Measure Of Irregularity Research Articles

A parametric study on the structural noise radiation characteristics of a steel spring floating slab track

It is a common experience that the interior noise of an underground train vehicle increases suddenly when it just runs into a track section with steel spring floating slabs, significantly reducing passengers’ comfort. This paper investigates the reasons, and control, of such noise through field tests and model simulations. The effect of rail surface irregularity on interior noise was investigated first by analysing data collected from interior noise tests, rail surface irregularity measurement and by analysing the modal behaviour of the slab. Noise radiation of the track slab was then analysed using the acoustic boundary element method while the vibration of the steel spring floating slab track is predicted based on an infinitely long periodic structure model; And finally with the prediction models, a parameter study was performed for the steel spring floating slab, on the purpose of improving the parameters to make the track less noisy. The research presented in this paper may provide a reference for the design of steel spring floating slabs in terms of noise control.

Sensitivity analysis to near-surface velocity errors of the Kirchhoff single-stack redatuming of multi-coverage seismic data

We present a study of a Kirchhoff-type redatuming approach for irregular multi-coverage land seismic data to remove the disturbing influence of rugged topography and a considerable near-surface velocity inhomogeneity, like a thick weathering layer, on the subsurface reflections. The redatuming method is physically more justified than the commonly used field-static correction methods that provide - in the indicated situation - non-physical temporal adjustments (field statics) by shifting traces in time, which lead to sub-optimal imaging and inversion of the reflections. The redatuming procedure studied here is different: It transforms the multi-coverage seismic reflection data on the irregular measurement surface to a regular measurement configuration on a pre-specified datum, below the inhomogeneous velocity overburden. This is done by stacking/summing the input traces with certain operators or traveltime curves which are calculated by ray tracing. In this way, incoherent subsurface reflections in the input data become coherent and thus recognizable in the output data. To have this happen, the redatuming operation requires a good approximation of the overburden velocity model and a reasonable one of the sub-burden homogeneous velocity model (below the datum). Then the multi-coverage output data on the new flat datum can be imaged and inverted much better than with standard data processing, i.e., with field-static correction. This work is dedicated to explaining the single-stack Kirchhoff-type redatuming approach kinematically using a synthetic model and data based on a real geological situation. However, our main aim is to study the sensitivity of this redatuming method to errors of the overburden velocity model, which is determined from multi-coverage seismic data using first arrivals traveltime tomography commonly used in the oil industry. We apply the redatuming process to a multi-coverage synthetic seismic dataset generated from the models that represent the real geological situation with irregular measurement surfaces and inhomogeneous overburden or near-surface velocity. To assess accuracy, the redatumed data was migrated in depth-domain and the results showed the correct positioning of the reflectors. The results revealed that this redatuming method produces reliable and accurate redatumed data even using approximate velocity models, which can be useful for prestack time and depth imaging.

Measuring Railway Track Irregularities at High Accuracy and Efficiency Based on GNSS/INS/TS Integration

Measuring the railway track irregularities (abbr. Irr) with sub-millimeter accuracy is vital for the travel safety of high-speed trains. In comparison to the traditional total station (TS)-based track inspection trolley (TIT) for track irregularity examination, the integration of global navigation satellite system (GNSS) and inertial navigation system (INS) is more time-efficient but less accurate in longwave irregularity detection. To achieve high accuracy and high survey efficiency, a GNSS/INS/TS integrated irregularity measuring (GIT-Irr) system is proposed based on the attitude-inferred irregularity measuring method utilizing TS constraint. In addition, to reduce the conversion error of TS coordinates from local engineering frame to local geo-frame, TS-aided calibration in the deviation domain is recommended in the GIT-Irr system. A field test was carried out near the Baotou East railway station in China in order to validate the performance of the proposed system. Four sets of independent measurements are collected together with an external reference from a TS-based TIT. The results demonstrate that the proposed system increases the surveying ability of shortwave and longwave alignment irregularities by about 5% and 75%, respectively. Under a 3-sigma confidence level, shortwave alignment irregularities are 0.50 mm and 0.57 mm in horizontal and vertical directions, respectively, while those of the longwaves are 1.78 mm and 2.06 mm. Therefore, the proposed GIT-Irr system provides great potential for highly efficient sub-millimeter track irregularity measurement.

A Feedback Control Strategy for a Fed-Batch Monoclonal Antibody Production Process Utilising Infrequent and Irregular Sampled Measurements

The ability to take non-invasive Raman measurements presents a unique opportunity to use one Raman probe across multiple vessels in parallel, reducing costs but making measurements infrequent. Under these conditions, infrequent and irregular feedback signals can result in poor closed-loop control performance. This study addressed the issue of infrequent and irregular Raman measurements using a linear dynamic model developed from interpolated data to predict more frequent measurements of the controlled variable. The simulated monoclonal antibody production was sampled hourly with white noise added to the simulated glucose concentration to replicate real Raman measurements. The hourly samples were interpolated into 15 min intervals and a linear dynamic model was developed to predict the glucose concentration at 15 min intervals. These predicted values were then used in a feedback control loop by using model predictive control or a conventional proportional and integral controller to control the glucose concentration at 15 min sampling intervals. For setpoint tracking, the model predictive control reduced the integral of absolute errors to 14,600 from 15,900 (with a 1 h sampling time) or 8.2% reduction. With adaptive model predictive control, the integral of absolute errors was reduced from 14,500 (1 h sampling time) to 14,200 for setpoint tracking and from 13,500 (1 h sampling time) to 13,300 for disturbance rejection. A final comparison demonstrated that the proposed method can also cope with random variations in the sampling time.

Power-Efficient Trainable Neural Networks towards Accurate Measurement of Irregular Cavity Volume

Irregular cavity volume measurement is a critical step in industrial production. This technology is used in a wide variety of applications. Traditional studies, such as waterflooding-based methods, have suffered from the following shortcomings, i.e., significant measurement error, low efficiency, complicated operation, and corrosion of devices. Recently, neural networks based on the air compression principle have been proposed to achieve irregular cavity volume measurement. However, the balance between data quality, network computation speed, convergence, and measurement accuracy is still underexplored. In this paper, we propose novel neural networks to achieve accurate measurement of irregular cavity volume. First, we propose a measurement method based on the air compression principle to analyze seven key parameters comprehensively. Moreover, we integrate the Hilbert–Schmidt independence criterion (HSIC) into fully connected neural networks (FCNNs) to build a trainable framework. This enables the proposed method to achieve power-efficient training. We evaluate the proposed neural network in the real world and compare it with typical procedures. The results show that the proposed method achieves the top performance for measurement accuracy and efficiency.

Multitemporal Change Detection and Irregular Land Shape Area Measurement from Multispectral Sensor Images through BSO Algorithm

Pixel-based classification and area measurement play a vital role in satellite image processing. The accuracy in classification and area measurement is required for remote monitoring various regions such as water area, cultivation regions, reservoir water spread, and disease spread in cultivation area. Traditionally, Google Earth Pro-based area measurement has error in area measurement due to curvature nature and irregular land surface. Moreover, exact point identification on land surface on Earth pro is difficult due to frequent changes on the land surface. The problem in the land area measurement is mostly affected due to the man-made changes in that particular land area. In this paper, we solve land area measurement error problem through the automated single and multithresholding pixels of different iterations on the land surface by the BSO algorithm. The land cover region pixel intensity changes on the curvature region of land surface with respect to spatial and temporal variations which are identified through BSO optimization-based image segmentation for exact area measurement. For the experimentation of accurate measurement, land area images such as urban, semiurban, hill, and coastal region from LANDSAT and SENTINEL images for period 2016 to 2019 are taken for the land area measurement study. BSO enhances and segments the land regions such as road, building, water body, vegetation, bare land, hill, and coastal region of about 32% more than particle swarm optimization (PSO) algorithm. Furthermore, the urban land area measurement accuracy increases to about 97% than the irregular land surface area.

Evaluation of Dynamic Foraminal Stenosis with Positional MRI in Patients with C6 Radiculopathy-Mimicking Pain: A Prospective Radiologic Cohort Study.

Objective Patients with a C6 radiculopathy-mimicking complaint are always in the gray zone if the diagnosis is not clear. The aim of the study is to make the diagnosis clear if the neck and shoulder pain is caused by a dynamic stenosis of the neural foramen at the C5-C6 level. Methods Patients with a C6 radiculopathy-mimicking complaint were included in the study. Patients had a cervical spine magnetic resonance imaging (MRI) at the normal limits, or a minimal protrusion at the C5-C6 level underwent a dynamic MRI procedure. We measured the foraminal area and spinal cord diameter (SCD) at the C5-C6 level by using the PACS system ROI irregular are determination integral embedded to PACS. Inter- and intraobserver reliability of measurements was evaluated. Results were analyzed statistically, and a p value< 0.05 was accepted as statistically meaningful. Results A total of 23 patients between January 2019 and June 2019 were included in the study. There were 10 men and 13 women, and the mean age was 41.3 (range 33-53). Foraminal area decrease at C5-C6 in extension and increase in flexion when compared with the neutral position was statistically significant (p < 0.001). Foraminal area changes between the complaint side and the opposite side was not statistically different (p > 0.05). Interobserver and intraobserver reliability of measurements were classified as in almost perfect agreement. Conclusions Our present work presented dynamic and positional foraminal changes in MRI with radiculopathy-mimicking patients. Soever, we did not find a difference between the clinical complaint side and the opposite side in radiculopathy-mimicking patients. Cervical radiculopathy pain should not be attributed only to foraminal sizes. PACS embedded irregular area measurement integral allows the easy measure of a big number of patients without additional set-up and digital work requirements.

Ensemble clustering of longitudinal bivariate HIV biomarker profiles to group patients by patterns of disease progression

This paper describes an ensemble cluster analysis of bivariate profiles of HIV biomarkers, viral load and CD4 cell counts, which jointly measure disease progression. Data are from a prevalent cohort of HIV positive participants in a clinical trial of vitamin supplementation in Botswana. These individuals were HIV positive upon enrollment, but with unknown times of infection. To categorize groups of participants based on their patterns of progression of HIV infection using both biomarkers, we combine univariate shape-based cluster results for multiple biomarkers through the use of ensemble clustering methods. We first describe univariate clustering for each of the individual biomarker profiles, and make use of shape-respecting distances for clustering the longitudinal profile data. In our data, profiles are subject to either missing or irregular measurements as well as unobserved initiation times of the process of interest. Shape-respecting distances that can handle such data issues, preserve time-ordering, and identify similar profile shapes are useful in identifying patterns of disease progression from longitudinal biomarker data. However, their performance with regard to clustering differs by severity of the data issues mentioned above. We provide an empirical investigation of shape-respecting distances (Fréchet and dynamic time warping (DTW)) on benchmark shape data, and use DTW in cluster analysis of biomarker profile observations. These reveal a primary group of ‘typical progressors,’ as well as a smaller group that shows relatively rapid progression. We then refine the analysis using ensemble clustering for both markers to obtain a single classification. The information from joint evaluation of the two biomarkers combined with ensemble clustering reveals subgroups of patients not identifiable through univariate analyses; noteworthy subgroups are those that appear to represent recently and chronically infected subsets.

Entropy in Science and How It Applies to Vocalization

Entropy is a scientific measure of uncertainty or irregularity in a system. It applies to the transmission of a signal, in particular the transmission of music and speech to a listener. This article explains entropy from a universal physical point of view. Beginning with the perspectives of famous scientists like Boltzmann and Shannon, some applications to voice and speech are given.

Phase irregularity: A conceptually simple and efficient approach to characterize electroencephalographic recordings from epilepsy patients.

The severe neurological disorder epilepsy affects almost 1% of the world population. For patients who suffer from pharmacoresistant focal-onset epilepsy, electroencephalographic (EEG) recordings are essential for the localization of the brain area where seizures start. Apart from the visual inspection of the recordings, quantitative EEG signal analysis techniques proved to be useful for this purpose. Among other features, regularity versus irregularity and phase coherence versus phase independence allowed characterizing brain dynamics from the measured EEG signals. Can phase irregularities also characterize brain dynamics? To address this question, we use the univariate coefficient of phase velocity variation, defined as the ratio of phase velocity standard deviation and the mean phase velocity. Beyond that, as a bivariate measure we use the classical mean phase coherence to quantify the degree of phase locking. All phase-based measures are combined with surrogates to test null hypotheses about the dynamics underlying the signals. In the first part of our analysis, we use the Rössler model system to study our approach under controlled conditions. In the second part, we use the Bern-Barcelona EEG database which consists of focal and nonfocal signals extracted from seizure-free recordings. Focal signals are recorded from brain areas where the first seizure EEG signal changes can be detected, and nonfocal signals are recorded from areas that are not involved in the seizure at its onset. Our results show that focal signals have less phase variability and more phase coherence than nonfocal signals. Once combined with surrogates, the mean phase velocity proved to have the highest discriminative power between focal and nonfocal signals. In conclusion, conceptually simple and easy to compute phase-based measures can help to detect features induced by epilepsy from EEG signals. This holds not only for the classical mean phase coherence but even more so for univariate measures of phase irregularity.

Measurement and Characterization Method of Permanent Magnetic Guideway Irregularity in HTS Maglev System

Measurement and Characterization Method of Permanent Magnetic Guideway Irregularity in HTS Maglev System

On total irregularity index of trees with given number of segments or branching vertices

A non-negative graph invariant IM is said to be an irregularity measure of a graph G if the following condition holds: IM(G)=0 if and only if G is regular. There exist many irregularity measures in the literature and the Albertson index is probably the most studied such measure. In order to overcome several drawbacks of the Albertson index, a variant of the Albertson index was recently introduced under the name ǣtotal irregularityǥ. The total irregularity index of a graph G is defined as 12∑u,w∈V(G)|degG(u)−degG(w)|, where degG(w) is the degree of a vertex w∈V(G). By an n-vertex tree, we mean a tree of order n. In the present study, the best possible sharp upper and lower bounds on the total irregularity index of n-vertex trees with fixed number of segments or branching vertices are derived.

Benefits of Telemonitoring of Pulmonary Function-3-Month Follow-Up of Home Electronic Spirometry in Patients with Duchenne Muscular Dystrophy.

Background: In patients with Duchenne Muscular Dystrophy (DMD), the respiratory system determines the quality and length of life; therefore, the search for easy and safe everyday monitoring of the pulmonary function is currently extremely important, particularly in the COVID-19 pandemic. The aim of the study was to evaluate the influence of a three-month home electronic spirometry (e-spirometry) monitoring of the pulmonary function and strength of respiratory muscles as well as the patients’ benefits from this telemetric program. Methods: Twenty-one boys with DMD (aged 7–22; non-ambulatory-11) received a remote electronic spirometer for home use with a special application dedicated for patients and connected with a doctor platform. Control of the hospital spirometry (forced vital capacity-FVC, forced expiratory volume in 1 second-FEV1, peak expiratory flow-PEF) and respiratory muscle strength (maximal inspiratory-MIP and expiratory pressures-MEP) before and after the three-month monitoring were performed as well telemonitoring benefit survey. Results: A total of 1403 measurements were performed; 15 of the participants were able to achieve correct attempts. There were no differences between the hospital and the home spirometry results as well as between respiratory muscle strength during v1 vs. v2 visits for the whole study group (all parameters p > 0.05); the six participants achieved increased value of FVC during the study period. There was a positive correlation between ΔFVC and the number of assessments during the home spirometry (r = 0.7, p < 0.001). Differences between FVC and MIPcmH2O (r = 0.58; p = 0.01), MEPcmH2O (r = 0.75; p < 0.001) was revealed. The mean general satisfaction rating of the telemonitoring was 4.46/5 (SD 0.66) after one month and 4.91/5 (SD 0.28) after three months. The most reported benefit of the home monitoring was the improvement in breathing (38% of participants after one month, 52% after three months of telemonitoring). Forgetting about the procedures was the most common reason for irregular measurements; the participants reported also increased motivation but less time to perform tests. Conclusions: The study indicates high compliance of the home telemonitoring results with the examination in the hospital. Benefits from home spirometry were visible for all participants; the most important benefit was breathing improvement. The remote home spirometry is usable for everyday monitoring of the pulmonary function in DMD patients as well can be also treated as respiratory muscle training.

Irregularity molecular descriptors of Cerium oxide CeO2 based on mathematical model and calculation

The irregularities of graphs and their dependencies on the size parameters are recently attracting attention of not only mathematician but also of theoretical chemists. It is found that these irregularities are related with the properties of the substance involved. Cerium oxide is a rare-earth metal having formula C e O 2 and it is light yellow-white powder. In the present article we are concerned with computing the closed forms of irregularity measures of general form of crystal structure of Cerium Oxide C e O 2 based on mathematical model and calculation.

Minimum Total Irregularity Index Of The Tricyclic Graphs

The quantitative characterization of the topological structures of irregular graphs has been demonstrated through several irregularity measures. In the literature, not only different chemical and physical properties can be well comprehended but also quantitative structure-activity relationship (QSPR) and quantitative structure-property relationship (QSAR) are documented through these measures. A simple graph G = (V;E) is a collection of V and E as vertex and edge sets respectively, with no multiple edges or loops. Keeping in view the importance of various irregularity measures, in (Abdo and Dimitrov 2012) the authors defined the total irregularity of a simple graph G = G(V;E) as irrt(G) = 1 2 P u;v2V jdG(u) 􀀀 dG(v)j; where dG(u) indicates the degree of the vertex u, where u 2 V (G). In this paper, we have determined the first minimum, second minimum and third minimum total irregularity index of the tricyclic graphs on the n vertices.

Breast cancer classification using improved fuzzy c means algorithm

Abnormal growth in the breast tissue prompts to the strange cell development in the breast. The researchers typically research for the size of the tumour in a mammogram, because mammograms contain irregular measurements of large scale and smaller scale calcifications. The nearness of these irregular measures of calcium stores in the breast ought to never be ignored as these are indications of early breast malignancy. To decipher this statement in a mammogram precisely, the quality of the pictures ought to be at its incomparable. The proposed research work is conveyed out for examinations of different screening strategies to recognize the unique phases of breast malignancy. In India for every 4 minutes, the women are diagnosed with this disease. And a woman dies with this disease for every 13 minutes. This disease is prominent with the people living in the ruler area while comparing the people in the urban areas. Therefore, it is very important to find and treat this disease as early as possible. The breast tumour region, perimeter and breadth are assessed from mammogram picture databases. The Bits Errors Degree (BER),), Highest Indication to Clatter Percentage (PSNR) and Callous Tetragonal Inaccuracy (MSE) values are determined for both Abnormal and normal images. These analyses were used to approve the presence or absence of the disease and to support the evaluation process for finding the disease. This quality assessment is used to understand the reality on Earth for a specific diagnosis that is a specific type of chromatin in a carcinogenic core that may indicate an irregular protein sequence

COVID-19 Changed Prevalence, Disease Spectrum and Management Strategies of Ocular Trauma

The coronavirus disease 2019 (COVID-19) pandemic has significantly impacted the health of people around the world and has reshaped social behaviors and clinical practice. The purpose of this perspective is to provide epidemiologists and clinicians with information about how the spectrum of ocular trauma diseases changed, as well as to optimize management for improving patient prognosis during this crisis. Analysis of current studies revealed that the prevalence of eye trauma decreased overall, with a trend of delayed medical treatment during the COVID-19 era. Irregular epidemic prevention and control measures, unprotected home activities, and unusual mental states are the main causes of ocular trauma. Strategies for reducing morbidity are also discussed, including popularizing the use norms of prevention and control supplies, taking heed to the safety of family activities, highlighting the special status of child protection, and paying attention to previous case data to implement region-specific precautions. The procedure of ophthalmological emergency and outpatient management should also be optimized, and mental health should be emphasized during this pandemic.

A Practical Root Cause Diagnosis Framework for Quality-Related Faults in Manufacturing Processes With Irregular Sampling Measurements

In actual manufacturing processes, because of the difference of sampling intervals of different process and quality variables, most of the collected data have characteristics of multiple sampling rates. How to fully use those irregular sampling data to realize nonlinear dynamic causality modeling for root cause location and propagation path identification of quality-related faults remains an open question. For this end, a practical root cause diagnosis framework is developed for manufacturing processes with irregular sampling measurements, in which a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest mutual information (MI)-based optimal variable division method is proposed to obtain quality-related and quality-unrelated variables. Then, a sampling interval attention network is designed for adaptively adjusting the dynamic temporal relationships among consecutive samples. Subsequently, a gated recurrent neural network (RNN) for improved design of conditional Granger causality (GC) analysis models is designed for diagnosing root causes and identifying propagation pathways of quality-related faults. Finally, the hot rolling process (HRP) is taken as an example for verification, and the accuracy rate of root cause diagnosis reaches 90.91% and 92.31%, which indicates its practicability and feasibility.

Investigation of GPS Loss of Lock occurrence and Its characteristics over Ethiopia using Geodetic GPS receivers of the IGS network

In this paper, we investigated the occurrence of GPS Loss of Lock(LOL) events and its characteristics in Ethiopia using three dual-frequency geodetic GPS receivers in Bahir Dar (geographic 11.599°N,37.359°E), Debark (geographic 13.150°N, 37.893°E), and Assosa (geographic 10.051°N,34.553°E) during the period from January to December for the year 2015. On average at the three stations,we confirmed that LOL events occurred within the hours of 19:00 LT-25:00 LT,while the maximum number of LOL events had been localized within the hours of 21:00 LT-23:00 LT. This implice that LOL events were more common during the post-sunset hours. On monthly basis, a maximum number of LOL events were observed in the months of April,and March, with a limited number of LOL events observed in the remaining months of the year at low latitudes,such as Bahir Dar, Debark, and Assosa stations. Seasonally, at the three stations and throughout 2015 studied maximum number of LOL occurrences were observed during equinoxes and substantially decreased during solstices. In particular, the months around the December solstice recorded the smallest number of LOL events for each station included here. We found that over the length of the 12 months of data included in this analysis, 551,782 and 160 LOL events had been observed in the Bahir Dar, Debark, and Assosa stations, respectively, inflicting extreme damage to GPS navigation. This implice that Debark station had the most LOL events, while Assosa station had the least number of LOL events, and Bahir Dar station had intermediate number of LOL events. We also found that the majority of LOL events in all stations took place when the elevation angle was low (between 30° and 40°),but Debark also has some LOL cases at higher elevations(greater than 50°) than other stations and Assosa had no LOL occurrences for higher elevations angle. Aside from that, LOL events show special azimuth distribution characteristics, with the discovered LOL events at the three stations concentrated in the northern areas of the sky between North-West and North-East [330°-360°; 0°-30°]. Finally, it may also open up the opportunity of the usage of the quantity of LOL events as a proxy measure of ionospheric irregularity or ionospheric scintillation for low latitudes such as Ethiopia’s Bahir Dar, Debark, and Assosa.

Permutation Entropy for Graph Signals

Entropy metrics (for example, permutation entropy) are nonlinear measures of irregularity in time series (one-dimensional data). Some of these entropy metrics can be generalised to data on periodic structures such as a grid or lattice pattern (two-dimensional data) using its symmetry, thus enabling their application to images. However, these metrics have not been developed for signals sampled on irregular domains, defined by a graph. Here, we define for the first time an entropy metric to analyse signals measured over irregular graphs by generalising permutation entropy, a well-established nonlinear metric based on the comparison of neighbouring values within patterns in a time series. Our algorithm is based on comparing signal values on neighbouring nodes, using the adjacency matrix. We show that this generalisation preserves the properties of classical permutation for time series and the recent permutation entropy for images, and it can be applied to any graph structure with synthetic and real signals. We expect the present work to enable the extension of other nonlinear dynamic approaches to graph signals.