспетчеризации заданий PBS Professional, позволяющая организовать эффективное распределение аппаратных ресурсов кластера между пользовательскими задачами. Реализованное программное окружение кластера с системой комплексного планирования заданий рассчитано на работу с широким перечнем компьютерных приложений, включая программы, построенные с использованием различных технологий параллельного программирования. Для эффективного исполнения в данной среде решений на основе машинного обучения, глубокого обучения и искусственного интеллекта применены технологии виртуализации. С использованием возможностей среды контейнеризации Singularity сформирован специализированный стек программного обеспечения и реализован особый режим его работы в формате единой вычислительной цифровой платформы. Purpose. Improving the technology of machine learning, deep learning and artificial intelligence plays an important role in acquiring new knowledge, technological modernization and the digital economy development.An important factor of the development in these areas is the availability of an appropriate highperformance computing infrastructure capable of providing the processing of large amounts of data. The creation of coprocessorbased hybrid computing systems, as well as new parallel programming technologies and application development tools allows partial solving this problem. However, many issues of organizing the effective multiuser operation of this class of systems require a separate study. The current paper addresses research in this area. Methodology. Using the OpenPOWER architecturebased cluster in the Shared Services Center The Data Center of the Far Eastern Branch of the Russian Academy of Sciences, the features of the functioning of hybrid computing systems are considered and solutions are proposed for organizing their work in a multiuser mode. Based on the virtual nodes concept, an adaptation of the PBS Professional job scheduling system was carried out, which provides an efficient allocation of cluster hardware resources among user tasks. Application virtualization technology was used for effective execution of machine learning and deep learning problems. Findings. The implemented cluster software environment with the integrated task scheduling system is designed to work with a wide range of computer applications, including programs built using parallel programming technologies. The virtualization technologies were used in this environment for effective execution of the software, based on machine learning, deep learning and artificial intelligence. Having the capabilities of the container Singularity, a specialized software stack and its operation mode was implemented for execution machine learning, deep learning and artificial intelligence tasks on a unified computing digital platform. Originality. The features of hybrid computing platforms functioning are considered, and the approach for their effective multiuser work mode is proposed. An effective resource manage model is developed, based on the virtualization technology usage.

scholarly journals Deep learning enabled inverse design in nanophotonics

Nanophotonics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 1041-1057 ◽  
Author(s):  
Sunae So ◽  
Trevon Badloe ◽  
Jaebum Noh ◽  
Jorge Bravo-Abad ◽  
Junsuk Rho
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Language Processing ◽  
Research Area ◽  
Inverse Design ◽  
Complex Data ◽  
Nanophotonic Devices ◽  
Nature Language Processing ◽  
Nanophotonic Structures ◽  
Learning Forward

AbstractDeep learning has become the dominant approach in artificial intelligence to solve complex data-driven problems. Originally applied almost exclusively in computer-science areas such as image analysis and nature language processing, deep learning has rapidly entered a wide variety of scientific fields including physics, chemistry and material science. Very recently, deep neural networks have been introduced in the field of nanophotonics as a powerful way of obtaining the nonlinear mapping between the topology and composition of arbitrary nanophotonic structures and their associated functional properties. In this paper, we have discussed the recent progress in the application of deep learning to the inverse design of nanophotonic devices, mainly focusing on the three existing learning paradigms of supervised-, unsupervised-, and reinforcement learning. Deep learning forward modelling i.e. how artificial intelligence learns how to solve Maxwell’s equations, is also discussed, along with an outlook of this rapidly evolving research area.

scholarly journals Improving deep learning performance by using Explainable Artificial Intelligence (XAI) approaches

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Vitor Bento ◽  
Manoela Kohler ◽  
Pedro Diaz ◽  
Leonardo Mendoza ◽  
Marco Aurelio Pacheco
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Surface Defect ◽  
Learning Performance ◽  
Learning Models ◽  
Surveillance Camera ◽  
Wide Range ◽  
Classification Tasks ◽  
Low Performance ◽  
Deep Learning Model

AbstractIn this work we propose a workflow to deal with overlaid images—images with superimposed text and company logos—, which is very common in underwater monitoring videos and surveillance camera footage. It is demonstrated that it is possible to use Explaining Artificial Intelligence to improve deep learning models performance for image classification tasks in general. A deep learning model trained to classify metal surface defect, which previously had a low performance, is then evaluated with Layer-wise relevance propagation—an Explaining Artificial Intelligence technique—to identify problems in a dataset that hinder the training of deep learning models in a wide range of applications. Thereafter, it is possible to remove this unwanted information from the dataset—using different approaches: from cutting part of the images to training a Generative Inpainting neural network model—and retrain the model with the new preprocessed images. This proposed methodology improved F1 score in 20% when compared to the original trained dataset, validating the proposed workflow.

scholarly journals Digital TV Network Management System Based on WEB

2022 ◽  
Vol 2146 (1) ◽  
pp. 012024
Author(s):  
Wei Qi ◽  
Chun Ying ◽  
Sheng Yong ◽  
Guizhi Zhao ◽  
Lihua Wang
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Control Method ◽  
Digital Tv ◽  
Network Management System ◽  
Snake Robot ◽  
Intelligent Machines ◽  
Artificial Intelligence Technology ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle

Abstract With the development and popularization of computer artificial intelligence technology, more and more intelligent machines are gradually produced. These intelligent machines have brought great convenience to people’s lives. This paper studies the control method of snake robot based on environment adaptability, which mainly explains the construction and stability of multi-modal CPG model. In addition, this paper also studies the trajectory tracking and dynamic obstacle avoidance of mobile robot based on deep learning.

Computational Intelligence for Detection of Skin Cancer using Deep Learning Classifiers

2021 ◽  
Vol 7 (1) ◽  
pp. 10-18
Author(s):  
S. Archana ◽  
◽  
N. Shyamsundar ◽  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Skin Cancer ◽  
Environmental Factors ◽  
Computational Intelligence ◽  
Skin Diseases ◽  
Research Field ◽  
Important Research ◽  
Wide Range ◽  
Computer Aided

Artificial intelligence (AI) has been recognized as an important research field in computer science. Although AI has been around for a while and has been used in many disciplines of medicine, its usage in dermatology is very recent and constrained. Dermatology is a field of bioscience concerned with the diagnosis and treatment of skin diseases. The wide range of dermatologic diseases changes regionally and seasonally according to temperature, humidity, and other environmental factors. Dermatological illnesses have been shown to have major impacts on the behavior of millions of individuals since nearly all forms of skin problems affect everyone every year. Because human analysis of such diseases requires time and effort, and existing techniques are only utilized to analyze certain types of skin diseases, there is a need for higher-level computer-aided skills in the analysis and diagnosis of multi-type skin disorders.

scholarly journals The Role of Deep Learning Method Based on Environmental Geochemical Data in Resource

2021 ◽  
Vol 245 ◽  
pp. 02001
Author(s):  
Pei Yao
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Environmental Science ◽  
Mineral Resources ◽  
Environmental Geochemistry ◽  
Geochemical Data ◽  
Environmental Investigation ◽  
Wide Range ◽  
Important Branch

In recent years, with the continuous development of artificial intelligence, deep learning, as an important method of artificial intelligence learning, has made great progress. At present, deep learning has been successfully applied in many engineering fields. Environmental science itself involves a wide range, among which environmental geochemistry is an important branch. The combination of environmental geochemical problems and deep learning can better study the role of geochemical problems in environmental investigation, and also can use the basic situation of regional environmental elements to predict mineral resources.

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