An accurate and low-cost PM2.5 estimation method based on Artificial Neural Network

2015 ◽  
Author(s):  
Lixue Xia ◽  
Rong Luo ◽  
Bin Zhao ◽  
Yu Wang ◽  
Huazhong Yang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Estimation Method ◽  
Artificial Neural

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

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

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 Discrimination of Object Curvature Based on a Sparse Tactile Sensor Array

Micromachines ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 583
Author(s):  
Weiting Liu ◽  
Binpeng Zhan ◽  
Chunxin Gu ◽  
Ping Yu ◽  
Guoshi Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sensor Array ◽  
Estimation Method ◽  
Tactile Sensor ◽  
Radius Of Curvature ◽  
Curvature Radius ◽  
Average Curvature ◽  
Artificial Neural ◽  
Tactile Sensor Array

Object curvature plays an important role in grasping and manipulation. To be more exact, local curvature is a more useful information for grasping practically. Vision and touch are the two main methods to extract surface curvature of an object, but vision is often limited since the complete contact area is invisible during manipulation. In this paper, the authors propose an object curvature estimation method based on an artificial neural network algorithm through a lab-developed sparse tactile sensor array. The compliant layer covering on the sensor is indispensable for fitting the curved surface. Three types (plane, convex sphere, and convex cylinder) of sample and each type of sample including 30 different radiuses (1 mm to 30 mm) were used in the experiment. The overall classification accuracy was 93.1%. The average curvature radius estimating error based on an artificial neural network (ANN) algorithm was 1.87 mm. When the radius of curvature was bigger than 5 mm, the average relative error was smaller than 20%. As a comparison, the sensor array density we used in this paper was less than 9/cm2, which was smaller than the density of human SAII receptors, but the discrimination result was close to the SAII receptors. Comparison with the curvature discrimination ability of the human body showed that this method has a promising application prospect.

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