In submitting conference proceedings to Engineering Proceedings, the Volume Editors of the proceedings would like to certify to the publisher that all papers published in this volume have been subjected to peer review by the designated expert referees and were administered by the Volume Editors [...]
We developed a Python-integrated closed-loop industrial safety system that bridges real-time helmet-compliance detection with immediate machine control. The custom Python application serves as critical middleware, orchestrating the complete pipeline from You Only Look Once Version 8 computer vision inference to industrial automation by trans-lating AI detection results into Object Linking and Embedding for Process Control Unified Architecture communications with a Mitsubishi programmable logic controller (PLC). The Python framework implements configurable safety policies through polygonal zones with authorized helmet colors, incorporates persistence filtering to prevent nuisance trips, and ensures deterministic translation from probabilistic AI outputs to Boolean PLC con-trol signals. Validation demonstrates reliable, low-latency safety actuation with clear ar-chitectural separation between vision processing, Python-mediated policy enforcement, and PLC-based deterministic control.
In road planning and landscape design, data collection emphasizes existing site conditions, particularly in projects involving modifications rather than new construction, as such data directly inform subsequent planning decisions. Beyond conventional surveying techniques, large-scale street-region digital elevation models can be generated using aerial imagery acquired from unmanned aerial vehicles. The point clouds derived from these aerial photographs provide a basis for constructing spatial models applicable to street landscape and road planning. In this study, aerial data were processed using Pix4D software 4.8.4 to generate the initial spatial model, which was subsequently integrated into a building information modeling-based design framework in Autodesk Revit 2022. This approach enabled rapid and precise design outputs, while the resulting BIM model was further applied to mapping applications to establish a foundational database for regional public works.
The conservation of endemic tree species in the Philippines requires lightweight and localized technological solutions. Therefore, we benchmark three Convolutional Neural Network (CNN) architectures: MobileNetV2, EfficientNetB0, and NASNetMobile, using 1800 leaf images from five endangered Dipterocarpaceae species and an unknown class. The models were trained with transfer learning and deployed on a Raspberry Pi prototype. Validation accuracies exceeded 97.00%, with NASNetMobile reaching 99.44%. Field testing produced lower accuracies of 85.00% for NASNetMobile, 77.78% for MobileNetV2, and 66.11% for EfficientNetB0. One-way analysis of variance with Tukey’s Honestly Significant Difference showed no statistically significant differences at the 0.05 level. These findings highlight the importance of field validation and statistical testing in CNN benchmarking for embedded biodiversity monitoring.
Biodiversity monitoring in tropical ecosystems is constrained by limited infrastructure, insufficient localized datasets, and reliance on cloud-based tools. We introduce BEMAX, a lightweight convolutional neural network for offline classification of endangered tree species in the Bohol Biodiversity Complex, Philippines. A curated leaf-image dataset from five species and an unknown class was collected using a Raspberry Pi camera. The MobileNetV2-based model achieved a 93.89% validation accuracy and an 88.33% field accuracy. Deployed on a Raspberry Pi 4 with touchscreen and camera integration, BEMAX demonstrates embedded AI as a replicable framework for conservation in data-scarce environments.
