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SSRN 2023年

作者： Yang, Shaopeng Guo, Yakun Yang, Lipeng Yu, Guoliang College of Ocean Science and Engineering Shandong University of Science and Technology Qingdao266590 China School of Naval Architecture Ocean &Civil Engineering Shanghai Jiao Tong University Shanghai200240 China Faculty of Engineering &Informatics University of Bradford BD7 1DP United Kingdom Qingdao Municipal Transport Development Centre Qingdao266100 China

This study employs numerical method to further elucidate the variation of local scour hole surrounding the piggyback pipeline under constant current conditions, considering the installation angle (α) of the small pipe ranging from 180° to 360°. Results reveal that a significant variation in the stagnation point of the main pipeline is observed as α ranges from 0° to 75° and from 315° to 360°, with a peak value occurring at α=60° and a minimum value at α=318°. For α varying from 180°to 360°, the scour hole is smaller than or proximate to that of the single pipe, indicating that positioning the small pipe beneath the center line of the main pipe exerts a certain inhibitory effect on scour. When α changes within range of 0°~360°, the piggyback pipeline exhibits its minimum values for local scour depth and width at α=222°. Under identical conditions, it has been observed that the scour hole resulting from positioning the small pipe above the center line of the primary pipe (α=0°~180°) is larger than when it is situated below (α=180°~360°). Similarly, the scour hole profile of the small pipe positioned upstream of the main pipe (α=270°~360°) exhibits greater dimensions compared to that located downstream (α=180°~270°). © 2023, The Authors. All rights reserved.

关键词： Numerical methods

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Adaptive deep neural network using genetics algorithms for solving linear and non-linear ordinary differential equations

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AIP Conference Proceedings 2022年第1期2472卷

作者： Pratama A. Danang Maharani A. Bakar 1Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu 21300 Kuala Terengganu Malaysia

The numerical solution of a ordinary differential equation (ODE) basically describes any physical phenomenon of interest. Traditionally, the numerical solution of ODE is solved by a discretization process using the classical finite difference approximation. Recently, many studies have used deep neural networks (DNN) to approximate ODE solutions without discretization. However, without the optimization process, DNN cannot run independently. In this work, we proposed an alternative method that combines DNN with a genetic algorithm (GA) as an optimization method. GA initiates a population of DNN trainable parameters, making the DNN training process more streamlined on a single-generation GA. We focused on DNN-GA hybrid solution for linear and nonlinear ordinary differential equations (ODE‘s). Our method is achieved by solving several test problems and demonstrating a comparison with stochastic gradient descent (SGD) optimizers. In most ODE problems, using the same number of iterations and architecture, DNN-GA produces a better loss function than using DNN-SGD.

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Simulation of coupled spring-mass system using modified multistep deep neural networks

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AIP Conference Proceedings 2023年第1期2484卷

作者： Danang A. Pratama Maharani A. Bakar 1Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Nerus Terengganu 21300 Malaysia 2Special Interest Group Modelling and Data Analytics Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Nerus Terengganu 21300 Malaysia

Deep Neural Networks (DNN) have a long time to be proven as universal function estimators. Recently, researchers have emerged DNN as an approximator for differential equation (DE) solutions which improved the conventional numerical methods without the discretization process. In this paper, we modified the DNN architecture to solve the DE problems by using a scalable hyperparameter in the activation function, which can be optimized to achieve the best performance of the DNN. In this case, we chose the Multistep time-stepping scheme to find a good solution of the DE problems, which is then continued by DNN to optimize the accuracy of the solutions. The proposed method called Multistep DNN is implemented in the problem of coupled spring-mass systems. The system is measuring the mass motion using a couple of the first-order systems of DE. The motion of the mass is described with and without any damping into the system, Some numerical results consist of the solutions of some variety model problems, both with and without damping and with the variation of mass and spring constant arranged vertically position. The modified Multistep DNN has better learning capabilities than the original one (without adaptive activation function) and classical multistep as it improves greatly the convergence rate, as well as the solution accuracy.

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Simulation of heat flow in welding using hybrid restarting FDM-SVR-Lanczos

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AIP Conference Proceedings 2023年第1期2484卷

作者： Rehana Thalib Maharani A. Bakar 1Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Nerus Terengganu 21300 Malaysia 2Special Interest Group Modelling and Data Analytics Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Nerus Terengganu 21300 Malaysia

This paper discusses the numerical simulations of heat transfer problem, particularly during the welding materials processing. This process highly depends on the temperature to determine the metallurgical properties, the strength, and the sureness of the joint during welding; hence it is desirable to study its heat flow. Finite difference method (FDM) is used to discretize partial differential equations (PDEs) to obtain the systems of linear equations (SLEs), which can be solved by an iterative method. Here, we used a variant of Lanczos based methods, called Restarting-FDM-SVR-Lanczos to solve two-dimensional heat flow in welding. The proposed method is a combination of Lanczos method and machine learning under the support vector regression (SVR) method, where the SVR plays important role in predicting the non-existing solutions of the Lanczos iterates, due to the breakdown issue in Lanczos method. Numerical results were presented and showed that Hybrid Restarting FDM-SVR-Lanczos performed better in solving the heat flow in welding problem compared with the single FDM and Lanczos methods, with small residual norms.

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A two-phase controlled drug release model in a one-compartmental system

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AIP Conference Proceedings 2023年第1期2484卷

作者： Amanina Setapa Shalela Mohd Mahali Fatimah Noor Harun 1Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Malaysia 2Management Science Research Group Universiti Malaysia Terengganu Malaysia

Small intestine is the main absorptive organ of orally consumed drugs in the body. Hydrogel is one of the materials that commonly used as a controlled drug release device that able to deliver drugs to be absorbed in the small intestine. This material swells to enable the drug release, leading to a dynamic diffusivity. In this paper, we aimed to simulate the controlled drug release from a swelling hydrogel considering the dynamic diffusion coefficient in a one-compartmental system of small intestine. The small intestine is considered as a single compartment where the controlled drug release process takes place simultaneously with the drug absorption and elimination process. We had formulated a two-phase controlled drug release model with two different constant diffusion coefficients using the advection-diffusion equation. The two-phase model was then adapted into the one-compartmental system and is solved using the forward finite difference method. The numerical simulation shows that the suggested mathematical model able to express the complex process of the controlled drug delivery in the small intestine.

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Modelling and simulation investigation of multi-string photovoltaic array system with MPPT controlled DC/DC converter

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AIP Conference Proceedings 2023年第1期2484卷

作者： N. Anang W. M. W. Muda. 1Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Malaysia. 2Renewable Energy and Power Research Interest Group (REPRIG) Eastern Corridor Renewable Energy SIG Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Malaysia.

This paper presents the simulation of power generation in a photovoltaic (PV) system that applies maximum PV power point tracking (MPPT) in a DC/DC boost converter. The MPPT is responsible to obtain the possible maximum PV power to be extracted to the input of the DC/DC boost converter. Although there are equations available to calculate the value of components in DC/DC circuit in literature, the validation of input parameters of the converter which are crucial in the design process was not properly presented, especially for different solar irradiance and temperature. Thus, this paper shows a step-by-step simulation of multi-string PV with converter, from designing the converter based on the PV specification and constructing the model by using MATLAB/Simulink software system. Next, the algorithm of the incremental conductance method was developed in MPPT to control the duty cycle of the converter. The result shows that the developed system has successfully output the maximum power under different solar irradiance and temperature with high efficiency of 99.0%. Simulation results were validated using the P-V curves of the selected PV modules. Then, the proposed model can then be connected to an inverter for a complete grid-connected PV system in the future.

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Plasma-enhanced metamaterial for antenna application

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AIP Conference Proceedings 2022年第1期2454卷

作者： Ahmad Nazri Dagang Tan Swe Lan Iswan Lapagang 1Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu 21030 Kuala Nerus Terengganu Malaysia

Metamaterials is a lefthanded media that can be applied in antenna application to overcome the traditional design which is bigger in size and untunable. The wireless industry could overcome this problem since metamaterial antenna is designed with high permittivity dielectric substrate, using shorting well, shorting pins, and some deformation. Furthermore, antenna design not only reduces in size, but has high directivity, tunable frequency, and high performance to solve those aforementioned problems. Metamaterial antenna consists of hundred features which tunable to catch signals coming from any direction. This study proposed different structures of metamaterials using plasma to enhance its capability by using Computer Simulation technology (CST) simulation software. The aims are to analyze and compare antenna performance of the different structures in terms of return loss, gain, radiation pattern, as well as to investigate antenna performance of different plasma in terms of its plasma properties. This study used four different metamaterials structures namely split ring structure, Symmetrical structure, Omega structure, and S structure. There are 12-type of plasma conditions labeled with form Plasma A to L, with different plasma frequency and collision frequency. These materials were simulated using CST to calculate their return loss, gain, and radiation pattern, in order to identify the best antenna performance condition. The split ring structure has the highest return loss at -74.8 dB (Plasma D) while the S structure gives the best antenna gain at 7.9 dB (Plasma F). This is thought due to the electromagnetic properties in the split ring structure, and the high collision and plasma frequencies of plasma in the S structure. With 12 types of plasma parameters value, combined with 4 types of structures, it can be said that Plasma F, gave the best results in terms of the combination of antenna gain (average at 7.2 dB) and return loss value (average at -33.6 dB). In term

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Blood cell detection and counting in microscopy images using overlapping object recognizing model

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AIP Conference Proceedings 2023年第1期2484卷

作者： Muhamad Munawar Yusro Rozniza Ali Muhammad Suzuri Hitam 1)Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Malaysia

Computer vision is one of the most striking areas in data science. Like other fields of data science, the application of this field has also become part of our personal lives. Classifying an overlapping object in cell detection is one of the main challenges faced by researchers who work in object detection and recognition. The white and red blood cells have a significant part in controlling the human body's framework. One of the essential tests for assessing human medical issue is checking the amount of white blood cells and red blood cells, as well as platelets number in the blood. In this project, we used one of the Convolutional Neural Network (CNN) techniques which is Faster Region CNN to recognize, classify, and count the blood cell elements. Our objective is to detect blood cell images from dataset, separate between overlapping cells and other objects, and then count the number of each blood cell. The researchers used 720 images containing 1603 labelled blood cells from 10 categories to train the model. Those images were marked by clinical the Hospital Clinic of Barcelona ‘s pathologists and were obtained from the dissemination of blood drawn from patients during 2015-2019. After training steps were performed on 60 images, and blood cell detection and segmentation were performed in the next steps. The average accuracy was 97%, and the verification loss was 18.4%. By using this our Faster R-CNN framework object detection method can enhance the accuracy and speed of cell detection.

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A review paper on image segmentation techniques based on colour and texture features

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AIP Conference Proceedings 2023年第1期2484卷

作者： Mohammad Sameer Aloun Muhammad Suzuri Hitam Wan Nural Jawahir Hj Wan Yussof Zainudin Bachok 1Faculty of Ocean Engineering Technology and Informatics (FTKKI) Universiti Malaysia Terengganu Malaysia 2Institute of Oceanography and Environment (INOS) Universiti Malaysia Terengganu (UMT) Malaysia

In image processing and computer vision, image segmentation plays a crucial role. It finds a place in many image applications. Texture and colour features and its combination are among the main features used in image segmentation. This paper presents the literature review for colour-based segmentation techniques and texture-based segmentation techniques. The main objective for this paper is to provide the recent trends of both the color and texture segmentation technique in image segmentation as well as presenting the most popular evaluation measurements used for image segmentation. At the end of this paper, the conclusion for future work and its direction is presented.

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Examining relative variable importance (RVI) and strength of interaction effects (SIE) of particulate matter (PM10) concentrations in moving vehicles at selected route in east coast of Malaysia

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AIP Conference Proceedings 2023年第1期2484卷

作者： Y. N. Zaitun W. M. W. Nurdiyana H. Fahimah I. Zulfadhli 1Senior Lecturer Faculty of Ocean Engineering Technology and Informatics University Malaysia Terengganu Malaysia 2Researcher Faculty of Ocean Engineering Technology and Informatics University Malaysia Terengganu Malaysia

The poor indoor air quality inside the cabin of a coach can be due to indoor air pollutants or to inadequate ventilation. Exposure to surrounding airborne particulate matter inside a long hour journey may increase health risk of the coach passengers. In this study, a one-minute gaseous (carbon monoxide (CO), carbon dioxide (CO2)), physical parameters (relative humidity, airflow, temperature), coach speeds, and number of occupants’ data were gathered two days trips for each route travelled coaches via motorways and local routes. The Route 1 journey started from Terminal Kota Bharu (TKB) to Terminal Bersepadu Selatan (TBS) and Route 2 link between Terminal Kuala Terengganu (TKT) and Terminal Bersepadu Selatan (TBS). Data were analysed to understand variability of the variables by using R software and its packages were applied temporally and statistically. An advance and a computational intelligence technique named the Boosted Regression Trees (BRT) was used to examine Relative Variable Importance (RVI) and estimation the Strength of Interaction Effects (SIE) inside cabin coaches. Results demonstrates significant variation in PM10 are distance (39.19%), elevation (15.19%) temperature (10.56%) humidity (6.65%) for Route 1 and 46.7% for CO (13.77 %), humidity (8.92 %), and elevation (8.01 %) for Route 2. It was found that strength of interactions (SIE) index was determined for humidity and temperature for both routes which range between 0.23 to 0.32, followed by humidity and airflow interactions (0.05) and CO and airflow (0.18) for Route 2. Since SIE or H-values fall within range from 0 to 1 which value of zero indicates that there are no interactions between the variables, the interaction between variables will be stronger if the value is closer to 1. It appears and promises that this machine learning method in particular BRT is able to examine in cabin pollutants and physical environment data.

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