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Beirami, Mohammadamin,Lee, Hee Yong,Yu, Yung-Ho The Korean Society of Marine Engineering 2015 한국마린엔지니어링학회지 Vol.39 No.5
This paper deals with development of an autopilot system for leisure yacht based on NMEA 2000 network and android platform. The developed system can operate both for manual steering and automatic navigation mode. In automatic steering mode, after manipulation of commands which are NMEA 0183 sentences by android platform, the developed system translates and sends the packets through NMEA 2000 network. Then the controller which is connected to NMEA 2000 network receives the commands and controls the boat's rudder system automatically. The automatic steering mode is achieved by cooperation of two controllers; one for controlling the rudder system, and the other for controlling the vessel's heading. To control the vessel's rudder and heading angle two PID controllers are developed with an adjustable dead-band gain. Also, in order to eliminate the steady-state error occurred by applying dead-band, an integral controller which specifically supervises the system's behavior inside the dead-band area is developed. In this paper, at the first stage, simulations are accomplished using computer in order to examine the feasibility of the proposed based on simulation results. In the next step, the system on a real hydraulic steering model is implemented and at the end the performance examination by implementing it on a real boat and doing test navigation is executed.
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Ali Asghar Moslemi Beirami,Vadim V. Ponkratov,Amir Ebrahim Akbari Baghal,Barno Abdullaeva,Mohammadali Nasrabadi 국제구조공학회 2023 Structural Engineering and Mechanics, An Int'l Jou Vol.88 No.2
Cost-effective high precision hybrid elements are presented in a hierarchical form for dynamic analysis of plates. The costs associated with controlling the vibrations of ferromagnetic plates can be minimized by adequate determination of the amount of electric current and magnetic field. In the present study, the effect of magnetic field and electric current on nonlinear vibrations of ferromagnetic plates is investigated. The general form of Lorentz forces and Maxwell's equations have been considered for the first time to present new relationships for electromagnetic interaction forces with ferromagnetic plates. In order to derive the governing nonlinear differential equations, the theory of third-order shear deformations of three-dimensional plates has been applied along with the von Kármán large deformation strain-displacement relations. Afterward, the nonlinear equations are discretized using the Galerkin method, and the effect of various parameters is investigated. According to the results, electric current and magnetic field have different effects on the equivalent stiffness of ferromagnetic plates. As the electric current increases and the magnetic field decreases, the equivalent stiffness of the plate decreases. This is a phenomenon reported here for the first time. Furthermore, the magnetic field has a more significant effect on the steady-state deflection of the plate compared to the electric current. Increasing the magnetic field and electric current by 10-times results in a reduction of about 350% and an increase of 3.8% in the maximum steady-state deflection, respectively. Furthermore, the nonlinear frequency decreases as time passes, and these changes become more intense as the magnetic field increases.
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Development of 3D Terrain Visualization for Navigation Simulation using a Unity 3D Development Tool
Shin, Il-Sik,Beirami, Mohammadamin,Cho, Seok-Je,Yu, Yung-Ho The Korean Society of Marine Engineering 2015 한국마린엔지니어링학회지 Vol.39 No.5
3D visualization of navigation simulation is to visualize the environment conditions (e.g. nearby ships, dynamic characteristics, environment, terrain, etc) for any users on ships at sea. Realistic 3D visualization enables the users to be immersed to it and guarantees the reliability of the simulation. In particular, terrain visualization contains many virtual objects, so it is time and cost-intensive for object modelling. This paper proposes a 3D terrain visualization method that can be realized in a short time and with low cost by using the Unity 3D development tool. The 3D terrain visualization system requires bathymetric and elevation terrains, and Aids to Navigations (AtoNs) to be realized. It also needs to include 3D visualization objects including bridges, buildings and port facilities for more accurate simulation. Bathymetric and AtoN elements are acquired from ENC, and the elevation element is acquired from SRTM v4.1 digital elevation chart database developed by NASA. Then, the bathymetric and elevation terrains are generated, and the satellite images are superposed by using this terrain information. The longitudinal and latitudinal information of the AtoNs are converted to the 3-axis information to position the AtoN locations. The 3D objects such as bridges, buildings and port facilities are generated and the terrain visualization is completed. The proposed method realizes more realistic 3D terrain visualization of Busan Port.
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Seyed Vahab Shojaedini,Mohamad Javad Beirami 대한의용생체공학회 2020 Biomedical Engineering Letters (BMEL) Vol.10 No.3
Automated recognition of daily human tasks is a novel method for continuous monitoring of the health of elderly people. Nowadays mobile devices (i.e. smartphone and smartwatch) are equipped with a variety of sensors, therefore activity classification algorithms have become as useful, low-cost, and non-invasive diagnostic modality to implement as mobile software. The aim of this article is to introduce a new deep learning structure for recognizing challenging (i.e. similar) humanactivities based on signals which have been recorded by sensors mounted on mobile devices. In the proposed structure, theresidual network concept is engaged as a new substructure inside the main proposed structure. This part is responsible toaddress the problem of accuracy saturation in convolutional neural networks, thanks to its ability in jump over some layerswhich leads to reducing vanishing gradients eff ect. Therefore the accuracy of the classifi cation of several activities isincreased by using the proposed structure. Performance of the proposed method is evaluated on real life recorded signalsand is compared with existing techniques in two diff erent scenarios. The proposed structure is applied on two well-knownhuman activity datasets that have been prepared in university of Fordham. The fi rst dataset contains the recorded signalswhich arise from six diff erent activities including walking, jogging, upstairs, downstairs, sitting, and standing. The seconddataset also contains walking, jogging, stairs, sitting, standing, eating soup, eating sandwich, and eating chips. In the fi rstscenario, the performance of the proposed structures is compared with deep learning schemes. The obtained results show thatthe proposed method may improve the recognition rate at least 5% for the fi rst dataset against its own family alternatives indistinguishing challenging activities (i.e. downstairs and upstairs). For the second data set similar improvements is obtainedfor some challenging activities (i.e. eating sandwich and eating chips). These superiorities even reach to at least 28% whenthe capability of the proposed method in recognizing downstairs and upstairs is compared to its non-family methods for thefi rst dataset. Increasing the recognition rate of the proposed method for challenging activities (i.e. downstairs and upstairs,eating sandwich and eating chips) in parallel with its acceptable performance for other non-challenging activities shows itseff ectiveness in mobile sensor-based health monitoring systems.
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Gholam Reza Khayati,Zahra Rajabi,Maryam Ehteshamzadeh,Hadi Beirami 한국콘크리트학회 2022 International Journal of Concrete Structures and M Vol.16 No.4
The use of reinforced concrete is common in marine structures. Failure of reinforcement due to corrosion has detrimental impacts on nearly all of these structures. Hence, proposing an accurate and reliable model was imperative. The goal of this paper is to develop a new hybrid model by combining Particle Swarm Optimization (PSO) with Dragonfly Algorithm (DA) for Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the corrosion current density (C11) of marine reinforced concrete. The neuro-fuzzy-based methods have emerged as suitable techniques for encountering uncertainties associated with the corrosion phenomenon in marine structures. To the best of our knowledge, this is the first research that predicts the C11 through a model integrating fuzzy learning, neural learning rules, and meta-heuristics. 2460 data are collected from 37 regions in Persian Gulf. The input parameters are age, concrete repairing history, height above the sea level, distance from sea, concrete compressive strength, rebar diameter, concrete cover depth, concrete electrical resistivity, chloride ion concentration and pH. The proposed rules for the estimation of C11 based on collected dataset are assessed based on the several metrics such as R2, efficiency, mean absolute percentage error (MAPE), and median of absolute error (MEDAE). According to the results, ANFIS-PSO–DA enables to predict C11 by R2 (0.92), MAPE (1.67), MEDAE (0.14), and EF (0.97). The results of sensitivity analysis revealed that concrete compressive strength and pH are the most effective parameters on the corrosion current density of reinforced concrete.
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