True Output Research Articles

Adaptive design of experiments to fit surrogate Gaussian process regression models allows fast sensitivity analysis of the input waveform for patient-specific 3D CFD models of liver radioembolization

Background and objectivePatient-specific 3D computational fluid dynamics (CFD) models are increasingly being used to understand and predict transarterial radioembolization procedures used for hepatocellular carcinoma treatment. While sensitivity analyses of these CFD models can help to determine the most impactful input parameters, such analyses are computationally costly. Therefore, we aim to use surrogate modelling to allow relatively cheap sensitivity analysis. As an example, we compute Sobol's sensitivity indices for three input waveform shape parameters. MethodsWe extracted three characteristic shape parameters from our input mass flow rate waveform (peak systolic mass flow rate, heart rate, systolic duration) and defined our 3D input parameter space by varying these parameters within 75 %-125 % of their nominal values. To fit our surrogate model with a minimal number of costly CFD simulations, we developed an adaptive design of experiments (ADOE) algorithm. The ADOE uses 100 Latin hypercube sampled points in 3D input space to define the initial design of experiments (DOE). Subsequently, we re-sample input space with 10,000 Latin Hypercube sampled points and cheaply estimate the outputs using the surrogate model. In each of 27 equivolume bins which divide our input space, we determine the most uncertain prediction of the 10,000 points, compute the true outputs using CFD, and add these points to the DOE. For each ADOE iteration, we calculate Sobol's sensitivity indices, and we continue to add batches of 27 samples to the DOE until the Sobol indices have stabilized. ResultsWe tested our ADOE algorithm on the Ishigami function and showed that we can reliably obtain Sobol's indices with an absolute error <0.1. Applying ADOE to our waveform sensitivity problem, we found that the first-order sensitivity indices were 0.0550, 0.0191 and 0.407 for the peak systolic mass flow rate, heart rate, and the systolic duration, respectively. ConclusionsAlthough the current study was an illustrative case, the ADOE allows reliable sensitivity analysis with a limited number of complex model evaluations, and performs well even when the optimal DOE size is a priori unknown. This enables us to identify the highest-impact input parameters of our model, and other novel, costly models in the future.

512 Length of Stay Prediction Based on an Artificial Neural Network

Abstract Introduction Hospital length of stay (LOS) is difficult to predict in patients with burn injuries due to the complexity of the injuries and the wide variability in patient outcomes. Predicting hospital LOS in patients with burn injuries can have numerous benefits for patients, physicians, and insurance companies. Artificial neural networks may offer insight into more accurate LOS predictions. Methods We obtained electronic health records (EHR) for burn patients from July 01, 2011 - July 01, 2021. An artificial neural network was used to model the relationship between the input features and LOS. The input features were preprocessed by normalizing each feature to the range [0, 1] and transformed using log normal. The preprocessed data was split into training (70%), validation (15%), and testing (15%) sets. The neural network consisted of one hidden layer with 13 neurons and a linear output layer. Results The neural network achieved an R-squared (R2) value of 0.72 on the training data, indicating that 72% of the variance in the data (n=1347). The validation and testing R2 values were 0.66 and 0.68, respectively. The overall R2 value, computed by combining the predictions on the training, validation, and testing data, was 0.70. The best performance, validation performance, and testing performance achieved by the neural network during the training process were 0.28, 0.27, and 0.30, respectively. These values indicate the average squared difference between the predicted output and the true output for each set, with lower values indicating better performance. Conclusions The results suggest that the neural network can model the relationship between the input features and LOS with reasonable accuracy. The R2 values indicate that the model explains a substantial amount of the variability in the data. Further testing and validation may be needed to assess the robustness and reliability of the model. Applicability of Research to Practice Patient outcomes are largely variable and determining LOS to a closer degree helps planning more robust treatment plans. Additionally, these models have the potential to serve as a more accurate gauge of whether they are following closely to their expected treatment timeline. As survival rates continue to improve, the improvement focus has and will continue to shift towards patient outcomes.

A CNN-based strategy to automate contour detection of the hip and proximal femur using DXA hip images from longitudinal databases (CLSA and CaMos)

ABSTRACT Hip fractures contribute significantly to mortality in older adults. New methods to identify those at risk use dual-energy X-ray absorptiometry (DXA) images and advanced image processing. However, DXA images have an overlapping femur and pelvis and may contain boundary lines, making automation challenging. Herein, a 5-layer U-net convolutional neural network (CNN) was developed to segment the femur from hip DXA images. Images were used from the Canadian Longitudinal Study on Aging (CLSA, N=104) and Canadian Multicentre Osteoporosis Study (CaMos, N=105) databases for training, with manual contour drawing defining the ‘true output’ of each image. An algorithm was then developed to mask each hip image, and trained to predict subsequent masks. The CNN was tested with 44 additional CLSA images and 42 CaMos images. This proposed approach had an accuracy and intersection over union (IoU) of 97% and 0.57, and 93% and 0.51, for CaMos and CLSA scans, respectively. Furthermore, a series of augmentation techniques was applied to increase the data size, with accuracies of 96% and 94%, and IoU of 0.53 and 0.50. Overall, our strategy automatically determined the contour of the proximal femur using various clinical DXA images, a key step to automate fracture risk assessment in clinical practice.

Hierarchical gradient‐ and least‐squares‐based iterative estimation algorithms for input‐nonlinear output‐error systems from measurement information by using the over‐parameterization

AbstractThis article investigates the parameter identification problems of the stochastic systems described by the input‐nonlinear output‐error (IN‐OE) model. This IN‐OE model consists of two submodels, one is an input nonlinear model and the other is a linear output‐error model. The difficulty in the parameter identification of the IN‐OE model is that the information vector contains the unknown variables, which are the noise‐free (true) outputs of the system, the approach taken here is to replace the unknown terms with the outputs of the auxiliary model. Based on the over‐parameterization model and the hierarchical identification principle, an over‐parameterization auxiliary model hierarchical gradient‐based iterative algorithm and an over‐parameterization auxiliary model hierarchical least‐squares‐based iterative algorithm are proposed to estimate the unknown parameters of the IN‐OE systems. Finally, two numerical simulation examples are given to demonstrate the effectiveness of the proposed algorithms.

Fast discrete Laplace transforms

The discrete Laplace transform (DLT) with M inputs and N outputs has a nominal computational cost of O(MN). There are approximate DLT algorithms with O(M+N) cost such that the output errors divided by the sum of the inputs are less than a fixed tolerance η. However, certain important applications of DLTs require a more stringent accuracy criterion, where the output errors divided by the true output values are less than η. We present a fast DLT algorithm combining two strategies. The bottom-up strategy exploits the Taylor expansion of the Laplace transform kernel. The top-down strategy chooses groups of terms in the DLT to include or neglect, based on the whole summand, and not just on the Laplace transform kernel. The overall effort is O(M+N) when the source and target points are very dense or very sparse, and appears to be O((M+N)1.5) in the intermediate regime. Our algorithm achieves the same accuracy as brute-force evaluation, and is typically 10–100 times faster.

Unbiased recursive least squares identification methods for a class of nonlinear systems with irregularly missing data

SummaryMissing data often occur in industrial processes. In order to solve this problem, an auxiliary model and a particle filter are adopted to estimate the missing outputs, and two unbiased parameter estimation methods are developed for a class of nonlinear systems (e.g., bilinear systems) with irregularly missing data. Firstly, an auxiliary model is constructed to estimate the unknown output, and an auxiliary model‐based multi‐innovation recursive least squares algorithm is presented by expanding the scalar innovation to an innovation vector. Secondly, according to the bias compensation principle, an auxiliary model‐based bias compensation multi‐innovation recursive least squares algorithm is proposed to compensate the bias caused by the colored noise. Thirdly, for further improving the parameter estimation accuracy, the unknown true output is estimated by a particle filter, and a particle filtering‐based bias compensation multi‐innovation recursive least squares algorithm is developed. Finally, a numerical example is selected to validate the effectiveness of the proposed algorithms. The simulation results indicate that the proposed algorithms have good performance in identifying bilinear systems with irregularly missing data.

Machine learning assisted delay minimization in full duplex energy constrained cooperative communication network

Cooperative communication is key in attaining high network throughput and better coverage area for a wireless powered communication network. The conventional solution methodologies proposed in the literature for these networks suffer from high run-time complexity. This paper presents an efficient polynomial-time machine learning approach for solving the total transmission time minimization problem through relay selection and scheduling of the users in a full duplex wireless powered cooperative communication network. Exploiting the simplicity offered by the tree ensemble models, a distributed gradient-boosted decision tree based model (XGBoost) is incorporated. The inputs of the model are the uplink channel coefficients and energy harvesting rates of the relays and the users, and outputs are the optimal completion time of the relays and users, relay selection and schedule of the users. Simulation results indicate that the proposed model very well approximates the true outputs, i.e., performs very close to the optimal solution of the optimization problem, while maintaining a very low run-time computational complexity. Specifically, for a network of 9 users, the run-time of the proposed model is just 25% of the optimal solution, and this ratio grows as the number of users increases in the network.

Numerical and experimental analysis of bead cross-sectional geometry in fused filament fabrication

PurposeThis paper aims to develop experimentally validated numerical models to accurately characterize the cross-sectional geometry of the deposited beads in a fused filament fabrication (FFF) process under various process conditions.Design/methodology/approachThe presented numerical model is investigated under various fidelity with varying computational complexity. To this end, comparisons between the Newtonian, non-newtonian, isothermal and non-isothermal computational models are presented for the extrusion of polylactic acid material in an FFF process. The computational model is validated through an experimental study on an off-the-shelf FFF printer. Microscope images of experimentally printed FFF bead cross-sections corresponding to various printing conditions are digitally processed for the validation. In the experimental study, common practical printing conditions for an FFF process are tested, and the results are compared to the numerical model.FindingsMicroscope image analyses of the cross-sectional geometries of deposited beads show that the numerical model provides a precise characterization of the cross-sectional geometry under varying process parameters in terms of the cross-section outline, bead height and width. The results show that the nozzle-to-table distance has a great effect on the bead shape when compared to the extrusion rate at a given nozzle-to-table distance. Comparison of the various computational models show that the non-Newtonian isothermal model provides the best tradeoff between computational complexity and model accuracy.Originality/valueThe authors provide detailed computational models, including the extruder nozzle geometry for cases ranging from Newtonian isothermal models to non-Newtonian non-isothermal models with experimental validation. The validation study is conducted for practical process parameters that are commonly used in FFF in practice and show that the computational models provide an accurate depiction of the true process outputs. As the developed models can accurately predict process outputs, they can be used in further applications for process planning and parameter tuning.

Monocular Real Time Full Resolution Depth Estimation Arrangement with a Tunable Lens

This work introduces a real-time full-resolution depth estimation device, which allows integral displays to be fed with a real-time light-field. The core principle of the technique is a high-speed focal stack acquisition method combined with an efficient implementation of the depth estimation algorithm, allowing the generation of real time, high resolution depth maps. As the procedure does not depend on any custom hardware, if the requirements are met, the described method can turn any high speed camera into a 3D camera with true depth output. The concept was tested with an experimental setup consisting of an electronically variable focus lens, a high-speed camera, and a GPU for processing, plus a control board for lens and image sensor synchronization. The comparison with other state of the art algorithms shows our advantages in computational time and precision.

An Observer to Detect Infrequently-Occurring Disturbances in Grinding Operations

Changes in ore properties create challenges for the control and optimization of comminution operations because they are generally difficult to measure in real time and have significant impacts on the grinding process and downstream operations. The effectiveness of a multi-model observer to detect and estimate step changes in the particle size distribution of the ore feed to a semi-autogenous grinding (SAG) mill using noisy measurements of the product particle size, is evaluated using a simulation model of the process. The observer maintains multiple hypotheses about the disturbance until their likelihood given the measurements can be determined and used to estimate the disturbance and the true process output. The results demonstrate that the multi-model observer has lower overall estimation errors than a single Kalman filter because it responds to changes in the output quickly without a compromised sensitivity to noise during steady-state. Real-time estimation of changes in ore feed properties in grinding operations could have significant benefits, however, more work is needed to characterize these disturbances, to determine if the process and disturbance models can be identified in practice, and to estimate the potential benefits.

GPTransformer: A Transformer-Based Deep Learning Method for Predicting Fusarium Related Traits in Barley.

Fusarium head blight (FHB) incited by Fusarium graminearum Schwabe is a devastating disease of barley and other cereal crops worldwide. Fusarium head blight is associated with trichothecene mycotoxins such as deoxynivalenol (DON), which contaminates grains, making them unfit for malting or animal feed industries. While genetically resistant cultivars offer the best economic and environmentally responsible means to mitigate disease, parent lines with adequate resistance are limited in barley. Resistance breeding based upon quantitative genetic gains has been slow to date, due to intensive labor requirements of disease nurseries. The production of a high-throughput genome-wide molecular marker assembly for barley permits use in development of genomic prediction models for traits of economic importance to this crop. A diverse panel consisting of 400 two-row spring barley lines was assembled to focus on Canadian barley breeding programs. The panel was evaluated for FHB and DON content in three environments and over 2 years. Moreover, it was genotyped using an Illumina Infinium High-Throughput Screening (HTS) iSelect custom beadchip array of single nucleotide polymorphic molecular markers (50 K SNP), where over 23 K molecular markers were polymorphic. Genomic prediction has been demonstrated to successfully reduce FHB and DON content in cereals using various statistical models. Herein, we have studied an alternative method based on machine learning and compare it with a statistical approach. The bi-allelic SNPs represented pairs of alleles and were encoded in two ways: as categorical (–1, 0, 1) or using Hardy-Weinberg probability frequencies. This was followed by selecting essential genomic markers for phenotype prediction. Subsequently, a Transformer-based deep learning algorithm was applied to predict FHB and DON. Apart from the Transformer method, a Residual Fully Connected Neural Network (RFCNN) was also applied. Pearson correlation coefficients were calculated to compare true vs. predicted outputs. Models which included all markers generally showed marginal improvement in prediction. Hardy-Weinberg encoding generally improved correlation for FHB (6.9%) and DON (9.6%) for the Transformer network. This study suggests the potential of the Transformer based method as an alternative to the popular BLUP model for genomic prediction of complex traits such as FHB or DON, having performed equally or better than existing machine learning and statistical methods.

Gonadotropin-releasing hormone (GnRH) measurements in pituitary portal blood: A history.

Much about the neuroendocrine control of reproduction is inferred from changes in the episodic release of luteinizing hormone (LH), as measured in samples of peripheral blood. This, however, assumes that LH precisely mirrors gonadotropin-releasing hormone (GnRH) release from the hypothalamus. Because GnRH is not measurable in peripheral blood, characterization of the relationship between these two hormones required the simultaneous measurement of GnRH and LH in pituitary portal and peripheral blood, respectively. Here, we review the history of why and how portal blood collection was developed, the aspects of the true output of the central component of the hypothalamic-pituitary-gonadal axis that this methodology helped clarify, and conditions under which the pituitary fails to serve as an adequate bioassay for the release pattern of GnRH.

Efficient and Secure Multi-party Computation for Heterogeneous Environment

Abstract: MPC (multi-party computation) is a comprehensive cryptographic concept that can be used to do computations while maintaining anonymity. MPC allows a group of people to work together on a function without revealing the plaintext's true input or output. Privacy-preserving voting, arithmetic calculation, and large-scale data processing are just a few of the applications of MPC. Each MPC party can run on a single computing node from a system perspective. Multiple parties' computing nodes could be homogenous or heterogeneous; nevertheless, MPC protocols' distributed workloads are always homogeneous (symmetric). We investigate the system performance of a representative MPC framework and a collection of MPC applications in this paper. On homogeneous and heterogeneous compute nodes, we describe the complete online calculation workflow of a state-of-the-art MPC protocol and examine the fundamental cause of its stall time and performance limitation. Keywords: Cloud Computing, IoT, MPC, Amazon Service, Virtualization.

Modified particle filtering‐based robust estimation for a networked control system corrupted by impulsive noise

AbstractThe non‐Gaussian characteristic of the impulsive noise significantly degrades the performance of the ‐norm‐based identification algorithms. To overcome the negative effects of impulsive noise to system identification, this article develops a modified particle filtering‐based robust multi‐innovation gradient (MPF‐MIG) algorithm for a networked control system corrupted by impulsive noise. The proposed algorithm is formulated based on a continuous logarithmic mixed p‐norm cost function, which can generate an adjustable gain that adapts to the data quality. A modified particle filtering is designed to estimate the unknown true output, which is corrupted by an impulsive noise without explicit probability density function. The simulation examples exhibit that the MPF‐MIG algorithm has better robustness and higher estimation accuracy than the conventional multi‐innovation gradient algorithm.

Validity and Reliability of the Computrainer Lab™ During Simulated 40 and 100 km Time-Trials.

The validity and reliability of the Computrainer Lab™ (CT) was assessed, for the first time, using a high-precision motor-driven calibration rig during simulated variable intensity 40 and 100 km time-trials (TTs). The load patterns imposed by the CT were designed from previously published studies in trained cyclists and included multiple 1 or 4 km bursts in power output. For the 40 and 100 km TTs, cluster-based analyses revealed a mean measurement error from the true workload of respectively 0.7 and 0.9%. However, measurement errors were dependent upon the workload variations, fluctuating from 0.2 to 5.1%. Average biases between repeated trials were contained within ± 1.1% for both TTs. In conclusion, using 40 and 100 km TTs containing 1 or 4 km bursts in power output, the present results indicate that (1) the CT can reliably be used by scientists to determine differences between research interventions; (2) the CT provides valid results of power output when data are being analyzed as a whole to derive one mean value of power output and; (3) variations in workload make it difficult to determine at any one time the veracity of the true power output produced by the athlete.

An online optimization method for extracting parameters of multi-parameter PV module model based on adaptive Levenberg-Marquardt algorithm

This paper proposes a novel method to estimate the optimal parameters of a single-diode photovoltaic (PV) module model as well as the active power of PV generation. First, a multi-parameter PV module model is established considering the changes of temperature and solar irradiance; thus, the model is adaptive to different environmental conditions. Second, the model parameter estimation is designed as an optimization problem. An online optimization scheme that only requires the normal operation information is proposed, which avoids the need for an offline experiment to determine the current–voltage (I-V) curve as in conventional PV modeling methods. Third, the adaptive Levenberg-Marquardt (ALM) method with an adaptative damping factor selection strategy is developed to solve the parameter optimization problem, which overcomes the singular issue of traditional LM methods. Moreover, the global sensitivity analysis (GSA) is leveraged to identify critical parameters affecting the PV generation output power, which provides information on model simplification. Finally, the proposed method is tested on data from real-world PV systems in Bend, Oregon, USA, under diverse conditions and scenarios. It is revealed that compared with commonly used methods, the proposed method obtains more accurate PV module parameters and offers PV generation outputs closer to the true outputs.

Identifying the Influential Inputs for Network Output Variance Using Sparse Polynomial Chaos Expansion

Sensitivity analysis (SA) is an important aspect of process automation. It often aims to identify the process inputs that influence the process output’s variance significantly. Existing SA approaches typically consider the input–output relationship as a black box and conduct extensive random sampling from the actual process or its high-fidelity simulation model to identify the influential inputs. In this article, an alternate, novel approach is proposed using a sparse polynomial chaos expansion-based model for a class of input–output relationships represented as directed acyclic networks. The model exploits the relationship structure by recursively relating a network node to its direct predecessors to trace the output variance back to the inputs. It, thereby, estimates the Sobol indices that measure the influence of each input on the output variance, accurately and efficiently. Theoretical analysis establishes the validity of the model as the prediction of the network output converges in probability to the true output under certain regularity conditions. Empirical evaluation of two manufacturing processes and a flooding process shows that the model estimates the Sobol indices accurately with far fewer observations than state-of-the-art black-box methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by the problem of automated identification of the inputs that influence the variance of the output for networked processes without feedback control. Such processes arise in various natural and engineered systems, of which manufacturing operations and flood mitigation are of particular interest to us, where the output variance represents the uncertainty in productivity, quality, or cost. Therefore, influential inputs’ identification allows us to quantify the effects of the various process parameters, such as operating conditions and physical properties, in determining the uncertainties in the process outputs. We show that our identification method is guaranteed to quantify the effects accurately and is expected to do so more efficiently (with fewer experimental observations) than widely used stochastic sampling techniques. In the future, we will like to evaluate the usefulness of the developed method on large-scale manufacturing and supply chain networks in actual production facilities as well as on critical infrastructures subject to cascading failures.

Estimating Ocean Surface Currents With Machine Learning

Global surface currents are usually inferred from directly observed quantities like sea-surface height, wind stress by applying diagnostic balance relations (like geostrophy and Ekman flow), which provide a good approximation of the dynamics of slow, large-scale currents at large scales and low Rossby numbers. However, newer generation satellite altimeters (like the upcoming SWOT mission) will capture more of the high wavenumber variability associated with the unbalanced components, but the low temporal sampling can potentially lead to aliasing. Applying these balances directly may lead to an incorrect un-physical estimate of the surface flow. In this study we explore Machine Learning (ML) algorithms as an alternate route to infer surface currents from satellite observable quantities. We train our ML models with SSH, SST, and wind stress from available primitive equation ocean GCM simulation outputs as the inputs and make predictions of surface currents (u,v), which are then compared against the true GCM output. As a baseline example, we demonstrate that a linear regression model is ineffective at predicting velocities accurately beyond localized regions. In comparison, a relatively simple neural network (NN) can predict surface currents accurately over most of the global ocean, with lower mean squared errors than geostrophy + Ekman. Using a local stencil of neighboring grid points as additional input features, we can train the deep learning models to effectively “learn” spatial gradients and the physics of surface currents. By passing the stenciled variables through convolutional filters we can help the model learn spatial gradients much faster. Various training strategies are explored using systematic feature hold out and multiple combinations of point and stenciled input data fed through convolutional filters (2D/3D), to understand the effect of each input feature on the NN's ability to accurately represent surface flow. A model sensitivity analysis reveals that besides SSH, geographic information in some form is an essential ingredient required for making accurate predictions of surface currents with deep learning models.

A novel neural network based on dynamic time warping and Kalman filter for real-time monitoring of supersonic inlet flow patterns

A supersonic inlet is one of the key components in a supersonic air-breathing propulsion system and is the basis for protection control. The overall system performance can be greatly influenced by its flow patterns, so it plays a crucial part and is necessary to develop methods for monitoring its flow patterns to ensure stable and safe operation. This issue can be viewed as a time series classification (TSC) task. Traditionally, several manually-engineered features are extracted as the indicators to evaluate the operation status, but this process can be heavily dependent on the professional experience. In this paper, a novel neural network called DTW-SLFN-KF is proposed, which integrates Dynamic Time Warping (DTW) and Kalman Filter (KF) into a single-hidden-layer neural network (SLFN) architecture to directly determine flow patterns from the dynamic sensor signals. The proposed network first adopts a DTW layer as the feature extractor to automatically extract robust features, and exploits the flexible alignment ability of DTW to keep the temporal continuity and deal with the temporal distortions. Then, these features are fed into an SLFN for classification. After that, to make full use of the extracted features and improve the classification performance of SLFN when the network structure is fixed, KF is applied as a linear post-processing technique to get the predicted output of SLFN closer to the true output. Experimental results demonstrate that the proposed DTW-SLFN-KF network has better comprehensive performance for monitoring the flow patterns of supersonic inlet in terms of monitoring accuracy and real-time performance when compared with other competitive methods.

Improvement of Algorithm for Formation of Orthogonal Components of Input Quantities in Microprocessor Protection

The use of orthogonal components (OC) underlies the construction of measuring elements of modern protection and automation devices. In most microprocessor-based protections, the orthogonal component of the input signal is extracted using a discrete Fourier transform (DFT). The DFT disadvantages are its low speed, which is more than one period of the fundamental frequency, as well as the sensitivity to the free aperiodic component, which creates significant conversion errors depending on the time constant of its decay. Such a settling time of the true output signal is often unacceptable for the design of high-speed measuring devices. The paper proposes to form the OC of the equivalent signal according to the values of the cosine and sine OC of the fundamental harmonic, formed using the DFT by multiplying them by the resulting correction factor. The developed algorithm for the formation of orthogonal components of input signals in microprocessor protections is characterized by high speed in transient modes and it has wide functionality. So, the proposed digital device for forming the orthogonal components of an equivalent signal, in comparison with digital filter based on the DFT, has an increased operating speed both in the mode of occurrence of a short circuit and during the decay of the monitored signal, while maintaining the same characteristics as in the DFT in other modes. A block diagram of the proposed digital device for forming the OC of an equivalent signal has been developed, all blocks of which can be implemented on a microelectronic and microprocessor element base. A digital model of the specified device has been developed in the dynamic modeling system MatLab-Simulink in accordance with the structural diagram. As a result of the calculations, a significant (up to two times) increase in the performance of the proposed digital device for forming the OC in transient modes has been established in comparison with the shapers based on the DFT.