• Earthquakes and Structures
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• v.24 no.2
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• pp.127-140
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• 2023

The vibration of microtubule in human cells is the source of electrical field around it and inside cell structure. The induction of electrical field is a direct result of the existence of dipoles on the surface of the microtubules. Measuring the electrical fields could be performed using nano-scale sensors and the data could be transformed to other computers using internet of things (IoT) technology. Processing these data is feasible by artificial intelligence-based methods. However, the first step in analyzing the vibrational behavior is to study the mechanics of microtubules. In this regard, the vibrational behavior of the microtubules is investigated in the present study. A shell model is utilized to represent the microtubules' structure. The displacement field is assumed to obey first order shear deformation theory and classical theory of elasticity for anisotropic homogenous materials is utilized. The governing equations obtained by Hamilton's principle are further solved using analytical method engaging Navier's solution procedure. The results of the analytical solution are used to train, validate and test of the deep neural network. The results of the present study are validated by comparing to other results in the literature. The results indicate that several geometrical and material factors affect the vibrational behavior of microtubules.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.122-128
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• 2020

This research investigates how a Virtual Reality (VR)-based simulation could be used to train safe operation skills for forklift operators. Forklift operation is categorized as high-risk work by many occupational health and safety regulators and authorities due to high injury and fatality rates involved with forklifts. Therefore, many safety guidelines have been developed for forklift operators. Typically, forklift operation safety training is delivered based on instructional texts or videos, which have limitations in influencing people's safety behavior. Against this background, we propose a VR-based forklift simulator that can enable safe operation skills training through a feedback system. The training program consists of several modules to teach how to perform the basic tasks of forklift operation, such as driving, loading and unloading, following the safety guidelines. The system provides instantaneous instructions and feedback regarding safe operation. This training system is based on the model of "learning-by-doing". The user can repeat the training modules as many times as necessary before being able to perform the given task without violating any safety guidelines. The last training module tests the user's acquisition of all safety skills required. The user feedback from several demonstration sessions showed the potential usefulness of the proposed training system.

• Steel and Composite Structures
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• v.49 no.1
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• pp.91-107
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• 2023

An effective approach to promoting sustainability within the construction industry is the use of recycled aggregate concrete (RAC) as a substitute for natural aggregates. Ensuring the frost resilience of RAC technologies is crucial to facilitate their adoption in regions characterized by cold temperatures. The main aim of this study was to use the Random Forests (RF) approach to forecast the frost durability of RAC in cold locations, with a focus on the durability factor (DF) value. Herein, three optimization algorithms named Sine-cosine optimization algorithm (SCA), Black widow optimization algorithm (BWOA), and Equilibrium optimizer (EO) were considered for determing optimal values of RF hyperparameters. The findings show that all developed systems faithfully represented the DF, with an R² for the train and test data phases of better than 0.9539 and 0.9777, respectively. In two assessment and learning stages, EO - RF is found to be superior than BWOA - RF and SCA - RF. The outperformed model's performance (EO - RF) was superior to that of ANN (from literature) by raising the values of R² and reducing the RMSE values. Considering the justifications, as well as the comparisons from metrics and Taylor diagram's findings, it could be found out that, although other RF models were equally reliable in predicting the the frost durability of RAC based on the durability factor (DF) value in cold climates, the developed EO - RF strategy excelled them all.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.33 no.4
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• pp.412-418
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• 2023

As advances in artificial intelligence (AI) increasingly approach areas once relegated to the realm of science fiction, there is growing public interest in using these technologies for practical everyday tasks in both the home and the workplace. This paper explores the applications of and implications for of using ChatGPT, a conversational AI model based on GPT-3.5 and GPT-4.0, in the field of occupational health and safety. After gaining over one million users within five days of its launch, ChatGPT has shown promise in addressing issues ranging from emergency response to chemical exposure to recommending personal protective equipment. However, despite its potential usefulness, the integration of AI into scientific work and professional settings raises several concerns. These concerns include the ethical dimensions of recognizing AI as a co-author in academic publications, the limitations and biases inherent in the data used to train these models, legal responsibilities in professional contexts, and potential shifts in employment following technological advances. This paper aims to provide a comprehensive overview of these issues and to contribute to the ongoing dialogue on the responsible use of AI in occupational health and safety.

• Annual Conference of KIPS
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• 2023.11a
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• pp.637-640
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• 2023

This paper presents multimodal image fusion with human pose for detecting abnormal human behaviors in low illumination conditions. Detecting human behaviors in low illumination conditions is challenging due to its limited visibility of the objects of interest in the scene. Multimodal image fusion simultaneously combines visual information in the visible spectrum and thermal radiation information in the long-wave infrared spectrum. We propose an abnormal event detection scheme based on the multimodal fused image and the human poses using the keypoints to characterize the action of the human body. Our method assumes that human behaviors are well correlated to body keypoints such as shoulders, elbows, wrists, hips. In detail, we extracted the human keypoint coordinates from human targets in multimodal fused videos. The coordinate values are used as inputs to train a multilayer perceptron network to classify human behaviors as normal or abnormal. Our experiment demonstrates a significant result on multimodal imaging dataset. The proposed model can capture the complex distribution pattern for both normal and abnormal behaviors.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.117-129
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• 2024

This paper introduces collective navigation through a narrow gap using a curriculum-based deep reinforcement learning algorithm for a swarm of unmanned aerial vehicles (UAVs). Collective navigation in complex environments is essential for various applications such as search and rescue, environment monitoring and military tasks operations. Conventional methods, which are easily interpretable from an engineering perspective, divide the navigation tasks into mapping, planning, and control; however, they struggle with increased latency and unmodeled environmental factors. Recently, learning-based methods have addressed these problems by employing the end-to-end framework with neural networks. Nonetheless, most existing learning-based approaches face challenges in complex scenarios particularly for navigating through a narrow gap or when a leader or informed UAV is unavailable. Our approach uses the information of a certain number of nearest neighboring UAVs and incorporates a task-specific curriculum to reduce learning time and train a robust model. The effectiveness of the proposed algorithm is verified through an ablation study and quantitative metrics. Simulation results demonstrate that our approach outperforms existing methods.

• Smart Structures and Systems
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• v.33 no.1
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• pp.65-91
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• 2024

The use of five optimization techniques for the prediction of a strength-based concrete mixture's best-fit model is examined in this work. Five optimization techniques are utilized for this purpose: Slime Mold Algorithm (SMA), Black Hole Algorithm (BHA), Multi-Verse Optimizer (MVO), Vortex Search (VS), and Whale Optimization Algorithm (WOA). MATLAB employs a hybrid learning strategy to train an artificial neural network that combines least square estimation with backpropagation. Thus, 72 samples are utilized as training datasets and 31 as testing datasets, totaling 103. The multi-layer perceptron (MLP) is used to analyze all data, and results are verified by comparison. For training datasets in the best-fit models of SMA-MLP, BHA-MLP, MVO-MLP, VS-MLP, and WOA-MLP, the statistical indices of coefficient of determination (R²) in training phase are 0.9603, 0.9679, 0.9827, 0.9841 and 0.9770, and in testing phase are 0.9567, 0.9552, 0.9594, 0.9888 and 0.9695 respectively. In addition, the best-fit structures for training for SMA, BHA, MVO, VS, and WOA (all combined with multilayer perceptron, MLP) are achieved when the term population size was modified to 450, 500, 250, 150, and 500, respectively. Among all the suggested options, VS could offer a stronger prediction network for training MLP.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.3
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• pp.131-139
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• 2024

Crew Transfer Vessels (CTVs) are primarily used for the maintenance of offshore wind farms. Despite being manually operated by professional captains and crew, collisions with other ships and marine structures still occur. To prevent this, the introduction of autonomous navigation systems to CTVs is necessary. In this study, research on the obstacle avoidance system of the autonomous navigation system for CTVs was conducted. In particular, research on obstacle avoidance simulation for CTVs using deep reinforcement learning was carried out, taking into account the currents and wind loads in offshore wind farms. For this purpose, 3 degrees of freedom ship maneuvering modeling for CTVs considering the currents and wind loads in offshore wind farms was performed, and a simulation environment for offshore wind farms was implemented to train and test the deep reinforcement learning agent. Specifically, this study conducted research on obstacle avoidance maneuvers using MATD3 within deep reinforcement learning, and as a result, it was confirmed that the model, which underwent training over 10,000 episodes, could successfully avoid both static and moving obstacles. This confirms the conclusion that the application of the methods proposed in this study can successfully facilitate obstacle avoidance for autonomous navigation CTVs within offshore wind farms.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.119-126
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• 2024

Disease caused by the coronavirus (COVID-19) is sweeping the globe. There are numerous methods for identifying this disease using a chest imaging. Computerized Tomography (CT) chest scans are used in this study to detect COVID-19 disease using a pretrain Convolutional Neural Network (CNN) ResNet50. This model is based on image dataset taken from two hospitals and used to identify Covid-19 illnesses. The pre-train CNN (ResNet50) architecture was used for feature extraction, and then fully connected layers were used for classification, yielding 97%, 96%, 96%, 96% for accuracy, precision, recall, and F1-score, respectively. When combining the feature extraction techniques with the Back Propagation Neural Network (BPNN), it produced accuracy, precision, recall, and F1-scores of 92.5%, 83%, 92%, and 87.3%. In our suggested approach, we use a preprocessing phase to improve accuracy. The image was enhanced using the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm, which was followed by cropping the image before feature extraction with ResNet50. Finally, a fully connected layer was added for classification, with results of 99.1%, 98.7%, 99%, 98.8% in terms of accuracy, precision, recall, and F1-score.

• Steel and Composite Structures
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• v.50 no.4
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• pp.443-458
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• 2024

The construction industry, one of the biggest producers of greenhouse emissions, is under a lot of pressure as a result of growing worries about how climate change may affect local communities. Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues connected to the manufacture of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete, which might be used in lieu of traditional concrete to reduce CO₂ emissions in the building industry. In the present work, the compressive strength (f_c) of GPC is calculated using random forests regression (RFR) methodology where natural zeolite (NZ) and silica fume (SF) replace ground granulated blast-furnace slag (GGBFS). From the literature, a thorough set of experimental experiments on GPC samples were compiled, totaling 254 data rows. The considered RFR integrated with artificial hummingbird optimization (AHA), black widow optimization algorithm (BWOA), and chimp optimization algorithm (ChOA), abbreviated as ARFR, BRFR, and CRFR. The outcomes obtained for RFR models demonstrated satisfactory performance across all evaluation metrics in the prediction procedure. For R² metric, the CRFR model gained 0.9988 and 0.9981 in the train and test data set higher than those for BRFR (0.9982 and 0.9969), followed by ARFR (0.9971 and 0.9956). Some other error and distribution metrics depicted a roughly 50% improvement for CRFR respect to ARFR.