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Experimental investigation on similar and dissimilar alloys of stainless steel joints by laser beam welding

ADVANCES IN MATERIALS AND PROCESSING TECHNOLOGIES 2022年第Supp 1期8卷 13-28页

作者： Bijivemula, Narayana Reddy Hema, P. Padmanabhan, G. SV Univ Dept Mech Engn Tirupati 517502 Andhra Pradesh India

Alloy and Stainless steel plates have to be welded to meet the present generation of smart manufacturing by Laser Beam Welding. Laser Beam Welding (LBW) plays an important role in joining of metals or welding the parts of similar/dissimilar metals. Due to its high-speed weldability with high laser beam power result in high strength and low heat affected zones with better appearance and quality. AISI Stainless steels which are widely used in various industries due to their resistance to corrosion. There is often a need to study the effect of process parameters, while fabricating stainless steel structures and/or when weld stainless steel to low-alloy steels. A solemn attempt is made in the present research work to join similar and dissimilar AISI 4130 Alloy and AISI 309 Stainless Steel joints by CO2 Laser Beam Welding with an objective to investigate the effects of process parameters. The weld joints are studied based on their Bead Width and Micro Hardness. The performance characteristic curves are drawn and analysed the influence of the process parameters on the weld joints. The whale optimisation algorithm is used to identify the optimal combination of process parameters based on the experimental output results of Bead Width and Micro Hardness.

关键词： Laser beam welding alloy steel stainless steel bead width micro hardness ANOVA whale optimisation algorithm

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Current Electric Load Forecasting Based on WOA-VMD-LSSVM 21

Current Electric Load Forecasting Based on WOA-VMD-LSSVM

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21st International Conference on Harmonics and Quality of Power

作者： Xie, Shixuan Liu, Liqun Wu, Qingfeng Taiyuan Univ Sci & Technol Fac Elect Informat Engn Taiyuan Peoples R China

ISBN: (纸本)9798350382570;9798350382563

In order to improvement of the accuracy of the short-term performance system load prediction and ensure a normal operation of daily power system, forecasting short term load model on the basis of WOA-VMD-LSSVM was proposed. To begin with, The whale optimisation algorithm (WOA) is used to automatically select the best search for a optimal core parameters (K-value and penalty factor a) of the the variational modal decomposition (VMD) to get the best decomposition subsequence, reduce the following applies trend information to the prediction precision, and decompose a data by using the optimized VMD;after that, the decomposed data sets are inputted into the model separately, and each subsequence's predictions are summed to produce the final *** that, decomposed groups about information is input in the pattern separately, and the forecasting scores of each of the sub-sequence summed to get the latest results of the forecast. To a certain extent, it provides a scientific decision-making basis for load management and optimal power dispatching.

关键词： forecasting model analysis whale optimisation algorithm variational modal decomposition combined forecasting models

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Research on Short-term Power Load Forecasting Using the WOA-BiLSTM-Attention Model 6

Research on Short-term Power Load Forecasting Using the WOA-...

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2023 IEEE 6th International Conference on Information Systems and Computer Aided Education, ICISCAE 2023

作者： Dai, Lei Xiao, Chengcheng Tang, Guohua China University of Geosciences Beijing School of Science Beijing China

ISBN: (纸本)9798350313444

The foundation of power system operation is short-term power load forecasting, and precise load forecasting can guarantee the power system's safe and stable operation. This paper chooses the bidirectional long- and short-term memory network model of the attention mechanism under the whale Optimization algorithm (WOA-BiLSTM-Attenion) for the evaluation of electric power forecasting in order to address the issues of low precision and poor accuracy of short-term load forecasting. Bi-directional long-short-term memory network modeling attentional mechanisms can lessen the loss of past information and boost the effect of important information. In order to minimize human intervention, the whale Optimization algorithm (WOA) is then applied for hyperparameter selection. The experimental results demonstrate that the WOA-BiLSTM-Attention model outperforms BP, LSTM, BiLSTM, and BiLSTM-Attention in terms of short-term power load forecasting, with an RMSE value of 5913.9446 and an R2 value of 0.9477. Its greater accuracy has some repercussions for other fields' forecasting research. © 2023 IEEE.

关键词： Attention mechanism BiLSTM deep learning load forecasting whale optimisation algorithm

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Gas Concentration Prediction Based on IWOA-LSTM-CEEMDAN Residual Correction Model

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SENSORS 2022年第12期22卷 4412-4412页

作者： Xu, Ningke Wang, Xiangqian Meng, Xiangrui Chang, Haoqian Anhui Univ Sci & Technol State Key Lab Min Response & Disaster Prevent & C Huainan 232000 Peoples R China Anhui Univ Sci & Technol Sch Comp Sci & Engn Huainan 232000 Peoples R China Anhui Univ Sci & Technol Sch Econ & Management Huainan 232000 Peoples R China

In this study, to further improve the prediction accuracy of coal mine gas concentration and thereby preventing gas accidents and improving coal mine safety management, the standard whale optimisation algorithm's (WOA) susceptibility to falling into local optima, slow convergence speed, and low prediction accuracy of the single-factor long short-term memory (LSTM) neural network residual correction model are addressed. A new IWOA-LSTM-CEEMDAN model is constructed based on the improved whale optimisation algorithm (IWOA) to improve the IWOA-LSTM one-factor residual correction model through the use of the complete ensemble empirical model decomposition with adaptive noise (CEEMDAN) method. The population diversity of the WOA is enhanced through multiple strategies and its ability to exit local optima and perform global search is improved. In addition, the optimal weight combination model for subsequence is determined by analysing the prediction error of the intrinsic mode function (IMF) of the residual sequence. The experimental results show that the prediction accuracy of the IWOA-LSTM-CEEMDAN model is higher than that of the BP neural network and the GRU, LSTM, WOA-LSTM, and IWOA-LSTM residual correction models by 47.48%, 36.48%, 30.71%, 27.38%, and 12.96%, respectively. The IWOA-LSTM-CEEMDAN model also achieves the highest prediction accuracy in multi-step prediction.

关键词： coal mine safety whale optimisation algorithm LSTM CEEMDAN decomposition and reconstruction gas concentration prediction

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Nature-inspired intrusion detection system for protecting software-defined networks controller

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COMPUTERS & SECURITY 2023年第1期134卷

作者： Kumar, Chandan Biswas, Soham Ansari, Md. Sarfaraj Alam Govil, Mahesh Chandra Natl Inst Technol Dept Comp Sci & Engn Sikkim 737139 India

Software Defined Networks (SDN) is a new emerging networking architecture facilitated by a separate controller. It has a centralized architecture that serves network management and demand fulfillment from a single point. Architecturally, SDN provides lofty benefits, but security measures are a severe concern that prevents universal adoption. If the SDN controller gets compromised, the intruders could ensign the route of network traffic based on their requirements which causes damage to the entire network. These security limitations of SDN get rectified by employing the network intrusion detection system (NIDS). Deploying ML techniques for NIDS helps us to detect the anomalies within SDN. Selecting a minimal number of features is important as it decreases the computation overhead and accelerates the NIDS performances. For feature selection, a combination of k optimal features is selected using the hit-and-trial approach. As a result, it influences the system's durability for intrusion detection and pushes the model toward poor performance. In this study, we present a new hybrid framework for feature selection using whale optimisation algorithm (WOA), Fisher Score (FS), and Information gain (IG), named whale Optimization algorithm-Fisher Feature Importance (WOA-(FI)-I-2). WOA-(FI)-I-2 addresses the challenge of selecting the.. number of an optimal subset of features. The evaluation result manifests the WOA-F-2 I feature selection technique that enhances the performance of the NIDS model, which backs the input for various classifiers-support vector machine, Naive Bayes, logistic regression, and stochastic gradient descent. The proposed feature selection technique also provides better performance for binary class and multi-class attack detection.

关键词： Software Defined Networks Intrusion Detection System whale optimisation algorithm Fisher score Information gain Feature selection Dimension reduction

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Enabling image optimisation and artificial intelligence technologies for better Internet of Things framework to predict COVID

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IET NETWORKS 2022年第0期

作者： Allayla, Noor M. Ibraheem, Farah Nazar Jaleel, Refed Adnan Univ Mosul Dept Comp Engn Mosul Iraq Al Nahrain Univ Dept Informat & Commun Engn Baghdad 10070 Iraq

Sensor technology advancements have provided a viable solution to fight COVID and to develop healthcare systems based on Internet of Things (IoTs). In this study, image processing and Artificial Intelligence (AI) are used to improve the IoT framework. Computed Tomography (CT) image-based forecasting of COVID disease is among the important activities in medicine for measuring the severity of variability in the human body. In COVID CT images, the optimal gamma correction value was optimised using the whale optimisation algorithm (WOA). During the search for the optimal solution, WOA was found to be a highly efficient algorithm, which has the characteristics of high precision and fast convergence. whale optimisation algorithm is used to find best gamma correction value to present detailed information about a lung CT image, Also, in this study, analysis of important AI techniques has been done, such as Support Vector Machine (SVM) and Deep-Learning (Deep-Learning (DL)) for COVID disease forecasting in terms of amount of data training and computational power. Many experiments have been implemented to investigate the optimisation: SVM and DL with WOA and without WOA are compared by using confusion matrix parameters. From the results, we find that the DL model outperforms the SVM with WOA and without WOA.

关键词： COVID deep learning image processing Internet of Things Support Vector Machine whale optimisation algorithm

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Wide area coordinated control for low-frequency oscillations damping in a wind-integrated power system

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JOURNAL OF ENGINEERING-JOE 2019年第18期2019卷 4941-4945页

作者： Gupta, Abhilash Kumar Verma, Kusum Niazi, Khaleequr Rehman Malaviya Natl Inst Technol Dept Elect Engn Jaipur Rajasthan India

The increasing wind integration in present grids to generate clean energy escalates the problem of low-frequency oscillations (LFOs) in the system. Here, a wide area-based coordinated control strategy of power system stabilisers (PSSs) and doubly fed induction generator's (DFIGs) power oscillation dampers (PODs) is presented to improve the system damping. An improved eigenvalue-based objective function is employed to optimally tune the variables of the controller using whale optimisation algorithm (WOA). The impact of DFIG location and thus the suitable wind farm locations are also found out using eigenvalue analysis and dynamic sensitivity analysis. The results are investigated on IEEE 68 bus test system highlighting the effectiveness of proposed strategy for a wide range of operating scenarios.

关键词： oscillations asynchronous generators sensitivity analysis optimisation damping power system stability power system control eigenvalues and eigenfunctions power generation control wind power plants clean energy low-frequency oscillations wide area-based coordinated control strategy power system stabilisers system damping DFIG location IEEE 68 bus test system wind-integrated power system doubly fed induction generator wind farm locations power oscillation dampers whale optimisation algorithm eigenvalue analysis dynamic sensitivity analysis operating scenarios objective function

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Optimal wavelet-based multi-modal medical image fusion with quantitative analysis for colour images using different colour models

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INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY 2022年第3期40卷 262页

作者： Nair, Rekha R. Singh, Tripty Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Comp Sci & Engn Bengaluru India

Colour is the component generally used to discriminate and recognise information and it is also considered as one of the most important aspects of vision. Abundant of information contained in the colour image can be utilised for multiple purposes applications such as image analysis, object identification and extraction of powerful details. This paper proposes an optimal wavelet colour image fusion (OWCIF) algorithm for multi-modal medical images that can work with source images of any size. The proposed algorithm works with greyscale and colour images. OWCIF is composed of the logarithmic and wavelet domain of the transformed colour model of source images. The local energy fusion rule provides sharp edge details. The experiment is conducted on eight colour models with four different proposed algorithms. The evaluation of the OWCIF algorithm performance is demonstrated with the help of four sets of colour standard dataset images. The images used in this work are MRA, MR-T1, CT, PET, MRI, and SPECT. Subjective evaluation of the fusion result is carried out by the assistance of expert radiologists. The four proposed OWCIF algorithms compared with each other to identify better algorithms and colour models for the set of given images.

关键词： medical image fusion logarithmic wavelet colour model whale optimisation algorithm

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Nature inspired-based remora optimisation algorithm for enhancement of density peak clustering

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COGENT ENGINEERING 2023年第2期10卷

作者： Anandarao, Sarvani Chellasamy, Sweetlin Hemalatha VIT AP Univ Sch Comp Sci & Engn Chennai India Vellore Inst Technol VIT Sch Comp Sci & Engn Chennai 600127 Tamil Nadu India

Density peak clustering (DPC) has shown promising results for many complex problems when compared with other existing clustering techniques. Inspite of many advantages, DPC suffers with lack of cluster centroids and cut-off distance identification. Cut-off distance is the prominent parameter used in the calculation of local density. The improper choice of cut-off distance leads to improper cluster results. Currently, the cut-off distance is selected using decision graph or delta density or knee point detection or silhouette score or kernel functions. The main problem with the above functions for selecting the cut-off distance in DPC is that they often rely on heuristic or visually subjective criteria, making the choice of the optimal cut-off distance challenging and potentially sensitive to data characteristics. By leveraging metaheuristic optimisation algorithms, the process of selecting the cut-off distance becomes less subjective and data-driven, potentially leading to improved clustering results in DPC. This motivated us to work on the choice of cut-off distance by the usage of remora optimisation algorithm (ROA). The cluster results are improved by the usage of remora in selection of reliable cut-off distance ( d c ) . The effectiveness of the updated DPC with ROA is evaluated by applying on the eight datasets and compared with K-means, traditional DPC, DPC merged with other optimisation results. The three parameters used here to check the quality of the cluster are homogeneity, completeness and silhouette analysis. ROA is new and built on the inspiration of remora which moves from one place to another using the sea fishes like shark, whale, sword fish, etc. It is clear from the results that DPC with ROA has produced the better homogeneity value of 0.807, completeness of 0.699 and silhouette analysis of 0.79 than the other clustering algorithms.

关键词： Density peak clustering (DPC) Remora optimisation algorithm (ROA) cut-off distance whale optimisation algorithm marine predator algorithm

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