Search Results (79)

Increasing mechanical design complexity and volume, particularly in component-based manufacturing, require scalable, traceable, and efficient design processes. In this research, a modular in-house automation platform using Autodesk Inventor’s Application Programming Interface (API) and Visual Basic for Applications (VBA) is developed to automate recurrent tasks such as CAD file generation, drawing production, structured archiving, and cost estimation. The proposed framework was implemented and tested on three real-world case studies in a turbocharger reconditioning unit with varying degrees of automation. Findings indicate remarkable time savings of up to 90% in certain documentation tasks with improved consistency, traceability, and reduced manual intervention. Moreover, the system also facilitated automatic generation of metadata-rich Excel and Word documents, allowing centralized documentation and access to data. In comparison with commercial automation software, the solution is flexible, cost-effective, and responsive to project changes and thus suitable for small and medium enterprises. Though automation reduced workload and rendered the system more reliable, some limitations remain, especially in fully removing engineering judgment, especially in complex design scenarios. Overall, this study investigates how API-based automation can significantly increase productivity and data integrity in CAD-intensive environments and explores future integration opportunities using AI and other CAD software. Full article

Automated story writing has been a subject of study for over 60 years. Today, large language models can generate narratively consistent and linguistically coherent short fiction texts. Despite these advancements, rigorous assessment of such outputs in terms of literary merit—especially concerning aesthetic qualities—has received scant attention. In this paper, we address the challenge of evaluating AI-generated microfiction (MF) and argue that this task requires consideration of literary criteria across various aspects of the text, including thematic coherence, textual clarity, interpretive depth, and aesthetic quality. To facilitate this, we present GrAImes: an evaluation protocol grounded in literary theory; specifically, GrAImes draws from a literary perspective to offer an objective framework for assessing AI-generated microfiction. Furthermore, we report the results of our validation of the evaluation protocol as answered by both literature experts and literary enthusiasts. This protocol will serve as a foundation for evaluating automatically generated microfiction and assessing its literary value. Full article

Pseudowords are essential in (psycho)linguistic research, offering a way to study language without meaning interference. Various methods for creating pseudowords exist, but each has its limitations. Traditional approaches modify existing words, risking unintended recognition. Modern algorithmic methods use high-frequency n-grams or syllable deconstruction but often require specialized expertise. Currently, no automatic process for pseudoword generation is designed explicitly for Greek, which is our primary focus. Therefore, we developed SyBig-r-Morph, a novel application that constructs pseudowords using syllables as the main building block, replicating Greek phonotactic patterns. SyBig-r-Morph draws input from word lists and databases that include syllabification, word length, part of speech, and frequency information. It categorizes syllables by position to ensure phonotactic consistency with user-selected morphosyntactic categories and can optionally assign stress to generated words. Additionally, the tool uses multiple lexicons to eliminate phonologically invalid combinations. Its modular architecture allows easy adaptation to other languages. To further evaluate its output, we conducted a manual assessment using a tool that verifies phonotactic well-formedness based on phonological parameters derived from a corpus. Most SyBig-r-Morph words passed the stricter phonotactic criteria, confirming the tool’s sound design and linguistic adequacy. Full article

High-resolution remote sensing imagery has become the primary data source for obtaining building information. Automatically extracting regularized building outline polygon vectors is crucial for improving vector mapping efficiency and geographic information system applications, but existing deep learning methods struggle to simultaneously achieve accurate detection, high pixel-level coverage, and geometric regularity. This paper proposes a novel two-stage building outline extraction method. In the first stage, the SegFormer model is used to extract image features, effectively capturing global context information. In the second stage, a polygon outline regularization model (PolyReg) based on a Masked Attention Encoder is innovatively introduced. The PolyReg model draws on the sequence generation idea from natural language processing, transforming the outline regularization task into a sequence generation problem. Through a cleverly designed self-attention mask matrix, it achieves an autoregressive output of regularized building outline coordinates, eliminating the need for cumbersome post-processing steps. Experimental results show that on the Inria Aerial Image Labeling Dataset, compared with traditional methods and existing deep learning methods, the proposed method demonstrates significant advantages in metrics such as IoU, C-IoU, and Hausdorff distance. It effectively improves the regularity and geometric accuracy of building outlines while maintaining high pixel-level coverage. Full article

This study proposes an automated three-dimensional (3D)-modeling method that combines convolutional neural networks (CNNs) with unmanned aerial vehicle (UAV) technology for the efficient management of thermal anomalies in building exteriors. Conventional 3D-modeling methods for thermal imaging management either require the processing of large volumes of data due to the use of thermal distribution information from entire image regions or involve increased processing time when architectural drawings are unavailable. In this study, RGB and infrared (IR) thermal images collected via UAVs were used to automatically detect windows and thermal anomalies using a CNN-based object detection model (YOLOv5). Subsequently, Global Navigation Satellite System (GNSS)-based coordinate data and image metadata were used to convert the resolution coordinates into actual spatial coordinates, which were then vectorized to automatically generate a 3D model. The resulting 3D model demonstrated high similarity to the actual building, accurately representing the locations of thermal anomalies. This method enabled faster, more objective, and more cost-effective maintenance compared to conventional methods, making it especially beneficial for efficiently managing difficult-to-access high-rise buildings. Full article

Voice activity detection (VAD) is the process of automatically determining whether a person is speaking and identifying the timing of their speech in an audiovisual data. Traditionally, this task has been tackled by processing either audio signals or visual data, or by combining both modalities through fusion or joint learning. In our study, drawing inspiration from recent advancements in visual-language models, we introduce a novel approach leveraging Contrastive Language-Image Pretraining (CLIP) models. The CLIP visual encoder analyzes video segments focusing on the upper body of an individual, while the text encoder processes textual descriptions generated by a Generative Large Multimodal Model, i.e., the Large Language and Vision Assistant (LLaVA). Subsequently, embeddings from these encoders are fused through a deep neural network to perform VAD. Our experimental analysis across three VAD benchmarks showcases the superior performance of our method compared to existing visual VAD approaches. Notably, our approach outperforms several audio-visual methods despite its simplicity and without requiring pretraining on extensive audio-visual datasets. Full article

Aging buildings pose a significant concern for many large developed cities, and the operation and maintenance (O&M) of mechanical, electrical, and plumbing (MEP) systems becomes critical. Building Information Modeling (BIM) facilitates efficient O&M for MEP. However, these numerous aging buildings were constructed without BIM, making BIM reconstruction a monumental undertaking. This research proposes an automatic approach for generating BIM based on 2D drawings. Semantic segmentation was utilized to identify MEP components in the drawings, trained on a custom-made MEP dataset, achieving an mIoU of 92.18%. Coordinates and dimensions of components were extracted through contour detection and bounding box detection, with pixel-level accuracy. To ensure that the generated components in BIM strictly adhere to the specifications outlined in the drawings, all model types were predefined in Revit by loading families, and an MEP component dictionary was built to match dimensions and model types. This research aims to automatically and efficiently generate BIM for MEP systems from 2D drawings, significantly reducing labor requirements and demonstrating broad application potential in the large-scale O&M of numerous aging buildings. Full article

To understand the topic of old age in the Middle Ages and the Renaissance, we can draw much information from relevant literary texts among other sources because the poets operated with general notions commonly subscribed to by their audiences. Old people appear in many different roles already in the pre-modern world, but here the focus will rest mostly on worthy, dignified, mighty, and even ferocious old warriors in heroic poetry. Those stand out because of their strength, their knowledge, their resolve, their wisdom, and their extensive and varied abilities, but this does not automatically mean that they were flawless. To round off this entry, the attention will finally turn to remarkable examples of old but highly respected people in the verse narratives by the German poet Heinrich Kaufringer, in Boccaccio’s Decameron, a harbinger of the Italian Renaissance, in Christine de Pizan’s didactic writings, and in the Old Norse Njál’s Saga. Full article

Speech-based communication between users and machines is a very lively branch of research that covers speech recognition, synthesis, and, generally, natural language processing. Speech corpora are needed for training algorithms for human–machine communication, especially for automatic speech recognition and for speech synthesis. Generative artificial intelligence models also need corpora for training for every language implemented. Therefore, speech corpora are constantly being created. In this paper, we discuss how to create high-quality corpora. The technical parameters of the recordings and audio files are addressed, and a methodology is proposed for planning speech corpus creation with an emphasis on usability. The proposed methodology draws the attention of potential creators of speech corpora to often neglected aspects of the corpus creation process. The criteria for a quality assessment of particular components are also discussed. The author recommends not combining all quality metrics into one (or at least allowing users to adjust particular weights), as different users might be interested in different quality components. The presented guidelines lead to obtaining high-quality corpora that meet the needs of their end users and are easy to use. Full article

This study performed a 3D CFD analysis on a GP3 rotary engine to determine the stroke and flow characteristics and examine the thermal- and flow-related design factors’ validity. The 3D CFD analysis was performed using the CONVERGE program, utilizing the automatic grid generation function based on the 3D engine design drawing, which is suitable for a rotating rotary engine geometry. The target species and error tolerance were selected based on the GRI-Mech 3.0 full reaction mechanism to validate the reaction model and define a reasonable range of target species and error tolerances. The RNG k-ε turbulence and SAGE combustion models were also employed to analyze the four-stroke characteristics for the GP3 engine by visualizing the internal flow. The various outcomes confirmed the rotary engine’s unique characteristics and were reasonably interpreted to validate the engine design factors. In particular, the EGR phenomenon in the intake and exhaust port overlap area and the interference phenomenon in the port overlap area between adjacent cylinders are unique to the engine, and were rationally analyzed to more accurately predict the engine’s performance. The results of this study regarding the flame quenching regions indicated power and efficiency, and the emission characteristics can be used to validate the design parameters. Full article

Fabrication drawings are essential for design evaluation, lean manufacturing, and quality detection of precast concrete (PC) components. Due to the complicated shape of PC components, the fabrication drawing needs to be customized to determine manufacturing dimensions and relevant assembly connections. However, the traditional manual drawing method is time-consuming, labor-intensive, and error-prone. This paper presents a BIM-based framework to automatically generate the readable drawing of PC components using building information modeling (BIM) and multi-agent reinforcement learning (MARL). Firstly, an automated generation method is developed to transform BIM model to view block. Secondly, a graph-based representation method is used to create the relationship between blocks, and a reward mechanism is established according to the drawing readability criterion. Subsequently, the block layout is modeled as a layout optimization problem, and the internal spacing and position of functional category blocks are regarded as agents. Finally, the agents collaborate and interact with the environment to find the optimal layout with the guidance of a reward mechanism. Two different algorithms are utilized to validate the efficiency of the proposed method (MADQN). The proposed framework is applied to PC stairs and a double-sided shear wall to demonstrate its practicability. Full article

The long-standing practice of document-based engineering has resulted in the accumulation of a large number of engineering documents across various industries. Engineering documents, such as 2D drawings, continue to play a significant role in exchanging information and sharing knowledge across multiple engineering processes. However, these documents are often stored in non-digitized formats, such as paper and portable document format (PDF) files, making automation difficult. As digital engineering transforms processes in many industries, digitizing engineering documents presents a crucial challenge that requires advanced methods. This research addresses the problem of automatically extracting textual content from non-digitized legacy engineering documents. We introduced an optical character recognition (OCR) system for text detection and recognition that leverages transformer-based generative deep learning models and transfer learning approaches to enhance text recognition accuracy in engineering documents. The proposed system was evaluated on a dataset collected from ships’ engineering drawings provided by a U.S. agency. Experimental results demonstrated that the proposed transformer-based OCR model significantly outperformed pretrained off-the-shelf OCR models. Full article

Material planning is important in construction, for it affects procurement, cost, and schedule. Proper planning of material supply and logistics helps streamline the performance of all tasks through the avoidance of excessive or insufficient material supply. Material planning relies on quantity takeoff (QTO) and project schedules. Conventionally, quantity takeoff was a manual process based on 2D drawings and human interpretation and was error-prone. Presently, with the popularity of Building Information Modelling (BIM), in BIM-based projects, using inbuilt quantity takeoff functions, quantities of work can be generated automatically from BIM models to aid the quantity takeoff. However, if those inbuilt QTO solutions are object-based, then the quantities of works extracted may not meet the requirements of the users in selected cases, e.g., in zone-based construction projects. Also, for estimating daily material requirements, the accuracy of the quantities of work becomes more important, not only for the purpose of efficient planning but also for reducing construction waste. Since works using the same type of material can go overlapping, in addition to estimating the amount of material for each work, the total amount of material for a day must also be calculated. Thus, this research aims to develop a framework for automatic extraction of zone-based concrete volumes and formwork positions for cast-in-place concrete structures using the data in BIM models, followed by linking them with project schedules for estimating daily concrete and formwork requirements. This framework extends the body of knowledge by introducing an innovative algorithm for automatically calculating overlapped areas between concrete members and a rule for naming tasks in the schedule, followed by evaluating the formwork requirements without drawing formwork in a 3D model. A software tool will be developed to achieve the aim, and a case study will be used to validate the proposed framework. Full article

Addressing the limitations of current multi-scale seabed terrain construction methods for a Digital Depth Model (DDM) and the low computational efficiency of automatic generalization algorithms, this paper draws on the concept of curvature simplification from 3D point cloud data processing and proposes a block-based DDM automatic generalization method that leverages surface curvature features. Initially, a clustering blocking model is established using an improved K-means algorithm for partitioning DDM data. Subsequently, a fitting surface is constructed based on the neighboring depth points within the blocked DDM to obtain the surface curvature characteristics of each depth point, which serve as the criterion for the DDM automatic generalization process. By integrating a multi-threaded parallel computation model, an efficient automated generalization workflow that encompasses data partitioning, fitting, computation, processing, and integration of the DDM is ultimately constructed. Furthermore, this paper designs validity and comparative experiments to analyze the proposed algorithm through experimental analysis. The results demonstrate that the algorithm can be applied to the multi-scale construction of DDM seabed terrain, while maintaining the integrity of both flat and complex seabed landforms, and significantly enhancing the computational efficiency of the DDM automatic generalization process. Full article

Changes in the jet stream not only affect the persistence of climate change and the frequency of extreme weather but are also closely related to climate change phenomena such as global warming. The manual way of drawing the jet stream axes in meteorological operations suffers from low efficiency and subjectivity issues. Automatic identification algorithms based on wind field analysis have some shortcomings, such as poor generalization ability, and it is difficult to handle merging and splitting. A semi-supervised learning jet stream axis identification method is proposed combining consistency learning and self-training. First, a segmentation model is trained via semi-supervised learning. In semi-supervised learning, two neural networks with the same structure are initialized with different methods, based on which pseudo-labels are obtained. The high-confidence pseudo-labels are selected by adding perturbation into the feature layer, and the selected pseudo-labels are incorporated into the training set for further self-training. Then, the jet stream narrow regions are segmented via the trained segmentation model. Finally, the jet stream axes are obtained with the skeleton extraction method. This paper uses the semi-supervised jet stream axis identification method to learn features from unlabeled data to achieve a small amount of labeled data to effectively train the model and improve the method’s generalization ability in a small number of labeled cases. Experiments on the jet stream axis dataset show that the identification precision of the presented method on the test set exceeds about 78% for SOTA baselines, and the improved method exhibits better performance compared to the correlation network model and the semi-supervised method. Full article