Parametric Generation Research Articles

Generating Game Levels by Defining Player Experiences

This paper proposes a novel pipeline for generating game levels that elicit predefined emotional experiences from players. Our approach uses evolutionary algorithms alongside data-driven persona agents, predictive emotional models, a PCG parametric level generator, and a newly defined language for the clear and computable definition of player emotional experiences: ExpREx (Experience Regular Expressions). Using these components, we evolve game levels to match the player experience goals specified using the ExpREx language, aiming to create levels that evoke specific emotional experiences for different subsets of players. The efficacy of our method was validated through a user study involving 101 participants, whose continuous annotations of emotional experience were collected and analyzed to assess the congruence between the actual emotional responses elicited and those targeted by our pipeline. We found that 93.73% of the ExpREx goals targeted were also reported by the user study subjects.

Traffic Awareness-Based Target Wake Time Scheduling in 802.11ax WLANs

In recent years, the rapid growth of mobile communication has led to the continuous expansion of network scale, and the energy consumption of wireless communication has also increased rapidly. Under the high-density networking conditions, frequent communication activities greatly increase the energy consumption. In order to reduce channel competition, optimize resource scheduling and reduce equipment power consumption, the target wake-up time (TWT) mechanism in IEEE 802.11ax system plays an important role through timely scheduling and transmission. To improve channel efficiency for multi-user (MU) transmission and tackle with complicated traffic conditions, this paper presents a traffic awareness-based TWT scheduling scheme (TAT). The traffic is predicted and classified using a spatio-temporal series model for traffic-based TWT parameter generation. Subsequently, the time–frequency scheduling table is developed to adaptively assign Resource Units (RUs) and time slices to active STAs. Simulation results show that TAT can guarantee the Quality of Service (QoS) under dynamic changes in power service and delay requirements while reducing power consumption, effectively meeting the energy-saving demands in the intelligent access scenarios for power terminals.

Rapid Optimization of Active Disturbance Rejection Controller Parameters for Quadrotor UAVs Using Kriging Surrogate Modeling

The Active Disturbance Rejection Controller (ADRC), celebrated for its superior resistance to interference, presents itself as an exemplary solution for the development of control systems that are designed to accommodate substantial changes in payload weight for quadrotor Unmanned Aerial Vehicles (UAVs) and to endure robust side winds along with other challenging operational scenarios. Despite the inherent complexity due to the numerous parameters required for the configuration of the ADRC, an innovative method utilizing the Kriging surrogate optimization algorithm has been introduced to automate and expedite the generation of these controller parameters. The development of the ADRC begins with the dynamics model of the quadrotor UAV, followed by the identification of key design parameters. These parameters are then rapidly optimized through the Kriging surrogate optimization algorithm. The controller’s effectiveness is confirmed by implementing the ADRC on Pixhawk flight control hardware, with a comparative analysis of the attitude response under various operating conditions, thereby validating the ADRC’s superior anti-disturbance performance.

SCARF: Scalable Continual Learning Framework for Memory‐efficient Multiple Neural Radiance Fields

AbstractThis paper introduces a novel continual learning framework for synthesising novel views of multiple scenes, learning multiple 3D scenes incrementally, and updating the network parameters only with the training data of the upcoming new scene. We build on Neural Radiance Fields (NeRF), which uses multi‐layer perceptron to model the density and radiance field of a scene as the implicit function. While NeRF and its extensions have shown a powerful capability of rendering photo‐realistic novel views in a single 3D scene, managing these growing 3D NeRF assets efficiently is a new scientific problem. Very few works focus on the efficient representation or continuous learning capability of multiple scenes, which is crucial for the practical applications of NeRF. To achieve these goals, our key idea is to represent multiple scenes as the linear combination of a cross‐scene weight matrix and a set of scene‐specific weight matrices generated from a global parameter generator. Furthermore, we propose an uncertain surface knowledge distillation strategy to transfer the radiance field knowledge of previous scenes to the new model. Representing multiple 3D scenes with such weight matrices significantly reduces memory requirements. At the same time, the uncertain surface distillation strategy greatly overcomes the catastrophic forgetting problem and maintains the photo‐realistic rendering quality of previous scenes. Experiments show that the proposed approach achieves state‐of‐the‐art rendering quality of continual learning NeRF on NeRF‐Synthetic, LLFF, and TanksAndTemples datasets while preserving extra low storage cost.

Research on Digital Construction Technology for Special-Shaped Shell Pipe Structures

The aesthetic appeal of special-shaped shell pipe structures makes them highly favored by architects and holds promising prospects for various applications. In the detailed design stage, NURBS curves should be divided into multiple continuous arcs due to the limitations of current steel structure fabrication equipment, which can only accommodate pipes with equal-curvature bends. However, the traditional manual fitting methods suffer from several issues including low efficiency, undercutting at the interface, poor smoothness of curves, and lack of control over tolerances. Furthermore, the weaker out-of-plane stiffness and utilization of bending arc pipe sections pose significant challenges in terms of spatial positioning and installation accuracy that need to be addressed. The study focuses on addressing these challenges by investigating digital construction technology for special-shaped shell pipe structures and developing a parametric algorithm that enables automatic fitting of spatial NURBS curves into multiple arcs, thereby achieving seamless curve fitting. A post-processing program was developed to enable the parametric generation of fabrication and installation information for structural members, which can be seamlessly integrated into the BIM database. Finally, structural position control technology is proposed to improve assembly efficiency and ensure consistency between the completed construction state and the design shape. The above digital construction technology has been applied in projects such as the Haihua Island International Conference Center. It can provide complete technical solutions for modeling of special-shaped shell pipe structures, including establishment of a member information database, fabrication at the workshop and installation on site, construction organization management, as well as installation accuracy control.

Investigation of heat generation and radiation effects on boundary layer flow of Prandtl liquid with Cattaneo–Christov double diffusion models

In this analysis, a bidirectional stretched sheet is used to produce a 3D\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal{D}$$\\end{document} flow of Prandtl liquid with improve mass diffusion and heat conduction models. This study advances knowledge of magnetohydrodynamics, radiation impacts, and heat production in fluid dynamics and transport processes. The Prandtl fluid model is critical for modeling non-Newtonian fluids, capturing its viscoelastic features, and allowing for precise simulation in industrial applications. It gives a mathematical foundation for analyzing complex fluid behaviors, which is necessary for optimizing operations utilizing such fluids. It uses the boundary layer method to simplify the fundamental equations and Cattaneo–Christov double diffusion models. The optimal homotopy analysis method is used to solve nonlinear ODEs caused by non-dimensional similarity variables. This investigation undertakes the calculation of drag coefficient for surfaces mass transfer rates and heat transfer rates proximate to the solid boundary. Furthermore, it conducts a comprehensive analysis of the influences exerted by various parameters on concentration and temperature profiles employing graphical representations for a rigorous examination. The Prandtl fluid model describes the viscoelastic characteristics of non-Newtonian fluids through constitutive equations, dimensional evaluation, and numerical simulations, which are frequently validated by experiments. The results show that changes in thermal and concentration relaxation parameters are accompanied by a decline in temperature. Temperature field rises as the thermal radiation parameter and heat generation increases. The work's novelty consists in its advanced modeling of Prandtl non-Newtonian fluids via Cattaneo–Christov double diffusion models, which incorporate magnetohydrodynamics and radiation effects and use optimal homotopy analysis for accurate parametric analyses of heat and mass transfer.

10 W, 2 mJ-level ns all-fiber amplifier at 2.8 µm.

In this Letter, we demonstrate a single-stage erbium-doped fluoride fiber amplifier composed of two spliced large core fibers with respective diameters of 85 and 130 µm. An optical parametric generator (OPG) operating at a 5 kHz repetition rate, and providing ∼2 ns pulse duration, and an average output power of 500 mW at a wavelength of 2.8 µm was used as a seed source. This nanosecond amplifier configuration achieved an average output power of 10 W with a record pulse energy of 2 mJ, corresponding to 1 MW of output peak power. The maximum slope efficiency of this hybrid amplifier is 21% with respect to the total incident pump power at 940 nm. The presented master-oscillator power-amplifier (MOPA) configuration shows its potential for further power and energy scaling.

Public Authentic-Replica Sampling Mechanism in Distributed Storage Environments

With the rapid development of wireless communication and big data analysis technologies, the storage of massive amounts of data relies on third-party trusted storage, such as cloud storage. However, once data are stored on third-party servers, data owners lose physical control over their data, making it challenging to ensure data integrity and security. To address this issue, researchers have proposed integrity auditing mechanisms that allow for the auditing of data integrity on cloud servers without retrieving all the data. To further enhance the availability of data stored on cloud servers, multiple replicas of the original data are stored on the server. However, in existing multi-replica auditing schemes, there is a problem of server fraud, where the server does not actually store the corresponding data replicas. To tackle this issue, this paper presents a formal definition of authentic replicas along with a security model for the authentic-replica sampling mechanism. Based on time-lock puzzles, identity-based encryption (IBE) mechanisms, and succinct proof techniques, we design an authentic replica auditing mechanism. This mechanism ensures the authenticity of replicas and can resist outsourcing attacks and generation attacks. Additionally, our schemes replace the combination of random numbers and replica correspondence tables with Linear Feedback Shift Registers (LFSRs), optimizing the original client-side generation and uploading of replica parameters from being linearly related to the number of replicas to a constant level. Furthermore, our schemes allow for the public recovery of replica parameters, enabling any third party to verify the replicas through these parameters. As a result, the schemes achieve public verifiability and meet the efficiency requirements for authentic-replica sampling in multi-cloud environments. This makes our scheme more suitable for distributed storage environments. The experiments show that our scheme makes the time for generating copy parameters negligible while also greatly optimizing the time required for replica generation. As the amount of replica data increases, the time spent does not grow linearly. Due to the multi-party aggregation design, the verification time is also optimal. Compared to the latest schemes, the verification time is reduced by approximately 30%.

Homotopic dynamic solution of hydrodynamic nonlinear natural convection containing superhydrophobicity and isothermally heated parallel plate with hybrid nanoparticles

Abstract The purpose of this work is to investigate the effect of thermal radiation on convective heat transfer for an electrically conducting hybrid nanofluid moving perpendicularly through a microchannel with parallel plates heated under isothermal conditions, while subjected to a transverse magnetic field. In this case, one surface exhibited a superhydrophobic slip and temperature jump, whereas the others did not. The objective of the study was to determine the impacts of thermal radiation, heat generation, and magnetism on the volume flow rate, velocity, and bulk temperature when either surface is heated by a constant wall temperature. Our findings reveal that the radiation parameter significantly influences both the Nusselt number and skin friction differently depending on the surface conditions, while also reducing flow rate and bulk temperature. The heat generation parameter similarly affects these variables but varies with the type of surface being heated. Additionally, both the velocity and temperature profiles increase with the porosity parameter and heat generation coefficient, regardless of the heated surface. Statistical analysis confirms the significance of magnetic and heat generation parameters in determining skin friction and the Nusselt number, and streamlines and isotherms illustrating the effects of thermal radiation and magnetism on two different hybrid nanofluids when heated on nonslip and superhydrophobic surfaces were provided. These insights provide valuable information for the design and maintenance of mini- and microdevices in the fields of nanoscience and nanotechnology.

Performance studies on group-velocity-matched femtosecond optical parametric generation

We present a comparative study and detailed characterization of high-power femtosecond optical parametric generation (OPG) at 10 MHz repetition rate in the near and mid-infrared by exploiting near-zero group-velocity-mismatch (GVM) in the nonlinear crystals of PPLN and MgO:PPLN. Using a microchip-started amplified Mamyshev fiber oscillator delivering 198 fs pulses at 1064 nm as the pump source and deploying 19-mm-long PPLN and 42-mm-long MgO:PPLN as gain media, we study in detail the influence of crystal length and pump pulse duration on femtosecond group-velocity-matched interaction in the OPG process. The OPG source is tunable across 1445–1577 nm in the signal and 3318–4412 nm in the idler, and can provide average output powers of up to 439 mW in the signal at 1530 nm and 197 mW in the idler at 3550 nm, at slope efficiencies of ∼50% and ∼20%, respectively. Signal pulses as short as 275 fs are obtained using the shorter crystal, while longer signal pulses with similar output powers are generated with the longer crystal. Experimental results are supported by theoretical simulations, providing good agreement. The OPG source exhibits excellent power stability with high spatial quality of M2<1.5 in the signal and idler beams.

AI-optimized elliptic curve with Certificate-Less Digital Signature for zero trust maritime security

The proliferation of sensory applications has led to the development of the Internet of Things (IoT), which extends connectivity beyond traditional computing platforms and connects all kinds of everyday objects. Marine Ad Hoc Networks are expected to be an essential part of this connected world, forming the Internet of Marine Things (IoMaT). However, marine IoT systems are often highly distributed, and spread across large sparse areas which makes it challenging to implement and manage centralized security measures. Despite some ongoing efforts to establish network connectivity in such environment, securing these networks remains an unreached goal. The use of Certificate-Less Digital Signatures (CLDS) with Elliptic Curve Cryptography (ECC) shows great promise in providing secure communication in these networks and achieving zero trust IoMaT security. By eliminating the need for certificates and associated key management infrastructure, CLDS simplifies the key management process. ECC also enables secure communication with smaller key sizes and faster processing times, which is crucial for resource-limited IoMaT devices. In this paper, we introduce CLDS using ECC as a means of securing IoT networks in a marine environment, creating a zero trust security framework for Internet of Marine Things (IoMaT). To increase security and robustness of the framework, we optimize the ECC parameters using two vital artificial intelligence algorithms, namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Evaluation results demonstrate a reduction in ECC parameter generation time by over 40% with GA optimization and 20% with PSO optimization. Additionally, the computational cost and memory usage for major ECC attacks increased significantly by up to 40% and 67% for Rho attacks, 34% and 53% for brute-force attacks, and 30% and 67% for improved hybrid attacks, respectively.

High-efficiency mid-infrared CW-seeded optical parametric generation in PPLN waveguide

Mid-infrared optical sources have been extensively used in a variety of applications, including gas sensing and metrology, by exploiting the fingerprint fundamental vibrational absorption bands of molecules in this spectral region. Parametric frequency conversion techniques, such as optical parametric generation (OPG) and optical parametric oscillator (OPO), represent common methodologies for mid-infrared generation. Notably, continuous-wave (CW)-seeded OPG exhibits superior stability and performance compared to conventional OPG. Here, we present a simple method for mid-infrared source generation based on femtosecond OPG in periodically poled lithium niobate (PPLN) waveguides. In addition to its robustness and simplicity, mid-infrared CW-seeded OPG features a high quantum conversion efficiency of 46.5% and a low threshold. The phase of CW-seeded OPG was passively referenced to the CW laser, making it suitable for dual-comb applications. This system shows great promise in the miniaturization of mid-infrared combs and will be applied to non-laboratory applications, including open-path atmospheric gas sensing and portable environmental monitoring.

Accelerating large-scale multi-scalar multiplication in Zk-SNARK through exploiting its multilevel parallelism

In the context of zk-SNARK, MSM emerges as a major computational bottleneck, particularly due to its high computational and memory overhead. In this work, we exploit multiple levels of parallelism to effectively accelerate largescale MSM in zk-SNARKs. Firstly, a distributed parameter generation method is proposed in this paper to replace that of centralized method. Based on this methodology, the paper realizes a system with extraordinary scalability, capable of computing large-scale MSMs. Subsequently, the approach presented in this paper elevates the computation of Bellperson, the most prominent zero-knowledge proof system in real-world applications, from a single-node computation to a clustering mode, significantly enhancing its computational performance – a crucial advancement for practical applications. Finally, we implement a multi-level, fully parallelised MSM computing system by leveraging hierarchical sub-task partitioning and cross-node communication optimization, thereby thoroughly exploiting parallelism at diverse granularities. Experimental results show that in the cluster scenario, the proposed approach achieves acceleration ratios of approximately 3.60 and 6.50 times compared to the cutting-edge heterogeneous version Bellperson in dual-node and quad-node settings, respectively. On a single node, the proposed optimization approach achieves an acceleration ratio of 1.38 times compared to the current State-of-the-Art MSM calculation module of cuZK, outperforms the industry-popular Bellman by 186 times and the Bellperson by 1.96 times.

Vector competence of Aedes albopictus field populations from Reunion Island exposed to local epidemic dengue viruses

Dengue virus (DENV) is the most prevalent mosquito-borne Flavivirus that affects humans worldwide. Aedes albopictus, which is naturally infected with the bacteria Wolbachia, is considered to be a secondary vector of DENV. However, it was responsible for a recent DENV outbreak of unprecedented magnitude in Reunion Island, a French island in the South West Indian Ocean. Moreover, the distribution of the cases during this epidemic showed a spatially heterogeneous pattern across the island, leading to questions about the differential vector competence of mosquito populations from different geographic areas. The aim of this study was to gain a better understanding of the vector competence of the Ae. albopictus populations from Reunion Island for local DENV epidemic strains, while considering their infection by Wolbachia. Experimental infections were conducted using ten populations of Ae. albopictus sampled across Reunion Island and exposed to three DENV strains: one strain of DENV serotype 1 (DENV-1) and two strains of DENV serotype 2 (DENV-2). We analyzed three vector competence parameters including infection rate, dissemination efficiency and transmission efficiency, at different days post-exposition (dpe). We also assessed whether there was a correlation between the density of Wolbachia and viral load/vector competence parameters. Our results show that the Ae. albopictus populations tested were not able to transmit the two DENV-2 strains, while transmission efficiencies up to 40.79% were observed for the DENV-1 strain, probably due to difference in viral titres. Statistical analyses showed that the parameters mosquito population, generation, dpe and area of sampling significantly affect the transmission efficiencies of DENV-1. Although the density of Wolbachia varied according to mosquito population, no significant correlation was found between Wolbachia density and either viral load or vector competence parameters for DENV-1. Our results highlight the importance of using natural mosquito populations for a better understanding of transmission patterns of dengue.

Nonlinear parametric generation and optical vortex transfer in graphene ensemble under Landau quantization

We explore the dynamics of nonlinear parametric generation and light beam propagation in a Landau-quantized graphene structure with three energy levels interacting with two laser pulses, utilizing the Maxwell-Bloch equations. By applying a laser field to one transition of the graphene sample while keeping the second beam initially absent, the distinctive preparation of the graphene sample, coupled with its weak interaction with laser radiation, results in the parametric generation of a new laser beam in a different transition. We investigate the influence of diverse system parameters on both the efficiency of the generated beam and the propagation dynamics of both beams. Our findings reveal that manipulating these parameters can induce oscillations in the intensity of propagated beams, mitigate absorption losses during propagation allowing for earlier relaxation, and enhance the efficiency of energy transfer from the initial to the generated beam. Additionally, we demonstrate the transfer of optical vortices within the graphene ensemble by introducing an optical vortex to the initial beam. This scheme holds promise for applications in high-dimensional quantum information processing.

Locally verifiable approximate multi-member quantum threshold aggregation digital signature scheme

Locally verifiable aggregate signature primitives can reduce the complexity of aggregate signature verification by computing locally open algorithms to generate auxiliary parameters. However, the breakthrough results of quantum computers at this stage indicate that it will be possible for quantum computers to break through the security of traditional hardness-based aggregated signature schemes. In order to solve the above problems, this paper proposes for the first time a new locally verifiable class of multi-member quantum threshold aggregated digital signature scheme based on the property that the verification of quantum coset states is a projection on the trans-subspace. Combined with the idea of auxiliary parameter generation in traditional locally verifiable aggregated signatures, it makes the current stage of threshold quantum digital signatures realize the aggregated features, and reduces the complexity of the verification of aggregated signatures while realizing post-quantum security. In addition, the verification of the signature key (quantum state) of the signature members does not require measurement operations, and the generated signatures are classical, so the communication between the trusted third center (TC), the set of signature members, the classical digital signature verifier (CV), and the third-party trusted aggregation generator (TA) are all classical, simplifying the communication model. In the performance analysis we make this quantum aggregation signature scheme more flexible as well as less quantum state preparation compared to other schemes.

Longitudinal propagation of aquatic disturbances following the largest wildfire recorded in New Mexico, USA

Wildfire disturbance propagation along fluvial networks remains poorly understood. We use incident, atmospheric, and water-quality data from the largest wildfire in New Mexico’s history to quantify how this gigafire affected surface runoff processes and mobilized wildfire disturbances into fluvial networks after burning 1382 km2. Surface runoff post-fire increased compared to pre-fire conditions, and precipitation events that are frequently observed in the affected watershed (<2-year recurrence) and fell during the post-fire first rainy season resulted in uncorrelated, less frequently observed runoff events (10-year recurrence). Besides these shifts in runoff generation, the magnitude and fluctuation of daily water quality parameters and relevant ecosystem processes also shifted over multiple months, even at sites located >160 km downstream of the burn perimeter. Our findings emphasize the need to incorporate spatially resolved longitudinal sampling designs into wildfire water quality research and highlight the spatiotemporal co-dependency among atmospheric, terrestrial, and aquatic processes in defining the net outcome of wildfire disturbance propagation along impacted fluvial networks.

HAPI-DE: Differential evolution with hierarchical archive based mutation strategy and promising information

Differential Evolution (DE), as a population-based meta-heuristic global optimization technique, has shown excellent performance in handling optimization problems in continuous spaces. Despite its effectiveness, the DE algorithm suffers from shortcomings such as complexity of parameter selection and limitations of the mutation strategy. Therefore, this paper presents a new strategy for generating trial vectors based on a hierarchical archive, which integrates promising information during evolution with current populations to obtain a good perception of the objective landscape. Moreover, to mitigate mis-scaling by scale factor, an adaptive parameter generation mechanism with hierarchical selection (APSH) is proposed. Furthermore, a novel population diversity metric technique and a restart mechanism based on wavelet functions is introduced in this paper. Comparative experiments were conducted to evaluate the performance of the proposed algorithm using 100 benchmark functions from the CEC2013, CEC2014, CEC2017, and CEC2022 test suites. The results demonstrate that the HAPI-DE algorithm outperforms or is on par with 6 recent powerful DE variants. Additionally, HAPI-DE was utilized in parameter extraction for the photovoltaic model STP6-120/36. The findings suggest that our algorithm, HAPI-DE, demonstrates competitiveness when compared to the 6 other DE variants.

Research on the Parametric Generation Method of Model with Multi-Information for the Wooden Architectural Heritage

ABSTRACT There are many traditional wooden buildings with complex structural systems, which leads to tedious modelling processes, and challenges to achieve multidisciplinary collaboration between architecture and structure. This study establish a multi-information model using information and parameterization methods. First, this study identifies typical traditional Chuan-Dou style wood frames in Jingdezhen as an example and uses the parameterization method to quickly generate building information models based on the composition rules of traditional wooden buildings using the Revit-Dynamo platform. Second, by embedding mechanical parameters in the model components, the building and structure information are integrated into a multidimensional integrated model. Finally, a method for applying the generated multi-information model in the project is discussed. This building and structure multi-information model verifies the rationality of the component structure in the building model in real time, thereby optimizing the design scheme and achieving the full-scale recording of the detailed statistics of the building and structural information of the components. These research results have significantly improved the modelling speed of traditional wooden buildings, simplified the tedious process from architectural design to structural calculation, and provided an important research foundation for the intelligent and digital protection of architectural heritage.

Magnetohydrodynamics bio-convection flow at Casson fluid stagnation point in porous medium: Cross-diffusion effect and heat production

This study examines the effect of heat production and radiation absorption on the magnetohydrodynamic Casson fluid flow at the stagnation point in a porous medium. We convert the group of fluid flow equations, which are non-linear partial differential equations with suitable boundary constraints, into a set of non-linear ordinary differential equations using similarity transformations. The homotopy analysis method (HAM) solves the converted system of ordinary differential equations. We draw graphs for numerous values of non-dimensional parameters and tables of surface drag force, rates of heat transfer, and mass transfer to analyze the relationship between velocity field, temperature field, concentration field, and other essential parameters involved in the study. We have proven that the Dufour number, radiation parameter, and heat generation parameter elevate the fluid temperature, whereas the magnetic parameter lowers it. The Casson fluid parameter, buoyancy force parameter, and mixed convection parameter all promote fluid movement throughout the flow field. The presented tabular data allows us to see the trend of heat and mass transfer rates, as well as drag force rates, against important parameters, enhancing our understanding of these rates.