scholarly journals Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning

2022 ◽  
Vol 14 (2) ◽  
pp. 870
Author(s):  
Mohammad Alsarayreh ◽  
Omar Mohamed ◽  
Mustafa Matar
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Deep Neural Network ◽  
Control Valve ◽  
Dual Fuel ◽  
Dynamic Neural Network ◽  
Fuel Gas ◽  
Wide Range

Accurate simulations of gas turbines’ dynamic performance are essential for improvements in their practical performance and advancements in sustainable energy production. This paper presents models with extremely accurate simulations for a real dual-fuel gas turbine using two state-of-the-art techniques of neural networks: the dynamic neural network and deep neural network. The dynamic neural network has been realized via a nonlinear autoregressive network with exogenous inputs (NARX) artificial neural network (ANN), and the deep neural network has been based on a convolutional neural network (CNN). The outputs selected for simulations are: the output power, the exhausted temperature and the turbine speed or system frequency, whereas the inputs are the natural gas (NG) control valve, the pilot gas control valve and the compressor variables. The data-sets have been prepared in three essential formats for the training and validation of the networks: normalized data, standardized data and SI units’ data. Rigorous effort has been carried out for wide-range trials regarding tweaking the network structures and hyper-parameters, which leads to highly satisfactory results for both models (overall, the minimum recorded MSE in the training of the MISO NARX was 6.2626 × 10−9 and the maximum MSE that was recorded for the MISO CNN was 2.9210 × 10−4, for more than 15 h of GT operation). The results have shown a comparable satisfactory performance for both dynamic NARX ANN and the CNN with a slight superiority of NARX. It can be newly argued that the dynamic ANN is better than the deep learning ANN for the time-based performance simulation of gas turbines (GTs).

A Single Valve Gas Fuel Flow Control for Gas Turbines

1993 ◽  
Author(s):  
William O. Statler
Keyword(s):  
Flow Control ◽  
Gas Turbine ◽  
Mass Flow ◽  
Gas Turbines ◽  
Control Valve ◽  
Control Loop ◽  
Fuel Flow ◽  
Wide Range ◽  
Gas Fuel ◽  
Flow Element

A method of mass flow control of fuel gas to a gas turbine has been developed and applied in control retrofits to existing gas turbines. Unlike other gas flow control systems in use on gas turbines this system actually measures the mass flow going into the turbine combustion system and uses this value as the feedback in a control loop to modulate a single throttling control valve. The system utilizes a common venturi flow element to develop a differential pressure which, along with inlet pressure and temperature, is used to compute the mass flow. Locating this flow element downstream of the control valve where the pressure is low at low flows reduces the usual problem of the wide range of delta-pressure (proportional to the square of the mass flow) to a workable level. This extends the range of this common type of flow measurement system enough that it becomes practical to apply it to the gas fuel flow control loop of a gas turbine.

scholarly journals A deep neural network approach to predicting clinical outcomes of neuroblastoma patients

2019 ◽  
Author(s):  
Léon-Charles Tranchevent ◽  
Francisco Azuaje ◽  
Jagath C. Rajapakse
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Clinical Outcomes ◽  
Deep Neural Network ◽  
Superior Performance ◽  
Support Vector ◽  
Disease Etiology ◽  
Topological Features ◽  
Wide Range

AbstractThe availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and disease recurrence. Such methods are also important to better understand the biological mechanisms underlying disease etiology and development, as well as treatment responses. Recently, different predictive models, relying on distinct algorithms (including Support Vector Machines and Random Forests) have been investigated. In this context, deep learning strategies are of special interest due to their demonstrated superior performance over a wide range of problems and datasets. One of the main challenges of such strategies is the “small n large p” problem. Indeed, omics datasets typically consist of small numbers of samples and large numbers of features relative to typical deep learning datasets. Neural networks usually tackle this problem through feature selection or by including additional constraints during the learning process.We propose to tackle this problem with a novel strategy that relies on a graph-based method for feature extraction, coupled with a deep neural network for clinical outcome prediction. The omics data are first represented as graphs whose nodes represent patients, and edges represent correlations between the patients’ omics profiles. Topological features, such as centralities, are then extracted from these graphs for every node. Lastly, these features are used as input to train and test various classifiers.We apply this strategy to four neuroblastoma datasets and observe that models based on neural networks are more accurate than state of the art models (DNN: 85%-87%, SVM/RF: 75%-82%). We explore how different parameters and configurations are selected in order to overcome the effects of the small data problem as well as the curse of dimensionality. Our results indicate that the deep neural networks capture complex features in the data that help predicting patient clinical outcomes.

scholarly journals A deep neural network approach to predicting clinical outcomes of neuroblastoma patients

2019 ◽  
Vol 12 (S8) ◽  
Author(s):  
Léon-Charles Tranchevent ◽  
Francisco Azuaje ◽  
Jagath C. Rajapakse
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Clinical Outcomes ◽  
Deep Neural Network ◽  
Superior Performance ◽  
Support Vector ◽  
Disease Etiology ◽  
Topological Features ◽  
Wide Range

Abstract Background The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and disease recurrence. Such methods are also important to better understand the biological mechanisms underlying disease etiology and development, as well as treatment responses. Recently, different predictive models, relying on distinct algorithms (including Support Vector Machines and Random Forests) have been investigated. In this context, deep learning strategies are of special interest due to their demonstrated superior performance over a wide range of problems and datasets. One of the main challenges of such strategies is the “small n large p” problem. Indeed, omics datasets typically consist of small numbers of samples and large numbers of features relative to typical deep learning datasets. Neural networks usually tackle this problem through feature selection or by including additional constraints during the learning process. Methods We propose to tackle this problem with a novel strategy that relies on a graph-based method for feature extraction, coupled with a deep neural network for clinical outcome prediction. The omics data are first represented as graphs whose nodes represent patients, and edges represent correlations between the patients’ omics profiles. Topological features, such as centralities, are then extracted from these graphs for every node. Lastly, these features are used as input to train and test various classifiers. Results We apply this strategy to four neuroblastoma datasets and observe that models based on neural networks are more accurate than state of the art models (DNN: 85%-87%, SVM/RF: 75%-82%). We explore how different parameters and configurations are selected in order to overcome the effects of the small data problem as well as the curse of dimensionality. Conclusions Our results indicate that the deep neural networks capture complex features in the data that help predicting patient clinical outcomes.

scholarly journals A Method for Wheat Head Detection Based on Yolov4

2020 ◽  
Author(s):  
Bo Gong ◽  
Daji Ergu ◽  
Ying Cai ◽  
Bo Ma
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Detection Method ◽  
Computer Hardware ◽  
Detection Methods ◽  
Plant Phenotyping ◽  
Wide Range ◽  
Head Detection ◽  
Multi Level

Abstract Background: Plant phenotyping by deep learning has increased attention. The detection of wheat head in the field is an important mission for estimating the characteristics of wheat heads such as the density, health, maturity, and presence or absence of awns. Traditional wheat head detection methods have problems such as low efficiency, strong subjectivity, and poor accuracy. However, with the development of deep learning theory and the iteration of computer hardware, the accuracy of object detection method using deep neural networks has been greatly improved. Therefore, using a deep neural network method to detect wheat heads in images has a certain value. Results: In this paper, a method of wheat head detection based on deep neural network is proposed. Firstly, for improving the backbone network part, two SPP networks are introduced to enhance the ability of feature learning and increase the receptive field of the convolutional network. Secondly, the top-down and bottom-up feature fusion strategies are applied to obtain multi-level features. Finally, we use Yolov3's head structures to predict the bounding box of object. The results show that our proposed detection method for wheat head has higher accuracy and speed. The mean average precision of our method is 94.5%, and the detection speed of our proposed method is 88fps. Conclusion: The proposed deep neural network can accurately and quickly detector the wheat head in the image which is based on Yolov4. In addition, the training dataset is a wheat head dataset with accurate annotations and rich varieties, which makes the proposed method more robust and has a wide range of application values. The proposed detector is also more suitable for wheat detection task, with the deeper backbone networks. The use of spatial pyramid pooling (SPP) and multi-level features fusion, which all play a crucial role in improving detector performance. Our method provides beneficial help for the breeding of wheat

scholarly journals p1-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning

2019 ◽  
Vol 2019 (3) ◽  
pp. 191-209 ◽  
Author(s):  
Se Eun Oh ◽  
Saikrishna Sunkam ◽  
Nicholas Hopper
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Machine Learning Techniques ◽  
Feature Engineering ◽  
Engineering Process ◽  
Learning Techniques ◽  
Wide Range

Abstract Recent advances in Deep Neural Network (DNN) architectures have received a great deal of attention due to their ability to outperform state-of-the-art machine learning techniques across a wide range of application, as well as automating the feature engineering process. In this paper, we broadly study the applicability of deep learning to website fingerprinting. First, we show that unsupervised DNNs can generate lowdimensional informative features that improve the performance of state-of-the-art website fingerprinting attacks. Second, when used as classifiers, we show that they can exceed performance of existing attacks across a range of application scenarios, including fingerprinting Tor website traces, fingerprinting search engine queries over Tor, defeating fingerprinting defenses, and fingerprinting TLS-encrypted websites. Finally, we investigate which site-level features of a website influence its fingerprintability by DNNs.

Development and Field Validation of a Large-Frame Gas Turbine Power Train for Steel Mill Gases

2011 ◽  
Author(s):  
Joel M. Hall ◽  
Robert T. Thatcher ◽  
Sergey V. Koshevets ◽  
Larry L. Thomas ◽  
Robert M. Jones
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Product Line ◽  
Similar Process ◽  
Development Effort ◽  
Design Development ◽  
Steel Mill ◽  
Data Set ◽  
Fuel Gas ◽  
Wide Range

As of September 2009, GE Energy (GE) has successfully expanded its large-frame gas turbine product line to burn ultra-low calorific steel mill gas fuel blends, especially mixtures of Blast Furnace Gas (BFG) and Coke Oven Gas (COG). The first two GE frame 9E Gas Turbines in China with this capability have thus far accumulated more than 8000 hours operating on BFG/COG blends. The China site comprises two complete power trains, including GE 9E gas turbines, generators, fuel cleaning equipment, and fuel gas compressors. Since startup, combustion operating parameters have remained within design limits, consistent with the extensive full-scale lab testing GE conducted during the turbine’s design development effort, and comparable to fleet experience on natural gas fired GE gas turbines. Based on this accumulated data set, especially the wide range of gas compositions tested in the combustion lab, similar process gases such as corex and finex gases, and air-blown synthetic gases are operable in this system. The GE 9E platform targets the 50Hz market. For 60Hz applications, a 7EA BFG product is available.

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.

scholarly journals Anomaly Detection Using Deep Neural Network for IoT Architecture

2021 ◽  
Vol 11 (15) ◽  
pp. 7050
Author(s):  
Zeeshan Ahmad ◽  
Adnan Shahid Khan ◽  
Kashif Nisar ◽  
Iram Haider ◽  
Rosilah Hassan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Deep Neural Network ◽  
Detection System ◽  
Traffic Monitoring ◽  
Detection Accuracy ◽  
Learning Models ◽  
Prime Concern ◽  
Entry Points

The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16–35 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features.

Design, Development and Application of Vehicle Gas Turbine Engines

1966 ◽  
Vol 181 (1) ◽  
pp. 149-172
Author(s):  
P. A. Phillips ◽  
Peter Spear
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Low Cost ◽  
Engine Performance ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Design Development ◽  
Turbine Engine ◽  
Wide Range ◽  
Cycle Temperature

After briefly summarizing worldwide automotive gas turbine activity, the paper analyses the power plant requirements of a wide range of vehicle applications in order to formulate the design criteria for acceptable vehicle gas turbines. Ample data are available on the thermodynamic merits of various gas turbine cycles; however, the low cost of its piston engine competitor tends to eliminate all but the simplest cycles from vehicle gas turbine considerations. In order to improve the part load fuel economy, some complexity is inevitable, but this is limited to the addition of a glass ceramic regenerator in the 150 b.h.p. engine which is described in some detail. The alternative further complications necessary to achieve satisfactory vehicle response at various power/weight ratios are examined. Further improvement in engine performance will come by increasing the maximum cycle temperature. This can be achieved at lower cost by the extension of the use of ceramics. The paper is intended to stimulate the design application of the gas turbine engine.

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