• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.157-164
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• 2022

The proposed privacy preserving scheme has identified the drawbacks of existing schemes in Vehicular Networks. This prototype enhances the number of nodes by decreasing the cluster size. This algorithm is integrated with the whale optimization algorithm and Block Chain Technology. A set of results are done through the NS-2 simulator in the direction to check the effectiveness of proposed algorithm. The proposed method shows better results than with the existing techniques in terms of Delay, Drop, Delivery ratio, Overhead, throughout under the denial of attack.

• Structural Engineering and Mechanics
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• 제88권1호
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• pp.43-52
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• 2023

This paper handles the results of an extensive parametric study on the rotational stiffness of the flexible base connection using ABAQUS program. The results of the parametric study show the relation between the applied moment and the relative rotation for 96 different base connections. The configurations of the studied connections considered different numbers, diameters, and spacing of the anchor bolts along with different thicknesses of the base plate to investigate the effect of these parameters on the rotational stiffness behavior. The results of the previous parametric research used through the whale optimization algorithm (WOA) to detect different equation formulation of the moment-rotation (M-Ɵ_r) equation to detect optimum equation simulates the general nonlinear rotational behavior of the flexible base connection considering all variables used in the parametric study. WOA is a relatively new promising algorithm, which is used in different types of optimization problems. For more verification, the classical genetic algorithm (GA) is used to make a comparison with WOA results. The results show that WOA is capable of getting an optimum equation of the M-Ɵ_r relation, which can be used to simulate the actual rotational stiffness of the flexible base connections. The rotational stiffness at H/150 can be calculated using WOA (1) method and be used as a design aid for engineering design.

• Smart Structures and Systems
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• 제25권1호
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• pp.47-56
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• 2020

This paper proposes a model-based approach for structural damage identification and quantification. Using pseudo modal strain energy and mode shape vectors, a damage-sensitive objective function is introduced which is suitable for damage estimation and quantification in shear frames. Whale optimization algorithm (WOA) is used to solve the problem and report the optimal solution as damage detection results. To illustrate the capability of the proposed method, a numerical example of a shear frame under different damage patterns is studied in both ideal and noisy cases. Furthermore, the performance of the WOA is compared with particle swarm optimization algorithm, as one the widely-used optimization techniques. The applicability of the method is also experimentally investigated by studying a six-story shear frame tested on a shake table. Based on the obtained results, the proposed method is able to assess the health of the shear building structures with high level of accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권5호
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• pp.1886-1908
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• 2020

With the technical advances, the amount of big data is increasing day-by-day such that the traditional software tools face a burden in handling them. Additionally, the presence of the imbalance data in big data is a massive concern to the research industry. In order to assure the effective management of big data and to deal with the imbalanced data, this paper proposes a new indexing algorithm for retrieving big data in the MapReduce framework. In mappers, the data clustering is done based on the Sparse Fuzzy-c-means (Sparse FCM) algorithm. The reducer combines the clusters generated by the mapper and again performs data clustering with the Sparse FCM algorithm. The two-level query matching is performed for determining the requested data. The first level query matching is performed for determining the cluster, and the second level query matching is done for accessing the requested data. The ranking of data is performed using the proposed Monarch chaotic whale optimization algorithm (M-CWOA), which is designed by combining Monarch butterfly optimization (MBO) [22] and chaotic whale optimization algorithm (CWOA) [21]. Here, the Parametric Enabled-Similarity Measure (PESM) is adapted for matching the similarities between two datasets. The proposed M-CWOA outperformed other methods with maximal precision of 0.9237, recall of 0.9371, F1-score of 0.9223, respectively.

• Structural Engineering and Mechanics
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• 제75권4호
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• pp.445-454
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• 2020

Structural damage identification (SDI) is a crucial step in structural health monitoring. However, some of the existing SDI methods cannot provide enough identification accuracy and efficiency in practice. A novel whale optimization algorithm (WOA) based method is proposed for SDI by weighting modal data and flexibility assurance criterion in this study. At first, the SDI problem is mathematically converted into a constrained optimization problem. Unlike traditional objective function defined using frequencies and mode shapes, a new objective function on the SDI problem is formulated by weighting both modal data and flexibility assurance criterion. Then, the WOA method, due to its good performance of fast convergence and global searching ability, is adopted to provide an accurate solution to the SDI problem, different predator mechanisms are formulated and their probability thresholds are selected. Finally, the performance of the proposed method is assessed by numerical simulations on a simply-supported beam and a 31-bar truss structures. For the given multiple structural damage conditions under environmental noises, the WOA-based SDI method can effectively locate structural damages and accurately estimate severities of damages. Compared with other optimization methods, such as particle swarm optimization and dragonfly algorithm, the proposed WOA-based method outperforms in accuracy and efficiency, which can provide a more effective and potential tool for the SDI problem.

• 한국항행학회논문지
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• 제24권4호
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• pp.291-298
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• 2020

본 연구는 입자 무리 최적화 (PSO; particle swarm optimization) 알고리즘을 이용하여 기존의 MPPT 알고리즘보다 신속하게 MPP를 추적할 수 있는 모델을 제안하였다. 제안 모델은 PSO 알고리즘에서 gbest 및 pbest의 가속 상수를 높게 설정하여 신속하게 MPP 지점을 추적하고 이로 인한 전력 불안정 문제점을 제거하였다. 또한, 일사량의 급격한 변화에 따른 태양광 패널의 전력 변화를 감지하여 알고리즘을 다시 실행하였다. 실험결과, 일사량이 691.5W/m2에 대해서 MPPT 시간이 0.03초와 전력이 131.65로서 기존의 P&O와 INC 알고리즘보다 높은 전력과 빠른 속도로 MPP를 추적하였으며, 일사량 변화에 따라 신속하게 MPP를 추적하였다. 제안 모델은 태양광 패널이 병렬로 연결되어 있는 태양광 발전소에서 부분적인 음영에 의해 전력량의 변화를 감지하였을 경우에도 적용할 수 있다. 본 연구는 MPPT 알고리즘을 개선하기 위해 MFO (moth flame optimization) 및 WOA (whale optimization algorithm)와 같은 최적화 알고리즘에 대한 비교 연구가 필요하다.

• Smart Structures and Systems
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• 제24권3호
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• pp.303-317
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• 2019

Magneto-rheological fluid (MRF) rotatory dampers are normally used for controlling the constant rotation of machines and engines. In this research, such a device is proposed to act as variable stiffness device to alleviate the rotational oscillation existing in the many engineering applications, such as motor. Under such thought, the main purpose of this work is to characterize the nonlinear torque-angular displacement/angular velocity responses of an MRF based variable stiffness device in oscillatory motion. A rotational hysteresis model, consisting of a rotatory spring, a rotatory viscous damping element and an error function-based hysteresis element, is proposed, which is capable of describing the unique dynamical characteristics of this smart device. To estimate the optimal model parameters, a modified whale optimization algorithm (MWOA) is employed on the captured experimental data of torque, angular displacement and angular velocity under various excitation conditions. In MWOA, a nonlinear algorithm parameter updating mechanism is adopted to replace the traditional linear one, enhancing the global search ability initially and the local search ability at the later stage of the algorithm evolution. Additionally, the immune operation is introduced in the whale individual selection, improving the identification accuracy of solution. Finally, the dynamic testing results are used to validate the performance of the proposed model and the effectiveness of the proposed optimization algorithm.

• Journal of Power Electronics
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• 제18권6호
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• pp.1805-1818
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• 2018

This paper proposes a novel hybrid maximum power point tracking (MPPT) algorithm combining a Whale Optimization Algorithm (WOA) and the conventional Perturb & Observation (P&O) to track/extract the highest amount of power from a solar photovoltaic (SPV) system working under partial shading conditions (PSCs). The proposed hybrid algorithm is based on a WOA which predicts the initial global peak (GP) and is followed by P&O in the final stage to achieve a quicker convergence to a GP. Thus, this hybrid algorithm overcomes the computational burden encountered in a standalone WOA, grey wolf optimization (GWO) and hybrid GWO reported in the literature. The conventional algorithm searches for the maximum power point (MPP) in the predicted region by the WOA. The proposed MPPT technique is modelled and simulated using MATLAB/Simulink for simulating an environment to check its effectiveness in accurately tracking the MPP during the GP region. This hybrid algorithm is compared with a standalone WOA, GWO and hybrid GWO. From the simulating results, it is shown that the proposed algorithm offers high tracking performance and that it increases the output power level of a SPV system under partial shading. The algorithm also verified experimentally on various PSCs.

• Structural Engineering and Mechanics
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• 제83권4호
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• pp.537-549
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• 2022

In this paper, a novel multi-feature model predictive control (MPC) framework with real-time and adaptive performances is proposed for intelligent structural control in which some drawbacks of the algorithm including, complex control rule and non-optimality, are alleviated. Hence, Linear Programming (LP) is utilized to simplify the resulted control rule. Afterward, the Whale Optimization Algorithm (WOA) is applied to the optimal and adaptive tuning of the LP weights independently at each time step. The stochastic control rule is also achieved using Kalman Filter (KF) to handle noisy measurements. The Extreme Learning Machine (ELM) is then adopted to develop a data-driven and real-time control algorithm. The efficiency of the developed algorithm is then demonstrated by numerical simulation of a twenty-story high-rise benchmark building subjected to earthquake excitations. The competency of the proposed method is proven from the aspects of optimality, stochasticity, and adaptivity compared to the KF-based MPC (KMPC) and constrained MPC (CMPC) algorithms in vibration suppression of building structures. The average value for performance indices in the near-field and far-field (El earthquakes demonstrates a reduction up to 38.3% and 32.5% compared with KMPC and CMPC, respectively.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.53-64
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• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.