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Ramlal SD, Sachdeva J, Ahuja CK, Khandelwal N. Multimodal Medical Image Fusion Using Nonsubsampled Shearlet Transform and Smallest Uni-Value Segment Assimilating Nucleus. INT J PATTERN RECOGN 2022. [DOI: 10.1142/s0218001422570014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This paper presents a new fusion scheme for medical (CT-MRI) images which is based on the nonsubsampled shearlet transform (NSST). The various image pairs to be fused are obtained from primary and internet sources. Initially, the images are decomposed through NSST into general and detailed features. The smallest uni-value segment assimilating nucleus (SUSAN) and local sum of Gaussian weighted pixel intensities-based activity measures are proposed to fuse the detailed sub-bands and low-frequency sub-band of NSST, respectively, for faster execution of the algorithm. Visual and parametric comparison of the proposed scheme is done through five traditional fusion algorithms using nine fusion performance parameters. In addition, Wilcoxon signed ranks test is also applied to compare different methods scientifically with the proposed fusion scheme. It is observed that the presented method is better in retaining bone, calcification, cerebrospinal fluid (CSF), edema and tumor details of the source images and is faster than other classical fusion schemes. The fused images of the proposed method are suitable for locating the site of biopsy externally or incision location in the bone of the brain skull with minimum diagnostic time.
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Affiliation(s)
- Sharma Dileepkumar Ramlal
- Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, India
- Electronics and Telecommunication Engineering Department, Eternal University, Baru Sahib, H.P., India
- Chitkara University Institute of Engineering & Technology, Baddi, HP, India
| | - Jainy Sachdeva
- Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, India
| | - Chirag Kamal Ahuja
- Department of Radio-Diagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, India
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Hou G, Gong L, Wang M, Yu X, Yang Z, Mou X. A novel linear active disturbance rejection controller for main steam temperature control based on the simultaneous heat transfer search. ISA TRANSACTIONS 2022; 122:357-370. [PMID: 34083082 DOI: 10.1016/j.isatra.2021.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 02/03/2021] [Accepted: 05/03/2021] [Indexed: 06/12/2023]
Abstract
The main steam temperature of boiler outlet has been deemed as a significant parameter of the safety and economic performances in the thermal power plant operation. The complex working status of the thermal generation endures highly uncertain factors and remarkable disturbance, which call for effective controlling approaches in the corresponding temperature management. The linear active disturbance rejection controller (LADRC) is a conducive and powerful controlling method, whereas strong correlation between LADRC parameters leads to difficulties in optimally determining the controller parameters. Aiming at eliminating the negative effects on main steam temperature control caused by uncertainties factors and disturbances, a high performance LADRC based on a novel parameters optimization strategy, the simultaneous heat transfer search (SHTS) algorithm, is designed to deliver a stability, rapidity, and precision of control process. In the presented SHTS algorithm, all the three phases of heat transfer are randomly and parallel operated, providing a significant improvement towards the optimization performance. The proposed algorithm is first verified on various benchmark functions contrasted to state-of-the-art counterparts in performance validating, and then adopted in the parameter selection of LADRC in the main steam temperature control system. The excellent control performance, strong robustness and disturbance rejection ability of the designed approach are illustrated through the simulation results on main steam temperature control system.
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Affiliation(s)
- Guolian Hou
- School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
| | - Linjuan Gong
- School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
| | - Mengyi Wang
- Digital Energy China, Schneider Electric China Operations Shanghai Branch, Shanghai, China
| | | | - Zhile Yang
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen, China.
| | - Xiaolin Mou
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen, China.
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Fault Detection of Wind Turbine Electric Pitch System Based on IGWO-ERF. SENSORS 2021; 21:s21186215. [PMID: 34577420 PMCID: PMC8469195 DOI: 10.3390/s21186215] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 08/25/2021] [Accepted: 08/25/2021] [Indexed: 11/22/2022]
Abstract
It is difficult to optimize the fault model parameters when Extreme Random Forest is used to detect the electric pitch system fault model of the double-fed wind turbine generator set. Therefore, Extreme Random Forest which was optimized by improved grey wolf algorithm (IGWO-ERF) was proposed to solve the problems mentioned above. First, IGWO-ERF imports the Cosine model to nonlinearize the linearly changing convergence factor α to balance the global exploration and local exploitation capabilities of the algorithm. Then, in the later stage of the algorithm iteration, α wolf generates its mirror wolf based on the lens imaging learning strategy to increase the diversity of the population and prevent local optimum of the population. The electric pitch system fault detection method of the wind turbine generator set sets the generator power of the variable pitch system as the main state parameter. First, it uses the Pearson correlation coefficient method to eliminate the features with low correlation with the electric pitch system generator power. Then, the remaining features are ranked by the importance of the RF features. Finally, the top N features are selected to construct the electric pitch system fault data set. The data set is divided into a training set and a test set. The training set is used to train the proposed fault detection model, and the test set is used for testing. Compared with other parameter optimization algorithms, the proposed method has lower FNR and FPR in the electric pitch system fault detection of the wind turbine generator set.
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Albert Jesuwaram AM, Maria Sebastin GP. Implementation analysis of pixel‐level image processing based on multiscale transforms. Comput Intell 2021. [DOI: 10.1111/coin.12384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Tawfik N, Elnemr HA, Fakhr M, Dessouky MI, Abd El-Samie FE. Hybrid pixel-feature fusion system for multimodal medical images. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021; 12:6001-6018. [DOI: 10.1007/s12652-020-02154-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Accepted: 05/25/2020] [Indexed: 09/02/2023]
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Abstract
AbstractSoftware usability is usually used in reference to the hierarchical software usability model by researchers and is an important aspect of user experience and software quality. Thus, evaluation of software usability is an essential parameter for managing and regulating a software. However, it has been difficult to establish a precise evaluation method for this problem. A large number of usability factors have been suggested by many researchers, each covering a set of different factors to increase the degree of user friendliness of a software. Therefore, the selection of the correct determining features is of paramount importance. This paper proposes an innovative metaheuristic algorithm for the selection of most important features in a hierarchical software model. A hierarchy-based usability model is an exhaustive interpretation of the factors, attributes, and its characteristics in a software at different levels. This paper proposes a modified version of grey wolf optimisation algorithm (GWO) termed as modified grey wolf optimization (MGWO) algorithm. The mechanism of this algorithm is based on the hunting mechanism of wolves in nature. The algorithm chooses a number of features which are then applied to software development life cycle models for finding out the best among them. The outcome of this application is also compared with the conventional grey wolf optimization algorithm (GWO), modified binary bat algorithm (MBBAT), modified whale optimization algorithm (MWOA), and modified moth flame optimization (MMFO). The results show that MGWO surpasses all the other relevant optimizers in terms of accuracy and produces a lesser number of attributes equal to 8 as compared to 9 in MMFO and 12 in MBBAT and 19 in MWOA.
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Liu X, Wang N. A novel gray wolf optimizer with RNA crossover operation for tackling the non-parametric modeling problem of FCC process. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.106751] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Tawfik N, Elnemr HA, Fakhr M, Dessouky MI, Abd El-Samie FE. Survey study of multimodality medical image fusion methods. MULTIMEDIA TOOLS AND APPLICATIONS 2021; 80:6369-6396. [DOI: 10.1007/s11042-020-08834-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 01/13/2020] [Accepted: 03/06/2020] [Indexed: 09/02/2023]
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Du J, Fang M, Yu Y, Lu G. An adaptive two-scale biomedical image fusion method with statistical comparisons. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 196:105603. [PMID: 32570007 DOI: 10.1016/j.cmpb.2020.105603] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 06/06/2020] [Indexed: 06/11/2023]
Abstract
Two-scale image representation of base and detail in the spatial-domain is a well-known decomposition scheme for its lower computational complexity than that performed in the transform-domain in the field of image fusion. Unfortunately, for a pseudo-colour input image, the base and detail images in the spatial-domain obtained via image decomposition scheme always display in greyscale. In this paper, a two-scale image fusion method with adaptive threshold obtained by Otsu's method is proposed for pseudo-colour image in the colour space domain. For greyscale image, detail and base image are obtained using structural information extracted from the difference image between a global and a local patch size. Consequently, local edge-preserving filter for preserving luminance information and local energy with the discussed window size are adopted to combine base and detail image. Experimental results show that structural and luminance information has been better preserved in terms of subjective and objective evaluations for medical image and protein image fusion. Specially, a two-step non-parametric statistical test (Friedman test and Nemenyi post-hoc test) with p-values is adopted to analysis the statistical significant of the relative difference between the proposed and compared methods in terms of values of objective metrics including 30 co-registered pairs of imaging data.
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Affiliation(s)
- Jiao Du
- School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China.
| | - Meie Fang
- School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China
| | - Yufeng Yu
- Department of Statistics, Guangzhou University, Guangzhou 510006, China
| | - Gang Lu
- Laboratory of Image Science and Technology, Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing 210096, China
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A novel reinforcement learning based grey wolf optimizer algorithm for unmanned aerial vehicles (UAVs) path planning. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106099] [Citation(s) in RCA: 84] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Liang Y, Wang X, Zhao H, Han T, Wei Z, Li Y. A covariance matrix adaptation evolution strategy variant and its engineering application. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105680] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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A non-convex economic load dispatch problem with valve loading effect using a hybrid grey wolf optimizer. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04284-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Three Dimensional Pulse Coupled Neural Network Based on Hybrid Optimization Algorithm for Oil Pollution Image Segmentation. REMOTE SENSING 2019. [DOI: 10.3390/rs11091046] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This paper proposes a three dimensional pulse coupled neural network (3DPCNN) image segmentation method based on a hybrid seagull optimization algorithm (HSOA) to solve the oil pollution image. The image of oil pollution is taken by the unmanned aerial vehicle (UAV) in the oil field area. The UAV is good at shooting the ground area, but its ability to identify the oil pollution area is poor. In order to solve this problem, a 3DPCNN-HSOA algorithm is proposed to segment the oil pollution image, and the oil pollution area is segmented to identify the dirty oil area and improve the inspection of environmental pollution. The 3DPCNN image segmentation method has simple structure and good segmentation effect, but it has many parameters and poor segmentation effect for complex oil images. Therefore, we apply HSOA algorithm to optimize the parameters of 3DPCNN algorithm, so as to improve the segmentation accuracy and solve the segmentation of oil pollution images. The experimental results show that the 3DPCNN-HSOA model can separate the oil pollution area from the complex background.
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Zhao YT, Li WG, Liu A. Improved grey wolf optimization based on the two-stage search of hybrid CMA-ES. Soft comput 2019. [DOI: 10.1007/s00500-019-03948-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Lu C, Gao L, Pan Q, Li X, Zheng J. A multi-objective cellular grey wolf optimizer for hybrid flowshop scheduling problem considering noise pollution. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2018.11.043] [Citation(s) in RCA: 77] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Ibrahim RA, Elaziz MA, Lu S. Chaotic opposition-based grey-wolf optimization algorithm based on differential evolution and disruption operator for global optimization. EXPERT SYSTEMS WITH APPLICATIONS 2018; 108:1-27. [DOI: 10.1016/j.eswa.2018.04.028] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Cheng J, Wang L, Jiang Q, Xiong Y. A novel cuckoo search algorithm with multiple update rules. APPL INTELL 2018. [DOI: 10.1007/s10489-018-1198-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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A Hybrid Seasonal Mechanism with a Chaotic Cuckoo Search Algorithm with a Support Vector Regression Model for Electric Load Forecasting. ENERGIES 2018. [DOI: 10.3390/en11041009] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Liu X, Mei W, Du H. Detail-enhanced multimodality medical image fusion based on gradient minimization smoothing filter and shearing filter. Med Biol Eng Comput 2018; 56:1565-1578. [DOI: 10.1007/s11517-018-1796-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Accepted: 01/27/2018] [Indexed: 10/18/2022]
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