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An imaging algorithm for high-resolution imaging sonar system

MULTIMEDIA TOOLS AND APPLICATIONS 2024年第11期83卷 31957-31973页

作者： Yang, Peixuan Acoust Signal & Elect Sci & Technol Corp Xijin Rd Lanzhou 730050 Peoples R China

Multireceiver synthetic aperture sonar (SAS) can produce high-resolution images by coherently superposing successive echoed signal. However, multireceiver SAS imaging is a difficult issue since the system can be divided into many bistatic SASs. Traditional imaging algorithms cannot be directly used to process the multireceiver SAS echoed signal, as they are just applied to monostatic sonar system. In order to solve this issue, this paper proposes an imaging algorithm based on Loffeld's bistatic formula (LBF) for multireceiver SAS system. With the LBF method, the point target reference spectrum (PTRS) of multireceiver SAS system can be split into quasi-monostatic term and the bistatic deformation term. The quasi-monostatic term is similar to the spectrum of monostatic SAS while the bistatic deformation term is caused by the displacement of transmitter and receiver. In this paper, the data of multireceiver SAS is firstly coerced into monostatic one by compensating the bistatic deformation term. After this step, the chirp scaling (cs) algorithm based on monostatic SAS can be directly applied. At last, simulations are exploited to demonstrate the presented method. Compared to traditional multireceiver SAS cs algorithms, the presented method significantly improves the imaging performance. Both ghosts target and higher sidelobes that traditional multireceiver SAS cs algorithms suffer from can be successfully suppressed. Furthermore, the presented method can obtain high-resolution results which are nearly identical to the benchmark of BP algorithm.

关键词： Multireceiver Synthetic aperture sonar Loffeld's bistatic formula Imaging cs algorithm

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Optimization Method for Digital Scheduling of Oilfield Sewage System

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WATER 2024年第18期16卷 2623页

作者： Chen, Shuangqing Zhou, Shun Li, Yuchun Jiang, Minghu Guan, Bing Xi, Jiahao Northeast Petr Univ Sch Petr Engn Daqing 163318 Peoples R China Postdoctoral Programme Daqing Oilfield Daqing 163318 Peoples R China Daqing Oilfield Design Inst Co Ltd Daqing 163318 Peoples R China Northeast Petr Univ Key Lab Continental Shale Hydrocarbon Accumulat & Daqing 163318 Peoples R China Northeast Petr Univ Sch Comp & Informat Technol Daqing 163318 Peoples R China

Oilfield Sewage System Scheduling is a complicated, large-scale, nonlinear system problem with multiple variables. The complexity of the sewage system pipeline network connection grows along with the ongoing building of oilfield stations, and the shortcomings of the sewage system water quantity scheduling program based on human experience decision-making become increasingly apparent. The key to solving this problem is to realize the digital and intelligent scheduling of sewage systems. Taking the sewage system of an oil production plant in Daqing oilfield as the research object, the water scheduling model of the sewage system is established in this paper. Aiming at the complex nonlinear characteristics of the model, the Levy flight speed updating operator, the adaptive stochastic offset operator, and the Brownian motion selection optimization operator are established by taking advantage of the particle swarm optimization (PSO) and the cuckoo search (cs) algorithm. Based on these operators, a hybrid PSO-cs algorithm is proposed, which jumps out of the local optimum and has a strong global search capability. Comparing PSO-cs with other algorithms on the CEC2022 test set, it was found that the PSO-cs algorithm ranked first in all 12 test functions, proving the excellent solving performance of the PSO-cs algorithm. Finally, the PSO-cs is applied to solve a water scheduling model for the sewage system of an oil production plant in Daqing Oilfield. It is found that the scheduling plan optimized by PSO-cs has a 100% water supply rate to the downstream water injection station, and the total energy consumption of the scheduling plan on the same day is reduced from 879.95 x 106 m5/d to 712.84 x 106 m5/d, which is a 19% reduction in energy consumption. The number of water balance stations in the sewage station increased by 7, which effectively improved the water resource utilization rate of the sewage station.

关键词： oilfield sewage system scheduling optimization PSO algorithm cs algorithm algorithm improvement

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Cuckoo search algorithm based satellite image contrast and brightness enhancement using DWT-SVD

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ISA TRANSACTIONS 2014年第4期53卷 1286-1296页

作者： Bhandari, A. K. Soni, V. Kumar, A. Singh, G. K. PDPM Indian Inst Informat Technol Design & Mfg Jabalpur 482011 India Indian Inst Technol Roorkee Dept Elect Engn Uttrakhand 247667 India

This paper presents a new contrast enhancement approach which is based on Cuckoo Search (cs) algorithm and DWT-SVD for quality improvement of the low contrast satellite images. The input image is decomposed into the four frequency subbands through Discrete Wavelet Transform (DWT), and cs algorithm used to optimize each subband of DWT and then obtains the singular value matrix of the low-low thresholded subband image and finally, it reconstructs the enhanced image by applying IDWT. The singular value matrix employed intensity information of the particular image, and any modification in the singular values changes the intensity of the given image. The experimental results show superiority of the proposed method performance in terms of PSNR, MSE, Mean and Standard Deviation over conventional and state-of-the-art techniques. (C) 2014 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Adaptive learning cs algorithm Wavelet thresholding DWT-SVD Image equalization and remote sensing image contrast enhancement

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Widened nested array: configuration design, optimal array and DOA estimation algorithm

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IET MICROWAVES ANTENNAS & PROPAGATION 2020年第5期14卷 440-447页

作者： Lin, Xinping Gong, Pan He, Lang Li, Jianfeng Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing Peoples R China Nanjing Univ Aeronaut & Astronaut Minist Ind & Informat Technol Key Lab Dynam Cognit Syst Electromagnet Spectrum Nanjing Peoples R China

A generalised nested array (GNA) configuration with large minimum inter-element spacing is attractive to alleviate mutual coupling. In this study, by decomposing and rearranging the sub-arrays of GNA, the authors propose a new nested array configuration named as widened nested array (WNA). The WNA enables to provide the reduced mutual coupling, enhanced degrees of freedom (DOFs), extended array aperture and improved direction of arrival (DOA) estimation performance compared to GNA. The closed-form expressions for sensor positions of WNA are given and the optimal array configuration for largest DOFs is derived. Meanwhile, the authors propose a partial angular compressed sensing(cs) based DOA estimation algorithm for WNA. Specifically, discrete Fourier transform method is first derived for consecutive DOFs of WNA to obtain initial DOA estimates. Subsequently, the initial estimates are utilised to compress the angular range of overcomplete dictionary of the cs algorithm, which is exploited to obtain the fine DOA estimates. Extensive simulation results testify that the proposed algorithm has the same estimation accuracy as cs algorithm with lower complexity.

关键词： array signal processing discrete Fourier transforms direction-of-arrival estimation correlation methods matrix algebra widened nested array configuration design DOA estimation algorithm generalised nested array configuration GNA minimum inter-element spacing WNA reduced mutual coupling extended array aperture arrival estimation performance optimal array configuration initial DOA estimates initial estimates cs algorithm fine DOA estimates estimation accuracy

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Detection and prevention of black hole and selective forwarding attack in clustered WSN with Active Trust

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第1-Sup期22卷 1313-1328页

作者： Mehetre, Deepak C. Roslin, S. Emalda Wagh, Sanjeev J. Sathyabama Univ Comp Sci Dept Chennai Tamil Nadu India Shivaji Univ Informat Technol Dept Govt COE Karad India

In the recent era, security is the major problem in sensor networks. Wireless sensor networks (WSNs) are mostly used for various real-world applications. However, WSNs face a lot of insider and outsider attacks, and it is complex to identify and protect towards insider attacks. Generally, an insider attack, in which the intruders choose several received data packets to drop, threatens the clustered WSNs. This situation has occurred because of the unattended clustered environments in the network. To overcome this problem, this paper proposes a trustable and secure routing scheme using two-stage security mechanism, and dual assurance scheme, for selecting the node and securing the data packet for WSNs. Both schemes are based on Active Trust to protect several kinds of attacks, such as black hole attack, and selective forwarding attack, during routing. Therefore, this paper identifies the trusted path and provides the secure routing paths using trust and Cuckoo search algorithm. Energy is the performance parameter utilized in the proposed scheme. The experimental result proves that proposed system provides the assurance to prolong the network lifespan and the probability of secure routing path in the network.

关键词： Routing Active Trust Security Black hole attack Selective forwarding attack cs algorithm

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An improved segmentation technique for multilevel thresholding of crop image using cuckoo search algorithm based on recursive minimum cross entropy

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IET SIGNAL PROCESSING 2022年第6期16卷 630-649页

作者： Kumar, Arun Kumar, Anil Vishwakarma, Amit Lee, Heung-No PDPM Indian Inst Informat Technol Design & Manage Dept ECE Jabalpur Madhya Pradesh India Gwangju Inst Sci & Technol Sch Elect Engn & Comp Sci Gwangju South Korea

Crop image segmentation is widely used for the analysis of crops. A wide variety of crops are present in the agriculture field, which varies in intensity and complex backgrounds. The thresholding method based on entropy is quite popular for the segmentation of an image. Among all, minimum cross entropy has been widely used. However, the complexity of computation increases when it is used for multilevel thresholding (MLT). Recursive minimum cross entropy is used to resolve the complexity of computation, and cuckoo search (cs) using Levy flight is used to find the optimal threshold for this objective function. Because real-time applications require less processing time while maintaining high performance, which is validated by the cs algorithm using recursive minimum cross entropy (R-MCE-cs) without constraint. The proposed method uses one constraint based on the structural similarity index (SSIM), which leads to an increment in the accuracy for a higher level of thresholding. The accuracy of the proposed method has been tested over 10 crop images with complex backgrounds and high dimensions of colour intensity space. The outcome of the proposed technique has been compared with five algorithms such as wind-driven optimisation (WDO), bacterial foraging optimisation (BFO), differential evolution (DE), artificial bee colony (ABC), and firefly algorithm (FFA). The result shows that the proposed method gives the most promising result, and the accuracy is also improved.

关键词： agriculture image segmentation recursive minimum cross entropy colour intensity space multilevel thresholding improved segmentation technique cuckoo search algorithm statistical distributions entropy SSIM R-MCE-cs MLT structural similarity index crops crop image segmentation cs algorithm Levy flight search problems crop analysis image colour analysis

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Two Subpopulations Cuckoo Search algorithm Based on Mean Evaluation Method for Function Optimization Problems

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2020年第8期34卷

作者： Liu, Jingsen Liu, Xiaozhen Li, Yu Henan Univ Coll Software Inst Intelligent Network Syst Kaifeng 475004 Peoples R China Henan Univ Coll Software Kaifeng 475004 Peoples R China Henan Univ Inst Management Sci & Engn Kaifeng 475004 Peoples R China

In order to better apply the cuckoo search (cs) algorithm in solving the problem of function extremism optimization, and further improve the phenomenon of low precision and slow convergence in the optimization process of algorithm, the two subpopulations cs algorithm based on mean value evaluation is proposed. On the one hand, the algorithm introduces dynamic inertia weight to adjust the levy flight mechanism, thus dynamically constraining the moving step-size of each generation of population, so that the algorithm has certain self-adaptability. On the other hand, the algorithm changes the way of mutation in the preference random walk. First, the average fitness evaluation mechanism is used to divide the current population into two subpopulations: good and bad. Then, it adopts a directional mutation strategy for the better population, so that the individual can search purposefully. The worse population uses differential mutation mechanism of the disturbance items with the t-distribution characteristics, and makes the individual to search in the best orientation of current, so as to enhance the local search performance and accelerate the convergence rate of the algorithm. Theoretical analysis proves the convergence and time complexity of the algorithm in this paper. The simulation results show that the improved algorithm has good applicability in solving the function optimization problem, and the optimization results and convergence speed have been significantly improved in the algorithm.

关键词： cs algorithm functions optimization dynamic inertia weight targeted mutation differential mutation

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Comprehensive study of linear antenna array optimisation using the cuckoo search algorithm

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IET MICROWAVES ANTENNAS & PROPAGATION 2019年第9期13卷 1325-1333页

作者： Khodier, Majid Jordan Univ Sci & Technol Dept Elect Engn Irbid 22110 Jordan

This study presents a comprehensive study of the design and optimisation of linear antenna arrays using the cuckoo search (cs) algorithm. The method is used to control important radiation characteristics of the antenna arrays (such as side-lobe level, beamwidth, nulls) by optimising the parameters of the array elements (amplitude, phase, and position). Different design examples are presented that illustrate the robustness of the cs algorithm and its ability in achieving the design goals. The considered design examples clearly show that the cs algorithm is a simple and yet a powerful optimisation tool that can be used in antenna array design problems with great confidence.

关键词： antenna radiation patterns linear antenna arrays antenna arrays optimisation search problems linear antenna array optimisation cuckoo search algorithm comprehensive study linear antenna arrays important radiation characteristics side-lobe level array elements different design examples cs algorithm design goals considered design examples powerful optimisation tool antenna array design problems

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Optimisation of antenna arrays using the cuckoo search algorithm

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IET MICROWAVES ANTENNAS & PROPAGATION 2013年第6期7卷 458-464页

作者： Khodier, Majid Jordan Univ Sci & Technol Dept Elect Engn Irbid 22110 Jordan

The goal of this article is to introduce and use the cuckoo search (cs) as an optimisation algorithm for the electromagnetics and antenna community. The cs is a new nature-inspired evolutionary algorithm (EA) for solving N-dimensional optimisation problems. Compared with other nature-inspired algorithms, the cs algorithm is easy to understand and implement and has minimum number of parameters to tune. Different examples are presented that illustrate the use of the cs algorithm, and the results are compared with results obtained using other optimisation methods. Preliminary results suggest that the cs algorithm can in some cases outperform other EAs, at least for the examples studied in this article.

关键词： antenna arrays evolutionary computation optimisation search problems antenna array optimisation cuckoo search algorithm electromagnetics community antenna community N-dimensional optimisation problems nature-inspired evolutionary algorithm cs algorithm

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Prediction of mechanical properties of hot rolled strip based on DBN and composite expectile regression

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FUNCTIONAL MATERIALS 2022年第2期29卷 279-290页

作者： Huang, Siyu He, Xiaoxia Zhang, Xin Li, Weigang Wuhan Univ Sci & Technol Coll Sci Wuhan 430065 Hubei Peoples R China Wuhan Univ Sci & Technol Prov Key Lab Syst Sci Met Proc Wuhan 430065 Hubei Peoples R China Wuhan Univ Sci & Technol Engn Res Ctr Met Automat & Measurement Technol Minist Educ Wuhan 430081 Hubei Peoples R China

In this paper, a DBN-CER19 model (deep belief network and composite expectile regression) is proposed to predict the tensile strength of hot rolled strip. The model takes full advantage that the quadratic loss function used by expectile regression can be solved by standard gradient optimization algorithm, and that DBN can learn more abstract hidden layer information from the underlying data. At the same time, the model combines the cs algorithm (Cuckoo Search) to select the number of DBN hidden layer nodes to improve the network structure. In order to demonstrate the superiority of this method, we use root mean squared error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE)as measurement indicators for empirical analysis. The results of the proposed model on the test set are 21.4381, 2.5251and 13.5035, respectively. The empirical results show that the DBN-CER19 model has higher prediction performance than previous models such as BP neural network(BPNN), quantile regression neural network(QRNN), expectile regression neural network(ERNN), and DBN.

关键词： deep belief network composite expectile regression cs algorithm mechanical properties of steel

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