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Item type:Publication, Correction: Sustainable urban waste collection using a hybrid heuristic–genetic approach: a Bangkok case study (Frontiers in Sustainability, (2026), 6, (1716538), 10.3389/frsus.2025.1716538)(2026-01-01); Waste Management An incorrect number was provided for School of Engineering, King Mongkut's Institute of Technology Ladkrabang. The correct number is 2565-02-01-074. The original version of this article has been updated.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Lightweight Transformer-Based Efficient Two-Step Network for Temporal Action Segmentation in Ultralow Frame Rate Excavator Work Videos at the Construction Site(2026-01-01); Itayama, YuichiroIn this paper, we propose a novel lightweight Transformer-based architecture for Temporal Action Segmentation (TAS) in an ultralow frame rate video setting, called a Transformer-Based Efficient Two-Step Network (ETSNFormer). This study makes three main contributions to the literature. First, we demonstrate that the choice of the temporal window size for feature extraction significantly affects the segmentation performance in an ultralow frame rate video setting. Second, we enhance the Efficient Two-Step Network (ETSN) baseline by integrating a modified Transformer-based decoder block, yielding improved segmentation accuracy while using fewer computational resources. Third, we employ a genetic algorithm-based hyperparameter-Tuning approach to automatically tune the hyperparameters of Local Burr Suppression (LBS), which is the post-processing method used in the ETSN baseline to mitigate the over-segmentation problem. On our ultralow frame rate excavator video dataset, ETSNFormer achieved state-of-The-Art results while using fewer parameters than prior approaches: A frame-wise accuracy of 89.91%, an edit score of 72.09%, and segmental F1 scores of 79.11%, 78.42%, and 73.51% at thresholds of 0.1, 0.25, and 0.5, respectively. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Co-Gasification of Plastic Waste Blended with Biomass: Process Modeling and Multi-Objective Optimization(2024-09-01); Wu, WeiMixed plastic/biomass co-gasification stands out as a promising and environmentally friendly technology, since it reduces wide solid wastes and produces green hydrogen. High-quality syngas can be obtained by virtue of the process design and optimization of a downdraft fixed-bed co-gasifier. The design is based on the actual reaction zones within a real gasifier to ensure accurate results. The methodology shows that (i) the co-gasifier modeling is validated using the adiabatic RGibbs model in Aspen Plus, (ii) the performance of the co-gasifier is evaluated using cold-gas efficiency (CGE) and carbon conversion efficiency (CCE) as indicators, and (iii) the multi-objective optimization (MOO) is employed to optimize these indicators simultaneously, utilizing a standard genetic algorithm (GA) combined with response surface methodology (RSM) to identify the Pareto frontier. The optimal conditions, resulting in a CGE of 91.78% and a CCE of 83.77% at a gasifier temperature of 967.89 °C, a steam-to-feed ratio of 1.40, and a plastic-to-biomass ratio of 74.23%, were identified using the technique for order of preference by similarity to ideal solution (TOPSIS). The inclusion of plastics enhances gasifier performance and syngas quality, leading to significant improvements in CGE and CCE values. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Data-driven approach to solve vertical drain under time-dependent loading(2021-06-01); Nguyen-Xuan, H.Currently, the vertical drain consolidation problem is solved by numerous analytical solutions, such as time-dependent solutions and linear or parabolic radial drainage in the smear zone, and no artificial intelligence (AI) approach has been applied. Thus, in this study, a new hybrid model based on deep neural networks (DNNs), particle swarm optimization (PSO), and genetic algorithms (GAs) is proposed to solve this problem. The DNN can effectively simulate any sophisticated equation, and the PSO and GA can optimize the selected DNN and improve the performance of the prediction model. In the present study, analytical solutions to vertical drains in the literature are incorporated into the DNN—PSO and DNN—GA prediction models with three different radial drainage patterns in the smear zone under time-dependent loading. The verification performed with analytical solutions and measurements from three full-scale embankment tests revealed promising applications of the proposed approach.