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This paper presents an object attitude estimation method using a 2D object image for a Laser Beam Control Research Testbed (LBCRT). Motivated by emerging Deep Learning (DL) techniques, a DL model that can estimate the attitude of a rotating object represented by Euler angles is developed. Instead of synthetic data for training and validation of the model, customized data is experimentally created using the laboratory testbed developed at the Naval Postgraduate School. The data consists of Short Wave Infra-Red (SWIR) images of a 3D-printed Unmanned Aerial Vehicle (UAV) model with varying attitudes and associated Euler angle labels. In the testbed, the estimated attitude is used to aim a laser beam to a specific point of the rotating model UAV object. The attitude estimation model is trained with 1684 UAV images and validated with 421 UAV images not used in the model training. The validation results show the Root-Mean-Square (RMS) angle estimation errors of 6.51 degrees in pitch, 2.74 degrees in roll, and 2.51 degrees in yaw. The Extended Kalman Filter (EKF) is also integrated to show the reduced RMS estimation errors of 1.36 degrees in pitch, 1.20 degrees in roll, and 1.52 degrees in yaw.

This paper presents a deep learning-based Scan-vs-BIM methodology for evaluating structural integrity through the extraction of features from As-Built scan and As-Planned Building Information Modeling (BIM) comparison data. Traditional Scan-vs-BIM frameworks often rely on Scan-to-BIM processes to generate point cloud-based mesh models for comparison, which significantly impairs computational efficiency. In contrast, the proposed streamlined Scan-vs-BIM framework incorporates a deep neural network (DNN) model consisting of two neural networks: one for structural integrity assessment and another for error type analysis. The model evaluates the structural integrity of individual components in a sequential manner, repeating the process across all elements to comprehensively assess the entire structure. Rather than converting point cloud data into mesh models for comparison, this approach directly measures the spatial discrepancies between the As-Built point cloud and As-Planned BIM, analyzing the distribution tendencies of these distance values. Experimental validation on actual steel structures demonstrated that the proposed method effectively predicts structural integrity, providing significant improvements in both accuracy and computational performance. [ABSTRACT FROM AUTHOR]

In architectural engineering, triangular tessellation using polygon mesh topology is one of the commonly used computational geometric approaches to simplify a free curved building façade into flat triangular facets and their subsequent straight edges. In such a façade system, exterior panels are supported by a network of profiles that correspond to their edges hidden behind the panels at an offset distance. A group of profiles, derived from the edges common to a node point of tessellated panels (i.e., the outermost panels enveloping the building), may dislocate from each other when offset from their original locations due to non-coplanar alignment and unique offset directions and distances. This dislocation problem gives rise to geometric complications in nodal connector design in addition to varying in the connected profile count and orientations. Design considerations regarding the effects of 'offset vertex dislocation' (i.e., the dislocation of the edges when it offsets from the original topology due to incoherent normal direction) should incorporate proper variables in the correct sequence based on a fundamental understanding that causes the dislocation problem. However, it is very often these topological problems pertaining to offset that are neglected, leading to subsequent design flaws. Such oversights diminish the inherent strengths of DfMA (design for manufacture and assembly) and design automation. This study develops a computational mathematical approach aimed at addressing the geometric complexities in nodal connector design. It focuses on two main areas: the precise positioning of substructure profiles essential for the design and a design automation approach that minimizes the length of the nodal connector arms to enhance 3D printing productivity. A life-scale proof-of-concept structure based on an automated parametric design process that implements the research findings demonstrates the application, incorporating 3D-printed PA12 (Polyamide-12) nodal connectors. [ABSTRACT FROM AUTHOR]

In the construction industry, customer satisfaction is of paramount importance, as it significantly impacts company success and reputation. In Korea's competitive apartment market, customer satisfaction—particularly feedback on newly built apartments—is vital for construction companies, as it fosters growth and customer loyalty. To gain an understanding of the sentiments and patterns within this feedback, text mining can be utilized. This study aims to extract such insights from textual data on apartment building defect complaints, using text mining and ensemble learning to develop models with high prediction accuracy. It analyzes the accuracy of the Word2Vec and term frequency–inverse document frequency (TF-IDF) models, as well as the individual performance of different classification models, including naïve Bayes, decision trees, logistic regression, k -nearest neighbors, support vector machines (SVMs), and random forests. This analysis was conducted to validate the effectiveness of ensemble learning. Data were collected from a total of 230 apartment building projects in South Korea between 2018 and 2023, resulting in a data set of 101,387 data points, which underwent analysis to validate the model. The validation results consistently showed that TF-IDF outperforms Word2Vec, with the SVM model achieving the highest performance, attaining an average F1 score of 0.7439. Ensemble learning models demonstrated an improvement in accuracy of up to 34% over single models, reaching an average accuracy of 97.47% after the removal of human error. While this study acknowledges its limitations, which include potential biases in the data set, the impact of language evolution on model precision, and difficulties in classifying complex defects, the ensemble model demonstrated substantial improvements in defect classification accuracy and provided practical insights for defect management in construction. Moving forward, future work could explore integrating multidimensional data, utilizing speech-to-text technology, prioritizing defects by severity, and employing artificial intelligence for real-time defect prediction to further enhance defect management practices. [ABSTRACT FROM AUTHOR]

Multiplexed bead assays for solution‐phase biosensing often encounter cross‐over reactions during signal amplification steps, leading to unwanted false positive and high background signals. Current solutions involve complex custom‐designed and costly equipment, limiting their application in simple laboratory setup. In this study, we introduce a straightforward protocol to adapt a multiplexed single‐bead assay to standard fluorescence imaging plates, enabling the simultaneous analysis of thousands of reactions per plate. This approach focuses on the design and synthesis of bright fluorescent and magnetic microspheres (MagSiGlow) with multiple fluorescent wavelengths serving as unique detection markers. The imaging‐based, single‐bead assay, combined with a scripted algorithm, allows the detection, segmentation, and co‐localization on average of 7500 microspheres per field of view across five imaging channels in less than one second. We demonstrate the effectiveness of this method with remarkable sensitivity at low protein detection limits (100 pg/mL). This technique showed over 85 % reduction in signal cross‐over to the solution‐based method after the concurrent detection of tumor‐associated protein biomarkers. This approach holds the promise of substantially enhancing high throughput biosensing for multiple targets, seamlessly integrating with rapid image analysis algorithms. [ABSTRACT FROM AUTHOR]

Agile project management pursues repetitive and continuous management through an empirical process control method considering the complexity of the project. This study aimed to propose a collaborative Building Information Modeling (BIM)-based work process, focusing on construction coordination tasks among participants during the construction phase of a project. The theoretical framework chosen for this endeavor was the Scrum framework, selected after analyzing previous research to align with the specific characteristics of construction coordination. Using the Scrum framework as a foundation, the study employed the exCPM methodology to model the collaborative BIM-based construction coordination progress. To validate the model, it underwent a transformation into a Petri-Nets model through the marked graph building block (MGBB) along with established rules for model conversion. The converted model was then subjected to validation through reachability tree analysis. The findings of this study hold applicability to diverse collaboration processes that leverage BIM in the construction industry. [ABSTRACT FROM AUTHOR]

The development of smart cities has led to transforming modern city operations by applying emerging technologies from the fourth industrial revolution such as the Internet of Things, BIM and machine learning. To prepare for the future development of cities, it is necessary to investigate the current status of city development, research topics and the direction toward future cities. In this study, a systematic literature review was conducted following the combination of literature review guides by Kitchenham and the PRISMA statement. The review identified 141 peer-reviewed academic papers from web-based archives, such as Web of Science and SCOPUS, that were categorized into five topics related to smart cities and urban areas. The technologies applied in the research were analyzed to determine the direction future cities should take based on current smart cities and emerging technologies that will shape individuals' daily lives. However, it was discovered that research papers on critical areas, such as mobility and management, were lacking compared to those on building design and urban planning. This paper concludes with an example of a future urban area that has been transformed due to adoption of future mobility technology to address the shortcomings of the reviewed literature on building and urban environments. [ABSTRACT FROM AUTHOR]