Neural Network Incorporating Meal Information Improves Accuracy of Short-Time Prediction of Glucose Concentration

Due to the random nature of ship motion in an open sea environment, the ship related maritime operations such as landing on an aircraft carrier, ship-borne helicopter recovery, cargo transfer between ships and so on, are usually very difficult. An accurate prediction of the motion will improve the operation safety and efficiency on board ships. This paper presents a research on the application of artificial neural network methods in the short-time prediction of ship pitching motion. The radial basis function (RBF) neural network is applied to develop a model for short-time prediction of ship pitching motion, and the other two kinds of artificial neural networks, i.e., back propagation (BP) neural network, Elman neural network are also applied for the same purpose. A comparative analysis among them is presented. It is shown that RBF neural network provides a more effective and accurate tool for predicting the ship pitching motion.

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

10.31227/osf.io/dp63x ◽

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.

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Real-time prediction of 1H and 13C chemical shifts with DFT accuracy using a 3D graph neural network

Chemical Science ◽

10.1039/d1sc03343c ◽

2021 ◽

Author(s):

Yanfei Guan ◽

S. V. Shree Sowndarya ◽

Liliana C. Gallegos ◽

Peter C. St. John ◽

Robert S. Paton

Keyword(s):

Neural Network ◽

Real Time ◽

Quantum Chemical ◽

Organic Molecules ◽

Chemical Shifts ◽

13C Chemical Shifts ◽

Time Prediction ◽

Nmr Data ◽

Proton Chemical Shifts

From quantum chemical and experimental NMR data, a 3D graph neural network, CASCADE, has been developed to predict carbon and proton chemical shifts. Stereoisomers and conformers of organic molecules can be correctly distinguished.

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Grinding Acoustic Emission Classification in Terms of Mechanical Behaviours

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.329.15 ◽

2007 ◽

Vol 329 ◽

pp. 15-20 ◽

Cited By ~ 2

Author(s):

Xun Chen ◽

James Griffin

Keyword(s):

Neural Network ◽

Acoustic Emission ◽

Material Removal ◽

Grinding Process ◽

Short Time Fourier Transform ◽

Fundamental Investigation ◽

Mechanical Behaviours ◽

Wavelets Transform ◽

Short Time ◽

Ae Signals

The material removal in grinding involves rubbing, ploughing and cutting. For grinding process monitoring, it is important to identify the effects of these different phenomena experienced during grinding. A fundamental investigation has been made with single grit cutting tests. Acoustic Emission (AE) signals would give the information relating to the groove profile in terms of material removal and deformation. A combination of filters, Short-Time Fourier Transform (STFT), Wavelets Transform (WT), statistical windowing of the WT with the kurtosis, variance, skew, mean and time constant measurements provided the principle components for classifying the different grinding phenomena. Identification of different grinding phenomena was achieved from the principle components being trained and tested against a Neural Network (NN) representation.

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Real Time Prediction of Ship Pitch Motion Based on NARX Neural Network

10.1109/ccdc52312.2021.9602831 ◽

2021 ◽

Author(s):

Wu Wenhao ◽

Li Lianbo ◽

Zhu Zhenyu ◽

Jiao Yang

Keyword(s):

Neural Network ◽

Real Time ◽

Pitch Motion ◽

Time Prediction ◽

Ship Pitch ◽

Narx Neural Network

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Earthquake forecasting and its verification

Nonlinear Processes in Geophysics ◽

10.5194/npg-12-965-2005 ◽

2005 ◽

Vol 12 (6) ◽

pp. 965-977 ◽

Cited By ~ 75

Author(s):

J. R. Holliday ◽

K. Z. Nanjo ◽

K. F. Tiampo ◽

J. B. Rundle ◽

D. L. Turcotte

Keyword(s):

Temporal Variations ◽

Earthquake Forecasting ◽

Earthquake Risk ◽

Decision Threshold ◽

New Approach ◽

Pi Method ◽

Time Prediction ◽

Short Time ◽

Large Earthquakes ◽

Proven Method

Abstract. No proven method is currently available for the reliable short time prediction of earthquakes (minutes to months). However, it is possible to make probabilistic hazard assessments for earthquake risk. In this paper we discuss a new approach to earthquake forecasting based on a pattern informatics (PI) method which quantifies temporal variations in seismicity. The output, which is based on an association of small earthquakes with future large earthquakes, is a map of areas in a seismogenic region ("hotspots'') where earthquakes are forecast to occur in a future 10-year time span. This approach has been successfully applied to California, to Japan, and on a worldwide basis. Because a sharp decision threshold is used, these forecasts are binary--an earthquake is forecast either to occur or to not occur. The standard approach to the evaluation of a binary forecast is the use of the relative (or receiver) operating characteristic (ROC) diagram, which is a more restrictive test and less subject to bias than maximum likelihood tests. To test our PI method, we made two types of retrospective forecasts for California. The first is the PI method and the second is a relative intensity (RI) forecast based on the hypothesis that future large earthquakes will occur where most smaller earthquakes have occurred in the recent past. While both retrospective forecasts are for the ten year period 1 January 2000 to 31 December 2009, we performed an interim analysis 5 years into the forecast. The PI method out performs the RI method under most circumstances.

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