Histogram Data Research Articles

Petrophysical analysis of well logs and core samples for reservoir evaluation: a case study of southern Issaran Field, Gulf of Suez province, Egypt

Understanding basic petrophysical properties may enhance the recovery of residual oil saturation and help in reservoir management. Reservoir oil saturation is the fraction of the porosity of a zone occupied by oil. The trapping may increase with the increase of porosity. Oil reservoirs represent a significant fraction of the reservoirs in the world-wide. We try to make a reservoir evaluation using petrophysical analysis of well logs and core samples at Issaran Field, South Gharib Formation, West of the Gulf of Suez, southeast Egypt. This area was heavily influenced by the structural setting of the Gulf of Suez’s central province, which is characterized by major SW dipping faults with a regional stratigraphic dip toward the NE. The high structural blocks are located in the center of the research area, and the low structural region is located in the southwest. South Gharib Formation is primarily composed of carbonates with few sulfates that grade upwards into sulfate-rich beds in the upper part. Laminated dolomitic limestone, with evaporites grades, changes downward into laminated dolomitic limestone and marly limestone, in the lower part. The anhydrite and dolomite units near the bottom have spotty oil stains. The structural setup is continued by the spread and deposition of this formation. We try to make analytical examination, of some reservoirs, accomplished by analyzing both well log data and core rock samples, vertically and laterally. According to the study of the horizontal permeability to vertical permeability ratio (Permeability anisotropy; lambda_{k}), the reservoir is laminated rock (lambda_{k} = 1.1 - 5), with a few data samples nearly isotropic (lambda_{k} = {1 mathord{left/ {vphantom {1 {1.1}}} right. kern-nulldelimiterspace} {1.1}}–1.1) and fractured (lambda_{k} = {1 mathord{left/ {vphantom {1 {2.5}}} right. kern-nulldelimiterspace} {2.5}}–1/1.1). Reservoir data histograms show that the most typical values of the data are 20–30% oil saturation and 7–10% porosity. The net-pay zone varies from four to 71 feet at different depths (897–1414 feet). Due to low shale content, water saturation, high effective porosity values, hydrocarbon saturation, may show higher net-pay thickness. These results may help to enhance the oil recovery. The area in the middle of the research area is suggested for future hydrocarbon development and using more petrophysical analysis.

RESEARCH ON NDVI NORMALIZATION METHOD BASED ON GF IMAGES

Abstract. The existing NDVI products have problems in terms of low spatial resolution and inconsistent values at a large geographical scale. Based on medium and high-resolution multi-source remote sensing data (GF-1 and GF-2 data), this paper normalized NDVI by combining absolute radiation normalization with relative radiation normalization. And the existing relative radiation normalization method, single-scene global linear normalization (SGloLM) method, is improved to adapt to the production of large-range high-resolution NDVI products. Aiming at the problem of obvious mosaic seams when the SGloLM method is applied to multi-scene images, it is mainly improved from two aspects. One is to improve the coefficient solution of the SGloLM algorithm and propose a new method considering the surrounding multi-scene data, the multi-scene global linear model (MGloLM). The other is to incorporate the Maximum Value Composite (MVC) method to synthesize the maximum value of NDVI at different times in a season, to represent the optimal situation of vegetation growth in the current season. In this study, combined experiments of different methods were performed, as well as qualitative and quantitative evaluations. The experimental results show that SGloLM+MVC and the MGloLM+MVC methods can better eliminate the mosaic seams, and their histogram is most similar to the histogram of standard data, and all quantitative evaluation indexes of SGloLM+MVC are optimal (CC=0.7804, MAD=0.0643, RMSE=0.1012).

The Cuts Selection Method Based on Histogram Segmentation and Impact on Discretization Algorithms.

The preprocessing of data is an important task in rough set theory as well as in Entropy. The discretization of data as part of the preprocessing of data is a very influential process. Is there a connection between the segmentation of the data histogram and data discretization? The authors propose a novel data segmentation technique based on a histogram with regard to the quality of a data discretization. The significance of a cut’s position has been researched on several groups of histograms. A data set reduct was observed with respect to the histogram type. Connections between the data histograms and cuts, reduct and the classification rules have been researched. The result is that the reduct attributes have a more irregular histogram than attributes out of the reduct. The following discretization algorithms were used: the entropy algorithm and the Maximal Discernibility algorithm developed in rough set theory. This article presents the Cuts Selection Method based on histogram segmentation, reduct of data and MD algorithm of discretization. An application on the selected database shows that the benefits of a selection of cuts relies on histogram segmentation. The results of the classification were compared with the results of the Naïve Bayes algorithm.

Acute and Late Pulmonary Effects After Radiation Therapy in Childhood Cancer Survivors: A PENTEC Comprehensive Review

The Pediatric Normal Tissue Effects in the Clinic (PENTEC) pulmonary task force reviewed dosimetric and clinical factors associated with radiation therapy (RT)-associated pulmonary toxicity in children. Comprehensive search of PubMed (1965-2020) was conducted to assess available evidence and predictive models of RT-induced lung injury in pediatric cancer patients (<21 years old). Lung dose for radiation pneumonitis (RP) was obtained from dose-volume histogram (DVH) data. RP grade was obtained from standard criteria. Clinical pulmonary outcomes were evaluated using pulmonary function tests (PFTs), clinical assessment, and questionnaires. More than 2,400 abstracts were identified; 460 articles had detailed treatment and toxicity data; and 11 articles with both detailed DVH and toxicity data were formally reviewed. Pooled cohorts treated during 1999 to 2016 included 277 and 507 patients age 0.04 to 22.7 years who were evaluable for acute and late RP analysis, respectively. After partial lung RT, there were 0.4% acute and 2.8% late grade 2, 0.4% acute and 0.8% late grade 3, and no grade 4 to 5 RP. RP risk after partial thoracic RT with mean lung dose (MLD) <14 Gy and total lung V20Gy <30% is low. Clinical and self-reported pulmonary outcomes data included 8,628 patients treated during 1970 to 2013, age 0 to 21.9 years. At a median 2.9- to 21.9-year follow-up, patients were often asymptomatic; abnormal PFTs were common and severity correlated with lung dose. At ≥10-year follow-up, multi-institutional studies suggested associations between total or ipsilateral lung doses >10 Gy and pulmonary complications and deaths. After whole lung irradiation (WLI), pulmonary toxicity is higher; no dose response relationship was identified. Bleomycin and other chemotherapeutics at current dose regimens do not contribute substantially to adverse pulmonary outcomes after partial lung irradiation but increase risk with WLI. After partial lung RT, acute pulmonary toxicity is uncommon; grade 2 to 3 RP incidences are <1%. Late toxicities, including subclinical/asymptomatic impaired pulmonary function, are more common (<4%). Incidence and severity appear to increase over time. Upon review of available literature, there appears to be low risk of pulmonary complications in children with MLD < 14 Gy and V20Gy <30% using standard fractionated RT to partial lung volumes. A lack of robust data limit guidance on lung dose/volume constraints, highlighting the need for additional work to define factors associated with RT-induced lung injury.

Tumor grading of soft tissue sarcomas: Assessment with whole-tumor histogram analysis of apparent diffusion coefficient

PurposeTo evaluate the usefulness of whole-tumor ADC histogram analysis based on entire tumor volume in determining the histologic grade of STS (soft tissue sarcoma)s. MethodsFrom January 2015 to December 2020, 53 patients with STS who underwent preoperative magnetic resonance imaging, including diffusion weighted imaging and ADC maps (b = 0 and 1400 s/mm2), within 1 month before surgical resection were included in the study. Regions of interest were drawn on every section of the ADC map containing tumor and were summated to derive volume-based histogram data of the entire tumor. Histogram parameters were correlated with histologic tumor grade using Kruskal-Wallis test and compared between high-(grade II and III) and low-grade STSs (grade I) using Mann-Whitney U test. Multivariable logistic regression analysis was applied to identify significant histogram parameters for high-grade STS prediction, and receiver operating characteristic curves (AUC) were constructed to determine optimum threshold. ResultsEight patients with low-grade STS (15.1%) and 45 with high-grade STS (26.4% [14/53] for grade II; 58.5% [31/53] for grade III) were included. High-grade STS showed positive skewness and low-grade STS showed negative skewness (0.503 vs −0.726, p=.001). High-grade STS showed lower mean ADC (p =.03) and 5th to 50th percentile values (p ≤. 03) than those of low-grade STS. Positive skewness was an independent predictor of high-grade STS (odds ratio: 6.704, p=.002) with 84.4% sensitivity and 87.5% specificity (cut-off values > -0.1757, AUC = 0.842). ConclusionSkewness is the most promising histogram parameter for discriminating high-grade from low-grade STS. The mean ADC values and lower half of percentile values are helpful for differentiating high from low-grade STSs.

Synthesis of pulses from particle detectors with a Generative Adversarial Network (GAN)

To address the possible lack or total absence of pulses from particle detectors during the development of its associate electronics, we propose a model that can generate them without losing the features of the real ones. This model is based on artificial neural networks, namely Generative Adversarial Networks (GAN). We describe the proposed network architecture, its training methodology and the approach to train the GAN with real pulses from a scintillator receiving radiation from sources of 137Cs and 22Na. The Generator was installed in a Xilinx’s System-On-Chip (SoC). We show how the network is capable of generating pulses with the same shape as the real ones that even match the data distributions in the original pulse-height histogram data.

Detection of the disease activity with ankylosing spondylitis through intravoxel incoherent motion diffusion-weighted MR imaging of sacroiliac joint.

To explore the value of the quantitative parameter of intravoxel incoherent motion diffusion (IVIM-DWI) at 3.0 T MRI of the sacroiliac joint in differentiating different disease activity statuses of ankylosing spondylitis (AS) and to compare it with traditional diffusion-weighted imaging (DWI) and Spondyloarthritis Research Consortium of Canada (SPARCC) score. 56 AS patients (active group, inactive group) and 24 healthy controls were included. Clinical data, quantitative parameters of IVIM-DWI MR images and the SPARCC scores were collected. The Kruskal-Wallis test was used to compare the differences between the groups. Receiver operating characteristic (ROC) curve analysis of histogram data and the SPARCC scores identified the efficacy of the three groups. The Spearman correlation coefficients were used to analyse the correlation between the quantitative IVIIM-DWI parameters and the SPARCC score. The f (10th percentile) and SPARCC score of the active group were significantly higher than those of the inactive group. The f (10th, 25th, 50th percentiles), Dslow (average, entropy, 10th ~ 90 th percentiles), Dfast (kurtosis, skewness), ADC (average, 10th ~ 90 th percentiles) and the SPARCC score of the active group were significantly higher than the control group (p < 0.05). The AUC of the SPARCC score was the highest (0.799) in the identification between the active and inactive groups, and the sensitivity and specificity were 69.23 and 82.35%, respectively, at the cut-off value of 12. The SPARCC score was positively correlated with each percentile and the average value. Quantitative IVIIM-DWI parameters are helpful for the identification of different AS disease activity levels and are superior to traditional DWI. IVIM-DWI quantitative parameters had a good correlation with the SPARCC score. A new MR technology-quantitative parameters of IVIM-DWI contribute to the identification of AS disease activity. IVIM-DWI quantitative parameters were well correlated with the SPARCC score.

Far-near combined positioning of picking-point based on depth data features for horizontal-trellis cultivated grape

For robotic harvesting of table grape in clusters usually by gripping and cutting the peduncle, the accurate cutting point positioning on the peduncle is crucial. In this paper, a new method for accurate positioning of picking-point based on the combination of far-view and near-view depth data features for horizontal-trellis cultivated grape was proposed. First, a far-near combination strategy for picking-point positioning which makes full use of the features of grape cluster and horizontal-trellis environment obtained from depth point cloud data was put forward. Then the special “eye-under-finger” structure to meet the needs of far-near combination was proposed, and, three key points of far-view point, near-view point and picking-point that determine the hand-eye path for far-near combination were defined. Finally, the far-near combined method composed of grape cluster detection in far view, far-near viewing switching and picking-point positioning based on depth data histogram in near view was established, which was realized by selecting the grape cluster bottom as the key clue. In field experiment of picking-point positioning in near view, the average running time of the algorithm is 0.29 s, and only 5 samples in total 100 failed to achieve accurate positioning. In lab experiments of far-near combined picking-point positioning, the success rate of accurate picking-point positioning reached 100%. This method is hopeful to overcome the deficiency of black-box processing of traditional image processing methods.

Multi-center analysis of machine-learning predicted dose parameters in brachytherapy for cervical cancer

Background and purposeImage-guided adaptive brachytherapy (IGABT) is a key component in the treatment of cervical cancer, but the nature of the clinical workflow makes it vulnerable to suboptimal plans, as the theoretical optimal plan depends heavily on organ configuration. Patient anatomy-based quality-assurance (QA) with overlap volume histograms (OVHs) is a promising tool to detect such suboptimal plans, and in this analysis its suitability as a multi-institutional clinical QA tool is investigated. Materials and methodsA total of 223 plans of 145 patients treated in accordance with the current state-of-the-art IGABT protocols from UMC Utrecht (UMCU) and Erasmus MC (EMC) were included. Machine-learning models were trained to predict dose D2cm3 to bladder, rectum, sigmoid and small bowel with the help of OVHs. For this strategy, points are sampled on the organs-at-risk (OARs), and the distances of the sampled points to the target are computed and combined in a histogram. Machine-learning models can then be trained to predict dose-volume histograms (DVHs) for unseen data. Single-center model robustness to needle use and applicator type and multi-center model translatability were investigated. Performance of models was assessed by the difference between planned (clinical) and predicted D2cm3 values. ResultsIntra-validation of UMCU data demonstrated OVH model robustness to needle use and applicator type. The model trained on UMCU data was found to be robust within the same protocol on EMC data, for all investigated OARs. Mean squared error between planned and predicted D2cm3 values of OARs ranged between 0.13 and 0.40 Gy within the same protocol, indicating model translatability. For the former protocol cohort of Erasmus MC large deviations were found between the planned and predicted D2cm3 values, indicating plan deviation from protocol. Mean squared error for this cohort ranged from 0.84 to 4.71 Gy. ConclusionOVH-based models can provide a solid basis for multi-institutional QA when trained on a sufficiently strict protocol. Further research will quantify the model’s impact as a QA tool.

Step Length Estimation Using the RSSI Method in Walking and Jogging Scenarios.

In this paper, human step length was estimated based on wireless channel properties and the received signal strength indicator (RSSI) method. Path loss between two ankles of the person under test was converted from the RSSI, which was measured using our developed wearable transceivers with embedded micro-controllers in four scenarios, namely indoor walking, outdoor walking, indoor jogging, and outdoor jogging. For brevity, we call it on-ankle path loss. The histogram of the on-ankle path loss showed clearly that there were two humps, where the second hump was closely related to the maximum path loss, which, in turn, corresponded to the step length. This histogram can be well approximated by a two-term Gaussian fitting curve model. Based on the histogram of the experimental data and the two-term Gaussian fitting curve, we propose a novel filtering technique to filter out the path loss outliers, which helps set up the upper and lower thresholds of the path loss values used for the step length estimation. In particular, the upper threshold was found to be on the right side of the second Gaussian hump, and its value was a function of the mean value and the standard deviation of the second Gaussian hump. Meanwhile, the lower threshold lied on the left side of the second hump and was determined at the point where the survival rate of the measured data fell to 0.68, i.e., the cumulative distribution function (CDF) approached 0.32. The experimental data showed that the proposed filtering technique resulted in high accuracy in step length estimation with errors of only 10.15 mm for the indoor walking, 4.40 mm for the indoor jogging, 4.81 mm for the outdoor walking, and 10.84 mm for the outdoor jogging scenarios, respectively.

A machine learning-based framework for online prediction of battery ageing trajectory and lifetime using histogram data

Accurately predicting batteries’ ageing trajectory and remaining useful life is not only required to ensure safe and reliable operation of electric vehicles (EVs) but is also the fundamental step towards health-conscious use and residual value assessment of the battery. The non-linearity, wide range of operating conditions, and cell to cell variations make battery health prediction challenging. This paper proposes a prediction framework that is based on a combination of global models offline developed by different machine learning methods and cell individualised models that are online adapted. For any format of raw data collected under diverse operating conditions, statistic properties of histograms can be still extracted and used as features to learn battery ageing. Our framework is trained and tested on three large datasets, one being retrieved from 7296 plug-in hybrid EVs. While the best global models achieve 0.93% mean absolute percentage error (MAPE) on laboratory data and 1.41% MAPE on the real-world fleet data, the adaptation algorithm further reduced the errors by up to 13.7%, all requiring low computational power and memory. Overall, this work proves the feasibility and benefits of using histogram data and also highlights how online adaptation can be used to improve predictions.

Towards a method to anticipate dark matter signals with deep learning at the LHC

We study several simplified dark matter (DM) models and their signatures at the LHC using neural networks. We focus on the usual monojet plus missing transverse energy channel, but to train the algorithms we organize the data in 2D histograms instead of event-by-event arrays. This results in a large performance boost to distinguish between standard model (SM) only and SM plus new physics signals. We use the kinematic monojet features as input data which allow us to describe families of models with a single data sample. We found that the neural network performance does not depend on the simulated number of background events if they are presented as a function of S/\sqrt{B}S/B, for reasonably large BB, where SS and BB are the number of signal and background events per histogram, respectively. This provides flexibility to the method, since testing a particular model in that case only requires knowing the new physics monojet cross section. Furthermore, we also discuss the network performance under incorrect assumptions about the true DM nature. Finally, we propose multimodel classifiers to search and identify new signals in a more general way, for the next LHC run.

Ultrasonic diagnosis of hypermobility of the cervical part of vertebral column and myofascial pain syndrome

The functional echospondylography method which makes it possible without the use of the x-ray method to diagnose in patients the hypermobility in the cervical part of vertebral column is suggested. The examination can be repeated in process treatment and after it. The ultrasonic histographic study of the myofascial trigger points makes it possible using the numerical indices to estimate the degree of their activity in comparison with the histogram data of the unchanged muscle part and to estimate the treatment results by the histogram change.

Information geometry approach to parameter estimation in hidden Markov model

We consider the estimation of the transition matrix of a hidden Markovian process by using information geometry with respect to transition matrices. In this paper, only the histogram of k-memory data is used for the estimation. To establish our method, we focus on a partial observation model with the Markovian process and we propose an efficient estimator whose asymptotic estimation error is given as the inverse of projective Fisher information of transition matrices. This estimator is applied to the estimation of the transition matrix of the hidden Markovian process. In this application, we carefully discuss the equivalence problem for hidden Markovian process on the tangent space.

Analyzing the Data of COVID-19 with Quasi-Distribution Fitting Based on Piecewise B-Spline Curves

Facing the worldwide coronavirus disease 2019 (COVID-19) pandemic, a new fitting method (QDF, quasi-distribution fitting) which can be used to analyze the data of COVID-19 is developed based on piecewise quasi-uniform B-spline curves. For any given country or district, it simulates the distribution histogram data which is made from the daily confirmed cases (or the other data including daily recovery cases and daily fatality cases) of COVID-19 with piecewise quasi-uniform B-spline curves. After using the area normalization method, the fitting curves could be regarded as a kind of probability density function (PDF): its mathematical expectation and the variance could be used to analyze the situation of the coronavirus pandemic. Numerical experiments based on the data of certain countries have indicated that the QDF method demonstrates the intrinsic characteristics of COVID-19 data of a given country or district, and because the interval of data used in this paper is over one year (500 days), it reveals the fact that after the multi-wave transmission of the coronavirus, the case fatality rate has obviously declined. These results show that the QDF method is effective and feasible as an appraisal method.

Information Classification Algorithm based on Project-based Learning Data-driven and Stochastic Grid

The adaptive partitioning algorithm of information set in simulation laboratory based on project-based learning data-driven and random grid is studied to effectively preprocess the information set and improve the adaptive partitioning effect of the information set. Using the improved fuzzy C-means clustering algorithm driven by project-based learning data, the fuzzy partition of information set in simulation laboratory is carried out to complete preprocessing of information set; The pre-processing information set space is roughly divided by the grid partitioning algorithm based on the data histogram; A random mesh generation algorithm based on uniformity is used to finely divide the coarse mesh cells; Taking the representative points of grid cells as the clustering center, the pre-processing information set is clustered by the density peak clustering algorithm to complete the adaptive partitioning of the information set in simulation laboratory. Experimental results show that this algorithm can effectively preprocess and adaptively partition the information set of simulation laboratory; For different dimension information sets, the evaluation index values of Rand index, Purity, standard mutual information, interval and Dunn index of the algorithm are all high, and the evaluation index values of compactness and Davidson's banding index are all low, so the algorithm has a high accuracy of adaptive partitioning of information sets.

A Novel Method Based on Kernel Density for Estimating Crown Base Height Using UAV-Borne LiDAR Data

As an essential parameter in forestry, crown base height (CBH) faces many tasks. The methods are still developing for estimating it. Unmanned aerial vehicles (UAVs) light detection and ranging (LiDAR) supplies new, massive, and high-density data for estimating CBH. Many methods had been generated to compute CBH indirectly using regression-based ways or directly using geometric/statistical LiDAR-based ways. However, there were few methods to deal with the problem of understory, trunk, and noise points caused by high-density UAV data. A robust method was first proposed in this study to directly estimate CBH from LiDAR data, which contained two significant skills: 1) understory vegetation removal for each tree using a polynomial curve and 2) computing CBH by kernel densification of the elevation frequency histogram of LiDAR data. It could tolerate the understory and trunk points better through kernel convolution. The method proposed in this study and a previous simple model were applied in a crabapple plot in the Huailai Remote Sensing Comprehensive Experimental Station, Hebei, China, and verified by field-measured data. It was inspiring that our method is slightly better, and the mean CBH of LiDAR-derived trees was only 1.60 cm higher than that of field-measured trees. The mean absolute error (MAE) of CBH was 4.91 cm, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}^{2}$ </tex-math></inline-formula> was 0.73, the root-mean-squared error (RMSE) was 8.29 cm, and the bias was 2.68% for these trees. Generally, this method showed strong usability for high-density UAV LiDAR data and high precision for measuring CBH of low trees.

Artificial intelligent investigations for the dynamics of the bone transformation mathematical model

In this study, the stochastic numerical solutions of the fractional myeloma bone disease system (FMBDS) have been presented. The fractional order investigation provides more accurate solutions of the FMBDS. The FMBDS is classified into three dynamics and the solution of each class is presented by using the artificial neural network enhanced by the scale conjugate gradient procedures (ANN-SCGPs). Three different fractional order performances have been used to present the solutions of the FMBDS by applying the ANN-SCGPs. The statics is chosen as 11%, 12% and 77% for training, testing and verification. Twelve number of hidden neurons with input and output layers have been proposed for the FMBDS. The comparison of proposed and reference solutions is performed that shows the accuracy of the ANN-SCGPs. The consistency, validity, precision, and capability of the ANN-SCGPs can be judged based on the state transitions values, regression actions, correlation behaviors, error histograms, and mean square error data.

Probabilistic constitutive law for masonry veneer wall ties

ABSTRACT In a masonry veneer wall system, tie strengths and stiffnesses vary randomly and so are not consistent for all ties throughout the wall. To ensure an economical and safe design, this paper uses tie calibration experimental approach in accordance with the standard AS2699.1 to investigate the tie failure load under compression and tension loading. Probabilistic wall tie characterisations are accomplished by estimating the mean, coefficient of variation and characteristic axial compressive and tensile strength from 50 specimens. The displacement across the cavity is recorded, which resulted the complete load versus displacement response. Using the maximum likelihood method, a range of probability distributions are fitted to tie strengths at different displacement histogram data sets, and a best-fitted probability distribution is selected for each case. The inverse cumulative distribution function plots are also used along with the Anderson-Darling test to infer a goodness-of-fit for the probabilistic models. An extensive statistical correlation analysis is also conducted to check the correlation between different tie strengths and associated displacement for both compression and tension loading. Based on the findings, a wall tie constitutive law is proposed to define probabilistic tie behaviour in numerical modelling.

Characterization of the Vaccine Abdala

Vaccines symbolize the main pandemic control tool in the world, which is why they are the object of study in many investigations. In this work, the Abdala vaccine was characterized in terms of adverse reactions by conducting a survey and statistical processing of the data taken through distribution graphs of the same, developed in the Minitab software 16. Of the people involved, 47.67% presented Symptoms that were mild to moderate with the presence of pain in the area of the injection, drowsiness and decay, mainly. Based on the distribution of the data in histograms, the duration of these symptoms was approximately 1 day. Likewise, it was observed that the symptoms occur mainly in females, for 33.72% of the cases in the first dose and 23.26% after the second dose was applied.