Algorithm For Automatic Generation Research Articles

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.

Application of the Contemporary Psychometrics for Assessing Economic Literacy

Currently, new skills and various types of "new literacies" relevant to the modern world are becoming issues of growing importance. One of them is economic literacy; however, there are only few assessment instruments that fulfil the academic requirements for its assessment among university students. One of such internationally established instruments is the Test of Understanding in College Economics (TUCE), which is a popular tool in empirical studies of economic literacy in many countries around the world. Despite its advantages, the currently available version of the TUCE designed for American colleges back in 2006, is prone to cheating and provides limited opportunities for formative feedback. The purpose of this paper is to present the Updated Test of Understanding in College Economics (U-TUCE). In developing the U-TUCE, we utilized the capabilities of contemporary psychometrics, which offer sufficient advances in overcoming all limitations of the original TUCE mentioned before. First, we present a revised theoretical framework of the U-TUCE, highlighting that the test measures different types of mastery of economic literacy. Second, we describe the approaches used for modifying the TUCE items and developing new items. A half of the original test items have been replaced or redesigned to reflect the economic context that has changed since 2006. Third, we utilize the logic of automatic item generation algorithms to increaseg the level of test protection against cheating. We made all changes in such a way as to maintain comparability with the previous versions of the TUCE test if necessary. Finally, the use of the Item Response Theory (IRT) is paired up with that of Cognitive Diagnostic Modeling (CDM) to ensure the quality of the U-TUCE and enhance its formative value. We show that IRT can be used to estimate the construct as a whole (which is of interest to researchers, administrators, and policy makers), while CDM provides information relating to each of the construct components, which are of interest to educational practitioners and students themselves. The results of the data analyses show that the test can be used for both purposes simultaneously.

Species selection for automatic chemical kinetic mechanism generation

AbstractMany important chemical kinetic systems require detailed chemical kinetic models to resolve. These detailed kinetic models can involve thousands of species and hundreds of thousands of chemical reactions, making them difficult to construct by hand. Modern automatic mechanism generation algorithms can mostly be divided into two classes: rule and rate based. Rule‐based generators choose species based on user defined constraints on species and reaction classes. Rate‐based generators generate a much larger set of potentially important species and reactions and then choose which ones to add based on running simulations of species and reactions deemed important and calculating the flux to potentially important species. In principle, the latter is preferable, as it requires the user to make far fewer assumptions about what is important in the system. However, while the effectiveness of the rate‐based approach has been demonstrated in a wide variety of systems, it has also been demonstrated to have difficulty picking up important low‐flux chemistries. Here we present a discussion of the challenges associated with rate‐based mechanism generation and new algorithms that are able to efficiently mitigate these challenges improving species selection during mechanism generation in a set of case studies.

Finite element mesh transition for local–global modeling of composite structures

This study presents an automatic mesh generation algorithm designed to address computational challenges in simulating small-scale defects within large composite structures. The algorithm seamlessly transitions from a coarse mesh, corresponding to the global structure, to a highly refined mesh in targeted local regions of interest. The transition element number and shape can be adjusted by the specified parameters. Tailored to complement this method for non-homogeneous composite models, which include multiple materials such as cohesive layers representing interlayer properties, a volume fraction calculator is integrated to automatically assign the mixture material property in each transition element. Entire processes are fully automated using a MATLAB script, eliminating the need to open the FEA software interface. The validation studies of the reconstructed two-dimensional models, assembled with the wrinkle-defect model, demonstrate their feasibility. The performance of the model is examined in terms of strain and displacement at the connecting boundaries, load–displacement curve, and interlayer failure prediction. The mesh transition model achieves agreeable results compared to a fully fine mesh model, and a 92% reduction in computational time in stress analysis, showing the efficiency of the mesh transition for local–global modeling of composite structures.

Surveying More Than Two Decades of Music Information Retrieval Research on Playlists

In this paper, we present an extensive survey of music information retrieval (MIR) research into music playlists. Our survey spans more than 20 years, and includes around 300 papers about playlists, with over 70 supporting sources. It is the first survey that is self-contained in the sense that it combines all the different MIR research into playlists. It embraces topics such as algorithms for automatic generation, for automatic continuation, for assisting with manual generation, for tagging and for captioning. It looks at manually constructed playlists, both those that are constructed for and by individuals, and those constructed in collaboration with others. It covers ground-breaking research into enhancing playlists by cross-fading consecutive songs and by interleaving consecutive songs with speech, similar to what happens on a radio show. Most significantly, it is the first survey that can fully incorporate the paradigm shift that has taken place in the way people consume recorded music: the shift from physical media to music streaming. This has wrought profound changes in the size of music collections available to listeners and thus the algorithms that support the construction, curation and presentation of playlists and the methods adopted by users when they also construct, curate and listen to playlists.

Automatic Generation Algorithm of Movie and TV Scripts Based on ChatGPT

A movie or TV script is a meticulously crafted document that serves as the blueprint for a visual storytelling project. It outlines the dialogue, actions, and scene descriptions that will unfold on screen, guiding actors, directors, and crew members in bringing the story to life. Each script is divided into scenes and acts, with clear instructions on character entrances and exits, camera angles, and pacing. This paper introduces a novel framework for enhancing the quality of movie and TV scripts through the integration of the Combinational Multi-Stage Genetic Optimization (CMSGO) model with ChatGPT, a state-of-the-art language generation model. The CMSGO model utilizes iterative optimization techniques to systematically refine and enhance script elements such as coherence, dialogue flow, character development, and overall narrative structure. The proposed CMSGO model comprises the Combinational model with the genetic optimization function. The function CMSGO model examines the fitness function with the Multi-Stage Optimization process. The proposed CMSGO model uses the estimation of features in the Multi-stage optimization model with the computation of features related to the scripts. Through 20 generations of optimization, the CMSGO model demonstrates its effectiveness in improving script quality, as evidenced by a steady increase in average script quality scores. Additionally, the multi-stage optimization approach targets specific aspects of script quality, allowing for targeted adjustments to parameters related to character motivations, plot coherence, and tone. Viewer opinions further validate the efficacy of the generated scripts, with positive evaluations across various aspects such as audience engagement, coherence, emotional impact, and originality. The proposed framework offers a robust and data-driven approach to scriptwriting, enabling the creation of high-quality movie and TV scripts that captivate and resonate with audiences, thus enriching the overall viewing experience.

Resolution Enhancement by Variable Zoom Trajectory in X-Ray Computed Tomography

Flat objects like electronic boards are challenging samples for high-resolution X-ray computed tomography scanning because their largest dimension significantly limits the magnification using circular trajectory scans. One way to improve spatial resolution for such samples is to utilize variable zoom trajectory. During variable zoom trajectory scanning, the source-to-object distance changes during the 360° rotation to maximize the magnification in the projections. Here, we propose an automatic variable zoom trajectory generation algorithm for arbitrary object and region of interest (ROI). We analyze how such a trajectory can enhance resolution in different cases and how isotropic is the resolution in the reconstructed volume. We demonstrate that the resolution can be improved without destroying the sample. However, the improvement is manifested mainly in directions in which we achieved the highest magnification in the projection.

Poster Design Research Based on Deep Learning Automatic Image Generation Algorithm

Although this research has made significant progress in image generation models, they still face issues such as insufficient diversity of generated images, poor quality of high-resolution images, and the need for a large amount of training data for model optimization. This paper studies poster design based on deep learning automatic image generation algorithm, using a recursive supervised image generation algorithm framework of generative adversarial networks for multi-view image generation and super-resolution generation tasks of small sample digital poster images. Various improvements have been proposed to enhance the performance of the GAN network model for poster design image generation tasks. Based on experimental research, this paper’s model uses generative adversarial networks to distinguish randomly cropped low resolution and high-resolution poster blocks, ensuring that high-resolution posters maintain their original resolution canvas texture and brush strokes, effectively improving the automatic generation effect of poster images. The evaluation results show that the quantitative evaluation of the proposed algorithm model in knowledge management is distributed in a reasonable range, which indicates that the proposed algorithm model has good performance in knowledge management. The poster design model based on deep learning automatic image generation algorithm proposed in this paper has certain effects. In subsequent practice, the automatic image generation algorithm can be combined with practical needs to improve the efficiency and design effect of poster design.

Microstructure generation and full-field multi-scale analyses for short fiber reinforced thermoplastics: Application to PA66GF composites

Short Fiber Reinforced Thermoplastics (SFRTs) like PA66GF composites are highly heterogeneous materials with a rather complex microstructure such that the characterization and the prediction of their mechanical behavior remain quite challenging. So far, most of the research efforts have employed phenomenological and mean-field multi-scale models to deal with SFRTs. The present contribution rather focuses on a full-field multi-scale approach that incorporates advanced techniques in terms of microstructural representation and material modeling, allowing a deep insight of the dominating deformation mechanisms occurring in PA66GF composites. The proposed approach is based on an automatic periodic mesh generation algorithm for matrix-inclusion Representative Volume Elements (RVE) with randomly positioned fibers that follow a given Orientation Distribution Function (ODF). At the microscopic scale, while the fibers are assumed to be elastic, the behavior of the thermoplastic matrix is described by a phenomenological multi-mechanism constitutive model accounting for viscoelasticity, viscoplasticity and ductile damage. It results an advanced multi-scale model that enables to visualize the local deformation and degradation mechanisms occurring at the microscopic scale while simultaneously analyzing their influence on the macroscopic response of the composite upon monotonic, persistent and cyclic loading. The potential of the proposed approach is further evaluated by comparing the predicted responses against experimental data.

Proposal and Definition of an Intelligent Clinical Decision Support System Applied to the Prediction of Dyspnea after 12 Months of an Acute Episode of COVID-19.

Long COVID is a condition that affects a significant proportion of patients who have had COVID-19. It is characterised by the persistence of associated symptoms after the acute phase of the illness has subsided. Although several studies have investigated the risk factors associated with long COVID, identifying which patients will experience long-term symptoms remains a complex task. Among the various symptoms, dyspnea is one of the most prominent due to its close association with the respiratory nature of COVID-19 and its disabling consequences. This work proposes a new intelligent clinical decision support system to predict dyspnea 12 months after a severe episode of COVID-19 based on the SeguiCovid database from the Álvaro Cunqueiro Hospital in Vigo (Galicia, Spain). The database is initially processed using a CART-type decision tree to identify the variables with the highest predictive power. Based on these variables, a cascade of expert systems has been defined with Mamdani-type fuzzy-inference engines. The rules for each system were generated using the Wang-Mendel automatic rule generation algorithm. At the output of the cascade, a risk indicator is obtained, which allows for the categorisation of patients into two groups: those with dyspnea and those without dyspnea at 12 months. This simplifies follow-up and the performance of studies aimed at those patients at risk. The system has produced satisfactory results in initial tests, supported by an AUC of 0.75, demonstrating the potential and usefulness of this tool in clinical practice.

Design and Implementation of Automatic Generation Algorithm for Advertising Artistic Design Based on Neural Networks

Design and Implementation of Automatic Generation Algorithm for Advertising Artistic Design Based on Neural Networks

Automatic Generation Algorithm for Digital Sculpture Based on CAD and Machine Vision

Automatic Generation Algorithm for Digital Sculpture Based on CAD and Machine Vision

A Study on an Intelligent Algorithm for Automatic Test Paper Generation and Scoring in University English Exams

This paper mainly studied the automatic test paper generation and scoring problems in university English exams. Firstly, an automatic test paper generation model was established. Then, an improved genetic algorithm (IGA) was designed for intelligent test paper generation, and it was also used to automatically score answers to Chinese-to-English translation questions in terms of syntax and semantics. It was found that compared with the traditional GA and particle swarm optimization algorithm, the IGA method was faster in generating test papers, with an average generation time of 25 s, and had a higher success rate (94%), suggesting higher validity, and the difficulty and differentiation degrees of the test papers were closer to the preset values. The results of automatic scoring also had a correlation of more than 0.8 with the manual scoring results. The results prove the effectiveness of the automatic test paper generation and scoring method. It can be promoted and applied in practice to enhance the security and fairness of large-scale English exams, as well as achieve objectivity and consistency in scoring.

Automatic Medical Report Generation Based on Cross-View Attention and Visual-Semantic Long Short Term Memorys.

Automatic medical report generation based on deep learning can improve the efficiency of diagnosis and reduce costs. Although several automatic report generation algorithms have been proposed, there are still two main challenges in generating more detailed and accurate diagnostic reports: using multi-view images reasonably and integrating visual and semantic features of key lesions effectively. To overcome these challenges, we propose a novel automatic report generation approach. We first propose the Cross-View Attention Module to process and strengthen the multi-perspective features of medical images, using mean square error loss to unify the learning effect of fusing single-view and multi-view images. Then, we design the module Medical Visual-Semantic Long Short Term Memorys to integrate and record the visual and semantic temporal information of each diagnostic sentence, which enhances the multi-modal features to generate more accurate diagnostic sentences. Applied to the open-source Indiana University X-ray dataset, our model achieved an average improvement of 0.8% over the state-of-the-art (SOTA) model on six evaluation metrics. This demonstrates that our model is capable of generating more detailed and accurate diagnostic reports.

Design of A Support Automatic Generation Algorithm Based on Layer Information

3D printing technology as a new type of manufacturing technology application field is getting larger and larger, and it also plays an important role in the field of large-scale construction machinery maintenance. In this chapter, the algorithm of support automatic generation of STL model is studied, and the algorithm of support automatic generation is designed based on the layer contour information. The problem of identification of suspension area is solved by the method of intersecting and comparing the contours of adjacent layers, which provides the algorithm basis for the application of subsequent improvement technology in the field of maintenance.

Gene expression programming with dual strategies and neighborhood search for symbolic regression problems

As an evolutionary algorithm for automatic generation of computer programs, Gene expression programming (GEP) has received a lot of attention because it can generate concise and highly accurate solutions. However, GEP also has some unsatisfactory aspects, such as too many control parameters and a lack of balance between exploration and exploitation. Therefore, a new GEP with dual strategies and neighborhood search (DNSGEP) is proposed in this paper for symbolic regression problems. As the core part, a new multi-parent crossover operator with neighborhood search is proposed to replace the original transposition and recombination operators in GEP, which greatly reduces the excessive control parameters in GEP. To assist the multi-parent crossover operator in selecting parents, selection weight is used to assess the quality of individuals in the population. The multi-parent crossover operator combines selection weight to learn more from the better parents. In order to satisfy the different needs of global search and local search of the algorithm, two different crossover strategies are extended on the basis of the multi-parent crossover operator. Furthermore, a population distribution entropy strategy to GEP is proposed for determining when the algorithm should choose which crossover strategy to use. DNSGEP is compared with GEP and three GEP variants on 14 symbolic regression problems and 6 even parity problems. Experimental results show that DNSGEP has the success rate and accuracy that are fully superior to GEP, GEPADF and OGEP, not inferior to SLGEP.

Algorithm for Automatic Generation of Oil Painting Guidance in Colleges and Art Culture Video Based on Intelligent Brush

This paper proposes a computer-generated algorithm for college oil painting style guidance and art culture video automatic generation with intelligent brushes. This paper proposes to use the tensor eigenvector value of the smart brush as the brush direction to draw multiple layers of input video frames to effectively simulate the fluid feel of Van Gogh's oil paintings. At the same time, an improved local illumination model is proposed. mapping technology to enhance the paint layering of drawn video frames. Firstly, the multispectral PCA spectral reconstruction algorithm is used to reconstruct the spectral reflectance of the oil painting surface, and then the image information and color information of the oil painting are reproduced; secondly, the combination of physical BRDF and multispectral images is used to reproduce the spatial information and color information of oil painting samples.

Deep learning’s shallow gains: a comparative evaluation of algorithms for automatic music generation

Deep learning methods are recognised as state-of-the-art for many applications of machine learning. Recently, deep learning methods have emerged as a solution to the task of automatic music generation (AMG) using symbolic tokens in a target style, but their superiority over non-deep learning methods has not been demonstrated. Here, we conduct a listening study to comparatively evaluate several music generation systems along six musical dimensions: stylistic success, aesthetic pleasure, repetition or self-reference, melody, harmony, and rhythm. A range of models, both deep learning algorithms and other methods, are used to generate 30-s excerpts in the style of Classical string quartets and classical piano improvisations. Fifty participants with relatively high musical knowledge rate unlabelled samples of computer-generated and human-composed excerpts for the six musical dimensions. We use non-parametric Bayesian hypothesis testing to interpret the results, allowing the possibility of finding meaningful non-differences between systems’ performance. We find that the strongest deep learning method, a reimplemented version of Music Transformer, has equivalent performance to a non-deep learning method, MAIA Markov, demonstrating that to date, deep learning does not outperform other methods for AMG. We also find there still remains a significant gap between any algorithmic method and human-composed excerpts.

An automatic text generation algorithm of technical disclosure for catenary construction based on knowledge element model

Preparing technical disclosure documents has always been a labor-intensive task in the site management practice of current OCS (Overhead Contact System) project construction, and the overall information level of construction management is not high. As a result, the technical disclosure documents are of poor quality, which significantly affects the effective play of technical documents in guiding construction activities. In view of the above limitations, this paper proposes a text-generation method for OCS engineering technical disclosure based on a knowledge element model. By investigating the characteristics of OCS engineering technical knowledge, a representation model is established to model the technical knowledge from multiple sources, such as case data, standard specifications, and design data. As OCS engineering is highly standardized, we propose a case-rule hybrid reasoning model for the reuse of OCS engineering case knowledge. The mining and utilization of earlier engineering technical knowledge are made possible by similar retrieval of precedent situations and adaptive rules modification. Finally, we suggest an automatic text-generating technique based on a configuration template for new building projects. The knowledge element model is converted into a technical disclosure document expressed in natural language using a two-level mapping process. The cantilever installation project is used as an illustration for empirical research, and relevant practitioners are invited to carry out a manual review by questionnaire from the perspectives of topic relevance, topic integrity, topic word implication, sentence smoothness, sentence continuity, and information volume. At the same time, standard assessment metrics such as BLEU and ROUGE are employed to compare with the neural network-based text generation approach. The outcomes demonstrate that the strategy suggested in this paper can generate technical disclosure text that performs well. Overall, its text integrity and readability may satisfy on-site management's demands and help on-site management lessen the workload of technical management staff.

Automatic Generation of Literary Sentences in French

In this paper, we describe a model for the automatic generation of literary sentences in French. Although there has been much recent effort directed towards automatic text generation in general, the generation of creative, literary sentences that is not restricted to a specific genre, which we approached in this work, is a difficult task that is not commonly treated in the scientific literature. In particular, our present model has not been previously applied to the generation of sentences in the French language. Our model was based on algorithms that we previously used to generate sentences in Spanish and Portuguese and on a new corpus, which we constructed and present here, consisting of literary texts in French, called MegaLitefr. Our automatic text generation algorithm combines language models, shallow parsing, the canned text method, and deep learning artificial neural networks. We also present a manual evaluation protocol that we propose and implemented to assess the quality of the artificial sentences generated by our algorithm, by testing if they fulfil four simple criteria. We obtained encouraging results from the evaluators for most of the desired features of our artificially generated sentences.