Artificial neural network implementation in single low-cost chip for the detection of insecticides by modeling of screen-printed enzymatic sensors response

2010 ◽  
Vol 74 (2) ◽  
pp. 223-229 ◽  
Author(s):  
Gustavo A. Alonso ◽  
Georges Istamboulie ◽  
Alfredo Ramírez-García ◽  
Thierry Noguer ◽  
Jean-Louis Marty ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Artificial Neural ◽  
Screen Printed

Low-Cost Hardware Implementation of Human Blood Identification Device Using Feed-Forward Artificial Neural Network on FPGA

2018 ◽  
Author(s):  
Rizki Eka Putri ◽  
Denny Darlis
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Image Resolution ◽  
Blood Type ◽  
Feed Forward ◽  
Testing Facility ◽  
System Input ◽  
Artificial Neural ◽  
Short Time

This article was under review for ICELTICS 2018 -- In the medical world there is still service dissatisfaction caused by lack of blood type testing facility. If the number of tested blood arise, a lot of problems will occur so that electronic devices are needed to determine the blood type accurately and in short time. In this research we implemented an Artificial Neural Network on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board to identify the blood type. This research uses blood sample image as system input. VHSIC Hardware Discription Language is the language to describe the algorithm. The algorithm used is feed-forward propagation of backpropagation neural network. There are 3 layers used in design, they are input, hidden1, and output. At hidden1layer has two neurons. In this study the accuracy of detection obtained are 92%, 92%, 92%, 90% and 86% for 32x32, 48x48, 64x64, 80x80, and 96x96 pixel blood image resolution, respectively.

Non-invasive intelligent monitoring system for fault detection in induction motor based on bio piezoelectric sensor using ANN

2022 ◽  
Author(s):  
Massine GANA ◽  
Hakim ACHOUR ◽  
Kamel BELAID ◽  
Zakia CHELLI ◽  
Mourad LAGHROUCHE ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Motor ◽  
Vibration Analysis ◽  
Low Cost ◽  
Piezoelectric Sensor ◽  
Integrated System ◽  
Easy Access ◽  
Non Invasive ◽  
Artificial Neural

Abstract This paper presents a design of a low-cost integrated system for the preventive detection of unbalance faults in an induction motor. In this regard, two non-invasive measurements have been collected then monitored in real time and transmitted via an ESP32 board. A new bio-flexible piezoelectric sensor developed previously in our laboratory, was used for vibration analysis. Moreover an infrared thermopile was used for non-contact temperature measurement. The data is transmitted via Wi-Fi to a monitoring station that intervenes to detect an anomaly. The diagnosis of the motor condition is realized using an artificial neural network algorithm implemented on the microcontroller. Besides, a Kalman filter is employed to predict the vibrations while eliminating the noise. The combination of vibration analysis, thermal signature analysis and artificial neural network provides a better diagnosis. It ensures efficiency, accuracy, easy access to data and remote control, which significantly reduces human intervention.

Univariate Artificial Neural Network in Forecasting Demand of Low Cost House in Petaling Jaya

2012 ◽  
pp. 1-16 ◽  
Author(s):  
Norhisham Bakhary ◽  
Khairulzan Yahya ◽  
Chin Nam Ng
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Housing Demand ◽  
Percentage Error ◽  
Potential Applications ◽  
Low Cost Housing ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Kebelakangan ini ramai penyelidik mendapati ‘Artificial Neural Network’ (ANN) untuk digunakan dalam berbagai bidang kejuruteraan awam. Banyak aplikasi ANN dalam proses peramalan menghasilkan kejayaan. Kajian ini memfokuskan kepada penggunaan siri masa ‘Univariate Neural Network’ untuk meramalkan permintaan rumah kos rendah di daerah Petaling Jaya, Selangor. Dalam kajian ini, beberapa kes bagi sesi latihan dan ramalan telah dibuat untuk mendapatkan model terbaik bagi meramalkan permintaan rumah. Nilai RMSE yang paling rendah yang diperolehi bagi tahap validasi adalah 0.560 dan nilai MAPE yang diperolehi adalah 8.880%. Hasil kajian ini menunjukkan kaedah ini memberikan keputusan yang boleh diterima dalam peramalan permintaan rumah berdasarkan data masa lalu. Kata kunci: Univariate Neural Network, permintaan rumah kos rendah, RMSE, MAPE Recently researchers have found the potential applications of Artificial Neural Network (ANN) in various fields in civil engineering. Many attempts to apply ANN as a forecasting tool has been successful. This paper highlighted the application of Time Series Univariate Neural Network in forecasting the demand of low cost house in Petaling Jaya district, Selangor, using historical data ranging from February 1996 to Appril 2000. Several cases of training and testing were conducted to obtain the best neural network model. The lowest Root Mean Square Error (RMSE) obtained for validation step is 0.560 and Mean Absolute Percentage Error (MAPE) is 8.880%. These results show that ANN is able to provide reliable result in term of forecasting the housing demand based on previous housing demand record. Key words: Time Series Univariate Neural Network, low cost housing demand, RMSE, MAPE

Optimization Design for a New Cascade of Helium Compressor With Enhanced Pressure Ratio

2010 ◽  
Author(s):  
Yanli Long ◽  
Limin Xu ◽  
Jinglei Yu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Design ◽  
Low Cost ◽  
Thickness Distribution ◽  
Pressure Ratio ◽  
Design Rule ◽  
Thermal Source ◽  
Stage Number ◽  
Artificial Neural

The High Temperature Gas-cooled Reactor (HTGR) is provided with good safety, high quality of thermal source and low cost of power generation in full life cycle. Furthermore, when the helium turbine is used for heat-work conversion, the efficiency of the HTGR is high and up to a magnitude of 50%. One of the key technologies of helium turbine is the helium compressor design. According to the conventional design rule of the air-compressor, the stage number of the helium compressor was too much excessive. Therefore, this thesis has analyzed and optimized a new cascade of helium compressor with enhanced pressure ratio in order to increase the pressure ratio and decrease the stage number. The Artificial Neural Network is used to build the approximate function which is based on database sample space. The Genetic Algorithm is used to search a new design, and the Artificial Neural Network is reused to predict the aerodynamic performance of the new design. The mean camber line and thickness distribution are optimized respectively, and the optimization results show that the total pressure loss coefficient can be reduced by 14.48% than that of the primary.

scholarly journals FORECASTING DEMAND FOR LOW COST CARRIERS IN AUSTRALIA USING AN ARTIFICIAL NEURAL NETWORK APPROACH

Aviation ◽  
2015 ◽  
Vol 19 (2) ◽  
pp. 90-103 ◽  
Author(s):  
Panarat Srisaeng ◽  
Glenn S. Baxter ◽  
Graham Wild
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Model Parameters ◽  
Back Propagation Algorithm ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Real Gdp ◽  
Feed Forward Network ◽  
Artificial Neural

This study focuses on predicting Australia‘s low cost carrier passenger demand and revenue passenger kilometres performed (RPKs) using traditional econometric and artificial neural network (ANN) modelling methods. For model development, Australia‘s real GDP, real GDP per capita, air fares, Australia‘s population and unemployment, tourism (bed spaces) and 4 dummy variables, utilizing quarterly data obtained between 2002 and 2012, were selected as model parameters. The neural network used multi-layer perceptron (MLP) architecture that compromised a multi-layer feed-forward network and the sigmoid and linear functions were used as activation functions with the feed forward‐back propagation algorithm. The ANN was applied during training, testing and validation and had 11 inputs, 9 neurons in the hidden layers and 1 neuron in the output layer. When comparing the predictive accuracy of the two techniques, the ANNs provided the best prediction and showed that the performance of the ANN model was better than that of the traditional multiple linear regression (MLR) approach. The highest R-value for the enplaned passengers ANN was around 0.996 and for the RPKs ANN was round 0.998, respectively.

scholarly journals Estimation of Axle Torque for an Agricultural Tractor Using an Artificial Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1989
Author(s):  
Wan-Soo Kim ◽  
Dae-Hyun Lee ◽  
Yong-Joo Kim ◽  
Yeon-Soo Kim ◽  
Seong-Un Park
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Physical Properties ◽  
Low Cost ◽  
Model Performance ◽  
Soil Physical Properties ◽  
Simple Method ◽  
Slip Ratio ◽  
Data Points ◽  
Artificial Neural

The objective of this study was to develop a model to estimate the axle torque (AT) of a tractor using an artificial neural network (ANN) based on a relatively low-cost sensor. ANN has proven to be useful in the case of nonlinear analysis, and it can be applied to consider nonlinear variables such as soil characteristics, unlike studies that only consider tractor major parameters, thus model performance and its implementation can be extended to a wider range. In this study, ANN-based models were compared with multiple linear regression (MLR)-based models for performance verification. The main input data were tractor engine parameters, major tractor parameters, and soil physical properties. Data of soil physical properties (i.e., soil moisture content and cone index) and major tractor parameters (i.e., engine torque, engine speed, specific fuel consumption, travel speed, tillage depth, and slip ratio) were collected during a tractor field experiment in four Korean paddy fields. The collected soil physical properties and major tractor parameter data were used to estimate the AT of the tractor by the MLR- and ANN-based models: 250 data points were used for developing and training the model were used, the 50 remaining data points were used to test the model estimation. The AT estimated with the developed MLR- and ANN-based models showed agreement with actual measured AT, with the R2 value ranging from 0.825 to 0.851 and from 0.857 to 0.904, respectively. These results suggest that the developed models are reliable in estimating tractor AT, while the ANN-based model showed better performance than the MLR-based model. This study can provide useful results as a simple method using ANNs based on relatively inexpensive sensors that can replace the existing complex tractor AT measurement method is emphasized.

scholarly journals Mitigating Construction Deficiencies: An Impact Analysis for Low-Cost Housing Developments Utilizing Artificial Neural Network

2021 ◽  
Author(s):  
Ramene U. Lim ◽  
Dante L. Silva ◽  
Kevin Lawrence M. De Jesus
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Housing Development ◽  
Development Projects ◽  
Computational Tool ◽  
Housing Developments ◽  
Low Cost Housing ◽  
Artificial Neural ◽  
To Come

The aim of this study is to be able to come up with a supplemental project management policy guidelines and computational tool that will address the two major concerns in construction of low-cost housing, construction delays and workmanship defects. Through assessment of previous studies, factors causing delays and defects from the two major stakeholders involved in housing development projects were identified. With the use of the five-point Likert Scale in survey forms distributed to 60 professionals involved in housing development projects, factors were classified and identified according to its degree of impact on the overall construction efficiency. The statistics of these factors were organized and used to develop an Artificial Neural Network Model. The relative importance of the factors was measured using Garson’s Algorithm. The derived equations from the developed ANN Model were used in formulating the computational tool and supplemental policy guidelines that can now be used to evaluate the workmanship defects and delay ratings of different housing developments. The computational tool was tested by 10 experts with their current projects and was able to receive a 4.6 out of 5 rubric evaluation rating, showing the tool’s effectiveness in identifying and assessing the probability and impact of construction deficiencies on their projects.

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