Two-step Optimization Process Research Articles

Calibration of Microscopic Traffic Simulation in an Urban Environment Using GPS-Data

Accurate traffic models are of decisive importance for well-founded traffic engineering and represent the basic framework for comprehensive simulation studies as modelling of traffic demand. Using traffic count and speed measurements of road segments is a common approach for the calibration of a realistic traffic simulation although the data acquisition process can be at very extensive costs. From an academical point of view, there have been many studies addressing the problem of calibration. In this respect, the microscopic simulation software SUMO offers the usage of the tools flowrouter and routesampler for generating network simulations on the base of traffic count measurements. In this paper, we propose a robust method for the calibration of microscopic traffic simulations by using vehicle count and speed measurements from collected GPS-data. The developed approach is a two-step optimization process: The application of integer linear programming (ILP) as a priori optimization is followed by adopting an evolutionary algorithm for minimizing the a posteriori deviation between real and simulated traffic data. As a proof of concept, the proposed method is tested in a subnet-work model of the inner city of Friedrichshafen and compared with the ready-to-use tools from SUMO. The suggested method indicates a promising correlation between simulated and real traffic data showing better calibration results in comparison to the aforementioned functions SUMO provides. Since the approach is network-independent, it also offers the possibility of large-scale traffic calibration.

Composing recurrent spiking neural networks using locally-recurrent motifs and risk-mitigating architectural optimization.

In neural circuits, recurrent connectivity plays a crucial role in network function and stability. However, existing recurrent spiking neural networks (RSNNs) are often constructed by random connections without optimization. While RSNNs can produce rich dynamics that are critical for memory formation and learning, systemic architectural optimization of RSNNs is still an open challenge. We aim to enable systematic design of large RSNNs via a new scalable RSNN architecture and automated architectural optimization. We compose RSNNs based on a layer architecture called Sparsely-Connected Recurrent Motif Layer (SC-ML) that consists of multiple small recurrent motifs wired together by sparse lateral connections. The small size of the motifs and sparse inter-motif connectivity leads to an RSNN architecture scalable to large network sizes. We further propose a method called Hybrid Risk-Mitigating Architectural Search (HRMAS) to systematically optimize the topology of the proposed recurrent motifs and SC-ML layer architecture. HRMAS is an alternating two-step optimization process by which we mitigate the risk of network instability and performance degradation caused by architectural change by introducing a novel biologically-inspired "self-repairing" mechanism through intrinsic plasticity. The intrinsic plasticity is introduced to the second step of each HRMAS iteration and acts as unsupervised fast self-adaptation to structural and synaptic weight modifications introduced by the first step during the RSNN architectural "evolution." We demonstrate that the proposed automatic architecture optimization leads to significant performance gains over existing manually designed RSNNs: we achieve 96.44% on TI46-Alpha, 94.66% on N-TIDIGITS, 90.28% on DVS-Gesture, and 98.72% on N-MNIST. To the best of the authors' knowledge, this is the first work to perform systematic architecture optimization on RSNNs.

A Model Predictive Control Scheme for Flying Capacitors and Neutral Point Voltages Control in Nested Piloted Neutral Point Clamped Inverter

Five-level nested piloted neutral point clamped (5L-NPNPC) inverter is a new version of nested neutral point clamped multilevel topologies. In 5L-NPNPC, voltage fluctuations in the flying capacitors (FCs) and neutral point (NP) increase as the output frequency decrease, which is not desirable for the safe operation of the inverter. In this paper, a new model predictive control-based solution is proposed to tackle this problem. First, feasible combinations of space voltage vectors are determined through which the reference voltage of the inverter is synthesized. Then, a two-step optimization process is proposed to select the optimal switching states. The voltages of FCs and NP are the control objects in the first and second optimization steps, respectively. The proposed control scheme mitigates FCs and NP voltage fluctuations in the entire frequency range while keeping the switching frequency fixed. Also, the peak value of common-mode voltage (CMV) is limited to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_dc</i> /6. Furthermore, the proposed control scheme divides the optimization process into two steps, leading to a significant reduction in the computational burden of the processor. The mentioned achievements are important in medium-voltage (MV) motor drive applications. The simulation results and scaled-down experiments are provided to support the above claims.

Direction Modulated Brachytherapy Tandem Model Applicators for Treatment Planning of Multi-Institutional Cervical Cancer Cases

Direction Modulated Brachytherapy (DMBT) offers a means of utilizing an anisotropic source to create more conformal dose distributions when integrated with image-guided adaptive brachytherapy (IGABT). Authors sought to validate the implementation of nine unique six-channeled, MRI-compatible, novel DMBT tandem applicators of varying physical dimensions within Varian's BrachyVision® (v16.1) treatment planning system (BV-TPS). A total of 110 retrospective clinically delivered high-dose-rate (HDR) cervical cancer brachytherapy plans, from three institutions, were re-planned for each of the nine DMBT tandem models within the BV-TPS, using the latest VEGO® inverse optimization algorithm, with dose heterogeneity accounted for through AcurosBV®. Plans consisted of both intracavitary (77 plans) and interstitial (33 plans) cases with an average prescription dose and high-risk clinical target volumes (CTVHR) of 607±113 cGy and 26.96±14.95 [range 6.70-69.58] cm3, respectively. During re-planning, the conventional tandems were replaced by one of the nine DMBT tandem models while leaving ovoids or rings, and needles (if present), in place. A two-step inverse optimization process was performed such that the lowest possible organs at risk (OAR) D2cc doses could be achieved while 1) keeping equivalent target coverage (ΔCTVHR-D90 to within ±0.5%) and, at the same time, 2) maintaining the general pear-shape dose distribution of the original plans. Noteworthy improvements in plan quality were achieved by all nine DMBT tandem models, which are presented in Table 1. Irrespective of the model, about ∼50 cGy reduction in D2cc across all OARs appear feasible. There is also a general trend of D2cc reductions' magnitude becoming smaller as the CTVHR volume increased due to loss in modulation at distance. Additionally, D2cc reductions in terms of EQD2 [Gy] were calculated assuming each re-plan was delivered throughout the course of treatment, which includes the external beam radiotherapy dose of 45 Gy and showed significant reductions of -6.29±4.38 Gy, -3.80±2.06 Gy, and -4.86±3.02 Gy for the bladder, rectum, and sigmoid, respectively, for DMBT model #9 for example. We have successfully incorporated nine DMBT tandem models into a commercial TPS and re-planned 110 cases, to a total of 990 plans. All nine DMBT tandem models were each able to generate notable D2cc reductions to OARs (∼50 cGy), without compromising target coverage, across plans from multiple institutions with various clinical/optimization practices. The results indicate both a promising impact and smooth integration of DMBT tandem technology into modern clinical IGABT workflow.

Combining Novel Direction Modulated Brachytherapy Tandem-and-Ovoids Applicators for Treatment Planning of Multi-Institutional Cervical Cancer Cases: Removing Needles in Intracavitary-Interstitial Techniques

Lack of standard guidelines for optimal needle insertion during high-dose-rate (HDR) intracavitary-interstitial (IC-IS) brachytherapy of the cervix means a sophisticated and technical skillset of inserting needles next to IC applicators must be developed to enhance plan quality. This study sought to evaluate the performance of two separate direction modulated brachytherapy (DMBT) tandem applicators used in conjunction with one set of novel DMBT ovoids, uniquely designed to effectively obviate the need for IS needles. A cohort of 32 retrospective clinical HDR brachytherapy plans, from three institutions, were re-planned with Varian's BrachyVision® (v16.1) treatment planning system (BV-TPS), using the latest VEGO® inverse optimization algorithm, with dose heterogeneity accounted for through the AcurosBV®. All plans consisted of IC-IS cases, with a range of 2-4 freehand-loaded needles, with an average prescription dose of 709±53 cGy, and with an average high-risk clinical target volume (HRCTV) of 36.73±17.15 [range 9.8-69.6] cm3. Two DMBT tandem models of 5.4-mm and 8.0-mm thicknesses along with a novel DMBT ovoids design, introduced for the first time, with 9 equi-angled grooves and 10-mm-diameter thickness. During re-planning, the conventional tandems, ovoids/rings, and all of the needles were replaced by one of the two DMBT tandem models and a set of DMBT ovoids. A two-step inverse optimization process was performed to achieve the lowest possible OAR D2cc doses while 1) keeping equivalent target coverage (ΔHRCTV-D90 to within ±0.5%) and 2) maintaining the general pear-shape dose distribution used by the original plans. For all plans, this process was repeated using each of the two DMBT tandem-and-ovoids combinations for a total re-planning of (32×2 =) 64 cases. On average, -47.15±29.61 (-40.40±34.90) cGy, -42.98±26.58 (-41.70±27.40) cGy, and -40.47±25.05 (-32.55±25.30) cGy reductions in D2cc across bladder, rectum, and sigmoid, respectively, were achieved for the 8-mm (5.4-mm) DMBT tandem-and-ovoids combinations while the average ΔHRCTV-D90 was +4.3±2.9 cGy (+0.5%±0.4%). Additionally, D2cc reductions in terms of EQD2 [Gy] were calculated and showed significant reductions of -4.05±2.47 (-3.37±2.83) Gy, -2.71±1.79 (-2.59±1.74) Gy, and -3.27±1.96 (-2.65±2.06) Gy for bladder, rectum, and sigmoid, respectively with an average net increase in total dwell times of 241.0±87.6 seconds at the luxury of avoiding IS needle insertions. It is clinically feasible to obviate the need for IS needles by incorporating the DMBT tandem-and-ovoids while producing lower OAR D2cc doses and maintaining equivalent target coverage.

Estimation of directional single crystal elastic properties from nano-indentation by correlation with EBSD and first-principle calculations

In this study, a two-step optimisation process for the estimation of the single crystal stiffness tensor from the indentation modulus is presented. This was accomplished by using a 2-dimensional data correlation method for nano-indentation, EBSD and ab initio data, as well as EDS for phase separation in a multi-phase material. Here, a single-phase Fe-24Ni-0.4C austenitic steel and a two-phase Fe-9.5Ni-0.5Co Seymchan meteorite were used as example materials.In a first step, the combination of high-speed nano-indentation mapping data with elemental and orientation distribution maps allowed the estimation of indentation moduli along specific crystal surface planes normal directions, (001), (011) and (111) using a least squares optimisation based on starting values from DFT or experimentally determined stiffness tensors. A second global optimisation step to estimate the single crystal stiffness tensor using pre-solved solution parameters of the Vlassak-Nix equations yielded reasonable correspondence between the experimentally determined stiffness tensors from correlative nano-indentation data and other methods. The presented method demonstrates generally the possibility to derive directionally sensitive elastic properties from high-speed nano-indentation for cubic materials statistically.

Phyto-assisted synthesis of CuO/industrial waste derived biochar composite for adsorptive removal of doxycycline hydrochloride and recycling of spent biochar as green energy storage device

The highest exposure of Endocrine disrupting compounds (EDC) into the water bodies as a result of extensive production and application of Covid-19 related drugs is a growing concern now a days. Herein, a novel nanocomposite material was developed by impregnating green synthesized copper oxide nanoparticles on the porous surface of fabric waste derived biochar to eliminate the concerned EDCs along with a sustainable disposal strategy for the spent adsorbent. Morphological characterizations by Field emission scanning electron microscopy confirmed the formation of hierarchical porous structured material. X-ray analysis revealed presence of both amorphous nature of biochar matrix as well as the crystalline nature attributed from monodispersion of copper oxide nanoparticles onto biochar surface. Batch sorption study showed removal of doxycycline hydrochloride (DOX) of >97% after 2 h at pH 7, 30 mg L−1 initial concentration of DOX and 2 g L−1 of adsorbent dose at room temperature after a two-step optimization process. Spectroscopic study and Raman shift suggested that pore filling, strong complexation and electrostatic interactions maximise the adsorption of DOX in the CuO/biochar composite as compared to the pristine biochar. However disposal of spent adsorbent is a crucial aspect for the environment and therefore, a sustainable recycling strategy for DOX loaded adsorbent as electrode material has been proposed for the first time in this study. Maximum specific capacitance value was observed in the range of 221.9–297.3 F g−1 for the DOX loaded nanocomposite at 1 mV s−1 comparable with other reported heteroatom-doped carbonaceous material as electrode. Therefore the excellent adsorption capacity of green synthesized CuO/biochar composite and its recycling after DOX adsorption can be recommended as a sustainable solution for mitigation of pharmaceuticals from wastewater. A detail study on degradation of DOX into eco-friendly products and its cost-effectiveness would be beneficial to suggest appropriate mitigation strategy for such compounds.

G2 Blending Ball B-Spline Curve by B-Spline

Blending two Ball B-Spline Curves(BBSC) is an important tool in modeling tubular objects. In this paper, we propose a new BBSC blending method. Our method has the following three main contributions: First, we use BBSC instead of ball Bézier to model the blending part to expand the solution space and make the resultant BBSC have better fairness. Second, we consider both the skeleton line and radius of BBSC, which makes the skeleton line and radius consistent. Thirdly, we propose a two-step optimization process to solve the problem of excessive amount of parameters brought by expanding the solution space, so that our method satisfies the real-time.

A two-step optimization process for efficient scheduling of heterogeneous fleets of electric buses with synchronized transfers

Public transport systems are undergoing a significant change in most cities thanks to the advances in technology. The focus is not only on sustainability but also on efficiency and the experience of the passenger. In this work, we propose a complete redesign of the public transportation that considers a heterogeneous fleet of electric buses that dynamically changes in terms of the needs of each bus line at different hours, as well as a new bus schedule that minimizes passenger waiting time at bus stops by synchronizing different bus lines. In order to get optimal solutions, two optimization steps are needed. Preliminary results suggest that it is possible to implement an electrical bus fleet with different passenger and battery capacities that addresses the passenger demand, including the transfers on extensive trips where the users reduce travel times due to the synchronized schedule.

Mechanical analysis and optimized performance of G-Code driven material extrusion components

In this work an end-to-end optimization procedure to maximize the mechanical performance of Additive Manufacturing (AM) components is presented. Material Extrusion (ME) is the selected demonstrative AM technology, but the approach is applicable to other AM processes where the manufacturing toolpaths of the geometry of the printed component are described in G-Code format. The proposed methodology is integrated into the AM workflow and drives a two-step optimization process in order to select the optimal printing orientation for a user defined case. The G-Code file containing the manufacturing toolpaths is used as input. This approach allows to operate as close as possible to the geometry of the actual component, avoiding the use of the STereoLithography (STL) geometry. A voxelized mesh is built from the G-Code by solving a modified 2.5D Shortest Path Problem (SPP) and high-fidelity Finite Element (FE) simulations are performed with the resulting mesh. A printing pattern-based material model that distinguishes three different zones of the printed component is used. Due to the orthotropic nature of the ME process, the Tsai–Wu failure criterion is applied to obtain the indicators of the mechanical performance of the component. These computed metrics are used to drive an optimization process where a robust criterion based on the Machine Learning (ML) algorithm Anomaly Detection (AD) is applied in order to select the optimal build direction from a prespecified span of orientations. Two test cases and one case study illustrate the performance of the proposed methodology. The results validate the approach against experiments, indicate that the selected optimization criterion is robust against factors alien to the actual physical problem and show that the accuracy of the voxelized method greatly improves the “traditional” STL-based simulations.

Aerodynamic optimization and analysis of quadrotor blades operating in the Martian atmosphere

The design of unmanned aerial systems (UAS) for flight in the Martian atmosphere is a relevant and current topic. The successful flight of the Ingenuity helicopter recently proved its applicability. We present a numerical approach to the aerodynamic optimization of blades for rotors working in flow regimes with Reynolds numbers smaller than 15,000. Due to the atmospheric gas density and viscosity on Mars and the required rotation speed and rotor diameter, rotary-wing works mainly in the so-called ultra-low Reynolds number regime (103<Re<104), where the flow is likely laminar. The tip region is in the lower range of the very-low Reynolds number regime (104<Re<105), where separation-induced laminar-turbulent transition may occur. Such conditions characterize rotor blades of UAS operating in the Martian atmosphere or at very high altitudes (30 km) in the Earth's atmosphere. The blade design procedure consists of a two-step optimization process. The process starts with an initial two-dimensional airfoil design analysis followed by three-dimensional Blade Element Momentum (BEM) simulations to achieve the chord and twist radial distributions necessary to define the blade geometry. Then, a procedure to perform three-dimensional adjoint-based CFD simulations enhances the performance. The aerodynamic characteristics of optimal blades are evaluated with Navier-Stokes (N-S) and Large Eddy Simulations (LES), demonstrating the negligible effect of turbulence in blade performance for these Reynolds numbers when the boundary layer is attached. The implemented BEM method and high-fidelity CFD simulations show a good agreement.

Two-step optimization (catalyst and process) to maximize the liquid yield from the waste plastics mixture by hydrocracking process using hierarchical zeolite catalysts

Two-step optimization (catalyst and process) to maximize the liquid yield from the waste plastics mixture by hydrocracking process using hierarchical zeolite catalysts

Focusing characteristics of cylindrical vector beams through a multi-focal all-dielectric grating lens.

A novel, to the best of our knowledge, type of multi-focal all-dielectric grating lens is proposed in this work, and focusing characteristics of cylindrical vector beams through the lens are investigated in detail. Based on the negative refraction mechanism of negative-first-order diffraction and Fermat's principle, a multi-focal lens is designed. By analyzing the diffraction effect of the grating, the essential factor that affects the focus quality is found. Through a two-step optimization process, secondary foci and the focal displacement of primary foci caused by high-order diffractions are overcome, and the quality of the focal field is significantly improved. This work provides a reference for micro-lens design for focus modulation, and the research results also have potential applications in the fields of light-field manipulation and optical tweezers.

CNN-Based Hyperspectral Pansharpening With Arbitrary Resolution

Traditional hyperspectral (HS) pansharpening aims at fusing a HS image with its panchromatic (PAN) counterpart, to bring the spatial resolution of the HS image to that of the PAN image. However, in many practical applications, arbitrary resolution HS (ARHS) pansharpening is required, where the HS and PAN images need to be integrated to generate a pansharpened HS image with arbitrary resolution (usually higher than that of the PAN image). Such an innovative task brings forth new challenges for the pansharpening technique, mainly including how to reconstruct HS images beyond the training scale and how to guarantee spectral fidelity at any spatial resolutions. To tackle the challenges, we present a novel convolutional neural network (CNN)-based method for ARHS pansharpening called ARHS-CNN. It is based on a two-step relay optimization process, which is associated with a multilevel enhancement subnetwork and a rescaling subnetwork. With a careful design following the thread, our ARHS-CNN is able to pansharpen HS images to any spatial resolutions using just a single CNN model trained on a limited number of scales while meantime to keep spectral fidelity at those resolutions, which wins an obvious advantage over traditional pansharpening methods. Experimental results obtained on several datasets verify the excellent performance of our ARHS-CNN method.

A two-step planning method to increase accessibility to medium complexity procedures for public secondary healthcare.

The specialized care level of the public Brazilian national health system is critical and chronically underfunded. Few studies have evaluated public secondary care planning on a strategic level, so there are open issues yet to examine. This study aims at locating medical centers and sizing equipment based on a two-step optimization process to meet the population's needs. The models consider physicians' propensity for working on a metropolis and the patients' choice on moving the least from their municipalities, therefore, conflicting decisions. The models provide the location of medical centers, the assignment of equipment to such locations, and the additional hours of specialists required to meet official standards of demand. Available equipment with idle capacity should partly satisfy the requirement for exams within the current infrastructure. For the remaining uncovered demand, the second step of the optimization model suggests the acquisition of additional equipment for the elected medical centers to meet established needs. The proposed location of secondary care facilities covers 834 municipalities, corresponding to 97.77% of the estate, with an average patient displacement of 58.73 km (CI95%: 56.18 km - 61.28 km). In general, 39 out of 77 health regions should hire additional hours of medical specialties. Pediatrics and gynecology represent the major gap.

Discovery and optimization of novel 3-benzyl-N-phenyl-1H-pyrazole-5-carboxamides as bifunctional antidiabetic agents stimulating both insulin secretion and glucose uptake.

A novel series of 3-benzyl-N-phenyl-1H-pyrazole-5-carboxamides was designed, synthesized and evaluated for their biological activities on glucose-stimulated insulin secretion (GSIS). The cytotoxicity of all 41 novel compounds was screened to assess their pharmacological safety in pancreatic β-cells. A two-step optimization process was carried out to establish the structure-activity relationship for this class and subsequently we identified the most active analogue 26. Further modification study of 26 evidenced the necessity of N-hydrogens in the core architecture. Protein expression analysis suggested that 26 increases insulin secretion via the activation of the upstream effector of pancreatic and duodenal homeobox 1 (PDX-1), which is an important factor promoting GSIS. Moreover, the administration of 26 effectively augmented glucose uptake in C2C12 myotube cells via the suppression of Mitsugumin 53 (MG53), an insulin receptor substrate 1 (IRS-1) ubiquitination E3 ligase.

Automotive frontal body structure optimisation in the Upper Legform to Bonnet Leading Edge test with the aim of reducing the pedestrian injuries

Automotive frontal body structure design and optimisation are absolutely essential towards reducing the risk of pedestrian injuries in road accidents. In this paper, a two-step optimisation process in combination with the finite element method (FEM) is utilized to enhance pedestrian protection for a class-B passenger car. In this way, the finite element (FE) model of the Upper Legform Impactor has been developed and verified according to the European Commission (EC) Regulation No 631/2009. The Upper Legform to Bonnet Leading Edge test has been numerically simulated using LS-DYNA software. Material and geometry of the frontal body structure components are considered as the design variables and optimum design points are identified using the Taguchi method through a two-step framework. The Analysis of variance (ANOVA) is also used to determine the percentage effect of each controllable factors on the desired output. The obtained results revealed that in the optimum design point in comparison with the base vehicle, more than 45% improvement in the pedestrian protection criterion is observed, while selected components experienced mass reduction about 9%.

Mathematical modeling and simulation of newly isolated bacillus cereus M1GT for tannase production through semi-solid state fermentation with agriculture residue triphala

In this present research, tannase producing bacteria were isolated from the samples of the gastrointestinal tract of a Goat. The liquid enrichment and spread plate technique were adopted and 6 distinct bacterial colonies were isolated on a nutrient agar plate. Based on qualitative and quantitative methods of screening, one isolate among six was found promising and identified as Bacillus cereus M1GT by using 16S rRNA gene sequencing technique. The cost-effective substrate Triphala was used as a tannin and energy source for tannase production through solid-state fermentation in shake flask independently. Process factors were screened by a two-step optimization process i.e. one factor at a time method and central composite design. The study of statistical experimental design with Triphala at optimized conditions showed 6.1-fold (0.116 U/gds) higher in tannase activity than that obtained in submerged fermentation. Further, the kinetics of Bacillus cereus M1GT under optimum process conditions were studied. The Logistic equation (growth, ‘µ’), Luedeking Piret equation (Tannase, ‘α’, and ‘β’) and Substrate utilization equation (Tannic acid, ‘m’ and ‘n’) of unstructured kinetic models were evaluated with the MATLAB program. The experimental and simulated values exhibited a good correspondence, indicating that models will define tannase production process.

Two-step optimization process for grass hydrolysate application as biodiesel feedstock with novel quality characteristics.

A major obstacle to biodiesel commercialization is supplying feedback which increases production costs. The potential of some oleaginous yeast for conversion of waste materials to biodiesel feedstock can overcome this problem. In this study, a potential oleaginous yeast strain was used for single-cell oil (SCO) production. Two sets of experiments were designed for the optimization process. According to the results obtained from the first experiment, lipid production and lipid content of this strain increased from 1.96g/L and 22.6% to 3.85g/L and 35.18% by optimization of grass hydrolysis, respectively. The results of the second experiment indicate an increase in SCO production and lipid content to 7.28g/L and 56.39%, respectively. These results were obtained when HNO3 was used for substrate pre-treatment. Lipid analysis by gas chromatography-mass spectrometry showed a suitable and high potential of fatty acid profile for biodiesel production, which was then confirmed by evaluating the physicochemical properties of the biodiesel obtained in compliance with the US and EU standards. Consumption of microbial oil and low-cost substrate can compensate the high costs of feedstock in biodiesel production.

Fast in-frame coding algorithm for HEVC based on probability of mode selection

Subject of Research. The in-frame encoding process in the standard of high-efficiency encoding of video images is considered. The analysis of mode selection probability is carried out; the algorithm of fast prediction mode selection is proposed. Method. It is proposed to select a set of 11 modes with the highest probability of selection of the 35 possible modes of the standard “default” algorithm, as input data for further refined selection. Depending on the results obtained, another stage is added, where the choice is reduced up to 4 modes. The selected modes are used in a two-stepoptimization process, which selects the best mode for the current encoding block. Main Results. The proposed algorithm is implemented in the C++ development environment. A number of standard video sequences with four quantization parameter values is used for testing. The results of the carried out experiments show that the proposed fast algorithm reduces the encoding time by 17.03 % compared to HM-16.20 reference software, with an increase in transmission speed by 1.26 % and a loss of quality only by 0.033 dB. Practical Relevance. The proposed fast algorithm provides a reduction in computational costs with an extremely negligible loss of quality of the restored video sequence. It can be used as a standard H265 video codec block for replacement of the existing in-frame prediction block in the reference program to speed up the video compression process.