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CONTENTS
Volume 29, Number 5, May 2022
 

Abstract
Gravel scattering that is generated during operation of high-speed railway vehicle is cause to damage of vehicle such as windows, axle protector and so on. Especially, those are frequently occurred in winter season when snow ice is generated easily. Above all, damage of vehicle windows has not only caused maintenance cost but also increased psychological anxiety of passengers. Various methods such as heating system using copper wire, heating jacket and heating air are applied to remove snow ice generated on the under-body of vehicle. However, the methods require much run-time and man power which can be low effectiveness of work. Therefore, this paper shows that large-area heating system was developed based on heating coat in order to fundamentally prevent snow ice damage on high-speed railway vehicle in the winter season. This system gives users high convenience because that can remotely control the heating system using IoT-based wireless communication. For evaluating the applicability to railroad sites, a field test on an actual high-speed railroad operation was conducted by applying these techniques to the brake cylinder of a high-speed railroad vehicle. From the results, it evaluated how input voltage and electric power per unit area of the heating specimen influences exothermic performance to draw the permit power condition for icing. In the future, if the system developed in the study is applied at the railroad site, it may be used as a technique for preventing all types of damages occurring due to snow ice in winter.

Key Words
brake cylinder; deicing technology; multi-layered paint-type exothermic coating; railway site; snow ice

Address
Heonyoung Kim: Research Institute, PILETA Co., Ltd., Daejeon 34016, Republic of Korea
Donghoon Kang: Railroad Safety Research Division, Korea Railroad Research Institute, Uiwang 16105, Republic of Korea
Chulmin Joo: Department of Mechanical Engineering, Yonsei University, Seoul 03722, Republic of Korea

Abstract
In this paper, an electric vehicle drives with efficient control and low cost hardware using four quadrant DC converter with Permanent Magnet Direct Current (PMDC) motor fed by DC boost converter is presented. The main idea of this work is to improve the energy efficiency of the conversion chain of an electric vehicle by inserting a boost converter between the battery and the four quadrant-DC motor chopper assembly. Consequently, this method makes it possible to maintain the amplification gain of the 4 quadrant chopper constant regardless of the battery voltage drop and even in the presence of a fault in the battery. One of the most important control problems is control under heavy uncertainty conditions. The higher order sliding mode control technique is introduced for the adjustment of DC bus voltage and mechanical motor speed. To implement the proposed approach in the automotive field, experimental tests were carried out. The performances obtained show the usefulness of this system for a better energy management of an electric vehicle and an ideal control under different operating conditions and constraints, mostly at nominal operation, in the presence of a load torque, when reversing the direction of rotation of the motor speed and even in case of battery chamber failure. The whole system has been tested experimentally and its performance has been analyzed.

Key Words
battery; DC boost converter; electric vehicle; four quadrant DC converter; higher order sliding mode control; mechanical speed and voltage control; sensor control of PMDC motor drives

Address
Karim Negadi: L2GEGI Laboratory, Department of Electrical Engineering, Faculty of Applied Science, University of Tiaret, BP 78 Zaaroura, 14000, Tiaret, Algeria
Mohamed Boudiaf: Department of Electrical Engineering, Ziane Achour University of Djelfa, Djelfa, Algeria
Rabah Araria: L2GEGI Laboratory, Department of Electrical Engineering, Faculty of Applied Science, University of Tiaret, BP 78 Zaaroura, 14000, Tiaret, Algeria
Lazreg Hadji: Department of Mechanical Engineering, University of Tiaret, BP 78 Zaaroura, Tiaret,14000, Algeria

Abstract
Water cycle algorithm (WCA) has been a very effective optimization technique for complex engineering problems. This study employs the WCA for simultaneous prediction of heating load (LH) and cooling load (LC) in residential buildings. This algorithm is responsible for optimally tuning a neural network (NN). Utilizing 614 records, the behavior of the LH and LC is explored and the captured knowledge is then used to predict for 154 unanalyzed building conditions. Since the WCA is a population-based algorithm, different numbers of the searching agents were tested to find the most optimum configuration. It was observed that the best solution is discovered by 500 agents. A comparison with five newly-developed benchmark optimizers, namely equilibrium optimizer (EO), multi-tracker optimization algorithm (MTOA), slime mould algorithm (SMA), multi-verse optimizer (MVO), and electromagnetic field optimization (EFO) revealed that the WCANN predicts the desired parameters with considerably larger accuracy. Obtained root mean square errors (1.4866, 2.1296, 2.8279, 2.5727, 2.5337, and 2.3029 for the LH and 2.1767, 2.6459, 3.1821, 2.9732, 2.9616, and 2.6890 for the LC) indicated that the most reliable prediction was presented by the proposed model. The EFONN, however, provided a more time-effective solution. Lastly, an explicit predictive formula was elicited from the WCANN.

Key Words
cooling load; energy performance; heating load; neural computing; water cycle algorithm

Address
Chang Lin and Junsong Wang: School of Architecture, South China University of Technology, 381 Wushan Road, Tianhe District, Guangzhou, Guangdong 510640, China

Abstract
This study investigates an effective approach to stabilize nonlinear systems. To ensure the asymptotic nonlinear stability in nonlinear discrete-time systems, the present study presents controller for an EBA (Evolved Bat Algorithm) NN (fuzzy neural network) in the algorithm. In fuzzy evolved NN modeling, the auxiliary circuit with high frequency LDI (linear differential inclusions) and NN model representation is developed for the nonlinear arbitrary dynamics. An example is utilized to demonstrate the system more robust compared with traditional control systems.

Key Words
artificial intelligence; fuzzy theory; intelligent algorithm; LDI; NN controller

Address
Z.Y. Chen, Ruei-Yuan Wang, Rong Jiang: School of Science, Guangdong University of Petrochemical Technology, Maoming, Guangdong 525000, China
Timothy Chen: Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA

Abstract
In this paper, the effect of magnetic field on the vibration behavior of a Magnetorheological elastomer (MRE) sandwich MEMS actuated by electrostatic actuation with conductive skins are examined within the multiple scales (MMS) perturbation method. Magnetorheological smart materials have been widely used in vibration control of various systems due to their mechanical properties change under the influence of different magnetic fields. To investigate the vibrational behavior of the movable electrode, the Euler-Bernoulli beam theory, as well as Hamilton's principle is used to derive the equations and the related boundary conditions governing the dynamic behavior of the system are applied. The results of this study show that by placing the Magnetorheological elastomer core in the movable electrode and applying different magnetic fields on it, its natural vibrational frequency can be affected so that by increasing the applied magnetic field, the system's natural frequency increases. Also, the effect of various factors such as the electric potential difference between two electrodes, changes in the thickness of the core and the skins, electrode length, the distance between two electrodes and also change in vibration modes of the system on natural frequencies have been investigated.

Key Words
magnetorheological elastomer; MEMS actuator; method of multiple scales; nonlinear dynamics; perturbation; sandwich beam

Address
Hossein Akhavan, Javad Ehyaei and Majid Ghadiri: Faculty of Engineering, Department of Mechanics, Imam Khomeini International University, 34148-96818, Qazvin, Iran

Abstract
Recently, numbers of long span pedestrian suspension bridges have been constructed worldwide. While recent tragedies regarding pedestrian suspension bridges have shown how these bridges can wreak havoc on the society, there are no specific guidelines for construction standards nor safety inspections yet. Therefore, a structural health monitoring system that could help ensure the safety of pedestrian suspension bridges are needed. System identification is one of the popular applications for structural health monitoring method, which estimates the dynamic system. Most of the system identification methods for bridges are currently adapting output-only system identification method, which assumes the dynamic load to be a white noise due to the difficulty of measuring the dynamic load. In the case of pedestrian suspension bridges, the pedestrian load is within specific frequency range, resulting in large errors when using the output-only system identification method. Therefore, this study aims to develop a system identification method for pedestrian suspension bridges considering both input and output of the dynamic system. This study estimates the location and the magnitude of the pedestrian load, as well as the dynamic response of the pedestrian bridges by utilizing artificial intelligence and computer vision techniques. A simulation-based validation test was conducted to verify the performance of the proposed system. The proposed method is expected to improve the accuracy and the efficiency of the current inspection and monitoring systems for pedestrian suspension bridges.

Key Words
computer vision; deep learning; pedestrian suspension bridge; structural health monitoring; system identification

Address
Jeonghyeok Lim and Hyungchul Yoon: Department of Civil Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju-si, Chungcheongbuk-do, Republic of Korea

Abstract
In this paper, a nonlinear structural model updating methodology based on the Deep Belief Network (DBN) is proposed. Firstly, the instantaneous parameters of the vibration responses are obtained by the discrete analytical mode decomposition (DAMD) method and the Hilbert transform (HT). The instantaneous parameters are regarded as the independent variables, and the nonlinear model parameters are considered as the dependent variables. Then the DBN is utilized for approximating the nonlinear mapping relationship between them. At last, the instantaneous parameters of the measured vibration responses are fed into the well-trained DBN. Owing to the strong learning and generalization abilities of the DBN, the updated nonlinear model parameters can be directly estimated. Two nonlinear shear-type structure models under two types of excitation and various noise levels are adopted as numerical simulations to validate the effectiveness of the proposed approach. The nonlinear properties of the structure model are simulated via the hysteretic parameters of a Bouc-Wen model and a Giuffre-Menegotto-Pinto model, respectively. Besides, the proposed approach is verified by a three-story shear-type frame with a piezoelectric friction damper (PFD). Simulated and experimental results suggest that the nonlinear model updating approach has high computational efficiency and precision.

Key Words
DBN; instantaneous parameters; nonlinear model updating; vibration responses

Address
Ye Mo: Department of Civil Engineering, Hefei University of Technology, Hefei, Anhui, 23009, China
Zuo-Cai Wang: Department of Civil Engineering, Hefei University of Technology, Hefei, Anhui, 23009, China; Anhui Engineering Research Center for Civil Engineering Disaster Prevention and Mitigation, Hefei, Anhui, 230009, China
Genda Chen: Department of Civil, Architectural, and Environmental Engineering, Missouri University of Science and Technology, Rolla, 65409, USA
Ya-Jie Ding: Department of Civil Engineering, Hefei University of Technology, Hefei, Anhui, 23009, China
Bi Ge: Department of Civil Engineering, Hefei University of Technology, Hefei, Anhui, 23009, China

Abstract
Hierarchical structure parameters, proposed in She-Leveque model, are investigated for velocity components obtained from different flow types over a large range of Reynolds numbers 255 < ReT < 720. The values of intermittency parameter B, with respect to a fixed velocity component, are observed nearly same for all four types of turbulence. The parameter Y, for streamwise velocity components is nearly the same but significantly different for vertical components in different flows. It is also observed that for both parameters, an obvious relation between the longitudinal and transverse components BT < BL (and YT < YL) always holds. However, the difference between BL and BT is found very small in all types of turbulent flows, we studied here. It is evidenced that at low Reynolds numbers, the deviations from K41 scaling are mainly due to the most intense structures and slightly because of more heterogeneous hierarchy of fluctuation structures. However, at higher Reynolds numbers the deviations seem as a consequence of the most intense structures only. Over all, the study suggests that the hierarchy parameter B may be consider as a universal constant.

Key Words
hierarchical structure parameters; intermittency; structure functions

Address
Imtiaz Ahmad: Department of Mathematics, Mirpur University of Science and Technology (MUST), Mirpur-10250 (AJK), Pakistan
Lamjed Hadj-Taieb: College of Engineering, Department of Mechanical Engineering, Prince Sattam Bin Abdulaziz University, 16273, AlKharj, Saudi Arabia; Laboratory of Applied Fluid Mechanics, Processes Engineering and Environment, Department of Mechanical Engineering, National Engineering School of Sfax, University of Sfax, Tunisia
Muzamal Hussain: Department of Mathematics, Government College Universit Faisalabad, Punjab, Pakistan
Mohamed A. Khadimallah: Civil Engineering Department, College of Engineering, Prince Sattam Bin Abdulaziz University, BP 655, Al-Kharj, 11942, Saudi Arabia
Muhammad Taj: Department of Mathematics, University of Azad Jammu and Kashmir, Muzaffarabad, 1300, Azad Kashmir, Pakistan
Adil Alshoaibi: Department of Physics, College of Science, King Faisal University, Al-Hassa, P.O. Box 400, Hofuf 31982, Saudi Arabia

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