scholarly journals Simulation Study of Utilizing X-ray Tube in Monitoring Systems of Liquid Petroleum Products

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 828
Author(s):  
Gholam Hossein Roshani ◽  
Peshawa Jammal Muhammad Ali ◽  
Shivan Mohammed ◽  
Robert Hanus ◽  
Lokman Abdulkareem ◽  
...  
Keyword(s):  
Gamma Ray ◽  
Mean Absolute Error ◽  
Volume Ratio ◽  
Absolute Error ◽  
Petroleum Products ◽  
X Ray ◽  
Radioisotope Source ◽  
Liquid Products ◽  
Mlp Neural Networks ◽  
Better Than

Radiation-based instruments have been widely used in petrochemical and oil industries to monitor liquid products transported through the same pipeline. Different radioactive gamma-ray emitter sources are typically used as radiation generators in the instruments mentioned above. The idea at the basis of this research is to investigate the use of an X-ray tube rather than a radioisotope source as an X-ray generator: This choice brings some advantages that will be discussed. The study is performed through a Monte Carlo simulation and artificial intelligence. Here, the system is composed of an X-ray tube, a pipe including fluid, and a NaI detector. Two-by-two mixtures of four various oil products with different volume ratios were considered to model the pipe’s interface region. For each combination, the X-ray spectrum was recorded in the detector in all the simulations. The recorded spectra were used for training and testing the multilayer perceptron (MLP) models. After training, MLP neural networks could estimate each oil product’s volume ratio with a mean absolute error of 2.72 which is slightly even better than what was obtained in former studies using radioisotope sources.

scholarly journals Velody 2—Resilient High-Capacity MIDI Steganography for Organ and Harpsichord Music

2020 ◽  
Vol 11 (1) ◽  
pp. 39
Author(s):  
Eric Järpe ◽  
Mattias Weckstén
Keyword(s):  
Mean Absolute Error ◽  
Signal To Noise Ratio ◽  
High Capacity ◽  
Absolute Error ◽  
Music Technology ◽  
Alternative Methods ◽  
File Size ◽  
User Friendly ◽  
Different Levels ◽  
Better Than

A new method for musical steganography for the MIDI format is presented. The MIDI standard is a user-friendly music technology protocol that is frequently deployed by composers of different levels of ambition. There is to the author’s knowledge no fully implemented and rigorously specified, publicly available method for MIDI steganography. The goal of this study, however, is to investigate how a novel MIDI steganography algorithm can be implemented by manipulation of the velocity attribute subject to restrictions of capacity and security. Many of today’s MIDI steganography methods—less rigorously described in the literature—fail to be resilient to steganalysis. Traces (such as artefacts in the MIDI code which would not occur by the mere generation of MIDI music: MIDI file size inflation, radical changes in mean absolute error or peak signal-to-noise ratio of certain kinds of MIDI events or even audible effects in the stego MIDI file) that could catch the eye of a scrutinizing steganalyst are side-effects of many current methods described in the literature. This steganalysis resilience is an imperative property of the steganography method. However, by restricting the carrier MIDI files to classical organ and harpsichord pieces, the problem of velocities following the mood of the music can be avoided. The proposed method, called Velody 2, is found to be on par with or better than the cutting edge alternative methods regarding capacity and inflation while still possessing a better resilience against steganalysis. An audibility test was conducted to check that there are no signs of audible traces in the stego MIDI files.

scholarly journals Charuco Board-Based Omnidirectional Camera Calibration Method

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 421 ◽  
Author(s):  
Gwon An ◽  
Siyeong Lee ◽  
Min-Woo Seo ◽  
Kugjin Yun ◽  
Won-Sik Cheong ◽  
...  
Keyword(s):  
Camera Calibration ◽  
Mean Absolute Error ◽  
Three Dimensional ◽  
Absolute Error ◽  
Calibration Method ◽  
Feature Points ◽  
Omnidirectional Camera ◽  
Reprojection Error ◽  
Conventional Methods ◽  
Better Than

In this paper, we propose a Charuco board-based omnidirectional camera calibration method to solve the problem of conventional methods requiring overly complicated calibration procedures. Specifically, the proposed method can easily and precisely provide two-dimensional and three-dimensional coordinates of patterned feature points by arranging the omnidirectional camera in the Charuco board-based cube structure. Then, using the coordinate information of the feature points, an intrinsic calibration of each camera constituting the omnidirectional camera can be performed by estimating the perspective projection matrix. Furthermore, without an additional calibration structure, an extrinsic calibration of each camera can be performed, even though only part of the calibration structure is included in the captured image. Compared to conventional methods, the proposed method exhibits increased reliability, because it does not require additional adjustments to the mirror angle or the positions of several pattern boards. Moreover, the proposed method calibrates independently, regardless of the number of cameras comprising the omnidirectional camera or the camera rig structure. In the experimental results, for the intrinsic parameters, the proposed method yielded an average reprojection error of 0.37 pixels, which was better than that of conventional methods. For the extrinsic parameters, the proposed method had a mean absolute error of 0.90° for rotation displacement and a mean absolute error of 1.32 mm for translation displacement.

scholarly journals Applications of Discrete Wavelet Transform for Feature Extraction to Increase the Accuracy of Monitoring Systems of Liquid Petroleum Products

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3215
Author(s):  
Mohammed Balubaid ◽  
Mohammad Amir Sattari ◽  
Osman Taylan ◽  
Ahmed A. Bakhsh ◽  
Ehsan Nazemi
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Volume Ratio ◽  
Petroleum Products ◽  
Discrete Wavelet ◽  
X Ray ◽  
Network Training ◽  
Transport Code ◽  
Pass Through

This paper presents a methodology to monitor the liquid petroleum products which pass through transmission pipes. A simulation setup consisting of an X-ray tube, a detector, and a pipe was established using a Monte Carlo n-particle X-version transport code to investigate a two-by-two mixture of four different petroleum products, namely, ethylene glycol, crude oil, gasoline, and gasoil, in deferent volumetric ratios. After collecting the signals of each simulation, discrete wavelet transform (DWT) was applied as the feature extraction system. Then, the statistical feature, named the standard deviation, was calculated from the approximation of the fifth level, and the details of the second to fifth level provide appropriate inputs for neural network training. Three multilayer perceptron neural networks were utilized to predict the volume ratio of three types of petroleum products, and the volume ratio of the fourth product could easily be obtained from the results of the three presented networks. Finally, a root mean square error of less than 1.77 was obtained in predicting the volume ratio, which was much more accurate than in previous research. This high accuracy was due to the use of DWT for feature extraction.

scholarly journals Automatically steering cardiac catheters in vivo with respiratory motion compensation

2020 ◽  
Vol 39 (5) ◽  
pp. 586-597 ◽  
Author(s):  
Paul M Loschak ◽  
Alperen Degirmenci ◽  
Cory M Tschabrunn ◽  
Elad Anter ◽  
Robert D Howe
Keyword(s):  
Motion Compensation ◽  
Respiratory Motion ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Respiratory Motion Compensation ◽  
X Ray ◽  
Cardiac Catheters ◽  
The Us ◽  
Predictive Filtering

A robotic system for automatically navigating ultrasound (US) imaging catheters can provide real-time intra-cardiac imaging for diagnosis and treatment while reducing the need for clinicians to perform manual catheter steering. Clinical deployment of such a system requires accurate navigation despite the presence of disturbances including cyclical physiological motions (e.g., respiration). In this work, we report results from in vivo trials of automatic target tracking using our system, which is the first to navigate cardiac catheters with respiratory motion compensation. The effects of respiratory disturbances on the US catheter are modeled and then applied to four-degree-of-freedom steering kinematics with predictive filtering. This enables the system to accurately steer the US catheter and aim the US imager at a target despite respiratory motion disturbance. In vivo animal respiratory motion compensation results demonstrate automatic US catheter steering to image a target ablation catheter with 1.05 mm and 1.33° mean absolute error. Robotic US catheter steering with motion compensation can improve cardiac catheterization techniques while reducing clinician effort and X-ray exposure.

scholarly journals Machine learning meets pKa

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 113 ◽  
Author(s):  
Marcel Baltruschat ◽  
Paul Czodrowski
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
External Validation ◽  
Absolute Error ◽  
Source Model ◽  
Squared Error ◽  
Fold Cross Validation ◽  
Better Than

We present a small molecule pKa prediction tool entirely written in Python. It predicts the macroscopic pKa value and is trained on a literature compilation of monoprotic compounds. Different machine learning models were tested and random forest performed best given a five-fold cross-validation (mean absolute error=0.682, root mean squared error=1.032, correlation coefficient r2 =0.82). We test our model on two external validation sets, where our model performs comparable to Marvin and is better than a recently published open source model. Our Python tool and all data is freely available at https://github.com/czodrowskilab/Machine-learning-meets-pKa.

Precision Measurement of Transition and Level Energies in 233U

1971 ◽  
Vol 26 (6) ◽  
pp. 1092-1093 ◽  
Author(s):  
T. von Egidy ◽  
O.W.B. Schult ◽  
W. Kallinger ◽  
D. Breitig ◽  
R.P. Sharma ◽  
...  
Keyword(s):  
Least Squares ◽  
Precision Measurement ◽  
Level Scheme ◽  
Gamma Ray ◽  
Crystal Spectrometer ◽  
Transition Energies ◽  
Annihilation Line ◽  
X Ray ◽  
Bent Crystal ◽  
Better Than

Abstract Gamma ray energies in 233U following the decay of 233Pa were measured with the Risö bent crystal spectrometer. These gamma lines were detected during the study of the 232Th(n,y)233Th reaction. Experimental details will be given elsewhere1. The energies were calibrated with the X-ray lines2 and the annihilation line3. The energies of seven transitions between 270 and 420 keV were determined with an accuracy which is up to a factor of 10 better than previous measurements by Albridge, Hollander, Gallagher, and Hamilton4. The transition energies are shown in the level scheme 4, 5 (Fig. 1 ) . A least squares program was used to fit the level energies to the seven transition energies obtained in this experiment and to four transition energies (below 103 keV) measured precisely by Albridge et al. 4. Level energies could be calculated with an accuracy of about 20 eV (Fig. 1).

scholarly journals Statistical Models in Estimating Air Temperature in a Mountainous Region of Greece

2017 ◽  
Vol 12 (3) ◽  
pp. 544-549 ◽  
Author(s):  
Stelios Maniatis ◽  
Kostas Chronopoulos ◽  
Aristidis Matsoukis ◽  
Athanasios Kamoutsis
Keyword(s):  
Air Temperature ◽  
Statistical Models ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Multi Layer Perceptron ◽  
Simple Linear Regression ◽  
The Mean ◽  
Of Greece ◽  
Better Than

The current work focuses on the estimation of air temperature (T) conditions in two high altitude (alt) sites (1580 m), each one at different orientation (southeast and northwest) in the mountain (Mt) Aenos in the island of Cephalonia, Greece, by using two well-known statistical models, simple linear regression (SLR) and multi-layer perceptron ( MLP), one of the most commonly used artificial neural networks. More specifically, the estimation of mean, maximum and minimum T in high alt sites was based on the respective T data of two lower alt sites (1100 m), the first at southeast and the second at northwest orientations, and was carried out separately for each orientation. The performance of both SLR and MLP models was evaluated by the coefficient of determination (R2) and the Mean Absolute Error (MAE). Results showed that the examined models (SLR and MLP) provided very satisfactory results with regard to the estimation of mean, maximum and minimum T, regarding southeast orientation (R2 ranging from 0.96 to 0.98), with mean T estimation being relatively better, as confirmed by the lowest MAE (0.83). Regarding northwest orientation, T estimation was less accurate (lower R2 and higher MAE), compared to the respective estimation of southeast orientation, but, the results were considered adequate (R2 and MAE ranging from 0.88 to 0.92 and 1.00 to 1.40, respectively). In general, the estimations of the mean T were better than those of the extreme ones (minimum and maximum T). In addition, better results (higher R2 and lower, in general, MAE) were obtained when T estimations were based on T data derived from sites located at areas with similar surroundings, as in the case of dense and tall vegetation of the sites at southeast orientation, irrespective of applied method.

Accounting for SNR in an Algorithm Using Wavelet Transform to Remove ECG Interference from EMG Signals

2019 ◽  
Vol 19 (01) ◽  
pp. 2050001
Author(s):  
Thandar Oo ◽  
Pornchai Phukpattaranont
Keyword(s):  
Wavelet Transform ◽  
Mean Absolute Error ◽  
Signal To Noise Ratio ◽  
Absolute Error ◽  
Pass Filter ◽  
Ecg Signals ◽  
Emg Signal ◽  
High Pass ◽  
Better Than ◽  
Ecg Interference

When the electromyography (EMG) signal is acquired from muscles in the torso, the electrocardiography (ECG) signal coming from heart activity can interfere. As a result, the EMG signal can be contaminated during data collection. In this paper, a technique based on discrete stationary wavelet transform (DSWT) is proposed to remove ECG interference from the EMG signal while taking into account the signal-to-noise ratio (SNR). The contaminated EMG signal is decomposed using 5-level DSWT with the Symlet wavelet function. The coefficients for levels 4 and 5, which are contaminated by ECG, are set to zero when their absolute values are less than or equal to a threshold determined for each SNR level. A clean EMG signal can then be obtained by inverse DSWT mapping of the new thresholded coefficients. We evaluated the performance of the proposed algorithm using simulated EMG contaminated with both simulated and real ECG signals, at 9 SNR levels from [Formula: see text]20 to 20[Formula: see text]dB with 5[Formula: see text]dB increments. The performance based on mean absolute error, correlation coefficient and relative error shows that the DSWT method is better than a high-pass filter.

scholarly journals Per-COVID-19: A Benchmark Dataset for COVID-19 Percentage Estimation from CT-Scans

2021 ◽  
Vol 7 (9) ◽  
pp. 189
Author(s):  
Fares Bougourzi ◽  
Cosimo Distante ◽  
Abdelkrim Ouafi ◽  
Fadi Dornaika ◽  
Abdenour Hadid ◽  
...  
Keyword(s):  
Mean Absolute Error ◽  
Pearson Correlation ◽  
Absolute Error ◽  
Ct Scans ◽  
Rt Pcr ◽  
X Ray ◽  
Fast Solution ◽  
Patient State ◽  
Polymerase Chain ◽  
Tomography Scan

COVID-19 infection recognition is a very important step in the fight against the COVID-19 pandemic. In fact, many methods have been used to recognize COVID-19 infection including Reverse Transcription Polymerase Chain Reaction (RT-PCR), X-ray scan, and Computed Tomography scan (CT- scan). In addition to the recognition of the COVID-19 infection, CT scans can provide more important information about the evolution of this disease and its severity. With the extensive number of COVID-19 infections, estimating the COVID-19 percentage can help the intensive care to free up the resuscitation beds for the critical cases and follow other protocol for less severity cases. In this paper, we introduce COVID-19 percentage estimation dataset from CT-scans, where the labeling process was accomplished by two expert radiologists. Moreover, we evaluate the performance of three Convolutional Neural Network (CNN) architectures: ResneXt-50, Densenet-161, and Inception-v3. For the three CNN architectures, we use two loss functions: MSE and Dynamic Huber. In addition, two pretrained scenarios are investigated (ImageNet pretrained models and pretrained models using X-ray data). The evaluated approaches achieved promising results on the estimation of COVID-19 infection. Inception-v3 using Dynamic Huber loss function and pretrained models using X-ray data achieved the best performance for slice-level results: 0.9365, 5.10, and 9.25 for Pearson Correlation coefficient (PC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE), respectively. On the other hand, the same approach achieved 0.9603, 4.01, and 6.79 for PCsubj, MAEsubj, and RMSEsubj, respectively, for subject-level results. These results prove that using CNN architectures can provide accurate and fast solution to estimate the COVID-19 infection percentage for monitoring the evolution of the patient state.

SVR Model for Prediction of Incidence Influenza Based on Automated Method

2014 ◽  
Vol 926-930 ◽  
pp. 1159-1163
Author(s):  
Jia Song
Keyword(s):  
Mean Absolute Error ◽  
Absolute Error ◽  
Public Health Issue ◽  
Support Vector ◽  
Automated Method ◽  
Significant Public Health ◽  
The Mean ◽  
Single Support Vector Machine ◽  
Absolute Percent Error ◽  
Better Than

As is a significant public health issue to predict the incidence of influenza, this paper present a supported vector regression (SVR) model based on an automated method which worked as the following steps: firstly, the automated method is used to select the texts which highly related to the influenza, and then the SVR algorithm will find out the nonlinear between each context. According to the result, when assessing by the root mean squared predict error, the mean absolute error and the mean absolute percent error of the whole system, the SVR performed much better than single support vector machine regression prediction. Also, the validity of this method is verified.

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