scholarly journals Cylinder Pressure Prediction of An HCCI Engine Using Deep Learning

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Halit Yaşar ◽  
Gültekin Çağıl ◽  
Orhan Torkul ◽  
Merve Şişci
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
High Performance ◽  
Internal Combustion Engines ◽  
Deep Neural Network ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Cylinder Pressure ◽  
Hcci Engine

AbstractEngine tests are both costly and time consuming in developing a new internal combustion engine. Therefore, it is of great importance to predict engine characteristics with high accuracy using artificial intelligence. Thus, it is possible to reduce engine testing costs and speed up the engine development process. Deep Learning is an effective artificial intelligence method that shows high performance in many research areas through its ability to learn high-level hidden features in data samples. The present paper describes a method to predict the cylinder pressure of a Homogeneous Charge Compression Ignition (HCCI) engine for various excess air coefficients by using Deep Neural Network, which is one of the Deep Learning methods and is based on the Artificial Neural Network (ANN). The Deep Learning results were compared with the ANN and experimental results. The results show that the difference between experimental and the Deep Neural Network (DNN) results were less than 1%. The best results were obtained by Deep Learning method. The cylinder pressure was predicted with a maximum accuracy of 97.83% of the experimental value by using ANN. On the other hand, the accuracy value was increased up to 99.84% using DNN. These results show that the DNN method can be used effectively to predict cylinder pressures of internal combustion engines.

Experimental Study of Spark Plasma Stretching and Combustion Variations Analysis Using Flame Luminosity Images From an Optically Accessible Internal Combustion Engine

2020 ◽  
Vol 142 (4) ◽  
Author(s):  
Behdad Afkhami ◽  
Yanyu Wang ◽  
Scott A. Miers ◽  
Jeffrey D. Naber
Keyword(s):  
Internal Combustion Engines ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Combustion Stability ◽  
Cylinder Pressure ◽  
Ic Engine ◽  
Flame Kernel ◽  
Spark Plasma ◽  
Intensity Images ◽  
Fuel Mixtures

Abstract Understanding the behavior of spark plasma and flame initiation in internal combustion engines leads to improvement in fuel economy and exhaust emissions. This paper experimentally investigated spark plasma stretching and cycle-to-cycle variations under various engine speed, load, and air–fuel mixtures using natural luminosity images. Natural luminosity images of combustion in an IC engine provide information about the flame speed, rate of energy release, and combustion stability. Binarization of the intensity images has been a desirable method for detecting flame front and studying flame propagation in combustors. However, binarization can cause a loss of information in the images. To study spark plasma stretching, the location of maximum intensity was tracked and compared to the trajectory of the flame centroid in binarized images as a representative for bulk flow motion. Analysis showed comparable trends between the trajectories of the flame centroid and spark stretching. From three air–fuel mixtures, the spark plasma for the lean mixture appeared to be more sensitive to the stretching. In addition, this research investigated combustion variations using two-dimensional (2D) intensity images and compared the results to coefficient of variation (COV) of indicated mean effective pressure (IMEP) computed from in-cylinder pressure data. The results revealed a good correlation between the variations of the luminosity field during the main phase of combustion and the COV of IMEP. However, during the ignition and very early flame kernel formation, utilizing the luminosity field was more powerful than in-cylinder pressure-related parameters to capture combustion variations.

scholarly journals A deep learning approach for detecting the behavior of people having personality disorders towards Covid-19 from Twitter

2021 ◽  
Author(s):  
Mourad Ellouze ◽  
Seifeddine Mechti ◽  
Moez Krichen ◽  
vinayakumar R ◽  
Lamia Hadrich Belguith
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Text Mining ◽  
Personality Disorders ◽  
Deep Neural Network ◽  
Selection Task ◽  
Learning Approach ◽  
Features Selection ◽  
Normal Behavior

This paper proposes an architecture taking advantage of artificial intelligence and text mining techniques in order to: (i) detect paranoid people by classifying their set of tweets into two classes (Paranoid/not-Paranoid), (ii) ensure the surveillance of these people by classifying their tweets about Covid-19 into two classes (person with normal behavior, person with inappropriate behavior). These objectives are achieved using an approach that takes advantage of different information related to the textual part, user and tweets for features selection task and deep neural network for the classification task. We obtained as an F-score rate 70% for the detection of paranoid people and 73% for the detection of the behavior of these people towards Covid-19. The obtained results are motivating and encouraging researchers to improve them given the interest and the importance of this research axis.

scholarly journals High-Performance Deep learning to Detection and Tracking Tomato Plant Leaf Predict Disease and Expert Systems

2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Yaser AbdulAali Jasim
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
High Performance ◽  
Plant Disease ◽  
Detection System ◽  
Open Data ◽  
Data Set ◽  
Performance Rate

Nowadays, technology and computer science are rapidly developing many tools and algorithms, especially in the field of artificial intelligence.  Machine learning is involved in the development of new methodologies and models that have become a novel machine learning area of applications for artificial intelligence. In addition to the architectures of conventional neural network methodologies, deep learning refers to the use of artificial neural network architectures which include multiple processing layers. In this paper, models of the Convolutional neural network were designed to detect (diagnose) plant disorders by applying samples of healthy and unhealthy plant images analyzed by means of methods of deep learning. The models were trained using an open data set containing (18,000) images of ten different plants, including healthy plants. Several model architectures have been trained to achieve the best performance of (97 percent) when the respectively [plant, disease] paired are detected. This is a very useful information or early warning technique and a method that can be further improved with the substantially high-performance rate to support an automated plant disease detection system to work in actual farm conditions.

scholarly journals A deep learning approach for detecting the behavior of people having personality disorders towards Covid-19 from Twitter

2021 ◽  
Author(s):  
Mourad Ellouze ◽  
Seifeddine Mechti ◽  
Moez Krichen ◽  
vinayakumar R ◽  
Lamia Hadrich Belguith
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Text Mining ◽  
Personality Disorders ◽  
Deep Neural Network ◽  
Selection Task ◽  
Learning Approach ◽  
Features Selection ◽  
Normal Behavior

This paper proposes an architecture taking advantage of artificial intelligence and text mining techniques in order to: (i) detect paranoid people by classifying their set of tweets into two classes (Paranoid/not-Paranoid), (ii) ensure the surveillance of these people by classifying their tweets about Covid-19 into two classes (person with normal behavior, person with inappropriate behavior). These objectives are achieved using an approach that takes advantage of different information related to the textual part, user and tweets for features selection task and deep neural network for the classification task. We obtained as an F-score rate 70% for the detection of paranoid people and 73% for the detection of the behavior of these people towards Covid-19. The obtained results are motivating and encouraging researchers to improve them given the interest and the importance of this research axis.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

МЕТОДЫ ДОСТИЖЕНИЯ МАКСИМАЛЬНОЙ ЭФФЕКТИВНОСТИ ПЛАТФОРМЫ ПРОТОТИПИРОВАНИЯ ВЫСОКОПРОИЗВОДИТЕЛЬНЫХ СИСТЕМ НА КРИСТАЛЛЕ НА ЗАДАЧАХ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

2020 ◽  
Vol 96 (3s) ◽  
pp. 585-588
Author(s):  
С.Е. Фролова ◽  
Е.С. Янакова
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computer Vision ◽  
High Performance ◽  
Systems On Chip ◽  
High Performance Systems ◽  
On Chip ◽  
Network Technologies ◽  
Neural Network Technologies

Предлагаются методы построения платформ прототипирования высокопроизводительных систем на кристалле для задач искусственного интеллекта. Изложены требования к платформам подобного класса и принципы изменения проекта СнК для имплементации в прототип. Рассматриваются методы отладки проектов на платформе прототипирования. Приведены результаты работ алгоритмов компьютерного зрения с использованием нейросетевых технологий на FPGA-прототипе семантических ядер ELcore. Methods have been proposed for building prototyping platforms for high-performance systems-on-chip for artificial intelligence tasks. The requirements for platforms of this class and the principles for changing the design of the SoC for implementation in the prototype have been described as well as methods of debugging projects on the prototyping platform. The results of the work of computer vision algorithms using neural network technologies on the FPGA prototype of the ELcore semantic cores have been presented.

scholarly journals Laminar Burning Velocity of Biogas-Containing Mixtures. A Literature Review

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 996
Author(s):  
Venera Giurcan ◽  
Codina Movileanu ◽  
Adina Magdalena Musuc ◽  
Maria Mitu
Keyword(s):  
Gas Turbines ◽  
Internal Combustion Engines ◽  
Fossil Fuels ◽  
Combustion Engine ◽  
Burning Velocity ◽  
Internal Combustion ◽  
Electricity Production ◽  
Engine Fuel ◽  
Laminar Burning Velocity ◽  
Waste Products

Currently, the use of fossil fuels is very high and existing nature reserves are rapidly depleted. Therefore, researchers are turning their attention to find renewable fuels that have a low impact on the environment, to replace these fossil fuels. Biogas is a low-cost alternative, sustainable, renewable fuel existing worldwide. It can be produced by decomposition of vegetation or waste products of human and animal biological activity. This process is performed by microorganisms (such as methanogens and sulfate-reducing bacteria) by anaerobic digestion. Biogas can serve as a basis for heat and electricity production used for domestic heating and cooking. It can be also used to feed internal combustion engines, gas turbines, fuel cells, or cogeneration systems. In this paper, a comprehensive literature study regarding the laminar burning velocity of biogas-containing mixtures is presented. This study aims to characterize the use of biogas as IC (internal combustion) engine fuel, and to develop efficient safety recommendations and to predict and reduce the risk of fires and accidental explosions caused by biogas.

scholarly journals Robust Approach to Supervised Deep Neural Network Training for Real-Time Object Classification in Cluttered Indoor Environment

2021 ◽  
Vol 11 (15) ◽  
pp. 7148
Author(s):  
Bedada Endale ◽  
Abera Tullu ◽  
Hayoung Shi ◽  
Beom-Soo Kang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Input Data ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Object Classification ◽  
Training Data ◽  
Gradient Descent Algorithm

Unmanned aerial vehicles (UAVs) are being widely utilized for various missions: in both civilian and military sectors. Many of these missions demand UAVs to acquire artificial intelligence about the environments they are navigating in. This perception can be realized by training a computing machine to classify objects in the environment. One of the well known machine training approaches is supervised deep learning, which enables a machine to classify objects. However, supervised deep learning comes with huge sacrifice in terms of time and computational resources. Collecting big input data, pre-training processes, such as labeling training data, and the need for a high performance computer for training are some of the challenges that supervised deep learning poses. To address these setbacks, this study proposes mission specific input data augmentation techniques and the design of light-weight deep neural network architecture that is capable of real-time object classification. Semi-direct visual odometry (SVO) data of augmented images are used to train the network for object classification. Ten classes of 10,000 different images in each class were used as input data where 80% were for training the network and the remaining 20% were used for network validation. For the optimization of the designed deep neural network, a sequential gradient descent algorithm was implemented. This algorithm has the advantage of handling redundancy in the data more efficiently than other algorithms.

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