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This paper uses big data technology to extract the electromechanical fault characteristics of metro vehicles and analyze the current situation under different fault conditions to ensure the operation quality and safety of metro operations. It also establishes a simulation model to simulate the current waveform of metro vehicles under different fault conditions and analyze the fault phenomenon. The simulation test results show that: (1) The current waveform of a single transistor with the hard fault is compared with the simulated current waveform under a normal state. The upper part of the A phase current waveform is lost when T1 fails. When T2 fails, the current waveform in the lower half of the C phase current is lost. When T3 fails, the upper half of the B phase current waveform is lost. (2) The current waveform in the hard fault's upper and lower bridge arms will have phase loss. In the T25 fault, the C phase current is completely lost. In the T14 fault, the phase A current waveform is completely lost. In the T36 fault, the phase B current is completely lost. (3) The current waveform of a single transistor with a soft fault is complete, but the overall current amplitude is reduced. When a T1 fails, the A phase current tends to rise first and then fall. Compared to normal, the amplitude of the current decreases, and the peak decreases slightly. (4) The current values of phase B and phase C of the two transistors on the same bridge above and below the soft fault are mostly the same. The phase A current output value decreases in both the positive and negative half cycles. This paper aims to improve the monitoring ability of the monitoring system of electromechanical equipment of metro vehicles, which plays a specific role in maintaining the safety of subway operations and improving the quality of subway operations. [ABSTRACT FROM AUTHOR]

Navigation and positioning is a new growth point of wireless services. And seamless positioning is the current development trend of navigation and positioning services. In view of the problems that dynamic handover cannot be performed during site movement, STA only selects the optimal AP according to the indication of received signal strength, and the handover delay is too long. This paper proposes a research on indoor and outdoor seamless positioning technology based on artificial intelligence and intelligent switching algorithm. The purpose is to study the influence of reducing the positioning data with too large error on the final positioning result. The method of this paper is to propose an intelligent switching algorithm and then discuss the research status and development direction of indoor positioning technology and outdoor positioning technology, respectively, and point out the switching methods between them. The role of these methods is to analyze the current situation of indoor and outdoor navigation technology, especially the development route of seamless positioning technology. It determines the strategy of integrating heterogeneous positioning systems to build a seamless positioning system to maximize the use of existing positioning system resources. This paper studies the design and implementation of the seamless positioning system through the simulation experiment of the intelligent switching algorithm and finally tests the system through the experimental vehicle. The online experiment results show that the system can achieve high positioning accuracy in indoor and outdoor environments, especially at its junction, and the positioning errors in all directions are within 0.2 m. [ABSTRACT FROM AUTHOR]

It is vital to consider acoustic scattering when using geometrical acoustic simulation techniques, such as ray tracing. However, there are few methods for modelling scattering, and most rely on strong assumptions of uniformity on the distribution of scattered energy. In this paper, a model for directional scattering in ray tracers is presented. The model is based on an idealized model of a 1D scatterer, which is then used to extend the most commonly used scattering algorithm in ray tracers today. The developed algorithm is implemented in a ray tracer and tested to evaluate its performance compared to existing methods. It is found that the directional scattering algorithm can be used to replicate measured effects on room acoustic parameters caused by changes in the orientation of 1D scatterers. [ABSTRACT FROM AUTHOR]

This paper does some research and discussion on the finite element analysis of a building structure, especially the computer graphics simulation method in building structure simulation. Moreover, with the support of BIM technology and computer finite element simulation technology, this paper constructs a building structure simulation system and analyzes the building structure simulation system based on actual conditions such as building structure and stress load. In addition, this paper improves the traditional structural analysis algorithm and designs experiments to evaluate the effect of the method proposed in this paper and analyze the data in the form of simulation to compare the validity of the test results. Finally, an experiment is designed to evaluate the data processing capability of the test system in this paper. The experimental analysis results verify the effectiveness of the method in this paper, which can provide relevant theoretical references for subsequent building structure simulation. [ABSTRACT FROM AUTHOR]

A nonparametric procedure to estimate the conditional probability that a nonstationary geostatistical process exceeds a certain threshold value is proposed. The method consists of a bootstrap algorithm that combines conditional simulation techniques with nonparametric estimations of the trend and the variability. The nonparametric local linear estimator, considering a bandwidth matrix selected by a method that takes the spatial dependence into account, is used to estimate the trend. The variability is modeled estimating the conditional variance and the variogram from corrected residuals to avoid the biasses. The proposed method allows to obtain estimates of the conditional exceedance risk in non-observed spatial locations. The performance of the approach is analyzed by simulation and illustrated with the application to a real data set of precipitations in the USA.Supplementary materials accompanying this paper appear on-line. [ABSTRACT FROM AUTHOR]

The aim of this systematic review is to synthesize and analyze interactive collaborative work in the virtual economy of the metaverse. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout May 2022, with search terms including "metaverse interactive environment" + "virtual employee engagement and immersive workspaces," "wearable augmented reality technology," and "spatial data mining and simulation modeling algorithms." As we analyzed research published in 2022, only 158 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 30, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Distiller SR, ROBIS, and SRDR. [ABSTRACT FROM AUTHOR]

Throughout the world, the reliability-based approach to safety design of aircraft systems is quite mature and widely used. However, there are still shortcomings in the reliability-based aircraft system safety analysis method. It cannot dynamically analyze the accident evolution process and lack consideration of the complex situation of multifactor coupling. On the basis of the original aircraft system safety analysis method, this paper innovatively proposes a functional hazard analysis (FHA) method based on the analytic hierarchy process (AHP) and multifactor fuzzy comprehensive assessment (FCA). The purpose is to improve the objectivity and quantification of the FHA method in the safety design of aircraft systems. At the same time, in the terminal airworthiness verification, this paper proposes a repeatable and controllable virtual test flight verification method, which aims to reduce the cost and cycle of the terminal airworthiness verification and expand the coverage of the envelope verification. Finally, combined with the clauses in MIL-HDBK-516B, a case calculation is carried out to verify the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

The quality of collision detection algorithm directly affects the performance of the whole simulation system. To address the low efficiency and low accuracy in detecting the collisions of flexible cloths in virtual environments, this paper proposes an oriented bounding box (OBB) algorithm with a simplified model, tree structure for a root-node double bounding box, and continuous collision detection algorithm incorporating an OpenNN-based neural network optimization. First, for objects interacting with the cloths with more complex modeling, the model is simplified with a surface simplification algorithm based on the quadric error metrics, and the simplified model is used to construct an OBB. Second, a bounding box technique commonly used for collision detection is improved, and a root-node double bounding box algorithm is proposed to reduce the construction time for the bounding box. Finally, neural networks are used to optimize the continuous collision detection algorithm, as neural networks can efficiently process large amounts of data and remove disjoint collision pairs. An experiment shows that the construction of an OBB using the simplified model is almost identical to that of the original model, but the taken to construct the OBB is reduced by a factor of approximately 2.7. For the same cloth, it takes 5.51%–11.32% less time to run the root-node double bounding box algorithm than the traditional-hybrid bounding box algorithm. With an average removal rate nearly identical to that of the traditional filtering method, the elapsed time is reduced by 7%–11% by using the continuous collision detection algorithm based on an OpenNN neural network optimization. The simulation results are realistic and in line with the requirements for real-time cloth simulations. [ABSTRACT FROM AUTHOR]

This paper introduces a novel and innovative approach to simulating random variates from two distinct probability distributions, namely the neutrosophic uniform distribution and the neutrosophic Weibull distribution. The primary objective of this research is to present a cutting-edge methodology for generating random variates by leveraging the accept-reject simulation method, particularly in the context of managing and addressing uncertainty. In addition to introducing the simulation methodology, this work will also provide comprehensive algorithms tailored to these proposed methods. These algorithms are essential for implementing the simulation techniques and will be instrumental in their practical applications. Furthermore, this study aims to explore the relationship between the level of indeterminacy and the resulting random variates. By investigating how varying degrees of indeterminacy impact random variates, we gain valuable insights into the dynamics of these distributions under different uncertainty conditions. Preliminary results suggest that random variates exhibit a trend of decreasing as indeterminacy levels increase, shedding light on the intriguing interplay between indeterminacy and random variate generation. [ABSTRACT FROM AUTHOR]

In this paper, the conceptualization of a control hardware architecture aimed to the implementation of integer- and fractional-order identification and control algorithms is presented. The proposed hardware architecture combines the capability of implementing PC-based control applications with embedded applications on microprocessor- and FPGA-based real-time targets. In this work, the potential advantages of this hardware architecture over other available alternatives are discussed from different perspectives. The experimental prototype that has been designed and built to evaluate the control hardware architecture proposed in this work is also described in detail. The thermal-based process taking place in the prototype is characterized for being reconfigurable and exhibiting fractional behaviour, which results in a suitable equipment for the purpose of fractional-order identification and control. In order to demonstrate the applicability and effectiveness of the proposed control hardware architecture, integer- and fractional-order identification and control algorithms implemented in various control technologies have been applied to the temperature-based experimental prototype described before. Detailed discussion about results and identification and control issues are provided. The main contribution of this work is to provide an efficient and practical hardware architecture for implementing fractional-order identification and control algorithms in different control technologies, helping to bridge the gap between real-time hardware solutions and software-based simulations of fractional-order systems and controllers. Finally, some conclusions and concluding remarks are offered in the industrial context. [ABSTRACT FROM AUTHOR]

The application of spin–orbit torque (SOT) devices to neuromorphic computing platforms is focused on the development of hardware circuit architectures. However, the inter-device variability, the integration modes of devices and peripheral circuits, and appropriate application scenarios are still unclear, limiting the development of SOT devices in neuromorphic computing. To solve this problem, this paper first proposes a circuit compensation scheme for the difference in resistance values of SOT devices, which solves this variability problem at the circuit level. Moreover, a synergistic scheme with the circuit is developed based on the correspondence between the multistate resistance characteristics of the SOT devices and a convolutional algorithm. To achieve this, a multichannel SOT convolutional kernel circuit architecture is built, which implements an image edge recognition application. Finally, based on a simulation model, an image edge recognition hardware circuit based on our CoPt-SOT devices is implemented, which is capable of performing image edge recognition with an accuracy of 96.33%. This scheme provides technical support and development prospects for SOT devices in neural network hardware applications. [ABSTRACT FROM AUTHOR]

The multi-state flow network (MSFN) serves as a fundamental framework for real-life network-structured systems and various applications. The system reliability of the MSFN, denoted as Rd, is defined as the probability of successfully transmitting at least d units of demand from a source to a terminal. Current analytical algorithms are characterized by their computational complexity, specifically falling into the NP-hard problem to evaluate exact system reliability. Moreover, existing analytical algorithms for calculating Rd are basically designed for predetermined values of d. This limitation hinders the ability of decision-makers to flexibly choose the most appropriate based on the specific characteristics of the given scenarios or applications. This means that these methods are incapable of simultaneously calculating system reliability for various demand levels. Therefore, this paper develops a simulation-based algorithm to estimate system reliability for all possible demand levels simultaneously such that we can eliminate the need to rely on repeat procedures for each specified d. An experimental investigation was carried out on a benchmark network and a practical network to validate the effectiveness and performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

To enhance the convergence performance of the affine projection sign subband adaptive filter (AP-SSAF) algorithm under highly correlated inputs, the novel AP-SSAF (NAP-SSAF) algorithm was proposed through fully utilizing the decorrelation characteristic of SAF, which obtains quicker convergence behavior than the AP-SSAF algorithm. However, under unknown system identification, the estimation accuracy of the NAP-SSAF algorithm may unavoidably decrease when the input of the adaptive filter is contaminated by noise. To alleviate this issue, in this paper, we propose the bias-compensated NAP-SSAF (BC-NAP-SSAF) algorithm to alleviate the estimated bias through employing unbiasedness criterion. Benefiting from the unbiased criterion, the proposed BC-NAP-SSAF algorithm achieves an asymptotically unbiased estimation. Moreover, the convergence bounds of the proposed BC-NAP-SSAF algorithm in the mean and mean-square senses are provided to ensure stability, which are obtained by resorting to Price's theorem along with some reasonable assumptions. The effectiveness of the derived theoretical convergence bounds is also corroborated. Finally, numerical simulations under system identification and acoustic echo cancellation applications demonstrate that the proposed BC-NAP-SSAF algorithm outperforms other algorithms in terms of estimation accuracy and tracking capability for noisy input under impulsive noise environment. [ABSTRACT FROM AUTHOR]

Digital image technology is penetrating into various fields of people's life, and it has been very mature and can effectively store and transmit data. Moreover, there are still various researches on image recognition, the core of this technology. The algorithm is mainly based on computer technology to obtain the target image for different scene categories, thus completely replacing the traditional classification form. Because of the limitations of traditional identification technology, there are some problems in the actual use. It does not depend on the prior knowledge requirements and can carry out complex feature space division. In this paper, an image recognition computer system is established by introducing an improved neural network algorithm. The algorithm is designed and tested, and the results show it has lower image recognition error rate. Subsequently, this research result is applied to the actual scene for testing. The test results show that the improved neural network optimization algorithm can make the extracted features more accurately expressed in the image processing, which is more effective than the traditional algorithm. [ABSTRACT FROM AUTHOR]

This paper describes the development of a new reference controller framework for fixed and floating offshore wind turbines that greatly facilitates controller tuning and represents standard industry practices. The reference wind turbine controllers that are most commonly cited in the literature have been developed to work with specific reference wind turbines. Although these controllers have provided standard control functionalities, they are often not easy to modify for use on other turbines, so it has been challenging for researchers to run representative, fully dynamic simulations of other wind turbine designs. The Reference Open-Source Controller (ROSCO) has been developed to provide a modular reference wind turbine controller that represents industry standards and performs comparably to or better than existing reference controllers. The formulation of the ROSCO controller logic and tuning processes is presented in this paper. Control capabilities such as tip speed ratio tracking generator torque control, minimum pitch saturation, wind speed estimation, and a smoothing algorithm at near-rated operation are included to provide modern controller features. A floating offshore wind turbine feedback module is also included to facilitate growing research in the floating offshore arena. All of the standard controller implementations and control modules are automatically tuned such that a non-controls engineer or automated optimization routine can easily improve the controller performance. This article provides the framework and theoretical basis for the ROSCO controller modules and generic tuning processes. Simulations of the National Renewable Energy Laboratory (NREL) 5MWreference wind turbine and International Energy Agency 15MW reference turbine on the University of Maine semisubmersible platform are analyzed to demonstrate the controller's performance in both fixed and floating configurations, respectively. The simulation results demonstrate ROSCO's peak shaving routine to reduce maximum rotor thrusts by over 10% compared to the NREL 5MWreference wind turbine controller on the land-based turbine and to reduce maximum platform pitch angles by nearly 30% when using the platform feedback routine instead of a more traditional low-bandwidth controller. [ABSTRACT FROM AUTHOR]

This paper presents a simulation scheme for water distribution systems based on loop flows algorithms. Water networks are large scale and non-linear systems. The operational control of such system has posed difficulties in the past to the human operator that had to take the right decisions, such as pumping more water or closing a valve, within a short period of time and quite frequently in the absence of reliable measurement information such as pressure and flow values. Computer simulations of such systems have alleviated these difficulties. They allowed 'what-if' scenarios to be run by the human operator, giving him the possibility to know in advance the operational problems that can arise in the real-life networks due to malfunctions of valves or burst in pipes. However these computer simulations are generally relying on solving a system of equations and the nodal heads variables have been the most frequently used variables in these computer simulations. More recently the numerical algorithms based on loop corrective flows have started to receive more attention due to the reduce size of the matrixes involved in the numerical computations, which relies on the loop structure of the water network rather than the nodes. This can improve the numerical properties of the algorithms that model the real-time behaviour of the network. It is shown that such an efficient simulation scheme can be developed in the context of the loop flows algorithms and some problems that appear from doing this are successfully solved. [ABSTRACT FROM AUTHOR]

This paper proposes the Marine Predators algorithm (MPA) linked with the reservoir simulation model and considering sedimentation, in order to improve reservoir rule curves. The release criteria of the hedging rule (HR) and standard operating policy (SOP) were investigated in this study. The results showed that the patterns of the new optimal rule curves from the MPA technique considering sedimentation and using HR were practically useful in this study, as were the patterns of the new optimal rule curves using SOP and those of the existing rule curves. Furthermore, the new optimal rule curves using HR criteria were able to alleviate both water scarcity and excess water situations better than both existing rule curves and optimal rule curves using SOP in terms of minimal average water shortage. The new curves reduced minimal average water shortage by 53% and excess release water deficit by 19%, whereas the frequency of water shortage term was increased by 3%. The results of rule curves efficiency from MPA were higher than GA and FPA techniques in terms of providing solutions. There was a significant difference in the efficiency of water problem alleviation between considering and not considering sedimentation. It can be concluded that the MPA linked with reservoir simulation using HR criteria and considering sedimentation can be used to find optimal rule curve solutions effectively. [ABSTRACT FROM AUTHOR]

In this paper, a symplectic algorithm is utilized to investigate constrained Hamiltonian systems. However, the symplectic method cannot be applied directly to the constrained Hamiltonian equations due to the non-canonicity. We firstly discuss the canonicalization method of the constrained Hamiltonian systems. The symplectic method is used to constrain Hamiltonian systems on the basis of the canonicalization, and then the numerical simulation of the system is carried out. An example is presented to illustrate the application of the results. By using the symplectic method of constrained Hamiltonian systems, one can solve the singular dynamic problems of nonconservative constrained mechanical systems, nonholonomic constrained mechanical systems as well as physical problems in quantum dynamics, and also available in many electromechanical coupled systems. [ABSTRACT FROM AUTHOR]

In order to obtain the initial condition of the traffic accident and reconstruct the collision process of motorcycle and car in reverse, this paper proposes a multi-objective optimisation algorithm based on the combination of NSGA-II and surrogate model. Aiming at a collision accident between a car and motorcycle, a simulation model of the human-vehicle-road coupling system is established. First, select the optimisation variables, optimisation objectives and constraints, define the variable range, and through the Opt-LHD to sample the optimisation variables. Then, obtain and fit the sample data, and take the sample data as the initial conditions into the established motorcycle traffic accident simulation model. Next, obtain the simulation data of the optimisation variables, and establish the response surface model of the optimisation objectives in MATLAB. Finally, using NSGA-II algorithm, the individuals in the population are solved based on the response surface model in turn, the Pareto solution set is obtained, and a group of satisfactory solutions is taken back to the simulation model for verification of the optimisation model, so as to prove the correctness of the satisfactory solution, and then the initial conditions of the collision between the motorcycle and the car are obtained. [ABSTRACT FROM AUTHOR]

Product line testing is challenging due to the potentially huge number of configurations. Several approaches have tackled this challenge; most of them focused on reducing the number of tested products by selecting a representative subset. However, little attention has been paid to product line test optimization using test results, while tests are executed. This paper aims at optimizing the testing process of product lines by increasing the fault detection rate. To this end we propose a dynamic test prioritization approach. In contrast to traditional static test prioritization, our dynamic test prioritization leverages information of tests being executed in specific products. Processing this information, the initially prioritized tests are rearranged in order to find non-discovered faults. The proposed approach is valid for any kind of product lines, but we have adapted it to the context of configurable simulation models, an area where testing is especially time-consuming and optimization methods are paramount. The approach was empirically evaluated by employing two case studies. The results of this evaluation reveal that the proposed test prioritization approach improves both the static prioritization algorithm and the selected baseline technique. The results provide a basis for suggesting that the proposed dynamic test prioritization approach is appropriate to optimize the testing process of product lines. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The stability of cable-net structures depends on the prestress of the system. Due to the large displacement and mutual effect of the cables, it is difficult to simulate the tensioning process and control the forming accuracy. The Backward Algorithm (BA) has been used to simulate the tensioning process. The traditional BA involves complicated and tedious matrix operations. In this paper, a new numerical method based on the Vector Form Intrinsic Finite Element (VFIFE) method is proposed for BA application. Moreover, the tensioning sequence of a complex cable-net structure is introduced. Subsequently, a new approach for BA application in the simulation of the tensioning process is presented, which combines the VFIFE approach and the notion of form-finding. Finally, a numerical example is simulated in detail and the results of different tensioning stages are analyzed to verify the feasibility of the proposed approach. This study provides a significant reference for improving the construction control and forming accuracy of complex prestressed cable-net structures. [ABSTRACT FROM AUTHOR]

With the development of 3D technology, the construction of 3D motion model has become an important research issue in many fields, such as virtual reality, animation and robot vision. In this paper, a 3D motion model construction method based on simulation algorithm is proposed. Firstly, the gravity simulation algorithm is used to simulate the trajectory of the object, and the position data of multiple moments with numerical accuracy of 10cm are obtained. Then, the interpolation algorithm is used to interpolate these position data into continuous curves, and the object trajectory with 1cm accuracy is obtained. Finally, we integrate the obtained trajectory data with 3D modeling software to generate a 3D motion model with real motion effect. The experimental results show that this method has high accuracy and reliability in constructing 3D motion model, and is suitable for many fields. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most of the existing smart grid authentication schemes suffer from high computational costs and complex authentication processes, which are not suitable for resource-constrained smart devices in the smart grid, while some lightweight schemes suffer from various security vulnerabilities, none of which can achieve the required trade-off between efficiency and security. To solve these problems, this paper designed an enhanced provably secure smart grid lightweight anonymous authentication scheme based on elliptic curve encryption algorithm. Introducing auxiliary validator to get rid of the reliance on the power supplier in the authentication phase and to achieve mutual authentication between the gateway and the smart meter under the condition of protecting the true identity of the smart meter. In addition, the scheme can trace and revoke the identity of malicious smart meters through pseudo identity. Through the security analysis under the random oracle model and simulation tool ProVerif verifies that the proposed scheme has high security properties. Performance analysis shows that the proposed scheme can meet the requirements for security and efficiency in smart grid environment. [ABSTRACT FROM AUTHOR]

The indirect identification of the geometric errors (GEs) in the rotary axis of a machine tool yields six equivalent GEs (EGEs) that are position-dependent; through an analytical proof, this study demonstrates that these errors also represent four position-independent GEs of the axis. Moreover, a novel algorithm using ball bar measurements to calculate the EGEs of a nutating rotary B -axis and a rotary C -axis is presented herein. This paper also presents a new analytical solution for the actual inverse kinematics of a nonorthogonal five-axis machine tool; this solution is used for GE compensation. The presented algorithms are implemented in a self-developed software that alters the nominal numerical control code in order to eliminate GEs. The compensation accuracy and efficiency are tested using a simulation system. The results demonstrate that the proposed compensation algorithm eliminates all identified GEs. Lastly, a cutting test executed on a machine confirms that the proposed algorithms considerably improve machining accuracy. [Display omitted] • Nonorthogonal five-axis machine tool (NOFAMT) with a nutating rotary B -axis. • Equivalent errors (EGEs) of rotary axes. • Proof that six EGEs of a rotary axis represent ten GEs of this axis. • Identification of the EGEs of the rotary axes measured indirectly. • Compensation for GEs of the NOFAMT analytically. [ABSTRACT FROM AUTHOR]

Studies on load flow in electrical distribution system have always been an area of interest for research from the previous few years. Various approaches and techniques are brought into light for load flow studies within the system and simulation tools are being used to work out on varied characteristics of system. This study concentrates on these approaches and the improvements made to the already existing techniques considering time and the algorithms complexity. Also, the paper explains the network reconfiguration (NR) techniques considered in reconfiguring radial distribution network (RDN) to reduce power losses in distribution system and delivers an approach to how various network reconfiguration techniques support loss reduction and improvement of reliability in the electrical distribution network. [ABSTRACT FROM AUTHOR]

Integrated energy systems (IESs) can improve energy efficiency and reduce carbon emissions, essential for achieving peak carbon emissions and carbon neutrality. This study investigated the characteristics of the CHP model considering P2G and carbon capture systems, and a two-stage robust optimization model of the electricity-heat-gas-cold integrated energy system was developed. First, a CHP model considering the P2G and carbon capture system was established, and the electric-thermal coupling characteristics and P2G capacity constraints of the model were derived, which proved that the model could weaken the electric-thermal coupling characteristics, increase the electric power regulation range, and reduce carbon emissions. Subsequently, a two-stage robust optimal scheduling model of an IES was constructed, in which the objective function in the day-ahead scheduling stage was to minimize the start-up and shutdown costs. The objective function in the real-time scheduling stage was to minimize the equipment operating costs, carbon emission costs, wind curtailment, and solar curtailment costs, considering multiple uncertainties. Finally, after the objective function is linearized with a ψ-piecewise method, the model is solved based on the C&CG algorithm. Simulation results show that the proposed model can effectively absorb renewable energy and reduce the total cost of the system. [ABSTRACT FROM AUTHOR]

The "shoeboxing" algorithm is a simplification approach capable of converting a building of any shape into a representative shoebox, with the aim of speeding-up Urban Building Energy Modeling simulations. The procedure works towards accurately predicting both annual energy needs and hourly thermal loads, going beyond the preliminary assessment of buildings' thermal performance at city-scale. Furthermore, the simplification has been particularly developed to work with buildings of complex geometry considering adjacencies and obstructions. After a first validation performed for stand-alone buildings, in this paper, the capabilities of the algorithm are evaluated at district-level on fictional parametrically generated layouts and in different climatic conditions. As a whole, the shoeboxing algorithm properly predicted both heating and cooling needs at building-level in all the considered climatic conditions, yielding annual differences within ±10% and ± 20%, respectively for cooling and heating. Moreover, both annual and hourly deviations showed to be similar in the different climates considered, suggesting that the simplification can be reliably employed worldwide. Finally, the thermal simulation time has been reduced up to 36 times. • District-scale validation of an algorithm for Urban Building Energy Modeling • Buildings are converted in shoeboxes including adjacencies and urban context • The simplification is validated on 64 districts of 324 buildings in three climates • Heating and cooling needs are predicted with differences within ±20% and ± 10% • The algorithm reduced the thermal simulation time up to 36 times [ABSTRACT FROM AUTHOR]

In recent years, cross-platform co-simulation has become an important development direction of the real-time simulation of power systems. Model segmentation is at the core of the realization of cross-platform joint simulation and parallel real-time simulation of these systems. In essence, it is based on the deep application of a system-decoupling algorithm. In order to solve problems that a single interface cannot, it considers the data interaction of large- and small-step systems at the same time This paper proposes an improved joint-simulation strategy based on the model-segmentation method for the cross-platform joint-simulation technology of a large-scale, flexible direct-power grid sent by the wind farms of RT-lab and Hypersim. Firstly, by studying several common interface algorithms in the current project, the adaptability of different interface algorithms is analyzed. Secondly, the problem of high-frequency oscillation caused by the inductance-decoupling algorithm is improved, and an improved segmentation-model algorithm is proposed. Finally, according to the adaptability, each interface algorithm is applied to the wind-power-based, flexible direct-transmission, dual-platform simulation model that was built for this study. The simulation results verify the feasibility of the improved interface in system decoupling and platform interfacing, and indicate the significantly improved accuracy and stability of the system. [ABSTRACT FROM AUTHOR]

The novel rotating rectangular aperture (RRA) system provides a good solution for space-based, large-aperture, high-resolution imaging tasks. Its imaging quality depends largely on the image synthesis algorithm, and the mainstream multi-frame deblurring approach is sophisticated and time-consuming. In this paper, we propose a novel full-aperture image synthesis algorithm for the RRA system, based on Fourier spectrum restoration. First, a numerical simulation model is established to analyze the RRA system's characteristics and obtain the point spread functions (PSFs) rapidly. Then, each image is used iteratively to calculate the increment size and update the final restored Fourier spectrum. Both the simulation's results and the practical experiment's results show that our algorithm performs well in terms of objective evaluation and time consumption. [ABSTRACT FROM AUTHOR]

The genomic scale metabolic networks of the microorganisms can be constructed based on their genome sequences, functional annotations, and biochemical reactions, reflecting almost all of the metabolic functions. Mathematical simulations of metabolic fluxes could make these functions be visualized, thereby providing guidance for rational engineering design and experimental operations. This review summarized recently developed flux simulation algorithms of microbial systems. For the single microbial systems, the optimal planning algorithm has low complexity because there is no interaction between microorganisms, and it can quickly simulate the stable metabolic states through the pseudo-steady hypothesis. Besides, the experimental conditions of single microbial systems are easier to reach or close to the optimal states of simulation, compared with polymicrobial systems. The polymicrobial culture systems could outcompete the single microbial systems as they could relieve metabolic pressure through metabolic division, resource exchange, and complex substrate coutilization. Besides, they provide varieties of intracellular production environments, which render them the potential to achieve efficient bioproduct synthesis. However, due to the quasi-steady hypothesis that restricts the simulation of the dynamic processes of microbial interactions and the algorithm complexity, there are few researches on simulation algorithms of polymicrobial metabolic fluxes. Therefore, this review also analyzed and combed the microbial interactions based on the commonly used hypothesis of maximizing growth rates, and studied the strategies of coupling interactions with optimal planning simulations for metabolism. Finally, this review provided new insights into the genomic scale metabolic flux simulations of polymicrobial systems. [ABSTRACT FROM AUTHOR]

This article presents a distributed model predictive control algorithm including fuzzy negotiation among subsystems and a dynamic setpoint generation method, applied to a simulated sewerage network. The methodology considers WWTP as an additional objective of control. To improve the performance of a DMPC using a hydraulic model for prediction, a more detailed model has been considered including suspended solids concentration (TSS). The results obtained with the proposed methodology have been validated on a benchmark simulation model for sewer systems developed to test and compare methodologies, showing good performance. [ABSTRACT FROM AUTHOR]

A novel hybrid transient adaptive subcell (TAS) direct simulation Monte Carlo (DSMC) algorithm is proposed to simulate rarefied gas flows in a wide range of Knudsen numbers. It is derived and analyzed by using a time and spatial discrete operator approach based on the non-homogeneous, local N-particle kinetic equation, first proposed by Stefanov. The novel algorithm is considered together with the standard and hybrid collision algorithms built on uniform grids. The standard collision algorithm uses only one single scheme—the NoTime Counter (NTC), or the Generalized or Simplified Bernoulli trials (GBT, SBT). The hybrid algorithm employs NTC, GBT, or SBT depending on the instantaneous number of particles in the considered cell. The novel hybrid TAS algorithm benefits from both the hybrid collision approach and the transient adaptive subcell grid covering each collision cell to achieve a uniform accuracy of order O(Δt, Δr) independently of the number of particles in the cells. To this aim, a local time step is defined as coherent with the TAS grid covering the corresponding collision cell. The novel hybrid TAS algorithm is tested on two-dimensional benchmark problems: supersonic rarefied gas flow past of a flat plate under an angle of incidence and pressure-driven gas flow in a microchannel. The results obtained by the hybrid TAS algorithm are compared to those obtained by the standard algorithms and the available Bird's DS2V code using nearest neighbor collision and open-source OpenFOAM code. The comparison shows an excellent accuracy of the suggested algorithm in predicting the flow field. [ABSTRACT FROM AUTHOR]

In order to alleviate the great harm caused by traditional fossil energy to the natural environment, photovoltaic power generation has become a good choice for human available resources. In this article, the photovoltaic array is used as the object of research, and the maximum power point tracking (MPPT) control algorithm of the photovoltaic array is used as the main research line. The photovoltaic array simulation model was simulated using the MATLAB/SIMULINK software to simulate the output characteristics. The following results were obtained. When the illumination intensity is fixed, the particle swarm optimization (POS) algorithm reaches the end condition after 0.015 s, stops the iteration, the photovoltaic array reaches the maximum power point, and the maximum power value found is 10.704 W, which is less than 1 W from the theoretical power value 10 W, with an error of 0.64%; this shows that the MPPT management approach based on the optimization algorithm has a positive effect. In the event of an immediate change in light, the particle optimization algorithm is re-optimized, with an optimization time of only 0.004 s, that is, it reaches the maximum power point and runs stably. This requires approximately 97% less time than the shock observation method and about 75% less time than the method required to increase the variable transmission, and the power curve is an almost smooth straight line. This shows that the PSO algorithm is able to quickly and accurately control the maximum power point in the event of a sudden change in lighting. The method optimized in this article is superior to the method of observing shocks in the case of stable lighting and sudden changes in light intensity also array and relatively high performance as well as high practical value. [ABSTRACT FROM AUTHOR]

Lagrangian particle tracking schemes allow a wide range of flow and transport processes to be simulated accurately, but a major challenge is numerically implementing the inter-particle interactions in an efficient manner. This article develops a multi-dimensional, parallelized domain decomposition (DDC) strategy for mass-transfer particle tracking (MTPT) methods in which particles exchange mass dynamically. We show that this can be efficiently parallelized by employing large numbers of CPU cores to accelerate run times. In order to validate the approach and our theoretical predictions we focus our efforts on a well known benchmark problem with pure diffusion, where analytical solutions in any number of dimensions are well established. In this work, we investigate different procedures for tiling the domain in two and three dimensions, (2-d and 3-d), as this type of formal DDC construction is currently limited to 1-d. An optimal tiling is prescribed based on physical problem parameters and the number of available CPU cores, as each tiling provides distinct results in both accuracy and run time. We further extend the most efficient technique to 3-d for comparison, leading to an analytical discussion of the effect of dimensionality on strategies for implementing DDC schemes. Increasing computational resources (cores) within the DDC method produces a trade-off between inter-node communication and on-node work. For an optimally subdivided diffusion problem, the 2-d parallelized algorithm achieves nearly perfect linear speedup in comparison with the serial run up to around 2700 cores, reducing a 5-hour simulation to 8 seconds, while the 3-d algorithm maintains appreciable speedup up to 1700 cores. [ABSTRACT FROM AUTHOR]

The purpose of this study is to examine road environment data, tracking control and spatial cognition algorithms, and virtual navigation and simulation modeling tools. In this article, I cumulate previous research findings indicating that urban transportation systems leverage autonomous vehicle decision-making algorithms, deep learning-based sensing technologies, and interactive data visualization tools. I contribute to the literature on vehicle and pedestrian detection tools, image processing and motion planning algorithms and deep learning and edge intelligence technologies by showing that autonomous vehicle routing and navigation develop on sensor data fusion algorithms, urban computing technologies, and data modeling tools. Throughout June 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “moral decision-making” + “autonomous mobility technologies” + “route planning algorithms,” “simulation modeling tools,” and “intelligent traffic monitoring systems.” As I inspected research published in 2022, only 174 articles satisfied the eligibility criteria. By removing controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 37, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, MMAT, and SRDR. [ABSTRACT FROM AUTHOR]

System-level simulations of sensors are valuables for optimizing device and system parameters and validating data-processing algorithms. Nowadays, the tendency of multi-sensor systems has increased the necessity of this type of simulations. In this work, a generic model for simulations of a sensor at system level is proposed. It allows implementing the complete behavior of a sensor including uncertainties and tolerances, nonlinearities, various noises and so on. This sensor model can be customized by extracting information from a datasheet, a power spectral density graph or an Allan deviation graph. Then, simulations of a single sensor or multi-sensor systems can be performed and data fusion algorithms can be tested for different applications. [ABSTRACT FROM AUTHOR]

In the field of radar reconnaissance, unsupervised recognition of radar signals is a particularly important method for classifying different signals and estimating signal parameters. For untagged signals, a new unsupervised recognition method based on the time‐frequency (TF) analysis and Multi‐view Low‐Rank Sparse Subspace Clustering (MLRSSC) is proposed. Specifically, the authors use image wavelet decomposition to create four views for TF images (TFIs), which introduces the concept of multi‐view subspace clustering into radar signal recognition. Compared with other advanced clustering algorithms, the authors' algorithm pays more attention to combining the inherent properties of TFIs. Aiming at the unique ridgeline distribution phenomenon of TFI, a multi‐block technology capable of robustly distributing TFIs subspaces is proposed. Then, Multi‐block and MLRSSC are combined to propose the Multi‐block Joint Multi‐view Subspace Clustering (M‐MLRSSC). The simulation shows that in the TFIs data set with a certain signal‐to‐noise ratio, M‐MLRSSC has obtained superior results, which is better than a variety of advanced subspace clustering algorithms. [ABSTRACT FROM AUTHOR]

Finite mixture models are a widely known method for modelling data that arise from a heterogeneous population. Within the family of mixtures of regression models, mixtures of linear mixed models have also been applied in different areas since, besides taking into consideration the heterogeneity in the population, they also allow to take into account the correlation between observations from the same individual. One of the main issues in mixture models concerns the estimation of the parameters. Maximum likelihood estimation is one of the most used methods in the estimation of the parameters for mixture models. However, the maximization of the log-likelihood function in mixture models is complex, producing in many cases infinite solutions whereby the maximum likelihood estimator may not exist, at least globally. For this reason, it is common to resort to iterative methods, in particular to the Expectation-Maximization (EM) algorithm. However, the slow convergence and the selection of initial values are two of biggest issues of the EM algorithm, the reason why some modified versions of this algorithm have been developed over the years. In this article we compare the performance of the EM, Classification EM (CEM) and Stochastic EM (SEM) algorithms in the estimation of the parameters for mixtures of linear mixed models. In order to evaluate their performance, we carry out a simulation study and a real data application. The results show that the CEM algorithm is the least computationally demanding algorithm, although the three algorithms provide similar maximum likelihood estimates for the parameters. [ABSTRACT FROM AUTHOR]

Computer experiments are often used as inexpensive alternatives to real‐world experiments. Statistical metamodels of the computer model's input‐output behavior can be constructed to serve as approximations of the response surface of the real‐world system. The suitability of a metamodel depends in part on its intended use. While decision makers may want to understand the entire response surface, they may be particularly keen on finding interesting regions of the design space, such as where the gradient is steep. We present an adaptive, value‐enhanced batch sequential algorithm that samples more heavily in such areas while still providing an understanding of the entire surface. The design points within each batch can be run in parallel to leverage modern multi‐core computing assets. We illustrate our approach for deterministic computer models, but it has potential for stochastic simulation models as well. [ABSTRACT FROM AUTHOR]

• A novel robust mixed-norm algorithm with affine projection (APRMNA) is proposed. • The APRMNA based on the generalized maximum correntropy criterion (APRMNA-GMC) is proposed. • The simplified APRMNA-GMC (S-APRMNA-GMC) is proposed. In this paper, a novel adaptive filtering algorithm combining both affine projection (AP) method and robust mixed-norm algorithm (RMNA) is proposed, which is called APRMNA. The AP method has the feature of fast convergence speed under colored inputs and the RMNA exhibits stable performance against noise interference. The proposed APRMNA algorithm not only combines the advantages of both AP and RMNA but also utilizes the ℓ 2 -norm constraint on the weight vector to avoid matrix inversion. Then, applying the generalized maximum correntropy (GMC) criterion to APRMNA, we also develop the APRMNA-GMC. Finally, a simplified version of the proposed APRMNA-GMC (S-APRMNA-GMC) is derived to reduce the computation complexity. Numerical simulations for system identification show that the proposed algorithms outperform other AP-type algorithms. [ABSTRACT FROM AUTHOR]

For the scenario where the overall layout is known and the obstacle distribution information is unknown, a dynamic path planning algorithm combining the A* algorithm and the proximal policy optimization (PPO) algorithm is proposed. Simulation experiments show that in all six test environments, the proposed algorithm finds paths that are on average about 2.04% to 5.86% shorter compared to the state‐of‐the‐art algorithms in the literature, and reduces the number of training epochs before stabilization from tens of thousands to about 4000. [ABSTRACT FROM AUTHOR]

In the far-field region, the wave propagation is modeled by the WKB solution described, while a successively cascaded Earth-ionosphere waveguide model is developed to determine the EM interference at the junction of the land and sea. Numerical modeling and simulation of many electromagnetic (EM) and multiphysics problems encounter challenges from geometrical and material complexities, large-scale issues, and multiscale issues. The mode-conversion coefficient ELF wave propagation over a mixed propagation path has been theoretically studied and applied to interpret the EM interference at various propagation distances. [Extracted from the article]

The increasing complexity of advanced devices and systems increases the scale of mathematical models used in computer simulations. Multiparametric analysis and study on long-term time intervals of large-scale systems are computationally expensive. Therefore, efficient numerical methods are required to reduce time costs. Recently, semi-explicit and semi-implicit Adams–Bashforth–Moulton methods have been proposed, showing great computational efficiency in low-dimensional systems simulation. In this study, we examine the numerical stability of these methods by plotting stability regions. We explicitly show that semi-explicit methods possess higher numerical stability than the conventional predictor–corrector algorithms. The second contribution of the reported research is a novel algorithm to generate an optimized finite-difference scheme of semi-explicit and semi-implicit Adams–Bashforth–Moulton methods without redundant computation of predicted values that are not used for correction. The experimental part of the study includes the numerical simulation of the three-body problem and a network of coupled oscillators with a fixed and variable integration step and finely confirms the theoretical findings. [ABSTRACT FROM AUTHOR]