PurposeThe goal of this study is to determine the values of the process parameters that should be used during the machining of ceramic tile using the abrasive water jet (AWJ) process in order to achieve the lowest pos...
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PurposeThe goal of this study is to determine the values of the process parameters that should be used during the machining of ceramic tile using the abrasive water jet (AWJ) process in order to achieve the lowest possible values for surface roughness and kerf taper ***/methodology/approachIn the present work, ceramic tile is processed by the AWJ process and experimental data were recorded using the RSM approach based Box-Behnken design matrix. The input process factors were water jet pressure, jet traverse speed, abrasive flow rate and standoff distance, to determine the surface roughness and kerf taper angle. ANOVA was used to check the adequacy of model and significance of process parameters. Further, the elite opposition-based learning grasshopper optimization (EOBL-GOA) algorithm was implemented to identify the simultaneous optimization of multiple responses of surface roughness and kerf taper angle in *** suggested EOBL-GOA algorithm is suitable for AWJ of ceramic tile, as evidenced by the error rate of & PLUSMN;2 percent between experimental and predicted solutions. The surfaces were evaluated with an SEM to assess the quality of the surface generated with the optimal settings. As compared with initial setting of the SEM image, it was noticed that the bottom cut surface was nearly smooth, with less cracks, striations and pits in the improved optimal results of the SEM image. The results of the analysis can be used to control machining parameters and increase the accuracy of AWJed ***/valueThe findings of this study present an innovative method for assessing the characteristics of the nontraditional machining processes that are most suited for use in industrial and commercial applications.
Due to the growing number of Industrial Internet of Things (IoT) devices, network attacks like denial of service (DoS) and floods are rising for security and reliability issues. As a result of these attacks, IoT devic...
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Due to the growing number of Industrial Internet of Things (IoT) devices, network attacks like denial of service (DoS) and floods are rising for security and reliability issues. As a result of these attacks, IoT devices suffer from denial of service and network disruption. Researchers have implemented different techniques to identify attacks aimed at vulnerable Industrial Internet of Things (IoT) devices. In this study, we propose a novel features selection algorithm FGOA-kNN based on a hybrid filter and wrapper selection approaches to select the most relevant features. The novel approach integrated with clustering rank the features and then applies the grasshopper algorithm (GOA) to minimize the top-ranked features. Moreover, a proposed algorithm, IHHO, selects and adapts the neural network's hyper parameters to detect botnets efficiently. The proposed Harris Hawks algorithm is enhanced with three improvements to improve the global search process for optimal solutions. To tackle the problem of population diversity, a chaotic map function is utilized for initialization. The escape energy of hawks is updated with a new nonlinear formula to avoid the local minima and better balance between exploration and exploitation. Furthermore, the exploitation phase of HHO is enhanced using a new elite operator ROBL. The proposed model combines unsupervised, clustering, and supervised approaches to detect intrusion behaviors. This combination can enhance the accuracy and robustness of the proposed model by identifying the most relevant features and detecting known and unknow botnet activity. The N-BaIoT dataset is utilized to validate the proposed model. Many recent techniques were used to assess and compare the proposed model's performance. The result demonstrates that the proposed model is better than other variations at detecting multiclass botnet attacks.
This research presents a fractional order (FO) controller for frequency control in microgrid. Four intelligent methodologies, namely, grasshopper optimization algorithm (GOA), Gravitational search algorithm (GSA), Gen...
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This research presents a fractional order (FO) controller for frequency control in microgrid. Four intelligent methodologies, namely, grasshopper optimization algorithm (GOA), Gravitational search algorithm (GSA), Genetic algorithm (GA), and Particle swarm optimization (PSO), are used for optimization-based tuning of FO controller. The proposed microgrid consists of wind turbine, solar photo voltaic system, aqua electrolyser, flywheel, ultra-capacitor, battery storage system, diesel engine, and fuel cell. GOA is employed to tune the parameters of the controllers. Application of GOA-based two-/multi-stage fractional order PID controller i.e., FOPID-(1 + PI) controller in frequency control for microgrid is a novel work. GOA-optimized FOPID-(1 + PI) controller unveils best results over the conventional PID/FOPID controller in terms of settling time, peak undershoot/overshoot, and performance index. Examination of dynamic responses for abrupt changes in load request divulges the pre-eminence of the proposed controller strategy with others. The robustness study implies that GOA-optimized fractional controller operates superbly and robustly under nonlinearities and perturbation in system parameters. In order to demonstrate the ascendancy of GOA, its results in terms of statistical parameters and performance index are compared with the results of GA, PSO, and GSA.
Currently, video and digital images possess extensive utility, ranging from recreational and social media purposes to verification, military operations, legal proceedings, and penalization. The enhancement mechanisms ...
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Currently, video and digital images possess extensive utility, ranging from recreational and social media purposes to verification, military operations, legal proceedings, and penalization. The enhancement mechanisms of this medium have undergone significant advancements, rendering them more accessible and widely available to a larger population. Consequently, this has facilitated the ease with which counterfeiters can manipulate images. Convolutional neural network (CNN)-based feature extraction and detection techniques were used to carry out this task, which aims to identify the variations in image features between modified and non-manipulated areas. However, the effectiveness of the existing detection methods could be more efficient. The contributions of this paper include the introduction of a segmentation method to identify the forgery region in images with the U-Net model's improved structure. The suggested model connects the encoder and decoder pipeline by improving the convolution module and increasing the set of weights in the U-Net contraction and expansion path. In addition, the parameters of the U-Net network are optimized by using the grasshopper optimization algorithm (GOA). Experiments were carried out on the publicly accessible image tempering detection evaluation dataset from the Chinese Academy of Sciences Institute of Automation (CASIA) to assess the efficacy of the suggested strategy. The results show that the U-Net modifications significantly improve the overall segmentation results compared to other models. The effectiveness of this method was evaluated on CASIA, and the quantitative results obtained based on accuracy, precision, recall, and the F1 score demonstrate the superiority of the U-Net modifications over other models.
X-rays are devices immensely used for diagnosis and treatment in medicinal field. X-rays are very popular as they are used in non-destructive testing. An X-ray device uses a high voltage power supply which can be prod...
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X-rays are devices immensely used for diagnosis and treatment in medicinal field. X-rays are very popular as they are used in non-destructive testing. An X-ray device uses a high voltage power supply which can be produced by DC-DC converters. Resonant converters are prominent among the dc-dc converters for X-ray application as the switching is done at zero current and zero voltage. This paper presents a LCC resonant converter based PID controller tuned with optimization algorithms for non-linear system. Differential evolution optimization (DEO), grey wolf optimization (GWO) and grasshopper optimization algorithms (GOA) are used to identify the local minima in time domain system. The objective functions considered are minimization of integral absolute error (IAE), integral square error (ISE) and integral time absolute error (ITAE). The convergence curve is also plotted for the different optimization algorithms using MATLAB Simulink to study the convergence time.
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