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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)
Hamontree, Chaowalit
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Koiwanit, Jarotwan
;
Sinchai, Ananta
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.
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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)
Sereepookkana, Kanok
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Orachon, Teerapong
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Doi, Shigeo
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Apivanichkul, Kamonchat
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Itayama, Yuichiro
In 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.
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Co-Gasification of Plastic Waste Blended with Biomass: Process Modeling and Multi-Objective Optimization
(2024-09-01)
Aentung, Tanawat
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Patcharavorachot, Yaneeporn
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Wu, Wei
Mixed 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.
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Data-driven approach to solve vertical drain under time-dependent loading
(2021-06-01)
Nghia-Nguyen, Trong
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Kikumoto, Mamoru
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Khatir, Samir
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Chaiyaput, Salisa
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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.
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Product recommendation based on genetic algorithm
(2019-07-01)
Janjarassuk, Udom
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Puengrusme, Sudatip
In this paper, we propose a product recommendation system based on genetic algorithm to find the best recommendation for a combination of products to the customers. The model evaluation relies on customer preferences and product requirements as well as feature ratings from the product experts. The system is tested by using a case study from recommendation of power unit selection for recording studio. Experimental results are provided.
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Diagnosing metabolic syndrome using genetically optimised Bayesian ARTMAP
(2019-01-01)
Kakudi, Habeebah Adamu
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Loo, Chu Kiong
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Moy, Foong Ming
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Masuyama, Naoki
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Pasupa, Kitsuchart
Metabolic Syndrome (MetS) constitutes of metabolic abnormalities that lead to non-communicable diseases, such as type II diabetes, cardiovascular diseases, and cancer. Early and accurate diagnosis of this abnormality is required to prevent its further progression to these diseases. This paper aims to diagnose the risk of MetS using a new non-clinical approach called 'genetically optimized Bayesian adaptive resonance theory mapping' (GOBAM). We evolve the Bayesian adaptive resonance theory mapping (BAM) by using genetic algorithm to optimize the parameters of BAM and its training input sequence. We use the GOBAM algorithm to classify individuals as either being at risk of MetS or not at risk of MetS with a related posterior probability, which ranges between 0 and 1. A data set of 11 237 Malaysians from the CLUSTer study stratified by age and gender into four subcategories was used to evaluate the proposed GOBAM algorithm. The comparative evaluation of our results suggested that the GOBAM performs significantly better than other classical adaptive resonance theory mapping models on the area under the receiver operating characteristic curves (AUC) and others criteria. Our algorithm gives an AUC of 86.42 %, 87.04 %, 91.08 %, and 89.24 % for the young female, middle aged female, young male, and middle-aged male subcategories, respectively. The proposed model can be used to support medical practitioners in accurate and early diagnosis of MetS.
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Improved Random Forest (RF) classifier for imbalanced classification of lung nodules
(2018-08-13)
Paing, May Phu
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Choomchuay, Somsak
Computer-aided detection (CAD) for lung cancer acts a dynamic research in biomedical engineering. These CADs generally occur imbalanced data classification because there are a large number of false lesions which are non-nodules (majority class), compared to the actual nodules (minority class). This paper proposes an improved random forest (RF) classifier to solve the learning bias problem of the imbalanced classification. The proposed RF applies sampling and three feature selection schemes namely Relief, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to improve the classification performance.
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A comparative study of mixed-integer linear programming and genetic algorithms for solving binary problems
(2018-06-15)
Kuendee, Punyisa
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Janjarassuk, Udom
This paper aims to investigate the capability of mixed-integer linear programming (MILP) method and genetic algorithm (GA) to solve binary problem (BP). A comparative study on the MILP method and GA with default and tuned setting to find out an optimal solution is presented. The mixed-integer programming library (MIPLIB 2010) is used to test and evaluate algorithms. The evaluation is shown in quality of the solution and the execution time of computation. The results show that GA is superior to MILP in execution time with inconsistent results. However, MILP is superior to GA in quality of the solution with more stable results.
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Multi-parents genetic algorithm optimization scheme for linear array antenna synthesis
(2017-04-26)
Sakonkanapong, Arnon
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Todnatee, Sarayoot
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Phongcharoenpanich, Chuwong
This research has proposed the improved iterative generic algorithm (GA) optimization scheme by using multi-parents crossover and adaptation mutation to synthesize the radiation pattern of an aperiodic (nonuniform) linear array antenna. The aim of the iterative optimization is to achieve a radiation pattern with a side lobe level (SLL) less than or equal to -20dB. In the optimization, the proposed scheme iteratively optimizes the array range (spacing) and the number of array elements, whereby the array element with the lowest absolute complex weight coefficient is first removed and then the second lowest and so on. The removal (the element reduction) is terminated once the SLL is greater than -20dB and the elemental increment mechanism is triggered. The results indicate that the proposed iterative GA optimization scheme is applicable to the non-uniform linear array antenna and also is capable of synthesizing the radiation pattern with SLL less than or equal to -20dB.
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Robust 2DOF fuzzy gain scheduling control for DC servo speed controller
(2016-11-01)
Chitsanga, Natchanon
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Kaitwanidvilai, Somyot
This paper proposes a new design method called “robust 2DOF fuzzy gain scheduling control” for a DC servo speed control system. The proposed technique utilizes the basic concept of 2DOF robust loop shaping, whose time-domain specifications are combined during the controller design using a reference model. In addition, the local controllers are fixed- structure robust controllers whose structure can be specified as for a simple controller. A fuzzy approach is adopted in both system identification process and global control structure to accomplish an entirely robust system. Although the design of robust control in a fuzzy system is not easy, genetic algorithms (GAs) simplify the control design problem to design the fuzzy controller such that the average stability margin is minimized. Implementation of a DC servo speed control was adopted to investigate the effectiveness of the proposed controller. As seen from the results, the proposed controller has more robust performance and can be adopted in applications with a wide operating range. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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