Automatic Generation Research Articles

Design optimization of a three-dimensional hexagonal braiding technique

The three-dimensional hexagonal braiding technique, utilizing an individually controllable horn gear mechanism, enables the automatic generation of a wide range of intricate fabric preforms for structural composites. However, the limited yarn-carrying capacity and the potential risk of collisions between horn gears hinder the development of this type of braiding machine. This study proposes an optimized hexagonal braiding technique to enhance the machinery and control system, aiming to improve the yarn-carrying capacity and prevent collisions. Specifically, the optimization includes modifying the switch device, developing a new algorithm for controlling each horn gear, and designing a control panel to facilitate human–computer interaction. Additionally, simulations of various fabric structures demonstrate improved braiding capability compared to traditional hexagonal braiders. Moreover, a prototype braider is assembled, capable of automatically braiding diverse fabrics using the control system, thereby demonstrating its potential to manufacture complex composite preforms.

Developmental Trajectories of Electric Vehicle Research in a Circular Economy: Main Path Analysis

This study explored the development history and future trends of academic research on electric vehicles (EVs) in a circular economy. We collected 4127 articles on circular economy and EVs from the Web of Science database, and main path analysis indicated that academic research in the field of EVs in a circular economy has covered the following topics in chronological order: EVs as a power resource; vehicle-to-grid (V2G) technology; renewable energy and energy storage grids; smart grid and charging station optimization; and sustainable development of energy, water, and environmental systems. Through cluster analysis and data mining, we identified the following main research topics in the aforementioned field: recycling and reuse of EV batteries, charging stations and energy management, V2G systems and renewable energy, power frequency control systems, dynamic economic emissions, and energy management. Finally, data mining and statistical analysis revealed the following emerging research topics in this field from 2020 to 2023: microgrids, deep learning, loop supply chain, blockchain, and automatic generation control. Various achievements have been attained in research on EVs in a circular economy; however, challenges related to aspects such as sustainable battery recycling charging infrastructure and renewable energy integration remain.

Safe dynamic optimization of automatic generation control via imitation-based reinforcement learning

IntroductionThe increasing penetration of distributed generation (e.g., solar power and wind power) in the energy market has caused unpredictable disturbances in power systems and accelerated the application of intelligent control, such as reinforcement learning (RL), in automatic generation control (AGC). However, traditional RL cannot ensure constraint safety during training and frequently violates the constraints (e.g., frequency limitations), further threatening the safety and stability of grid operation.MethodsTo address the safety issue, we propose a novel safe RL framework that combines expert experiences with the RL controller to achieve imitation-based RL. This method allows an initialized safe policy by imitating expert experiences to prevent random explorations at the beginning. Specifically, we first formulate the AGC problem mathematically as a Markov decision process. Then, the imitation mechanism is developed atop a soft actor–critic RL algorithm.Results and discussionFinally, numerical studies are conducted with an IEEE 39-bus network, which show that the proposed method satisfies the frequency control performance standard better and improves the RL training efficiency.

Automatic Test Pattern Generation for Robust Quantum Circuit Testing

Quantum circuit testing is essential for detecting potential faults in realistic quantum devices, while the testing process itself also suffers from the inexactness and unreliability of quantum operations. This article alleviates the issue by proposing a novel framework of automatic test pattern generation (ATPG) for robust testing of logical quantum circuits. We introduce the stabilizer projector decomposition (SPD) for representing the quantum test pattern and construct the test application (i.e., state preparation and measurement) using Clifford-only circuits, which are rather robust and efficient as evidenced in the fault-tolerant quantum computation. However, it is generally hard to generate SPDs due to the exponentially growing number of the stabilizer projectors. To circumvent this difficulty, we develop an SPD generation algorithm, as well as several acceleration techniques that can exploit both locality and sparsity in generating SPDs. The effectiveness of our algorithms are validated by (1) theoretical guarantees under reasonable conditions and (2) experimental results on commonly used benchmark circuits, such as Quantum Fourier Transform (QFT), Quantum Volume (QV), and Bernstein-Vazirani (BV) in IBM Qiskit.

Do Language Models Enjoy Their Own Stories? Prompting Large Language Models for Automatic Story Evaluation

Abstract Storytelling is an integral part of human experience and plays a crucial role in social interactions. Thus, Automatic Story Evaluation (ASE) and Generation (ASG) could benefit society in multiple ways, but they are challenging tasks which require high-level human abilities such as creativity, reasoning, and deep understanding. Meanwhile, Large Language Models (LLMs) now achieve state-of-the-art performance on many NLP tasks. In this paper, we study whether LLMs can be used as substitutes for human annotators for ASE. We perform an extensive analysis of the correlations between LLM ratings, other automatic measures, and human annotations, and we explore the influence of prompting on the results and the explainability of LLM behaviour. Most notably, we find that LLMs outperform current automatic measures for system-level evaluation but still struggle at providing satisfactory explanations for their answers.

Iterative LMI-Tuned PID Controller for Robust Decentralized Multi-Area Automatic Generation Control in Deregulated Electricity Markets

This study proposes an Iterative Linear Matrix Inequality (LMI)-Tuned Proportional-Integral-Derivative (PID) controller for robust decentralized AGC in the context of deregulated electricity markets. The study addresses the challenges posed by market uncertainties and diverse generation sources, aiming to enhance the stability and performance of power systems. This study is driven by the urgent need to develop robust AGC strategies that are adapted to the complex dynamics of decentralized power systems operating in deregulated markets. The conventional centralized control approaches are often inadequate to address the diverse and distributed nature of modern generation units. Furthermore, the intermittent and variable nature of renewable energy sources adds a layer of complexity that demands innovative control solutions. The impact of bilateral contracts on the dynamics is taken into consideration while modifying the conventional LFC of interconnected power networks. To model these bilateral contracts, the distribution company (DISCO) participation matrix (DPM) is presented, and the multi-area block diagram reflects these concepts. Simulation studies are conducted to evaluate and compare the performance of the proposed Iterative LMI-Tuned PID Controller and Conventional PID Controller. Results are analyzed against key performance metrics, considering scenarios reflective of diverse market conditions. Comparative studies with the conventional PID controller provide insights into the efficacy and robustness of the proposed approach.

Generating Java code pairing with ChatGPT

The Large Language Models (LLMs) like ChatGPT 3.5 have created a new era of automatic code generation. However, the existing research primarily focuses on generating simple code based on datasets (such as HumanEval, etc.). Most of approaches pay less attention to complex and practical code generation. Therefore, in this paper, we propose a new approach called “Xd-CodeGen” which can be used to generate large scale Java code. This approach is composed of four phases: requirement analysis, modeling, code generation, and code verification. In the requirement analysis phase, ChatGPT 3.5 is utilized to decompose and restate user requirements. To do so, a knowledge graph is developed to describe entities and their relationship in detail. Further, Propositional Projection Temporal Logic (PPTL) formulas are employed to define the properties of requirements. In the modeling phase, we use knowledge graphs to enhance prompts and generate UML class and activity diagrams for each sub-requirement using ChatGPT 3.5. In the code generation phase, based on established UML models, we make use of prompt engineering and knowledge graph to generate Java code. In the code verification phase, a runtime verification at code level approach is employed to verify generated Java code. Finally, we apply the proposed approach to develop a practical Java web project.

Retraction Note: Light image enhancement and virtual reality application in automatic generation of basketball game scenes and training data simulation

Retraction Note: Light image enhancement and virtual reality application in automatic generation of basketball game scenes and training data simulation

Retraction Note: Artificial intelligence recruitment text automatic generation based on light detection and improved neural network algorithm

Retraction Note: Artificial intelligence recruitment text automatic generation based on light detection and improved neural network algorithm

Automatic generation of diffusion tensor imaging for the lumbar nerve using convolutional neural networks

【Purpose】Diffusion Tensor Imaging (DTI) with tractography is useful for the functional diagnosis of degenerative lumbar disorders. However, it is not widely used in clinical settings due to time and health care provider costs, as it is performed manually on hospital workstations. The purpose of this study is to construct a system that extracts the lumbar nerve and generates tractography automatically using deep learning semantic segmentation. 【Methods】We acquired 839 axial diffusion weighted images (DWI) from the DTI data of 90 patients with degenerative lumbar disorders, and segmented the lumbar nerve roots using U-Net, a semantic segmentation model. Using five architectural models, the accuracy of the lumbar nerve root segmentation was evaluated using a Dice coefficient. We also created automatic scripts from three commercially available software tools, including MRICronGL for medical image viewing, Diffusion Toolkit for reconstruction of the DWI data, and Trackvis for the creation of the tractography, and compared the time required to create the tractography, and evaluated the quality of the automated tractography was evaluated. 【Results】Among the five models, the architectural model Resnet34 performed the best with a Dice = 0.780. The creation time for the automatic lumbar nerve tractography was 191 s, which was significantly shorter by 235 s than the manual time of 426 s (p < 0.05). Furthermore, the agreement between manual and automated tractography was 3.67 ± 1.53 (satisfactory). 【Conclusions】Using deep learning semantic segmentation, we were able to construct a system that automatically extracted the lumbar nerve and generated lumbar nerve tractography. This technology makes it possible to analyze lumbar nerve DTI and create tractography automatically, and is expected to advance the clinical applications of DTI for the assessment of the lumbar nerve.

Automatic source code generation for deterministic global optimization with parallel architectures

Trends over the past two decades indicate that much of the performance gains of commercial optimization solvers is due to improvements in x86 hardware. To continue making progress, it is critical to consider alternative/specialized massively parallel computing architectures. In this work, we detail the development of an open-source source code transformation approach built using Symbolics.jl to construct McCormick-based relaxations of functions that enables their effective parallelized evaluation. We then apply this approach in a novel parallelized branch-and-bound routine that offloads lower- and upper-bounding problems to a GPU. The effectiveness of this new approach is demonstrated on three nonconvex problems of interest, where it yields convergence time improvements of 22–118x compared to an equivalent serial CPU implementation and in two cases outperforms vanilla branch-and-bound versions of existing state-of-the-art solvers that use tighter bounding techniques. This work exemplifies how deterministic global optimizers using alternative hardware architectures can compete with—or eventually outclass—even the most powerful serial CPU implementations, and to the best of the authors' knowledge, represents the first successful demonstration of deterministic global optimization using a GPU.

Assessing the proficiency of large language models in automatic feedback generation: An evaluation study

Assessment feedback is important to student learning. Learning analytics (LA) powered by artificial intelligence exhibits profound potential in helping instructors with the laborious provision of feedback. Inspired by the recent advancements made by Generative Pre-trained Transformer (GPT) models, we conducted a study to examine the extent to which GPT models hold the potential to advance the existing knowledge of LA-supported feedback systems towards improving the efficiency of feedback provision. Therefore, our study explored the ability of two versions of GPT models – i.e., GPT-3.5 (ChatGPT) and GPT-4 – to generate assessment feedback on students' writing assessment tasks, common in higher education, with open-ended topics for a data science-related course. We compared the feedback generated by GPT models (namely GPT-3.5 and GPT-4) with the feedback provided by human instructors in terms of readability, effectiveness (content containing effective feedback components), and reliability (correct assessment on student performance). Results showed that (1) both GPT-3.5 and GPT-4 were able to generate more readable feedback with greater consistency than human instructors, (2) GPT-4 outperformed GPT-3.5 and human instructors in providing feedback containing information about effective feedback dimensions, including feeding-up, feeding-forward, process level, and self-regulation level, and (3) GPT-4 demonstrated higher reliability of feedback compared to GPT-3.5. Based on our findings, we discussed the potential opportunities and challenges of utilising GPT models in assessment feedback generation.

Visible Artificial Intelligence: Exploring the Enhancement of Chatgpt and Film Script Creation

As a joint link of artificial intelligence products, the emergence of ChatGPT triggered a global debate, and attracted the attention of practitioners all over the world. Meanwhile, multiple film and television production fields such as computer graphics processing, digital virtual shooting, CG special effects production, have embedded automatic content generation factors. However, artificial intelligence is also facing legal dilemmas and ethical constraints, researchers need to compare and analyze its advantages and problems, systematically consider the strengths and impacts, limitations and problems of AIGC, and provide reference and practical guidance for AI to better promote itself.

Digital Surface Model Generation from Satellite Images Based on Double-Penalty Bundle Adjustment Optimization

Digital Surface Model (DSM) generation from high-resolution optical satellite images is an important topic of research in the remote sensing field. In optical satellite imaging systems, the attitude information of the cameras recorded by satellite sensors is often biased, which leads to errors in the Rational Polynomial Camera (RPC) model of satellite imaging. These errors in the RPC model can mislead the DSM generation. To solve the above problems, we propose an automatic DSM generation method from satellite images based on the Double-Penalty bundle adjustment (DPBA) optimization algorithm. In the proposed method, two penalty functions representing the camera’s attitude and the spatial 3D points, respectively, are added to the reprojection error model of the traditional bundle adjustment optimization algorithm. Instead of acting on images directly, the penalty functions are used to adjust the reprojection error model and improve the RPC parameters. We evaluate the performance of the proposed method using high-resolution satellite image pairs and multi-date satellite images. Through some experiments, we compare the accuracy and completeness of the DSM generated by the proposed method, the Satellite Stereo Pipeline (S2P) method, and the traditional bundle adjustment (BA) method. Compared to the S2P method, the experiment results of the satellite image pair indicate that the proposed method can significantly improve the accuracy and the completeness of the generated DSM by about 1–5 m and 20%–60% in most cases. Compared to the traditional BA method, the proposed method improves the accuracy and completeness of the generated DSM by about 0.01–0.05 m and 1%–3% in most cases. The experiment results can be a testament to the feasibility and effectiveness of the proposed method.

Deformable patch-based garment design in immersive virtual reality

PurposeThis study aims to create and deform 3D garment apparel in an immersive virtual reality using head-mounted display and controllers. For this, adequate design methods for immersive virtual environment were explored and developed in order to confirm the suitability of the developed methods.Design/methodology/approachAn immersive virtual environment was prepared using Unreal Engine (UE) version 5.1 and Meta Human Lexi to create template garment that corresponds to the sizes of a human model. Dual quaternion skinning was adopted for pose deformation. For size deformation, patches were constructed with the measurement lines defined on Lexi. This patch-based approach was adopted not only for automatic generation but also for flat pattern projection of the template garment.FindingsThe research found that garment-making process can be brought into immersive virtual reality. Free use of one's hands and body made apparel deformation in an immersive environment conform with the real garment draping process.Originality/valueSimulating garment making in an immersive virtual reality has not previously been explored in detail. This research discovered, implemented and tested methods that best suit the environment where head-mounted display and controllers are essential in detail.

Automatic generation control of is-landed micro-grid using integral reinforcement learning-based adaptive optimal control strategy

Automatic generation control of is-landed micro-grid using integral reinforcement learning-based adaptive optimal control strategy

Automatización para el registro de resultados de laboratorio al utilizar la técnica de amplificación isotérmica mediada por bucle (LAMP)

Digital image processing has a variety of applications in different areas, such as engineering, robotics, industry, agriculture, and health, to mention a few. When researchers use some colorimetric techniques, certain situations arise in which the support of computational tools is required to be able to accurately identify the color and tone of the results obtained, thus reducing the probability of making errors. The LAMP technique is a tool applied to nucleic acid amplification and is an example of a colorimetric procedure. The proposed solution presented in this research report is the development of a computer program that uses digital image processing to collect the information obtained from the use of the LAMP technique. Feature extraction techniques based on color and shape are used as part of the solution methodology; with the goal of identifying the elements that belong to the samples of interest. After carrying out a series of tests with different configurations, the automatic generation of files that store the description of results from the LAMP technique was achieved.

VeriFog: A Generic Model-based Approach for Verifying Fog Systems at Design Time and Generating Deployment Configurations

Fog Computing is a paradigm decentralizing the Cloud by geographically distributing computation, storage, network resources and related services. It provides benefits such as reducing the number of bottlenecks, limiting unwanted data movements, etc. However, managing the size, complexity and heterogeneity of the Fog systems to be engineered is challenging and can quickly become costly. According to best practices in software engineering, verification tasks could be performed on a system design prior to its implementation and deployment. We propose a generic model-based approach for verifying Fog systems at design time, also enabling the automatic generation of corresponding deployment configuration files. Named VeriFog, this approach is notably based on a customizable Fog Modeling Language (FML). We experimented in practice by modeling three use cases, from three different application domains, and by considering three main types of non-functional properties to be verified. From this modeling and verification effort, we show that we are able to automatically generate usable deployment configuration files for different deployment tools. In direct collaboration with our industrial partner Smile, the approach and underlying language presented in this paper are necessary steps towards a more global model-based support for the complete life cycle of Fog systems.

The synergistic effect of artificial intelligence technology in the evolution of visual communication of new media art

This study aims to clarify the synergistic effect of artificial intelligence (AI) technology in the evolution of visual communication of new media art, thereby exploring an AI layout design method based on Convolutional Neural Network (CNN) in the practice of visual communication design. Firstly, this study designs an AI layout design model based on CNN, and trains and optimizes it with training data. Secondly, the automatic generation of layout design is realized by constantly adjusting the model parameters and network structure. Finally, various AI layout design algorithms are compared, and their effects and performances in layout design generation are analyzed. To verify the layout and composition matching model's performance, traditional layout design methods are selected for comparison (layout, comparison, harmonic composition, etc.). This study involved 20 design students as participants, evaluating them across three dimensions: overall comprehensive assessment, readability of text information, and rationality of visual path using a Likert 7-point scale. The results reveal that the proposed method's evaluation outcomes in these three aspects are 5.95, 5.68, and 5.74, respectively, higher than the traditional layout design methods. To sum up, the generative AI discussed here can automatically generate design elements and schemes through deep learning and big data analysis, thus providing a reference for the innovation of visual communication design.

Automated plan generation for prostate radiotherapy patients using deep learning and scripted optimization

Background and PurposeTreatment planning is a time-intensive task that could be automated. We aimed to develop a “single-click” workflow, fully deployed within a commercial treatment planning system (TPS), for autoplanning prostate radiotherapy treatment plans using predictions from a deep learning model (DLM). Materials and MethodsAutomatically generated treatment plans were created with a single script, executed from within a commercial TPS scripting environment, that performed two stages sequentially. Initially, a 3D dose distribution was predicted with a ResUNet DLM. The DLM was trained and validated using previously treated datasets (n = 120) which used 3D contours as inputs. Following this, dose predictions were converted into treatment plans by extracting dose-volume metrics from the predictions to use as objectives for the inverse optimizer within the TPS. An independent test dataset (n = 20) was used to evaluate the similarity between automated and clinical plans. ResultsFor planning target volumes, the median percentage difference and interquartile range between the automatically generated plans and clinical plans were 0.4% [0.2-1.1%] for the V100%, −0.5% [(−1.0)-(−0.2)%] for D99% and −0.5% [(−1.0)-(−0.2)%] for D95%. Bladder and rectum volume-at-dose objectives agreed within −6.1% [(−12.5)-0.9%]. The conversion of the DLM prediction into a treatment plan took 15 min [13-16 min]. ConclusionsAn automatic plan generation workflow that uses a DL model with scripted optimization was fully deployed in a commercial TPS. Autoplans were compared to previously treated clinical plans and were found to be non-inferior.