Traditional methods for producing architectural drawings require extensive manual labor. This paper proposes an automated method for generating a comprehensive set of three-view drawings, including the standardized labeling of doors and annotation of dimensions and areas. The output drawings are software-readable and editable, and the method is applicable to intricate structures with non-orthogonal or curved walls. To fully validate the accuracy of the proposed method, two distinct building scenarios were selected for experimentation.
Accurate prediction of the future trajectories of traffic agents is a critical aspect of autonomous vehicle navigation. However, most existing approaches focus on predicting trajectories from a static roadside perspective, ignoring the influence of autonomous vehicles’ future plans on neighboring traffic agents. To address this challenge, this paper introduces EPG-MGCN, an ego-planning-guided multi-graph convolutional network. EPG-MGCN leverages graph convolutional networks and ego-planning guidance to predict the trajectories of heterogeneous traffic agents near the ego vehicle. The model captures interactions through multiple graph topologies from four distinct perspectives: distance, visibility, ego planning, and category. Additionally, it encodes the ego vehicle's planning information via the planning graph and a planning-guided prediction module. The model is evaluated on three challenging trajectory datasets: ApolloScape, nuScenes, and next generation simulation (NGSIM). Comparative evaluations against mainstream methods demonstrate its superior predictive capabilities and inference speed.
The cover image is based on the Research Article Geoacoustic and geophysical data-driven seafloor sediment classification through machine learning algorithms with property-centered oversampling techniques by Junghee Park et al., https://doi.org/10.1111/mice.13126.
The cover image is based on the Research Article Urban risk assessment model to quantify earthquake-induced elevator passenger entrapment with population heatmap by Donglian Gu et al., https://doi.org/10.1111/mice.13287.
Dynamic cone resistance (DCR) is a recently introduced soil resistance index that has the unit of stress. It is determined from the dynamic response at the tip of an instrumented dynamic cone penetrometer. However, DCR evaluation is generally a manual, time-consuming, and error-prone process. Thus, this study investigates the feasibility of determining DCR using a stacked ensemble (SE) machine learning (ML) model that utilizes signals obtained from dynamic cone penetration testing. Two ML experiments revealed that using only force signals provides more accurate predictions of DCR. In addition, the SE technique outperformed the base learning algorithms in both cases. Overall, the findings suggest that ML techniques can be used to automate the analysis of DCR with force and acceleration signals.
The training workflow of neural networks can be quite complex, potentially time-consuming, and require specific hardware to accomplish operation needs. This study presents a novel analytical video-based approach for vehicle tracking and vehicle volume estimation at nighttime using a monocular traffic surveillance camera installed over the road. To build this approach, we employ computer vision-based algorithms to detect vehicle objects, perform vehicle tracking, and vehicle counting in a predefined detection zone. To address low-illumination conditions, we adapt and employ image noise reduction techniques, image binary conversion, image projective transformation, and a set of heuristic reasoning rules to extract the headlights of each vehicle, pair them belonging to the same vehicle, and track moving candidate vehicle objects continuously across a sequence of video frames. The robustness of the proposed method was tested in various scenarios and environmental conditions using a publicly available vehicle dataset as well as own labeled video data.
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