Code Optimization Research Articles

A Two-Stage Sub-Threshold Voltage Reference Generator Using Body Bias Curvature Compensation for Improved Temperature Coefficient

Leakage diodes cause deviations in the thermal drift of ultra-low-power two-transistor (2T) reference circuits, resulting in either convex or concave output voltages against temperature, depending on the reference transistor types (n-type/p-type). This paper investigates the combined application of the convexity and concavity properties exhibited by the output voltage of complementary 2T references, one n-type and one p-type. By exploiting the body bias effect, this approach mitigates variations in the output reference voltage caused by temperature fluctuations. Software optimization is also used to obtain the required aspect ratios after formulating the required criteria for drain-induced barrier lowering (DIBL) elimination in the first stage. The performance of the proposed reference is evaluated by post-layout Monte Carlo simulations. In the range of 0 °C to 100 °C, the output reference voltage has an average temperature coefficient (TC) of 26.7 ppm/°C without any temperature trim. The output reference voltage is 195.5 mV with a standard deviation of 13.6 mV. The line sensitivity (LS) is 17.1 ppm/V in the supply voltage range of 0.5 V to 2.1 V at 25 °C. At 25 °C and 0.5 V, the power consumption is 28.8 pW, increasing to a maximum of 1.3 nW at 100 °C and 2.1 V.

Structure optimization design and performance analysis of liquid cooling plate for power battery

The structural design of liquid cooling plates represents a significant area of research within battery thermal management systems. In this study, we aimed to analyze the cooling performance of topological structures based on theoretical calculation and simple structures based on design experience to achieve the best comprehensive performance and analyze their operating environment characteristics. The COMSOL Multiphysics software and topology optimization method were used to calculate the topology structure. To assess performance, the numerical computation method was employed to simulate and calculate the optimized structure, comparing its temperature, velocity, and pressure related parameters and changes. Moreover, the study analyzed the impact of inlet flow velocity, ambient temperature, and coolant temperature on heat dissipation performance. The results revealed a reduction of 0.31 % in the highest temperature and a 21.20 % decrease in pressure drop for the topology-based results compared to the post-processing results. However, the cooling performance advantage offered by the topology structure was not significant, the enhancement in cooling performance did not offset the increase in production cost. Furthermore, we found that variations in service conditions influence the heat dissipation performance of liquid cooling plates, requiring comprehensive consideration when selecting condition parameters.

GenArchBench: A genomics benchmark suite for arm HPC processors

Arm usage has substantially grown in the High-Performance Computing (HPC) community. Japanese supercomputer Fugaku, powered by Arm-based A64FX processors, held the top position on the Top500 list between June 2020 and June 2022, currently sitting in the fourth position. The recently released 7th generation of Amazon EC2 instances for compute-intensive workloads (C7 g) is also powered by Arm Graviton3 processors. Projects like European Mont-Blanc and U.S. DOE/NNSA Astra are further examples of Arm irruption in HPC. In parallel, over the last decade, the rapid improvement of genomic sequencing technologies and the exponential growth of sequencing data has placed a significant bottleneck on the computational side. While most genomics applications have been thoroughly tested and optimized for x86 systems, just a few are prepared to perform efficiently on Arm machines. Moreover, these applications do not exploit the newly introduced Scalable Vector Extensions (SVE).This paper presents GenArchBench, the first genome analysis benchmark suite targeting Arm architectures. We have selected computationally demanding kernels from the most widely used tools in genome data analysis and ported them to Arm-based A64FX and Graviton3 processors. Overall, the GenArch benchmark suite comprises 13 multi-core kernels from critical stages of widely-used genome analysis pipelines, including base-calling, read mapping, variant calling, and genome assembly. Our benchmark suite includes different input data sets per kernel (small and large), each with a corresponding regression test to verify the correctness of each execution automatically. Moreover, the porting features the usage of the novel Arm SVE instructions, algorithmic and code optimizations, and the exploitation of Arm-optimized libraries. We present the optimizations implemented in each kernel and a detailed performance evaluation and comparison of their performance on four different HPC machines (i.e., A64FX, Graviton3, Intel Xeon Skylake Platinum, and AMD EPYC Rome). Overall, the experimental evaluation shows that Graviton3 outperforms other machines on average. Moreover, we observed that the performance of the A64FX is significantly constrained by its small memory hierarchy and latencies. Additionally, as proof of concept, we study the performance of a production-ready tool that exploits two of the ported and optimized genomic kernels.

Closed-form solutions of consistency ratio in best worst method minmax optimization model: max of edge error matrix and minmax edge error determinant methods

The Best Worst Method (BWM), a reduced version of the AHP, is a recent multi-criteria decision-making tool based on pairwise comparisons with reference to the best and worst criteria. Consistency Ratio (CR) measurement for the rating quality and prioritizations is still a controversial topic. Firstly, the computation for the current CR of BWM must rely on a software optimization solver to find the optimal values, and the solver may not always guarantee the exact optimal solutions, especially if the computational cost settings are not large enough for higher number of criteria. Secondly, much effort to evaluate optimization algorithms is needed to find the best solutions with the least computational resources due to diverse solvers possibly leading to different results with different performances. Thirdly, optimization programming code is not trivial to be implemented for general BWM users. To address these issues, this paper presents the closed-form solutions, Max of Edge Error Matrix (MEEM) (Eq. (44) of Theorem 4) and Minmax Edge Error Determinant (MEED) (Algorithm 1), to replace the BWM optimization models to directly calculate the CR values. Two simulations have been performed with a basic laptop using a single process. One simulation of twenty thousand random pairs of vectors took 26.34 h to perform to verify that the approximate results are higher than or very close to the exact closed-form values of both methods when high computational cost is allocated for the solver to increase the precision. Another simulation of one million random pairs of vectors only took 1.27 h to perform to verify that the MEED and MEEM methods always produce the same results for the number of criteria up to nine. The computational time for the exact results is dramatically reduced when the solver is not needed. The advantages of the proposed solutions include the following: the software to solve the optimization model to obtain CR is unnecessary, and the proposed calculation is extremely efficient to obtain the exact accuracy. The two-step optimization model can preserve the fixed Minmax Edge Error to find the weights which add up to one, which is the condition to determine if the model reaches exact optimal solutions. As the CR optimization model produces multiple versions of weights, which are recommended not to be used, the new method does not need to compute the unnecessary weight values to get the Minmax Edge Error. With the provision of equations leading to closed forms, users can understand the properties of CR in much clearer perspectives. Due to the computational efficiency and explainability, the proposed closed forms can replace the CR optimization model to compute CR efficiently and accurately for all diverse applications using BWM.

A Comprehensive Survey on Loop Unrolling Technique In Code Optimization

A Comprehensive Survey on Loop Unrolling Technique In Code Optimization

The Design of FIR Filter Based on improved DA Algorithm and its FPGA implementation: REVIEW

The Design of FIR Filter Based on improved DA Algorithm and its FPGA implementation: REVIEW

Processing of a Part Such as a Composite Bearing Housing on a Milling Machine With a Digital Control Program

In the era of the industrial revolution of ndustry 4.0, the Republic of Kazakhstan is rapidly introducing automated robots and digital control programs in many of its production facilities. In this context, special attention is paid to modern technologies, and AKIRA SEIKI milling machines with FANUC control software stand out as advanced technologies in this process. They are becoming the most popular in the CIS countries due to the economic efficiency and intellectual support of the Taiwanese manufacturer. Practical aspects of constructing CNC milling machines and their practical use are presented in this article. Particular attention is paid to the functionality of milling machines using the example of processing such a part as a composite bearing housing. In addition, the process of writing processing operation codes in the CIMKO computer program is described. The code is provided with brief descriptions to help you understand the compiled workpiece processing operations. With the growing need for efficiency and precision in production, many companies and industrial plants in the Republic of Kazakhstan are considering AKIRA SEIKI milling machines with FANUC control software as a major investment. These machines not only provide high productivity, but also help automate complex technological processes. CIMKO, among other things, allows the development and optimization of machining code, which complements the efficiency and accuracy of the machines.

Hybrid of Meta-Heuristic Techniques Based on Cuckoo Search and Particle Swarm Optimizations for Solar PV Systems Subjected to Partially Shaded Conditions

Solar energy has a significant role in meeting rising energy demand while reducing environmental impact. Solar radiation and temperature are important factors on which PV energy production depends, but its optimal operation point is influenced by variations in the aforementioned environmental factors. The nonlinear behavior of the solar system and the variable nature of environmental conditions make determining the optimal operation point difficult. To overcome these difficulties, maximum power point tracking (MPPT) finding techniques are used to extract the optimal power from the photovoltaic energy system. The behavior of MPPT varies for different weather conditions, such as partial shading conditions (PSC), and uniform irradiance conditions. Conventional techniques are simple, quick, and efficient for tracing the MPP quickly, but they are limited to uniform weather conditions. In addition, these techniques don't achieve the Global Maxima (GM) and mostly stay stuck at the Local Maxima (LM). The Meta-Heuristic techniques aid in finding the GM, but their primary disadvantage is that they take a longer time to trace the Global Maxima. This study addresses the problem by combining Cuckoo Search (CS) and Particle Swarm Optimization (PSO) algorithms, leading to a hybrid (CSPSO) technique to extract the global maximum (GM). To verify the effectiveness of the suggested technique, its performance is examined under three different irradiance patterns for different PV array configurations (such as 3S and 4S3P) through MATLAB simulation. The outcomes of CSPSO are compared with the prior well-known Meta-Heuristic techniques such as Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Crow Search Algorithm (CSA). The results show the suggested technique excels over other techniques in terms of accuracy, tracking efficiency, and tracking speed. The suggested technique is capable of tracking GMPP with an average efficiency of 99.925% and an average tracking time of 0.13 s in all shading patterns studied.

Analysis of Energy Consumption of Robotic Welding Stations

Analysis of the energy consumption of industrial robots during the implementation of a technological task can increase efficiency and minimize production costs, as well as extend the service life of robots, taking into account the rational use of energy sources. An important challenge in designing sustainable production processes is the selection of energy-saving technological equipment supporting the work of robots. Additionally, optimization of robot program codes, including parameters and robot movement paths, determines the energy efficiency of the robotic station. Welding is one of the most popular areas of process robotization due to its ability to increase production efficiency and improve the quality of part assembly compared to manual work. In these studies, an exemplary process of robotic welding of aluminum alloy parts was designed, analyzed, and optimized with an orientation towards sustainable development guidelines. This work also presents a review of current publications discussing the authors’ contribution to achieving energy efficiency in robotic welding processes. The purpose of this analysis is also to indicate directions for the development of future research work in order to minimize energy consumption in production processes with an appropriate level of product quality and increase production efficiency.

A parallel programming application of the A* algorithm in digital rock physics

Pore-scale analysis and characterization of reservoir rocks provide valuable information for the definition and management of underground hydrogen storage and CO2 storage or sequestration.This article presents an optimized implementation of the A* algorithm, the most popular pathfinding method in the presence of obstacles. In this context, the algorithm is applied to recognize the minimum length connected paths through each flow direction of 3D images of a porous medium representative of a reservoir rock. The identification of the main paths allows the characterization of the pore space and the calculation of fundamental petrophysical properties such as tortuosity and effective porosity, which can be used for permeability estimation. Compared to other algorithms available for pore-scale characterization, such as the pore centroid, A* provides a better approximation of the pore space available for the flow and, therefore, a reliable characterization of the petrophysical properties. On the other hand, path identification is significantly consuming in terms of time and memory. In this paper, an efficient and optimized implementation based on C++/OpenMP programming language is presented.The proposed implementation aims to the analysis of large-scale models profiting from parallelization, memory optimization, and enhanced managing of dead paths. Three test cases of increasing sizes are presented, to analyze the advantages and the disadvantages of the algorithm as the number of explored points increases. The 3D binary images analyzed are related to a synthetic domain (1 million voxels) and two actual sandstone samples (about 4 and 64 million voxels respectively). The code is validated against a Matlab serial implementation, showing a significant improvement in efficiency. Remarkable test cases of several millions of voxels were afforded, overcoming the memory and execution slowness issues. Moreover, the proposed implementation is suitable for large pore-scale models run in HPC environments.

Research Progress on Thermal Conductivity of High-Pressure Die-Cast Aluminum Alloys

High-pressure die casting (HPDC) has been extensively used to manufacture aluminum alloy heat dissipation components in the fields of vehicles, electronics, and communication. With the increasing demand for HPDC heat dissipation components, the thermal conductivity of die-cast aluminum alloys is paid more attention. In this paper, a comprehensive review of the research progress on the thermal conductivity of HPDC aluminum alloys is provided. First of all, we introduce the general heat transport mechanism in aluminum alloys, including electrical transport and phonon transport. Secondly, we summarize several common die-cast aluminum alloy systems utilized for heat dissipation components, such as an Al–Si alloy system and silicon-free aluminum alloy systems, along with the corresponding composition optimizations for these alloy systems. Thirdly, the effect of processing parameters, which are significant for the HPDC process, on the thermal conductivity of HPDC aluminum alloys is discussed. Moreover, some heat treatment strategies for enhancing the thermal conductivity of die-cast aluminum alloys are briefly discussed. Apart from experimental findings, a range of theoretical models used to calculate the thermal conductivity of die-cast aluminum alloys are also summarized. This review aims to guide the development of new high-thermal-conductivity die-cast aluminum alloys.

Cracks-suppression perceptual geometry coding for dynamic point clouds

Dynamic point clouds can effectively describe 3D objects and natural scenes, providing users with an immersive visual experience, but their huge amount of data requires efficient compression tools. To this end, the video-based point cloud compression (V-PCC) standard was developed, however, it may lead to cracks in the compressed point cloud at low bitrates. To solve the problem, this paper proposes a cracks-suppression perceptual geometry coding method for dynamic point cloud. Firstly, considering that the edge regions of 2D geometry patches in V-PCC are prone to cracks, a prior knowledge based crack region detection and preprocessing scheme is designed to reduce cracks. Secondly, considering the unique perceptual geometry characteristics of point cloud, a projected geometry curvature based structural similarity (PGC-SSIM) is proposed to evaluate geometry quality of point clouds, which contains the geometric perception information of point cloud, and is more consistent with the human visual perception. Finally, based on the PGC-SSIM, an adaptive quantization parameter adjustment strategy is designed for rate-distortion optimization in geometry coding of dynamic point clouds. The experimental results show that the proposed method can effectively reduce cracks in the compressed point cloud without reducing the coding performance compared to the V-PCC anchor. Moreover, the presented PGC-SSIM can be used to improve the visual quality of the compressed point cloud.

Improving Data Delivery in Unreliable Networks Using Network Coding and Ant-Colony Optimization

Wireless Sensor Networks (WSNs) comprise interconnected wireless nodes that receive and transmit data across applications and platforms. This paper addressed the problem of link failures in WSNs that potentially could lead to the loss of data packets while still in transit. This was achieved through the use of network coding which is known to address capacity bottleneck problems in WSNs. In particular, a technique called Ant Agent-Assisted Network Coding (AAANC) is proposed that employs the ant colony optimization technique in addition to network coding operations. The main aim of AAANC is to facilitate the successful delivery and decoding of coded data packets in the presence of link failures. AAANC employs a packet route selection technique that is inspired by the social behavior of natural ants. For natural ants, a strong pheromone trail along a path indicates a promising route to a food source, and this is analogous to a reliable communication link for routing data packets in this paper. Through simulations, AAANC was compared to diagonal pseudorandom network coding (DNC) and triangular pseudorandom network coding (TNC), and it proved to have a superior performance in terms of packet delivery ratio and number of decoded packets. Significant performance gain can be achieved if AAANC algorithm is made to dynamically adapt the ant colony and network coding parameters in response to traffic changes.

Toward HPC application portability via C++ PSTL: the Gaia AVU-GSR code assessment

The computing capacity needed to process the data generated in modern scientific experiments is approaching ExaFLOPs. Currently, achieving such performances is only feasible through GPU-accelerated supercomputers. Different languages were developed to program GPUs at different levels of abstraction. Typically, the more abstract the languages, the more portable they are across different GPUs. However, the less abstract and co-designed with the hardware, the more room for code optimization and, eventually, the more performance. In the HPC context, portability and performance are a fairly traditional dichotomy. The current C++ Parallel Standard Template Library (PSTL) has the potential to go beyond this dichotomy. In this work, we analyze the main performance benefits and limitations of PSTL using as a use-case the Gaia Astrometric Verification Unit-Global Sphere Reconstruction parallel solver developed by the European Space Agency Gaia mission. The code aims to find the astrometric parameters of ∼108\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim10^8$$\\end{document} stars in the Milky Way by iteratively solving a linear system of equations with the LSQR algorithm, originally GPU-ported with the CUDA language. We show that the performance obtained with the PSTL version, which is intrinsically more portable than CUDA, is comparable to the CUDA one on NVIDIA GPU architecture.

Optimization of Reinforced Concrete Structures in Terms of Environmental Impacts, Durability and Cost

The paper deals with the methods of software optimization of the concrete structure in terms of environmental issues, durability, and cost. It links to previously developed software tool that enables multi-criteria optimization of a 1 m2 one-way slab. A feasibility study focused on the optimization of larger structural units, and it analyses and compares methods of concrete structures optimization in terms of environmental impacts, durability, and life-cycle cost.

Method for GPU-based spectral data cube reconstruction of integral field snapshot imaging spectrometers.

In this paper, the principles of spectral data cube reconstruction based on an integral field snapshot imaging spectrometer and GPU-based acceleration are presented. The primary focus is on improving the reconstruction algorithm using GPU parallel computing technology to enhance the computational efficiency for real-time applications. And the computational tasks of the spectral reconstruction algorithm were transferred to the GPU through program parallelization and memory optimization, resulting in significant performance gains. Experimental results indicate that the average processing time of the GPU-based parallel algorithm is approximately 29.43 ms, showing a substantial acceleration ratio of about 14.27 compared to the traditional CPU serial algorithm with an average processing time of around 420.46 ms. The study aims to refine the GPU parallelization algorithm for continued improvement in computational efficiency and overall performance. The anticipated applications of this research include providing crucial technical support for the perception and monitoring of crop growth traits in agricultural production, contributing to the modernization and advancement of intelligence in the field.

PREPARATION AND JUSTIFICATION OF NANOFIBRES-LOADED MAFENIDE USING ELECTROSPINNING TECHNIQUE TO CONTROL RELEASE

Objective: The primary objective was to fabricate a novel drug delivery system capable of providing a controlled and prolonged release of antibiotics. Methods: The experimental design was formulated using Design-Expert® software (version 13), enabling systematic and efficient fabrication process optimization. The study involved the preparation of various nanofiber formulations with different ratios of the three polymers to assess their impact on drug release behavior. Mafenide, a widely used antibiotic, was chosen as the model drug for this investigation. The electrospinning process allowed for producing uniform and fine nanofibers with a high surface area, ensuring a large drug-loading capacity. The synthesized nanofibers were characterized using scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR) to evaluate their morphology, chemical interactions, and thermal properties. The drug release kinetics of the antibiotic-loaded nanofibers were studied under different physiological conditions to assess their sustained release behavior. Results: The final nanofiber formula was successfully prepared using the electrospinning technique. The Fourier Transform Infrared Spectroscopy (FTIR) analysis was achieved to confirm the possibility of chemical interaction and bond formation between mafenide and the polymers. Present. The SEM picture of the optimized nanofiber formula showed the homogeneity and excellent entanglement of the electrospun nanofibers at a resolution of 5 µm. PVA/chitosan/HPMC and mafenide pure drug have been successfully fabricated with sufficient strength to resist swelling after absorbing wound exudate. The polymer network becomes more compact when chitosan and Hydroxypropyl Methyl Cellulose (HPMC) are combined with polyvinyl alcohol (PVA), enabling regulated swelling during solvent ingress. The polymer composite's three-dimensional network influenced how quickly the medication was released from the matrix. Sample 2's polymer network traps the medication, gradually releasing after controlled swelling, resulting in a sustained release profile compared to blank sample according to the cumulative release (%) study of mafenide loaded nanofiber and mafenide drug blank sample. Conclusion: This research successfully demonstrated the fabrication of sustained-release antibiotic nanofibers using electrospinning and three biocompatible polymers. The systematic optimization approach using Design-Expert® software proved effective in tailoring the drug release behavior of nanofibers. The developed drug delivery system holds great promise for pharmaceutical applications, particularly in improving antibiotic therapies and patient care.

Comparative techno-economic analyses and optimization of standalone and grid-tied renewable energy systems for South Asia and Sub-Saharan Africa

This study evaluates the economic, technical, and environmental performance of stand-alone and grid-tied HRES in Khiriya Bharka, India; Thayet Township, Myanmar; Lower Manya Krobo, Ghana; and Mamfe, Cameroon, considering different regional solar radiation, wind speed diversity, and climate. The HRES are designed and modeled using the Hybrid Optimization of Multiple Energy Resources software (HOMER PRO) to meet consumers' daily loads in the selected places. The analysis results are compared considering the levelized cost of energy (LCOE), net present cost (NPC), greenhouse gas (GHG) emission, renewable fraction (RF), and optimum system configuration. The optimal system configurations are the combination of PV-WT-DG-BAT-CON for grid-tied and stand-alone HRES in India, Ghana, and Cameroon, respectively, and the best configuration for Thayet Township, Myanmar, is WT-DG-BAT-CON. The LCOE of the considered places without grid connection is $0.127/kWh, $0.145/kWh, $0.174/kWh, and $0.143/kWh, respectively. The LCOE of grid-tied HRES is 0.026$/kWh, 0.0286$/kWh, 0.01$/kWh, and 0.0281$/kWh for Khiriya Bharka, Thayet Township, Lower Manya Krobo, and Mamfe, respectively. This research can be useful for planning grid-tied and stand-alone HRES between Asia and African countries by comparing grid-tied and stand-alone HRES to determine the optimum system configuration and finding the best optimization results under different climate conditions. These results are further validated by finding loss of power supply probability (LPSP) and LCOE using particle swarm optimization (PSO). To better inform policymakers and stakeholders in various regions, this research aims to assess and compare renewable energy solutions in terms of their feasibility, cost-effectiveness, environmental impact, and sustainability across a range of geographical, social, and economic contexts.

SPECIFICS OF IMPLEMENTING SMART MANUFACTURING IN INDUSTRIAL SMALL AND MEDIUM-SIZED ENTERPRISES: BENEFITS AND LIMITATIONS

Purpose. The aim of the article is to determine the mechanisms for solving the technological and technical limitations of data management during the automation of the production process at industrial enterprises, as well as the economic advantages and limitations created by the introduction of smart production at small and medium-sized industrial enterprises. Methodology of research. The theoretical and methodological basis of the research is the scientific works of leading scientists and practicing entrepreneurs regarding the features and technology of implementing smart production at industrial enterprises. The following general scientific methods were used in the research process: comparison – to study the approaches of the largest producing countries to the management and stimulation of the development of smart production; analysis and synthesis – to analyse the state of implementation of smart production at small and medium-sized industrial enterprises; abstract and logical – for expressing own assumptions and formulating conclusions, graphic – for visual illustration of the conceptual model of optimization of software and hardware of SMEs for data processing of enterprises. Findings. It has been established that the leading technologies forming the system of smart production - the industrial Internet of Things, neural networks based on artificial intelligence and blockchain technology – solve the following technological limitations that existed under the centralized server infrastructure: infrastructure overload due to the increase in the consumption of computing resources (solved as a result of optimization the speed of transaction accounting due to the introduction of artificial neural networks based on artificial intelligence); problems of stakeholder authentication and platform compatibility (solved as a result of automating registration of participants, transactions and as a result of automating the execution of transactions due to smart contracts within the framework of open blockchain technology); insufficient integrity and confidentiality of the data register as a result of compatibility and authentication problems (solved due to the technology of distributed accounting of transactions (blockchain), which allows to avoid falsification of data). It was found that the main economic advantage as a result of the implementation of the mentioned technologies within the framework of the smart production system is the possibility of mass customization of production at the level or almost at the level of the costs of standard mass production. It is specified that these technologies are appropriate for implementation by high-tech industrial enterprises producing products with high added value. Originality. The theoretical aspects of establishing direct and indirect economic benefits from the implementation of a smart production system at industrial small and medium-sized enterprises, approaches to optimizing production in the direction of product customization have gained further development. Practical value. Formulated approaches and recommendations for solving problems related to data processing during the automation of the production process - as a set of technological limitations, as well as an approach for solving technological limitations for the manufacture of customized products can be used at innovation-oriented industrial enterprises, in particular, medium and small ones. Key words: smart production, small and medium enterprises, industrial Internet of things, artificial intelligence, blockchain technology, automation, production, data processing.

Uncertainty modeling of connected and automated vehicle penetration rate under mixed traffic environment

Accurate knowledge of the penetration rate of connected and automated vehicles (CAVs) is crucial for effective control applications during the transition from mixed traffic to full CAV deployment. Previous studies have focused on characterizing or controlling mixed traffic with a fixed CAV penetration rate. However, in reality, the on-road penetration rate of CAVs varies, even if their market share remains constant. This study presents a mathematical model that estimates the CAV penetration rate while considering this variability. We propose an uncertainty-based penetration rate estimation model to assess the variability of CAV numbers on the road. This model utilizes a probabilistic modified random walk approach to estimate the distribution. To enhance the realism of mixed traffic flow, we incorporate realistic vehicle braking and starting behaviors using an improved cellular automata-based mixed traffic flow model. Simulation results demonstrate that our uncertainty-based penetration rate estimation model accurately describes CAV numbers and estimates the variability in mixed traffic flow, specifically within opened circular boundaries and on-ramps. Moreover, we demonstrate the practical applicability of the uncertainty models in real-world situations, showcasing their potential to enhance system optimizations.