• Smart Structures and Systems
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• v.25 no.6
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• pp.765-778
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• 2020

The present study intends to find a proper solution for the estimation of the physical behaviors of enlarged piles through a combination of small-scale laboratory tests and a hybrid computational predictive intelligence process. In the first step, experimental program is completed considering various critical influential factors. The results of the best multilayer perceptron (MLP)-based predictive network was implemented through three mathematical-based solutions of dragonfly algorithm (DA), whale optimization algorithm (WOA), and ant lion optimization (ALO). Three proposed models, after convergence analysis, suggested excellent performance. These analyses varied based on neurons number (e.g., in the basis MLP hidden layer) and of course, the level of its complexity. The training R² results of the best hybrid structure of DA-MLP, WOA-MLP, and ALO-MLP were 0.996, 0.996, and 0.998 where the testing R² was 0.995, 0.985, and 0.998, respectively. Similarly, the training RMSE of 0.046, 0.051, and 0.034 were obtained for the training and testing datasets of DA-MLP, WOA-MLP, and ALO-MLP techniques, while the testing RMSE of 0.088, 0.053, and 0.053, respectively. This obtained result demonstrates the excellent prediction from the optimized structure of the proposed models if only population sensitivity analysis performs. Indeed, the ALO-MLP was slightly better than WOA-MLP and DA-MLP methods.

• Advances in concrete construction
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• v.11 no.4
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• pp.347-356
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• 2021

To address the limitation of the commonly used steam curing of reactive powder concrete (SC-RPC) in engineering, a preparation technology of normal curing reactive powder concrete (NC-RPC) is proposed. In this study, an experimental comparative research on the mechanical properties of NC-RPC and SC-RPC under uniaxial tension is conducted. Under the premise of giving full play to the ultra-high performance of RPC, the paper tries to explore whether normal curing can replace steam curing. The results show that various mechanical indexes of NC-RPC (e.g., tensile strength, ultimate tensile strain, elastic modulus and deformation performance) could basically reach the mechanical index values in steam curing at 28d age, some performance is even better at a longer age. So it affirms the feasibility of normal curing. In this paper, the influence of normal curing age on the tensile properties of RPC is discussed, and the relationship between each index and age is introduced in detail. Based on the experimental data, the tensile mechanism of RPC is analyzed theoretically, and two kinds of tensile constitutive models for RPC are proposed, one is curvilinear model, and another one is polygonal line model. The validity of the two models is further verified by the test results of others.

• Journal of Convergence for Information Technology
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• v.9 no.8
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• pp.1-8
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• 2019

Dialog system is becoming a new dominant interaction way between human and computer. It allows people to be provided with various services through natural language. The dialog system has a common structure of a pipeline consisting of several modules (e.g., speech recognition, natural language understanding, and dialog management). In this paper, we tackle a task of domain classification for the natural language understanding module by employing machine learning models such as convolutional neural network and random forest. For our dataset of seven service domains, we showed that the random forest model achieved the best performance (F1 score 0.97). As a future work, we will keep finding a better approach for domain classification by investigating other machine learning models.

• Wind and Structures
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• v.28 no.4
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• pp.225-238
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• 2019

In this paper a novel and efficient computational framework to estimate the stress range versus number of cycles curves experienced by a cable due to external excitations (e.g., seismic excitations, traffic and wind-induced vibrations, among others) is proposed. This study is limited to the wind-cable interaction governed by the Vortex Shedding mechanism which mainly rules cables vibrations at low amplitudes that may lead to their failure due to bending fatigue damage. The algorithm relies on a stochastic approach to account for the uncertainties in the cable properties, initial conditions, damping, and wind excitation which are the variables that govern the wind-induced vibration phenomena in cables. These uncertainties are propagated adopting Monte Carlo simulations and the concept of importance sampling, which is used to reduce significantly the computational costs when new scenarios with different probabilistic models for the uncertainties are evaluated. A high fidelity cable model is also proposed, capturing the effect of its internal wires distribution and helix angles on the cables stress. Simulation results on a 15 mm diameter high-strength steel strand reveal that not accounting for the initial conditions uncertainties or using a coarse wind speed discretization lead to an underestimation of the stress range experienced by the cable. In addition, parametric studies illustrate the computational efficiency of the algorithm at estimating new scenarios with new probabilistic models, running 3000 times faster than the base case.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.169-176
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• 2021

Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation. We first generate gridded trajectory images by mapping the raw vessel trajectories into two dimensional matrix. Based on the gridded image input, we test the proposed model along with the other deep autoencoder-based models for the abnormal trajectory data generated through rotation and speed variation from normal trajectories. We show that the proposed model improves detection accuracy for the generated abnormal trajectories compared to the other models.

• Journal of Hospice and Palliative Care
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• v.26 no.1
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• pp.22-25
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• 2023

Although most patients prefer dying at home, patients whose condition rapidly becomes critical need care in the intensive care unit (ICU), and it is rare for them to die at home with their families. Therefore, interest in hospice and palliative care for patients in the ICU is increasing. Hospice and palliative care (PC) is necessary for all patients with life-threatening diseases. The following patients need palliative care in the ICU: patients with chronic critical illnesses who need tracheostomy, percutaneous gastrostomy tube, and extracorporeal life support; patients aged 80 years or older; stage 4 cancer patients; patients with specific acute diseases with a poor prognosis (e.g., anoxic brain injury and intracerebral hemorrhage requiring mechanical ventilation); and patients for whom the attending physician expects a poor prognosis. There are two PC models-a consultative model and an integrative model-in the ICU setting. Since these two models have advantages and disadvantages, it is necessary to apply the model that best fits each hospital's circumstances. Furthermore, interdisciplinary decision-making between the ICU care team and PC specialists should be strengthened to increase the provision of hospice and palliative care services for patients expected to have poor outcomes and their families.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.237-240
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• 2020

The fine-tuned neural network (NN) model for a whole temporal portion in a video does not always yield the best quality (e.g., PSNR) performance over all regions of each frame in the temporal period. For certain regions (usually homogeneous regions) in a frame for super-resolution (SR), even a simple bicubic interpolation method may yield better PSNR performance than the fine-tuned NN model. When there are multiple NN models available at the receivers where each NN model is trained for a group of images having a specific category of image characteristics, the performance of Quality enhancement can be improved by selectively applying an appropriate NN model for each image region according to its image characteristic category to which the NN model was dedicatedly trained. In this case, it is necessary to signal which NN model is applied for each region. This is very advantageous for image restoration and quality enhancement (IRQE) applications at user terminals with limited computing capabilities.

• Advances in Energy Research
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• v.8 no.2
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• pp.111-123
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• 2022

Solar Energy is the energy of solar radiation carried by them in the form of heat and light. It can be converted into electricity. Solar potential depends on the site's atmosphere; the solar energy distribution depends on many factors, e.g., turbidity, cloud types, pollution levels, solar altitude, etc. We estimated solar radiation with the help of the Ashrae clear-sky model for three locations in Pakistan, namely Pasni, Gwadar, and Jiwani. As these locations are close to each other as compared to the distance between the sun and earth, therefore a slight change of latitude and longitude does not make any difference in the calculation of direct beam solar radiation (BSR), diffuse solar radiation (DSR), and global solar radiation (GSR). A modified formula for declination angle is also developed and presented. We also created two different models for Ashrae constants. The values of these constants are compared with the standard Ashrae Model. A good agreement is observed when we used these constants to calculate BSR, DSR, GSR, the Root mean square error (RMSE), Mean Absolute error (MABE), Mean Absolute percent error (MAPE), and chisquare (χ²) values are in acceptance range, indicating the validity of the models.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.353-358
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• 2020

Recent efforts to develop a common standard for nuclear power plants (NPPs) with the aim of creating (1) a digital environment for a better understanding of NPPs life-cycle management aspect and (2) engineering data interoperability by using existing standards among different unspecified project participants (e.g., owners/operators, engineers, contractors, equipment suppliers) during plants' life cycle process (EPC, O&M, and decommissioning). In order to meet this goal, there is a need for formulating a standardized high-level physical breakdown structure (PBS) for NPPs project management office (PMO). However, high-level PBS must be comprehensive enough and able to represent the different types of plants and the new trends of technologies in the industry. This has triggered the need for addressing the issues of the recent operational NPPs and future technologies' ramification for evaluating the changes in the NPPs physical components in terms of structure, system, and component (SSC) configuration. In this context, this ongoing study examines the recent conventional NPPs and technological trends in the development of future NPPs facilities. New reactor models regarding the overlap of variant issues of nuclear technology were explored. Finally, issues on PBS for project management are explored by the examination of the configuration of NPPs primary system. The primary systems' configuration of different reactor models is assessed in order to clarify the need for analyzing the new trends in nuclear technology and to formulate a common high-level PBS. Findings and implications are discussed for further studies.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.596-601
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• 2013

In the current pavement construction practice, the state agencies traditionally determine the quality of the as-constructed pavement mix based on individual mixture material parameters (e.g., air voids, cement or asphalt content, aggregate gradation, etc.) and consider these parameters as key variables to influence payment schedule to the contractors and the present and future quality of the as-constructed mixture. A set of empirically pre-determined pay adjustment schedule for each parameter that was differently developed and being used by the individual agencies is then applied to a given project, in order to judge whether each parameter conforms to the designated specifications and consequently the contractor may either be rewarded or penalized in accordance with the payment schedule. With an improved quality assurance system, the Performance Related Specification, the individual parameters are not utilized as a direct judgment factor; rather, they become independent variables within a performance prediction function which is directly used to predict the performance. The quantified performance based on the prediction model is then applied to evaluate the pavement quality. This paper presents the brief history of the quality assurance in asphalt pavement construction including the Performance Related Specifications, statistical performance models in terms of fatigue and rutting distresses, as an example of the performance prediction models, and envisions the possibilities as to how this Performance Related Specification could be utilized in other infrastructures construction quality assurance.