scholarly journals An energy efficient time-mode digit classification neural network implementation

2019 ◽  
Vol 378 (2164) ◽  
pp. 20190163
Author(s):  
O. C. Akgun ◽  
J. Mei
Keyword(s):  
Neural Network ◽  
Energy Efficient ◽  
Supply Voltage ◽  
Single Layer ◽  
Activation Function ◽  
Monostable Multivibrator ◽  
Pulse Generators ◽  
Autonomous Computing ◽  
Handwritten Digit ◽  
Artificial Neural Network Ann

This paper presents the design of an ultra-low energy neural network that uses time-mode signal processing). Handwritten digit classification using a single-layer artificial neural network (ANN) with a Softmin-based activation function is described as an implementation example. To realize time-mode operation, the presented design makes use of monostable multivibrator-based multiplying analogue-to-time converters, fixed-width pulse generators and basic digital gates. The time-mode digit classification ANN was designed in a standard CMOS 0.18 μm IC process and operates from a supply voltage of 0.6 V. The system operates on the MNIST database of handwritten digits with quantized neuron weights and has a classification accuracy of 88%, which is typical for single-layer ANNs, while dissipating 65.74 pJ per classification with a speed of 2.37 k classifications per second. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.

Power Forecasting from Solar Panels Using Artificial Neural Network in UTHM Parit Raja

2021 ◽  
Vol 02 (01) ◽  
Author(s):  
Natasha Munirah Mohd Fahmi ◽  
◽  
Nor Aira Zambri ◽  
Norhafiz Salim ◽  
Sim Sy Yi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Climatic Factors ◽  
Activation Function ◽  
Solar Panels ◽  
Ann Model ◽  
Power Characteristics ◽  
Step Procedure ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper presents a step-by-step procedure for the simulation of photovoltaic modules with numerical values, using MALTAB/Simulink software. The proposed model is developed based on the mathematical model of PV module, which based on PV solar cell employing one-diode equivalent circuit. The output current and power characteristics curves highly depend on some climatic factors such as radiation and temperature, are obtained by simulation of the selected module. The collected data are used in developing Artificial Neural Network (ANN) model. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) are the techniques used to forecast the outputs of the PV. Various types of activation function will be applied such as Linear, Logistic Sigmoid, Hyperbolic Tangent Sigmoid and Gaussian. The simulation results show that the Logistic Sigmoid is the best technique which produce minimal root mean square error for the system.

scholarly journals Prediction of Locations in Medical Images Using Orthogonal Neural Networks

2021 ◽  
Author(s):  
Jong Soo Kim ◽  
Yongil Cho ◽  
Tae Ho Lim
Keyword(s):  
Neural Network ◽  
Diagnostic Performance ◽  
Medical Images ◽  
Selection Criterion ◽  
Activation Function ◽  
X Rays ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Image Localization

Abstract An orthogonal neural network (ONN), a new deep-learning structure for medical image localization, is developed and presented in this paper. This method is simple, efficient, and completely different from a convolution neural network (CNN). The diagnostic performance of ONN for detecting the location of pneumothorax in chest X-rays was assessed and compared to that of CNN. An area under the receiver operating characteristic (ROC) curve (AUC) of 0.870, an accuracy of 85.3%, a sensitivity of 75.0%, and a specificity of 86.5% were achieved; the ONN outperformed the CNN. The diagnostic performance of the ONN with a sigmoid activation function for all the nodes obviously outperformed the ONN with the rectified linear unit (RELU) activation function for all the nodes other than the output nodes. In addition, by applying ONN and CNN to predict the location of the glottis in laryngeal images, we achieved accurate and adjacent prediction rates of 70.5% and 20.5%, respectively, with the ONN. The prediction accuracy of the ONN was compared favorably with that of the CNN. Compared to a CNN, an ONN required only approximately 10% of the computations using a CNN trained on images with an input resolution of 256 × 256 pixels. A fully-connected small artificial neural network (ANN), selected by comparing the test results of several dozens of small ANN models, achieved the best location prediction performance on medical images. This study demonstrated that an ONN can be used as a quick selection criterion to compare ANN models for image localization since an ONN performed well compared decently with the selected ANN model.

Control Oriented Modelling of Engine IMEP Variation

2016 ◽  
Author(s):  
Qilun Zhu ◽  
Robert Prucka ◽  
Shu Wang ◽  
Michael Prucka ◽  
Hussein Dourra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Turbulent Combustion ◽  
Single Layer ◽  
Network Size ◽  
Operating Conditions ◽  
Premixed Turbulent Combustion ◽  
Spark Timing ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Engine cycle-by-cycle combustion variation is a potential source of emissions and drivability issues in automobiles, and has become an important concern for engine control engineers. The nature of turbulent combustion in IC engines means that combustion variations cannot be eliminated completely. Furthermore, it is inevitable for the engine to run at conditions with high combustion variations in most vehicle applications. For example, during gear shifts spark timing can be changed dramatically to help track the fast transitions of torque demand, often resulting in high Coefficient of Variation in Indicated Mean Effective Pressure (COV of IMEP). Under these circumstances, the control engineers have to weigh between combustion variation and other performance demands (i.e. fast torque tracking). An accurate online estimation of COV of IMEP can be beneficial to this process. A calibrated map of COV of IMEP versus engine operating conditions can be an option for engines with few control actuators. As the number of control actuators increases, combustion variation modelling using inputs with physical representations becomes favorable due to the potential for reduced calibration effort. However, since COV of IMEP is a stochastic variable describing the distribution of IMEP output, it can only be modelled empirically. This research proposes a control-oriented real-time COV of IMEP model based on an Artificial Neural Network (ANN) and inputs from turbulent combustion research. The effects of premixed turbulent combustion variation are analyzed with flame regime analysis in this research after a brief introduction of the experimental setup and engine information. In-cylinder thermodynamics are then evaluated to reveal how the changes of heat release transform into the variation of cylinder pressure, producing COV of IMEP. A range of model input parameters are assessed to determine the set that produces the most accurate prediction of IMEP variation with minimal computational requirements. An Artificial Neural Network (ANN) is applied to capture the nonlinear coupled correlations between COV of IMEP and model inputs. The ANN is combined with a regression pretreatment to reduce network size and improve extrapolation stability. This computationally efficient single-layer three-neuron ANN COV of IMEP model achieved 0.29% normalized Root Mean Square Error (RMSE). Dynamometer tests show that the model performs well outside the training region.

scholarly journals Use of Artificial Neural Network for Estimation of Propeller Torque Values in a CODLAG Propulsion System

2020 ◽  
Vol 58 (1) ◽  
pp. 25-38
Author(s):  
Sandi Baressi Šegota ◽  
Daniel Štifanić ◽  
Kazuhiro Ohkura ◽  
Zlatan Car
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Absolute Error ◽  
Activation Function ◽  
Propulsion System ◽  
Coefficient Of Determination ◽  
Decay State ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Torque Values

An artificial neural network (ANN) approach is proposed to the problem of estimating the propeller torques of a frigate using combined diesel, electric and gas (CODLAG) propulsion system. The authors use a multilayer perceptron (MLP) feed-forward ANN trained with data from a dataset which describes the decay state coefficients as outputs and system parameters as inputs – with a goal of determining the propeller torques, removing the decay state coefficients and using the torque values of the starboard and port propellers as outputs. A total of 53760 ANNs are trained – 26880 for each of the propellers, with a total 8960 parameter combinations. The results are evaluated using mean absolute error (MAE) and coefficient of determination (R2). Best results for the starboard propeller are MAE of 2.68 [Nm], and MAE of 2.58 [Nm] for the port propeller with following ANN configurations respectively: 2 hidden layers with 32 neurons and identity activation and 3 hidden layers with 16, 32 and 16 neurons and identity activation function. Both configurations achieve R2 value higher than 0.99.

scholarly journals Handwritten Digit Recognition using Convolutional Neural Network in Python with Tensorflow and Observe the Variation of Accuracies for Various Hidden Layers

2019 ◽  
Author(s):  
Fathma Siddique ◽  
Shadman Sakib ◽  
Md. Abu Bakr Siddique
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Handwritten Digit Recognition ◽  
Diverse Range ◽  
Digit Recognition ◽  
Handwritten Digit ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In recent times, with the increase of Artificial Neural Network (ANN), deep learning has brought a dramatic twist in the field of machine learning by making it more Artificial Intelligence (AI). Deep learning is used remarkably used in vast ranges of fields because of its diverse range of applications such as surveillance, health, medicine, sports, robotics, drones etc. In deep learning, Convolutional Neural Network (CNN) is at the center of spectacular advances that mixes Artificial Neural Network (ANN) and up to date deep learning strategies. It has been used broadly in pattern recognition, sentence classification, speech recognition, face recognition, text categorization, document analysis, scene, and handwritten digit recognition. The goal of this paper is to observe the variation of accuracies of CNN to classify handwritten digits using various numbers of hidden layer and epochs and to make the comparison between the accuracies. For this performance evaluation of CNN, we performed our experiment using Modified National Institute of Standards and Technology (MNIST) dataset. Further, the network is trained using stochastic gradient descent and the backpropagation algorithm.

scholarly journals Decentralized Identification and Control in Real-Time of a Robot Manipulator via Recurrent Wavelet First-Order Neural Network

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Luis A. Vázquez ◽  
Francisco Jurado ◽  
Alma Y. Alanís
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Robot Manipulator ◽  
Single Layer ◽  
Activation Function ◽  
Neural Structure ◽  
Neural Controller ◽  
First Order ◽  
Parallel Configuration ◽  
And Control

A decentralized recurrent wavelet first-order neural network (RWFONN) structure is presented. The use of a wavelet Morlet activation function allows proposing a neural structure in continuous time of a single layer and a single neuron in order to identify online in a series-parallel configuration, using the filtered error (FE) training algorithm, the dynamics behavior of each joint for a two-degree-of-freedom (DOF) vertical robot manipulator, whose parameters such as friction and inertia are unknown. Based on the RWFONN subsystem, a decentralized neural controller is designed via backstepping approach. The performance of the decentralized wavelet neural controller is validated via real-time results.

Block method and artificial neural network for processing and presenting monitoring data for subsidence during oil production

2020 ◽  
Vol 964 (10) ◽  
pp. 2-6
Author(s):  
V.N. Baranov ◽  
Jad Alkareem Kouteny
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Surface Deformation ◽  
Geodetic Network ◽  
Activation Function ◽  
Block Method ◽  
Different Types ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Observation Period

In order to optimize methods of geodetic supporting the monitoring and interpretation data in oil-producing area of a reservoir field, we proposed a modeling method enabling to optimize the construction of a geodetic network and raise the accuracy of determining the earth’s surface deformation using parameters of the model and apply the “block” method for its assessment. The relevance of the block method choice is obvious, its implementation, is to ensure high accuracy of determination and prediction of subsidence. The method enables specifying the re-observation period and dividing the area into parts, which increases the accuracy of the result. The method is effective when using an artificial neural network (ANN). In this case, the ANN consists of two layers, which can be increased in the form of a three-layer network when arranging the forecasting process. At the activation function choice, three similar expressions were considered; the symmetric Gauss function was adopted as the optimal one. In the process of setting up the network for the “block” method, the setting up parameter and the number of inputs (signals) for each individual block for different types of signals were determined.

scholarly journals Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers

2019 ◽  
Author(s):  
Fathma Siddique ◽  
Shadman Sakib ◽  
Md. Abu Bakr Siddique
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Text Categorization ◽  
Stochastic Gradient Descent ◽  
Diverse Range ◽  
Handwritten Digit ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In recent times, with the increase of Artificial Neural Network (ANN), deep learning has brought a dramatic twist in the field of machine learning by making it more Artificial Intelligence (AI). Deep learning is used remarkably used in vast ranges of fields because of its diverse range of applications such as surveillance, health, medicine, sports, robotics, drones etc. In deep learning, Convolutional Neural Network (CNN) is at the center of spectacular advances that mixes Artificial Neural Network (ANN) and up to date deep learning strategies. It has been used broadly in pattern recognition, sentence classification, speech recognition, face recognition, text categorization, document analysis, scene, and handwritten digit recognition. The goal of this paper is to observe the variation of accuracies of CNN to classify handwritten digits using various numbers of hidden layer and epochs and to make the comparison between the accuracies. For this performance evaluation of CNN, we performed our experiment using Modified National Institute of Standards and Technology (MNIST) dataset. Further, the network is trained using stochastic gradient descent and the backpropagation algorithm.

scholarly journals A Building Information Model (BIM) and Artificial Neural Network (ANN) Based System for Personal Thermal Comfort Evaluation and Energy Efficient Design of Interior Space

2019 ◽  
Vol 11 (18) ◽  
pp. 4972 ◽  
Author(s):  
Ma ◽  
Liu ◽  
Shang
Keyword(s):  
Neural Network ◽  
Thermal Comfort ◽  
Energy Efficient ◽  
Ann Model ◽  
Efficient Design ◽  
Interior Space ◽  
Building Information Model ◽  
Comfort Evaluation ◽  
Building Information ◽  
Artificial Neural Network Ann

It is crucial to evaluate indoor personal thermal comfort for a comfortable and green thermal environment. At present, the research on individual thermal comfort does not consider its implementation mode. Moreover, the improvement of energy saving efficiency under the premise of increasing human comfort is an urgent problem that needs to be solved. In this paper, we proposed a Building Information Model (BIM) and Artificial Neural Network (ANN) based system to solve this problem. The system consists of two parts including an ANN predictive model considering the Predicted Mean Vote (PMV) index, the persons’ position, and an innovative plugin of BIM to realize dynamic evaluation and energy efficient design. The ANN model has three layers, considering three environment parameters (air temperature, air humidity, and wind speed around the person), three human state parameters (human metabolism rate, clothing thermal resistance, and the body position) and four body parameters (gender, age, height, and weight) as inputs. The plugin provides two functions. One is to provide corresponding personal thermal comfort evaluation results with dynamic changes of parameters returned by Wireless Sensor Networks (WSN). The other one is to provide energy saving optimization suggestions for interior space design by simulating the energy consumption index of different design schemes. In the data test, the Mean Squared Error (MSE) of the established ANN model was about 0.39, while the MSE of traditional PMV model was about 2.1. The system realized the integration of thermal information and a building model, thereby providing guidance for the creation of a comfortable and green indoor environment.

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