Generation Algorithm Research Articles

A novel algorithm for generating random fiber distributions for fiber reinforced composites based on lattice partition

AbstractThis paper propose a random fiber distribution generation algorithm based on lattice partition, which addresses the issue of limited increase in fiber volume fraction caused by the random movement of fibers in previous methods. By partitioning the representative volume element (RVE) window into lattices and moving fibers directionally, the proposed algorithm significantly increases the number of fibers within the RVE window. The generated fiber distribution achieves a fiber volume fraction of up to 80%, with an average execution time of 243.4 s. The influence of algorithm parameters on the randomness of fiber distribution is examined through statistical analysis of the generated distributions. Finite element models with various parameters are established to predict the elastic properties of the composites. The average relative error between the predicted values and the experimental results is 8.6%, demonstrating the effectiveness of the proposed algorithm. This algorithm offers a simple and effective approach for generating fiber distributions in the numerical study of the micromechanics of composites.Highlights A new lattice‐partition method is proposed to generate fiber distributions. The fiber volume fraction of generated fiber distribution is up to 80%. The randomness of fiber distribution is analyzed through statistical analysis. The algorithm predicts elastic properties with an average relative error of 8.6%.

Cloud detection sample generation algorithm for nighttime satellite imagery based on daytime data and machine learning application.

Highly accurate nighttime cloud detection in satellite imagery is challenging due to the absence of visible to near-infrared (0.38-3μm, VNI) data, which is critical for distinguishing clouds from other ground features. Fortunately, Machine learning (ML) techniques can more effectively leverage the limited wavelength information and show high-accuracy cloud detection based on vast sample volume. However, accurately distinguishing cloud pixels solely through thermal infrared bands (8-14μm, TIR) is challenging, and acquiring numerous, high-quality and representative samples of nighttime images for ML proves to be unattainable in practice. Given the thermal infrared radiation transmission process and the fact that daytime and nighttime have the same source of radiance in TIR, we propose a sample generation idea that uses daytime images to provide samples of nighttime cloud detection, which is different from the traditional sample construction methods (e.g., manually label, simulation and transfer learning method), and can obtain samples effectively. Based on this idea, nighttime cloud detection experiments were carried out for MODIS, GF-5 (02) and Himawari-8 satellites, respectively. The results were validated by the Lidar cloud product and manual labels and show that our nighttime cloud detection result has higher accuracy than MYD35 (78.17%) and the ML model trained by nighttime manual labels (75.86%). The accuracy of the three sensors is 82.19%, 88.71%, and 79.34%, respectively. Moreover, we validated and discussed the performance of our algorithm on various surface types (vegetation, urban, barren and water). The results revealed that the accuracy of the three sensors over barren was found to be poor and varied with surface types but overall high. Our study can provide a novel perspective on nighttime cloud detection of muti-spectral satellite imagery.

Enhancing wildfire detection: a novel algorithm for controllable generation of wildfire smoke images

Background The lack of wildfire smoke image data is one of the most important factors hindering the development of image-based wildfire detection. Smoke image generation based on image inpainting techniques is a solution worthy of study. However, it is difficult to generate smoke texture with context consistency in complex backgrounds with current image inpainting methods. Aims This work aims to provide a wildfire smoke image database for specific scenarios. Methods We designed an algorithm based on generative adversarial networks (GANs) to generate smoke images. The algorithm includes a multi-scale fusion module to ensure consistency between the generated smoke and backgrounds. Additionally, a local feature-matching mechanism in the discriminator guides the generator to capture real smoke’s feature distribution. Key results We generated 13,400 wildfire smoke images based on forest background images and early fire simulation from the Fire Dynamics Simulator (FDS). Conclusions A variety of advanced object detection algorithms were trained based on the generated data. The experimental results confirmed that the addition of the generated data to the real datasets can effectively improve model performance. Implications This study paves a way for generating object datasets to enhance the reliability of watchtower or satellite wildfire monitoring.

Emergency Dispatch Strategy Considering Spatiotemporal Evolution of Power Grid Failures Under Typhoon Conditions

The increasing climate-change-induced tropical cyclone phenomena expose the power grid to significant operation risks by disrupting the normal operation of grid components. This paper considers the failure mechanism with respect to critical grid components and reveals a novel spatiotemporal revolution of grid failures during the passage of typhoons. Based on the spatiotemporal evolution of grid failures, a threshold-based emergency scenario set and the corresponding two-stage robust optimization-based emergency dispatch model are developed. The robust emergency dispatch strategy is obtained using a column-and-constraint generation (C&CG) algorithm. The simulation results show that the proposed robust emergency dispatch strategy can guarantee a considerable degree of robustness under multiple emergency scenarios driven by uncertain typhoon conditions.

Mitigating Risk and Emissions in Power Systems: A Two-Stage Robust Dispatch Model with Carbon Trading

The large-scale integration of renewable energy sources is crucial for reducing carbon emissions. Integrating carbon trading mechanisms into electricity markets can further maximize this potential. However, the inherent uncertainty in renewable power generation poses significant challenges to effective decarbonization, renewable energy accommodation, and the security and cost efficiency of power system operations. In response to these challenges, this paper proposes a two-stage robust power dispatch model that incorporates carbon trading. This model is designed to minimize system operating costs, risk costs, and carbon trading costs while fully accounting for uncertainties in renewable energy output and the effects of carbon trading mechanisms. This model is solved using the column-and-constraint generation algorithm. Validation of an improved IEEE 39-bus system demonstrates its effectiveness, ensuring that dispatch decisions are both robust and cost-efficient. Compared to traditional dispatch models, the proposed model significantly reduces system risk costs, enhances the utilization of renewable energy, and, through the introduction of a ladder carbon trading mechanism, achieves substantial reductions in carbon emissions during system operation.

Enhancing power quality in distributed generation systems using three-phase shunt active power filter

The current paper presents a new reference current generation algorithm for shunt active power filter (SAPF) combined with distributed generation systems using the instantaneous power theory (IPT) which is organized in two main blocks. The first is the synchronization technique; we propose a new quasi open loop technique that combines a Decoupled double self-tuning filter (DDSTF) and a single-phase frequency locked loop (FLL). The DDSTF is a new technique that extracts and separates the positive and negative sequence voltage under highly unbalanced and distorted conditions with no need to the symmetrical component and/or signals delay. The FLL achieve the grid frequency adaptation. Unlike the existing frequency estimators, this technique is motivated by its simplicity and the immunity to the voltage signal amplitude and phase variation or disturbance, which is a great advantage. The second is an ADALINE in power space to separate the fundamental harmonic from the distortion harmonics of the measured currents. The effectiveness of the proposed OLS technique in their application to the SAPF is validated by simulation.

An efficient certificateless anonymous signcryption communication scheme for vehicular adhoc network

Certificateless public key infrastructure (PKI) avoids the key escrow problem associated with identity-based PKI and has recently been widely employed in anonymous communication schemes for vehicular adhoc networks (VANETs). In existing certificateless anonymous signcryption schemes for VANETs, vulnerabilities such as potential attacks involving the substitution of pseudonyms and the forging of pseudonymous public-private key pairs exist due to the lack of “identity-key binding” and “non-linearity processing of public-private key pairs.” To address this issue, we propose an improved certificateless anonymous signcryption scheme based on elliptic curve cryptography. The scheme incorporates bilinear pairing as one of the authentication mechanisms, designs pseudonym generation algorithms and public-private key pair structures, and introduces a pseudonym verification mechanism. The correctness of the scheme is proven under the random oracle model, and its security is extensively demonstrated through detailed discussions on its confidentiality, authentication, unforgeability, anonymity, and traceability. Furthermore, the time and space complexity of the scheme are calculated. By comparing with recently published certificateless signcryption schemes, it is shown that the proposed scheme offers higher security with smaller computational and communication overheads. This certificateless vehicular network signcryption algorithm provides an efficient encryption solution for anonymous communication in vehicular networks, thereby ensuring the rapid development of secure technology for intelligent connected vehicle super terminals.

Low-carbon economic optimization strategy for industrial loads in parks considering source-load-price multivariate uncertainty

A low-carbon economic optimization method is proposed for industrial loads in parks considering multivariate uncertainty. Model of dynamic load node carbon intensity is proposed considering the uncertainty of clean energy and dynamic conventional units carbon intensity based on system currents. The robustness is improved based on multi-interval uncertainty set. The gap is filled by hybrid copula model with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) prices in the uncertainty model. The GARCH is used to built the marginal distribution function of prices, and a hybrid copula model is proposed to obtain the joint probability density function based on the weights calculated by Euclidean distance. The LNCI probability distributions of the regulation potential of loads are proposed with the virtual energy storage and the virtual power model to improve the descriptive ability of uncertainty based on the expansion and contraction functions. Finally, the typical scenarios are extracted based on Monte Carlo and Wasserstein measures. The Column sum Constraint Generation algorithm and strong duality theory are used to get the robust-stochastic optimization. The method proposed in the paper has a positive effect on enterprises to rationalize their production schedules, reduce electricity and carbon emissions costs and increase the revenue from participating in grid regulation.

Side-Scan Sonar Image Generation Under Zero and Few Samples for Underwater Target Detection

The acquisition of side-scan sonar (SSS) images is complex, expensive, and time-consuming, making it difficult and sometimes impossible to obtain rich image data. Therefore, we propose a novel image generation algorithm to solve the problem of insufficient training datasets for SSS-based target detection. For zero-sample detection, we proposed a two-step style transfer approach. The ray tracing method was first used to obtain an optically rendered image of the target. Subsequently, UA-CycleGAN, which combines U-net, soft attention, and HSV loss, was proposed for generating high-quality SSS images. A one-stage image-generation approach was proposed for few-sample detection. The proposed ADA-StyleGAN3 incorporates an adaptive discriminator augmentation strategy into StyleGAN3 to solve the overfitting problem of the generative adversarial network caused by insufficient training data. ADA-StyleGAN3 generated high-quality and diverse SSS images. In simulation experiments, the proposed image-generation algorithm was evaluated subjectively and objectively. We also compared the proposed algorithm with other classical methods to demonstrate its advantages. In addition, we applied the generated images to a downstream target detection task, and the detection results further demonstrated the effectiveness of the image generation algorithm. Finally, the generalizability of the proposed algorithm was verified using a public dataset.

Konnektor: A Framework for Using Graph Theory to Plan Networks for Free Energy Calculations.

Alchemical free energy campaigns can be planned using graph theory by building networks that contain nodes representing molecules that are connected by possible transformations as edges. We introduce Konnektor, an open-source Python package, for systematically planning, modifying, and analyzing free energy calculation networks. Konnektor is designed to aid in the drug discovery process by enabling users to easily setup free energy campaigns using complex graph manipulation methods. The package contains functions for network operations including concatenation of networks, deletion of transformations, and clustering of molecules along with a framework for combining these tools with existing network generation algorithms to enable the development of more complex methods for network generation. A comparison of the various network layout features offered is carried out using toy data sets. Additionally, Konnektor contains visualization and analysis tools, making the investigation of network features much simpler. Besides the content of the package, the paper also offers application examples, demonstrating how Konnektor can be used and how the different networks perform from a graph theory perspective. Konnektor is freely available via GitHub at https://github.com/OpenFreeEnergy/konnektor under the permissive MIT License.

Discrete Element Modeling of Irregular-Shaped Particles Using an Automatic Generation Algorithm-Based Multi-Sphere Model

Within the Discrete Element Method (DEM) framework, the multi-sphere model capable of creating irregular-shaped particles by several sub-spheres attracts increasing attention. Traditionally, the creation of irregular-shaped particles using the multi-sphere model requires manual determination of the position and diameter of each sub-sphere, which extremely increases the difficulty and workload of DEM modeling of irregular-shaped particles. To tackle this issue, a new automatic generation algorithm that helps to create arbitrary-shaped multi-sphere particles automatically was developed in this study. The quality of created multi-sphere particles can be easily controlled by adjusting the number of sub-spheres and size factor referred to as the size ratio of the smallest sub-sphere to the largest sub-sphere. To verify the effectiveness of the newly developed automatic generation algorithm, two verification tests were conducted in this study: (i) The packing behavior of irregular-shaped particles in a rectangular container and (ii) the mechanical behavior of irregular-shaped particles under the uniaxial compression. The fill heights of the simulations were in line with those of the experiments with an error of less than 8%. The mechanical behavior of irregular-shaped particles under the uniaxial compression can also be well reproduced. Besides, the influence of important parameters on simulation accuracy was also investigated. The results demonstrated that the newly developed automatic generation algorithm can accurately create complex-shaped particles with a small number of sub-spheres. Increasing the number of sub-spheres will improve the simulation accuracy but at the expense of computational efficiency. A larger size factor leads to higher computational efficiency in DEM simulation but is accompanied by a significant loss of accuracy.

Automatic Experimental Numerosity Generation and Numerical Training for Rodents.

Non-symbolic stimuli representing numerosities are invariably associated with continuous magnitudes, complicating the interpretation of experimental studies on numerosity perception. Although various algorithms for experimental numerosity generation have been proposed, they do not consider the quantifiable distribution of values of continuous magnitudes and the degree of numerosity-magnitudes association. Consequently, they cannot thoroughly exclude the possibility of magnitudes integration or strategy switch between different magnitudes in numerical stimulus perception. Here, we introduce a protocol for numerosity generation, animal training, and behavior outcomes analysis that takes the aforementioned issues into consideration. This protocol has been applied to rodents and is applicable to other animals in numerosity studies. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Algorithm for generating non-symbolic numerical stimuli Alternate Protocol: General algorithm for generating non-symbolic numerical stimuli Basic Protocol 2: Numerical training and testing for rodents.

Underwater virtual exploration of the ancient port of Amathus

Underwater cultural heritage sites, spanning from submerged settlements to ancient ports and shipwrecks, captivate researchers and the public, providing insight into civilizations along coastlines and riverbanks. However, their accessibility and exploration are hindered by the sea’s physical barrier. Virtual Reality (VR) offers a transformative solution by providing digital accessibility to these underwater artifacts, enabling immersive exploration without physical limitations. VR enables people to embark on virtual tours of these sites, fostering a deeper appreciation of maritime archaeology and cultural heritage. Yet, fully realizing VR’s potential in underwater environments poses challenges, such as realistic virtual reconstruction and accurate simulation of marine life and coral reefs. Photogrammetry emerges as an effective technique for creating detailed 3D models, although underwater conditions often hinder quality outcomes. To address these challenges, our work focuses on digital underwater cultural heritage, presenting a gamified VR exploration of the ancient harbor of Amathus in Cyprus. Through photogrammetry, our VR environment enables users to explore and interact with the historic site seamlessly. Integrated guided tours, procedural generation, and machine learning algorithms enhance realism and user engagement. Evaluation through user studies demonstrates high-quality VR experiences with minimal discomfort, highlighting the efficacy and potential impact of our approach in enhancing underwater exploration and conservation efforts.

UUD-Fusion: An unsupervised universal image fusion approach via generative diffusion model

Image fusion is a classical problem in the field of image processing whose solutions are usually not unique. The common image fusion methods can only generate a fixed fusion result based on the source image pairs. They tend to be applicable only to a specific task and have high computational costs. Hence, in this paper, a two-stage unsupervised universal image fusion with generative diffusion model is proposed, termed as UUD-Fusion. For the first stage, a strategy based on the initial fusion results is devised to offload the computational effort. For the second stage, two novel sampling algorithms based on generative diffusion model are designed. The fusion sequence generation algorithm (FSGA) searches for a series of solutions in the solution space by iterative sampling. The fusion image enhancement algorithm (FIEA) greatly improves the quality of the fused images. Qualitative and quantitative evaluations of multiple datasets with different modalities demonstrate the great versatility and effectiveness of UUD-Fusion. It is capable of solving different fusion problems, including multi-focus image fusion task, multi-exposure image fusion task, infrared and visible fusion task, and medical image fusion task. The proposed approach is superior to current state-of-the-art methods. Our code is publicly available at https://github.com/xiangxiang-wang/UUD-Fusion.

Playback method for dynamic star map simulation by fusing cosmic background radiation information

In this study, a playback method for dynamic star map simulation fusing cosmic background radiation information was proposed to improve the playback speed of star maps. The image distortion effect before and after the fusion of star maps and cosmic background radiation was evaluated using peak signal-to-noise ratios. The performance of the proposed and the global catalog traversal generation algorithms without cosmic background radiation simulation capability, as well as the indicators, were experimentally compared. The results showed that the average refresh rates of the view cone filtering star map fast-generation algorithm and the fusion playback algorithm of the star map and cosmic background radiation were 74.2 and 73.7 Hz, respectively. Compared with the entire catalog traversal generation algorithm, the instantaneous stability of the single-frame star map was improved by 3.045 and 3.305 times, respectively, and the consumed time consistency was improved by 4.646 and 5.068 times, respectively.

Distributionally robust single machine scheduling with release and due dates over Wasserstein balls

Single machine scheduling aims at determining the job sequence with the best desired performance, and provides the basic building block for more advanced scheduling problems. In the present study, a single machine scheduling model with uncertain processing time is considered by incorporating the job release time and due date. The job processing time follows unknown probability distribution, and can be estimated via the historical data. To model the uncertainty, the processing time distribution is defined over a Wasserstein ball ambiguity set, which covers all feasible probability distributions within the confidence radius of the empirical distribution, known as the center of the ball. Then a data-driven distributionally robust scheduling model is constructed with individual chance constraints. In particular, two equivalent reformulations are derived with respect to the ℓ1-norm and ℓ2-norm metrics of the Wasserstein ball, namely, a mixed-integer linear programming and a mixed-integer second order cone programming model, respectively. To accelerate the solving of large-scale instances, a tailored constraint generation algorithm is introduced. In the numerical analysis, the proposed distributionally robust scheduling approach is compared with the state-of-the-art methods in terms of out-of-sample performance.

Automated Grasp Labeling and Detection Framework with Pixel-Level Precision

Due to the differences in shape, material, and color of objects, the detection of planar grasps by robots remains challenging. Traditional methods rely on discrete grasp configurations for annotation, ignoring many possible grasp configurations. This leads to poor network generalization, making it challenging to handle a diverse range of grasped objects. Manual reannotation to continuous labels can effectively address this issue but comes with a significant cost. Therefore, this paper proposes a Pixel-level Grasp framework. Firstly, APGLG can automatically generate pixel-level grasp labels and discrete labels into continuous labels, effectively increasing the information content of individual data and improving the network generalization performance. Then, we propose the Max-Grasp-Net, built on the Multi-axis Vision Transformer and Dynamic Convolution Decomposition, to construct a U-shaped Network structure. A grasp decoder is incorporated, and deep supervision is applied to enhance network generalization. On the Cornell dataset, we achieve the best results, a grasping accuracy of 99.55%, an average success rate of 97.92% in single-object grasping, and 95.83% in multi-object grasping. We verified the effectiveness of our label generation algorithms and network innovation through actual grasp experiments.

Combinatorial transition testing in dynamically adaptive systems: Implementation and test oracle

Due to the large number of possible interactions and transitions among features in dynamically adaptive systems, testing such systems poses significant challenges. To verify that such systems behave correctly, combinatorial interaction testing (CIT) can create concise test suites covering all valid pairs of features of such systems. While CIT claims to find all errors caused by two features, it does not cover certain errors occurring only for specific transitions between features. To address this issue we study the technique of Combinatorial Transition Testing (CTT), which includes both generation and detection of what we call behavioural transition errors. From an initial generation algorithm that combines both interaction and transition coverage but lacks scalability, we propose an optimised version that enables CTT even for hundreds of features. From a valid test suite covering all transitions, we complete our testing approach with a test oracle that detects all behavioural transition errors without any prior knowledge of the system’s behaviour. After a comprehensive analysis over a large number of feature models, we conclude that size of CTT-generated test suites and test effort needed to use our test oracle are linearly correlated to CIT-generated ones and that CTT grows logarithmically in the number of features.

Mapping Drug Terms via Integration of a Retrieval-Augmented Generation Algorithm with a Large Language Model

Mapping Drug Terms via Integration of a Retrieval-Augmented Generation Algorithm with a Large Language Model

Adaptive robust self‐scheduling for a wind‐based GenCo equipped with power‐to‐gas system and gas turbine to participate in electricity and natural gas markets

AbstractIntegrating renewable energy, particularly wind generation, into power systems brings significant uncertainty and intermittency, challenging generation companies (GenCos) to maintain economic operations. This paper proposes a strategy for a wind‐based GenCo equipped with a gas turbine and a power‐to‐gas (PtG) system to balance wind variability. The gas turbine offsets power shortfalls by consuming natural gas, while the PtG system absorbs excess wind energy, converting it to natural gas and injecting it into the natural gas network as a form of storage. The GenCo operates in day‐ahead electricity and natural gas markets and the real‐time electricity market, addressing uncertainties from wind output, energy prices, and natural gas access. A tri‐level adaptive robust optimization model is introduced for the GenCo’s short‐term operational scheduling. At the first level, decisions related to the GenCo's participation in the day‐ahead electricity and natural gas markets are made. The second level deals with determining the worst‐case realization of the uncertainties, constrained by the budget of uncertainty and the limited range of variations of uncertain variables. The third level sets the operational strategy on the actual day, considering power and gas balance, as well as constraints for the wind farm, gas turbine, and PtG unit. A column & constraint generation (C&CG) algorithm is used to solve the model. Numerical results demonstrate the model’s effectiveness in various case studies, showing that the GenCo can manage wind uncertainties, benefit from arbitrage opportunities in electricity and gas markets, and improve economic outcomes. This approach supports the broader integration of renewable energy into power systems.