Ground Truth Values Research Articles

Near offset reconstruction for marine seismic data using a convolutional neural network

AbstractMarine seismic data is often missing near offset information due to separation between the source and receiver cables. To solve this problem, a convolutional neural network is trained on synthetic seismic data to reconstruct the near offset gap. The synthetic data is created using a two‐dimensional finite difference method within a heterogeneous velocity model. These synthetics are generated with a source‐over‐receiver acquisition geometry so that they contain complete near offset data. The convolutional neural network is then trained on input‐target synthetic pairs where the inputs are common midpoint gathers with the near offset section removed, and the targets are the same gathers with the near offset section retained. Following training, the robustness of the method is investigated with regards to common midpoint data sorting, normal moveout correction and changes in the velocity model. It is found that training on common midpoint‐sorted data results in 2.8 times lower error than training on shot gathers, that normal moveout correction of the training data makes no significant difference in error levels, and that the model can reconstruct realistic near offsets on synthetic data generated 10 km away within the heterogeneous velocity model. In field data testing, first a dataset with source‐over‐cable acquisition geometry from the Barents Sea is used to compare the reconstructed wavefields to ground truth values. Although the reconstructed amplitudes require minor scaling to match the true values, predictions on this dataset yield 2.5 times lower near offset reconstruction error compared to a simple Radon transform interpolation method. Furthermore, amplitude versus offset gradient and intercept sections from the Barents Sea dataset are estimated with half the error when including the convolutional neural network‐predicted near offset data, compared to only using the conventionally‐acquirable portion of the data (beyond 112.5 m of offset). In a secondary field data test, a conventional northern North Sea dataset is used to demonstrate how the method may be applied in practice. Here, the convolutional neural network generates more realistic predictions than the Radon method, and the gradient and intercept sections calculated using the convolutional neural network‐predicted traces have higher signal‐to‐noise ratios than the sections calculated using only the original data. The combination of high‐quality synthetic training data and interpolation in the common midpoint domain enables near offset reconstruction at significant depth (1 s of two‐way traveltime or more), which is demonstrated in both synthetic and field examples.

Comparison of ASI-PRISMA Data, DLR-EnMAP Data, and Field Spectrometer Measurements on “Sale ‘e Porcus”, a Salty Pond (Sardinia, Italy)

A comparison between the ASI-PRISMA (Agenzia Spaziale Italiana-PRecursore IperSpettrale della Missione Applicativa) DLR-EnMAP (German Aerospace Center—Environmental Mapping and Analysis Program) data and field spectrometer measurements has been performed. The test site, located at the “Sale ‘e Porcus” pond (hereafter SPp) in Western Sardinia, Italy, offers particularly homogenous characteristics, making it an ideal location not only for experimentation but also for calibration purposes. Three remote-sensed data acquisitions have been performed by these agencies (ASI and DLR) starting on 14 July 2023 and continuing until 22 July 2023. The DLR-EnMAP data acquired on 22 July overestimates both that of the ASI-PRISMA and the 14 July DLR-EnMAP radiance in the VNIR region, while all the datasets are close to each other, up to 2500 nm, for all considered days. The average absolute mean difference between the reflectance values estimated by the ASI-PRISMA and DLR-EnMAP, in the test area, is around 0.015, despite the small difference in their time of acquisition (8 days); their maximum relative difference value occurs at about 2100 nm. In this study, we investigate the relationship between the averaged ground truth value of reflectance, acquired by means of a portable ASD FieldSpec spectoradiometer, characterizing the test site and the EO reflectance data derived from the official datasets. FieldSpec measurements confirm the quality of both the ASI-PRISMA and DLR-EnMAP’s reflectance estimations.

Reproducibility of arterial spin labeling cerebral blood flow image processing: A report of the ISMRM open science initiative for perfusion imaging (OSIPI) and the ASL MRI challenge.

Arterial spin labeling (ASL) is a widely used contrast-free MRI method for assessing cerebral blood flow (CBF). Despite the generally adopted ASL acquisition guidelines, there is still wide variability in ASL analysis. We explored this variability through the ISMRM-OSIPI ASL-MRI Challenge, aiming to establish best practices for more reproducible ASL analysis. Eight teams analyzed the challenge data, which included a high-resolution T1-weighted anatomical image and 10 pseudo-continuous ASL datasets simulated using a digital reference object to generate ground-truth CBF values in normal and pathological states. We compared the accuracy of CBF quantification from each team's analysis to the ground truth across all voxels and within predefined brain regions. Reproducibility of CBF across analysis pipelines was assessed using the intra-class correlation coefficient (ICC), limits of agreement (LOA), and replicability of generating similar CBF estimates from different processing approaches. Absolute errors in CBF estimates compared to ground-truth synthetic data ranged from 18.36 to 48.12 mL/100 g/min. Realistic motion incorporated into three datasets produced the largest absolute error and variability between teams, with the least agreement (ICC and LOA) with ground-truth results. Fifty percent of the submissions were replicated, and one produced three times larger CBF errors (46.59 mL/100 g/min) compared to submitted results. Variability in CBF measurements, influenced by differences in image processing, especially to compensate for motion, highlights the significance of standardizing ASL analysis workflows. We provide a recommendation for ASL processing based on top-performing approaches as a step toward ASL standardization.

Real-time tracking of radial artery vessel wall using a Kalman filter-based ultrasound single-plane wave RF signal time-frequency information fusion algorithm

ObjectiveThe goal of our study was to achieve real-time dynamic tracking of the radial artery vessel wall in everyday life, which is crucial for blood pressure estimation and cardiovascular disease prediction. MethodsThe algorithm integrates time–frequency information from ultrasound single-plane wave RF (radiofrequency) signals and consists of three main components: feature frame ROI (region of interest) selection, inter-frame cross-correlation and intra-frame autocorrelation, and feature frame Kalman filtering. ResultsExperimental validation on a silicone gel ultrasound phantom with simulated blood vessels demonstrates an average diameter estimation error of less than 0.5% compared to ground truth values. Comparative experiments show similar relative errors (2.83%, 2.43%) to the optical flow method, with a computational speed 7.32 times faster. The algorithm accurately aligns extracted pulse waves with pressure pulse waves at six local feature points per cycle. ConclusionThe proposed method achieves a favorable balance between speed and accuracy in tracking the radial artery vessel wall. It holds significant potential for real-time monitoring of physiological health parameters, offering precise and dynamic tracking capabilities. SignificanceThis algorithm addresses the challenges of non-invasive real-time tracking, benefiting blood pressure estimation and cardiovascular disease prediction. Its integration of time–frequency information and efficient computational speed make it valuable for the scientific community and public in monitoring physiological health parameters.

Assessing accuracy of resonances obtained with reassigned spectrograms from the "ground truth" of physical vocal tract models.

The reassigned spectrogram (RS) has emerged as the most accurate way to infer vocal tract resonances from the acoustic signal [Shadle, Nam, and Whalen (2016). "Comparing measurement errors for formants in synthetic and natural vowels," J. Acoust. Soc. Am. 139(2), 713-727]. To date, validating its accuracy has depended on formant synthesis for ground truth values of these resonances. Synthesis is easily controlled, but it has many intrinsic assumptions that do not necessarily accurately realize the acoustics in the way that physical resonances would. Here, we show that physical models of the vocal tract with derivable resonance values allow a separate approach to the ground truth, with a different range of limitations. Our three-dimensional printed vocal tract models were excited by white noise, allowing an accurate determination of the resonance frequencies. Then, sources with a range of fundamental frequencies were implemented, allowing a direct assessment of whether RS avoided the systematic bias towards the nearest strong harmonic to which other analysis techniques are prone. RS was indeed accurate at fundamental frequencies up to 300 Hz; above that, accuracy was somewhat reduced. Future directions include testing mechanical models with the dimensions of children's vocal tracts and making RS more broadly useful by automating the detection of resonances.

Corridor-Level Auto Occupancy in Mobility Monitoring Efforts: A Crash-Based Approach

Vehicle occupancy (the number of persons per vehicle) is fundamental to evaluations of transportation system performance focused on the movement of people rather than solely vehicles. Historically, obtaining detailed estimates of occupancy has required manual (expensive) field data collection. This paper reports on a crash-based approach for estimating occupancy. Because some characteristics, such as the presence of multiple passengers aged 16 to 21, may affect the likelihood of crash involvement, the study examined ways to control possible crash bias using two methods. The first—heuristic bias correction, which entails synthesizing missing samples—is useful for smaller jurisdictions where there is no ground truth value readily available but an area-wide occupancy measure is needed. The second—statistical bias correction, which uses manual observations for calibration followed by the creation of a model—is suitable for obtaining occupancy at the corridor level. The approach is demonstrated with 12 years of fall and spring crash data for the Hampton Roads region of southeastern Virginia, U.S., representing 1.7 million people and 50,000 crashes. Testing showed estimated occupancies were within roughly 0.05 of true occupancy when corridor-specific calibration was performed. Further, the larger number of samples used for an area-wide occupancy means that heuristic bias correction is not always necessary: for cities with more than 200 samples, the difference between uncorrected and corrected occupancies was 0.02. For states that report the number of occupants for crashes with and without injury, this approach holds promise for estimating vehicle occupancy for both corridors and regions.

HGSMDA: miRNA-Disease Association Prediction Based on HyperGCN and Sørensen-Dice Loss.

Biological research has demonstrated the significance of identifying miRNA-disease associations in the context of disease prevention, diagnosis, and treatment. However, the utilization of experimental approaches involving biological subjects to infer these associations is both costly and inefficient. Consequently, there is a pressing need to devise novel approaches that offer enhanced accuracy and effectiveness. Presently, the predominant methods employed for predicting disease associations rely on Graph Convolutional Network (GCN) techniques. However, the Graph Convolutional Network algorithm, which is locally aggregated, solely incorporates information from the immediate neighboring nodes of a given node at each layer. Consequently, GCN cannot simultaneously aggregate information from multiple nodes. This constraint significantly impacts the predictive efficacy of the model. To tackle this problem, we propose a novel approach, based on HyperGCN and Sørensen-Dice loss (HGSMDA), for predicting associations between miRNAs and diseases. In the initial phase, we developed multiple networks to represent the similarity between miRNAs and diseases and employed GCNs to extract information from diverse perspectives. Subsequently, we draw into HyperGCN to construct a miRNA-disease heteromorphic hypergraph using hypernodes and train GCN on the graph to aggregate information. Finally, we utilized the Sørensen-Dice loss function to evaluate the degree of similarity between the predicted outcomes and the ground truth values, thereby enabling the prediction of associations between miRNAs and diseases. In order to assess the soundness of our methodology, an extensive series of experiments was conducted employing the Human MicroRNA Disease Database (HMDD v3.2) as the dataset. The experimental outcomes unequivocally indicate that HGSMDA exhibits remarkable efficacy when compared to alternative methodologies. Furthermore, the predictive capacity of HGSMDA was corroborated through a case study focused on colon cancer. These findings strongly imply that HGSMDA represents a dependable and valid framework, thereby offering a novel avenue for investigating the intricate association between miRNAs and diseases.

Deep learning algorithm for fully automated measurement of sagittal balance in adult spinal deformity

AimDeep learning (DL) algorithms can be used for automated analysis of medical imaging. The aim of this study was to assess the accuracy of an innovative, fully automated DL algorithm for analysis of sagittal balance in adult spinal deformity (ASD).Material and methodsSagittal balance (sacral slope, pelvic tilt, pelvic incidence, lumbar lordosis and sagittal vertical axis) was evaluated in 141 preoperative and postoperative radiographs of patients with ASD. The DL, landmark-based measurements, were compared with the ground truth values from validated manual measurements.ResultsThe DL algorithm showed an excellent consistency with the ground truth measurements. The intra-class correlation coefficient between the DL and ground truth measurements was 0.71–0.99 for preoperative and 0.72–0.96 for postoperative measurements. The DL detection rate was 91.5% and 84% for preoperative and postoperative images, respectively.ConclusionThis is the first study evaluating a complete automated DL algorithm for analysis of sagittal balance with high accuracy for all evaluated parameters. The excellent accuracy in the challenging pathology of ASD with long construct instrumentation demonstrates the eligibility and possibility for implementation in clinical routine.

Women's subsistence networks scaffold cultural transmission among BaYaka foragers in the Congo Basin.

In hunter-gatherer societies, women's subsistence activities are crucial for food provisioning and children's social learning but are understudied relative to men's activities. To understand the structure of women's foraging networks, we present 230 days of focal-follow data in a BaYaka community. To analyze these data, we develop a stochastic blockmodel for repeat observations with uneven sampling. We find that women's subsistence networks are characterized by cooperation between kin, gender homophily, and mixed age-group composition. During early childhood, individuals preferentially coforage with adult kin, but those in middle childhood and adolescence are likely to coforage with nonkin peers, providing opportunities for horizontal learning. By quantifying the probability of coforaging ties across age classes and relatedness levels, our findings provide insights into the scope for social learning during women's subsistence activities in a real-world foraging population and provide ground-truth values for key parameters used in formal models of cumulative culture.

Automatic detection of nostril and key markers in images

In the medical field, one of complex challenges in surgery involving the skin, cartilage, mucosa, and the skeletal platform is the cleft nasal deformity. The causes of deformity are nasal formed from improper fusion of the medial and lateral nasal prominences with the maxillary prominence during embryologic development. Primary rhinoplasty or first time of cleft nose repair is the nasal surgery for repairing the cleft nasal deformity. In post cheiloplasty, the primary rhinoplasty patients must use nasal splint for supporting surgical wound. However, regular nasal splints are expensive and only come with default nasal size. This limits nasal splint from supporting specific patient cases such as children. The goal of this study is to implement a program that can automatically detect the nostril and green marker in patient images for creating custom-made 3D nasal splints. The proposed method is created utilizing the CNN model. YOLO architecture is utilized because it is the one of several CNN architectures that works well with the face recognition task. The YOLOV5, YOLOV8, and YOLO-NAS are attempted and compared in the training phase. The model with a highest performance is selected, and fine-tuned for adjusting to be compatible with a patient dataset. The fine-tuned YOLOV8 reaches a mAP with 99.5%. The predicted images from fine-tuned model are used to perform body part segmentation like Otsu's thresholding and discover contour to locate essential features like the green marker and nostril in ellipse and bounding boxes. The nostril distance is calculated using the bounding box and ellipse. The columella distance or nostril gap is measured from the distance between nostrils in YOLO predicted label. Both distances are converted into centimeter scale and evaluated with ground truth value in volunteer and patient cases from nasal expert for inspecting efficiency between bounding box, ellipse, and size of marker. The result proves that the proposed model can detect crucial features with mean absolute error 0.102. Furthermore, the proposed model indicates that marker size has no effect on detection and distance, according to the findings of the experiments.

Faster region based convolution neural network with context iterative refinement for object detection

In this paper, proposed a novel method to improve the localization precision of identified objects. We present a framework for iteratively enhancing image region recommendations to meet ground truth values in this research. The Faster R–CNN (FR-CNN) seems to be an object recognition deep convolutional network. It gives the user the impression that the network is cohesive and single. The network can provide accurate and timely predictions about the whereabouts of a range of objects. We first build a unified model based on rapid predictions to relocate inaccurate area recommendations. Because the emphasis is on object detection, it may be utilised with a wide range of datasets and is compatible with various FR-CNN architectures. Second, we focus on the application of the joint score function to a variety of picture features. This joint score function depicts the location of the concealed object concerning other objects. The picture data and an updated structured production loss function are the only two inputs that influence the parameters of the joint scoring function. The join-score function and iterative context refinement (CIR) are used to generate our final unified model, which is then classified using Smooth Support Vector Machine (SSVM). We measured accuracy using the mean average precision after training FR-CNN + CIR and SSVM on a low-cost GPU using the PASCAL VOC 2012 dataset. Our results are 3.6 % more exact than rival deep learning algorithms on average.

Integrated Difference Autocorrelation: A Novel Approach to Estimate Shear Wave Speed in the Presence of Compression Waves.

In share wave elastography (SWE), the aim is to measure the velocity of shear waves, however unwanted compression waves and bulk tissue motion pose challenges in evaluating tissue stiffness. Conventional approaches often struggle to discriminate between shear and compression waves, leading to inaccurate shear wave speed (SWS) estimation. In this study, we propose a novel approach known as the integrated difference autocorrelation (IDA) estimator to accurately estimate reverberant SWS in the presence of compression waves and noise. The IDA estimator, unlike conventional techniques, computes the subtraction of velocity between neighboring particles, effectively minimizing the impact of long wavelength compression waves and other wide-area movements such as those caused by respiration. We evaluated the effectiveness of IDA by: (1) using k-Wave simulations of a branching cylinder in a soft background, (2) using ultrasound elastography on a breast phantom, (3) using ultrasound elastography in the human liver-kidney region, and (4) using magnetic resonance elastography (MRE) on a brain phantom. By applying IDA to unfiltered contaminated wave fields of simulation and elastography experiments, the estimated SWSs are in good agreement with the ground truth values (i.e., less than 2% error for the simulation, 9% error for ultrasound elastography of the breast phantom and 19% error for MRE). Our results demonstrate that IDA accurately estimates SWS, revealing the existence of a lesion, even in the presence of strong compression waves. IDA exhibits consistency in SWS estimation across different modalities and excitation scenarios, highlighting its robustness and potential clinical utility.

Estimating Per-Class Statistics for Label Noise Learning.

Real-world data may contain a considerable amount of noisily labeled examples, which usually mislead the training algorithm and result in degraded classification performance on test data. Therefore, Label Noise Learning (LNL) was proposed, of which one popular research trend focused on estimating the critical statistics (e.g., sample mean and sample covariance), to recover the clean data distribution. However, existing methods may suffer from the unreliable sample selection process or can hardly be applied to multi-class cases. Inspired by the centroid estimation theory, we propose Per-Class Statistic Estimation (PCSE), which establishes the quantitative relationship between the clean (first-order and second-order) statistics and the corresponding noisy statistics for every class. This relationship is further utilized to induce a generative classifier for model inference. Unlike existing methods, our approach does not require sample selection from the instance level. Moreover, our PCSE can serve as a general post-processing strategy applicable to various popular networks pre-trained on the noisy dataset for boosting their classification performance. Theoretically, we prove that the estimated statistics converge to their ground-truth values as the sample size increases, even if the label transition matrix is biased. Empirically, we conducted intensive experiments on various binary and multi-class datasets, and the results demonstrate that PCSE achieves more precise statistic estimation as well as higher classification accuracy when compared with state-of-the-art methods in LNL.

TPDC: Point Cloud Completion by Triangular Pyramid Features and Divide-and-Conquer in Complex Environments.

Point cloud completion recovers the complete point clouds from partial ones, providing numerous point cloud information for downstream tasks such as 3-D reconstruction and target detection. However, previous methods usually suffer from unstructured prediction of points in local regions and the discrete nature of the point cloud. To resolve these problems, we propose a point cloud completion network called TPDC. Representing the point cloud as a set of unordered features of points with local geometric information, we devise a Triangular Pyramid Extractor (TPE), using the simplest 3-D structure-a triangular pyramid-to convert the point cloud to a sequence of local geometric information. Our insight of revealing local geometric information in a complex environment is to design a Divide-and-Conquer Splitting Module in a Divide-and-Conquer Splitting Decoder (DCSD) to learn point-splitting patterns that can fit local regions the best. This module employs the Divide-and-Conquer approach to parallelly handle tasks related to fitting ground-truth values to base points and predicting the displacement of split points. This approach aims to make the base points align more closely with the ground-truth values while also forecasting the displacement of split points relative to the base points. Furthermore, we propose a more realistic and challenging benchmark, ShapeNetMask, with more random point cloud input, more complex random item occlusion, and more realistic random environmental perturbations. The results show that our method outperforms both widely used benchmarks as well as the new benchmark.

Transcranial Phase Correction Using Pulse-Echo Ultrasound and Deep Learning: A 2-D Numerical Study.

Phase aberration caused by human skulls severely degrades the quality of transcranial ultrasound images, posing a major challenge in the practical application of transcranial ultrasound techniques in adults. Aberration can be corrected if the skull profile (i.e., thickness distribution) and speed of sound (SOS) are known. However, accurately estimating the skull profile and SOS using ultrasound with a physics-based approach is challenging due to the complexity of the interaction between ultrasound and the skull. A deep learning approach is proposed herein to estimate the skull profile and SOS using ultrasound radiofrequency (RF) signals backscattered from the skull. A numerical study was performed to test the approach's feasibility. Realistic numerical skull models were constructed from computed tomography (CT) scans of five ex vivo human skulls in this numerical study. Acoustic simulations were performed on 3595 skull segments to generate array-based ultrasound backscattered signals. A deep learning model was developed and trained to estimate skull thickness and SOS from RF channel data. The trained model was shown to be highly accurate. The mean absolute error (MAE) was 0.15 mm (2% error) for thickness estimation and 13 m/s (0.5% error) for SOS estimation. The Pearson correlation coefficient between the estimated and ground-truth values was 0.99 for thickness and 0.95 for SOS. Aberration correction performed using deep-learning-estimated skull thickness and SOS values yielded significantly improved beam focusing (e.g., narrower beams) and transcranial imaging quality (e.g., improved spatial resolution and reduced artifacts) compared with no aberration correction. The results demonstrate the feasibility of the proposed approach for transcranial phase aberration correction.

Kompresija JPEG i BPG bez gubitka vizuelnih informacija na primeru baze KonJND-1k

Introduction/purpose: This paper presents the results of the research on visually lossless image compression which is of particular interest because it achieves a high degree of compression, while the visual quality of the image is not impaired, i.e., end users are very satisfied with the image quality. The analysis was carried out using the publicly available large-scale picture-wise KonJND-1k database which contains the results of subjective tests on JPEG and BPG compressed images. Methods: Thanks to the availability of images from the KonJND-1k database, the dependence of objective assessments of image quality on parameters that control the degree of compression of source signals (quality factor for JPEG and quantization parameter for BPG) is analyzed. The results of the visually lossless subjective tests are used for a deep analysis of the boundary and typical values of the parameters that control these two types of compression, as well as for the analysis of the corresponding values of the objective quality scores. Furthermore, reliable features for predicting the boundary between visually lossless and visually lossy compression have been identified. For that purpose, the degree of agreement between the predictions and the ground truth values of the peak signal-to-noise ratio (PSNR) and image representation in bits per pixel (bpp) is used. The visually lossless compression ratio is used to compare JPEG and BPG techniques. Results: It is shown that the boundary between visually lossless and visually lossy image compression is found in a wide range of PSNR values (about 20 dB for JPEG and 15 dB for BPG). The corresponding JPEG image compression quality factor values at this threshold also range widely from 31 to 79, with concentration between 40 and 45. For the BPG encoder, the values of the quantization parameter are grouped around 30, and the boundary values are 25 and 34. Furthermore, it is shown that this boundary can be reliably determined based on simple features derived from the original uncompressed image. Gradient-based features known as spatial frequency and spatial information proved to be the best predictors. The degree of agreement between the predictions obtained from these features with the ground truth values of PSNR and bpp in both types of compression is greater than 85%. A comparative analysis has showed that, using BPG compression, it is possible, on the average, to achieve a twice larger compression ratio of visually lossless compression than for JPEG (80 versus 40). Conclusion: Although a high degree of agreement is achieved between the predictions and the ground truth values of PSNR and bpp of the boundary between visually lossless and visually lossy compression, there is a need for the development of new prediction approaches, especially with the BPG technique, which through the compression ratio proved to be superior to the JPEG technique. The existing databases used for the analysis of visually lossless compression contain color images from the visible part of the electromagnetic spectrum. Considering the increasing use of images from the infrared part of the spectrum, there is a need to conduct similar tests in this spectral range.

Recurrent neural network for the dynamics of Zika virus spreading

<abstract> <p>Recurrent Neural Networks (RNNs), a type of machine learning technique, have recently drawn a lot of interest in numerous fields, including epidemiology. Implementing public health interventions in the field of epidemiology depends on efficient modeling and outbreak prediction. Because RNNs can capture sequential dependencies in data, they have become highly effective tools in this field. In this paper, the use of RNNs in epidemic modeling is examined, with a focus on the extent to which they can handle the inherent temporal dynamics in the spread of diseases. The mathematical representation of epidemics requires taking time-dependent variables into account, such as the rate at which infections spread and the long-term effects of interventions. The goal of this study is to use an intelligent computing solution based on RNNs to provide numerical performances and interpretations for the SEIR nonlinear system based on the propagation of the Zika virus (SEIRS-PZV) model. The four patient dynamics, namely susceptible patients S(y), exposed patients admitted in a hospital E(y), the fraction of infective individuals I(y), and recovered patients R(y), are represented by the epidemic version of the nonlinear system, or the SEIR model. SEIRS-PZV is represented by ordinary differential equations (ODEs), which are then solved by the Adams method using the Mathematica software to generate a dataset. The dataset was used as an output for the RNN to train the model and examine results such as regressions, correlations, error histograms, etc. For RNN, we used 100% to train the model with 15 hidden layers and a delay of 2 seconds. The input for the RNN is a time series sequence from 0 to 5, with a step size of 0.05. In the end, we compared the approximated solution with the exact solution by plotting them on the same graph and generating the absolute error plot for each of the 4 cases of SEIRS-PZV. Predictions made by the model appeared to be become more accurate when the mean squared error (MSE) decreased. An increased fit to the observed data was suggested by this decrease in the MSE, which suggested that the variance between the model's predicted values and the actual values was dropping. A minimal absolute error almost equal to zero was obtained, which further supports the usefulness of the suggested strategy. A small absolute error shows the degree to which the model's predictions matches the ground truth values, thus indicating the level of accuracy and precision for the model's output.</p> </abstract>

IFM calculator: An algorithm for interfragmentary motion calculation in finite element analysis

BackgroundInterfragmentary motion (IFM) is a complex state that significantly impacts the healing process of fractures following implant placement. It is crucial to fully consider the IFM state after implantation in the design and biomechanical testing of implants. However, current finite element analysis software lacks direct tools for calculating IFM, and existing IFM tools do not offer a comprehensive solution in terms of accuracy, functionality, and visualization. MethodsIn our study, we developed a Python-based algorithm for calculating IFM that addresses limitations. Our algorithm automatically calculated IFM distances, sliding distances, gaps, as well as the angles and rotation of the two fracture surfaces using all nodes on both sides of the fracture ends. Researchers could input data and selected desired parameters in the interface. The algorithm then performed the necessary calculations and presented the results in a clear and concise manner. The algorithm also provided comprehensive data export capabilities, allowing researchers to customize analyses based on specific needs.To provide a more intuitive demonstration of the calculation process and usage of IFM-Cal, we conducted simulations in Ansys using two rectangular blocks to compare the accuracy and function of three different methods (Point based method, contact tool and IFM-Cal). ResultsThe point-based method and the contact tool could not accurately calculate IFA, while IFM-Cal could provide a comprehensive evaluation of IFA. In simulation 1, the IFM distances calculated using the point sampling method, contact tool, and IFM-Cal were 2.00 mm, 3.15 mm, and 2.00 mm, respectively. In simulation 2, both the point sampling method and contact tool failed to calculate the interfragmentary angle (IFA), while the IFM-Cal algorithm estimated an angle of -7.87°, which had a small error compared to the ground-truth value of 7.9°. ConclusionWe have developed an algorithm for computing IFM which can be utilized in finite element analysis and biomechanical experiments. By conducting comparative simulations with other existing algorithms, we have demonstrated the superior accuracy and expanded evaluation capabilities of our algorithm.

Convolutional Neural Networks for Determining the Ion Beam Impact on a Space Debris Object

Introduction. Space debris is a serious problem that significantly complicates space activity. This problem can be mitigated by active space debris removal. The ion beam shepherd (IBS) concept assumes the contactless removal of a space debris object (SDO) by the plume of an ion thruster (IT). Techniques for determining the force impact from the IT to the SDO are of crucial importance for implementing the IBS concept.Problem Statement. A launcher’s upper stage, approximated by a cylinder, is considered an SDO deorbited by the plume of the IT. The SDO can change its orientation and position relative to the shepherd satellite. The shepherd satellite shall be able to determine the force transmitted to the SDO by the IT, using only SDO’s images as the input information.Purpose. The study aims to develop a neural net model that can map an SDO image to the force transmitted by an IT plume to this object and estimate the accuracy of such models.Material and Methods. Plasma physics methods are used to obtain ground truth values of the ion beam force. The deep learning methodology is applied to create neural net models.Results. Three different approaches for end-to-end ion force determination have been investigated. The first model uses a single convolutional neural net (CNN). The second model is an ensemble network consisting of four sub-models, and a classifier is used to pick the correct sub-model. The last model is similar to the first one but is trained on all images used for the second model. After training, all three models’ accuracy and computational complexity are estimated. These estimates demonstrate the acceptable performance of CNN-based models.Conclusions. This paper demonstrates that CNNs can be used to determine the force impact without knowledge about the SDO position and orientation and significantly faster than the previous methods.

EVALUATION OF PSNR VALUE FOR IMAGE SUPER-RESOLUTION USING DEEP LEARNING

Deep learning is a branch of study that simulates the human brain and allows learning from large data. In recent years, deep learning techniques have become more prevalent in a range of applications. These techniques have been widely employed in image processing, especially for object detection, image augmentation, and super-resolution. Single-image super-resolution, which converts low-resolution images into high-resolution ones, is a significant application of deep learning. Outstanding perceptual and antagonistic outcomes have been achieved using multiple single-image super-resolution models. In this study, several noteworthy methods, including Super Resolution with Convolutional Neural Network, Super Resolution Residual Network, Super Resolution Generative Adversarial Network, and Enhanced Super Resolution Generative Adversarial Network are used for single image super-resolution. Each layer in these deep learning models carries out certain operations on low-resolution images to transform them into high-resolution equivalents. As a result, many high-resolution images can be produced from a single low-resolution image. We created a dataset of random images for our research. The original low-resolution images are compared to the high-resolution images that were created using a deep learning technique. Peak Signal-to-Noise Ratio and the Structural Similarity Index are used in the comparison. Additionally, the evaluation process includes computing the ground truth value. The highest similarity indexed is achieved by VGG54 with 96% of similarity.