Jump neural network for online short-time prediction of blood glucose from continuous monitoring sensors and meal information

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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Examining Sensor Agreement in Neural Network Blood Glucose Prediction

Journal of Diabetes Science and Technology ◽

10.1177/19322968211018246 ◽

2021 ◽

pp. 193229682110182

Author(s):

Aaron P. Tucker ◽

Arthur G. Erdman ◽

Pamela J. Schreiner ◽

Sisi Ma ◽

Lisa S. Chow

Keyword(s):

Neural Network ◽

Blood Glucose ◽

Patient Specific ◽

Sensor Location ◽

Effective Care ◽

Freestyle Libre ◽

Patients With Diabetes ◽

Interstitial Glucose ◽

Continuous Glucose Monitors ◽

Glucose Prediction

Successful measurements of interstitial glucose are a key component in providing effective care for patients with diabetes. Recently, there has been significant interest in using neural networks to forecast future glucose values from interstitial measurements collected by continuous glucose monitors (CGMs). While prediction accuracy continues to improve, in this work we investigated the effect of physiological sensor location on neural network blood glucose forecasting. We used clinical data from patients with Type 2 Diabetes who wore blinded FreeStyle Libre Pro CGMs (Abbott) on both their right and left arms continuously for 12 weeks. We trained patient-specific prediction algorithms to test the effect of sensor location on neural network forecasting ( N = 13, Female = 6, Male = 7). In 10 of our 13 patients, we found at least one significant ( P < .05) increase in forecasting error in algorithms which were tested with data taken from a different location than data which was used for training. These reported results were independent from other noticeable physiological differences between subjects (eg, height, age, weight, blood pressure) and independent from overall variance in the data. From these results we observe that CGM location can play a consequential role in neural network glucose prediction.

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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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