Topology optimization against cascading failures on wireless sensor networks using a memetic algorithm

Fu, Xiuwen; Pace, Pasquale; Aloi, Gianluca; Yang, Lin; Fortino, Giancarlo

doi:10.1016/j.comnet.2020.107327

Cited by 157 publications

(68 citation statements)

References 34 publications

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“…Image mosaic algorithm based on gray area is developed from the field of image matching [1]. First, the reference image is segmented into a fixed geometric area, and then the regions of the same size are selected from the image to be stitched, and the similarity of the regions is calculated by using the topological characteristics of the two image regions [2]. In 1982, Rofenfied first proposed the cross-correlation similarity measure to calculate the similarity between regions.…”

Section: Introductionmentioning

confidence: 99%

Retracted: Application of Migration Image Registration Algorithm Based on Improved SURF in Remote Sensing Image Mosaic

2020

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The classical SURF algorithm has many disadvantages, such as high dimension of feature descriptor, large amount of computation, and low matching accuracy when the angle of rotation and angle of view is too large. To solve the above problems, an improved algorithm is proposed. Firstly, image preprocessing is carried out by image binarization, feature points are extracted by Hessian matrix, and then feature description is carried out by using circular neighborhood of feature points. Har wavelet response is used to establish descriptors for each feature point, and the normalized gray-level difference and second-order gradient in the neighborhood are calculated simultaneously to form a new feature descriptor. Finally, the RANSAC algorithm is used to eliminate the mismatch points. The algorithm does not Compared with the classical SURF algorithm, it has the advantage of speed, and makes full use of the gray level information and the detail information, so it has higher accuracy. Experimental results show that the algorithm has good robustness and stability to image blur, illumination difference, angle rotation, field of view transformation and so on. The algorithm is applied to remote sensing image stitching to obtain the stitched image with no obvious geometric shift and good edge connection. This algorithm is a kind of image registration algorithm with short time and high precision, which can meet the registration requirements of remote sensing image stitching.

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Section: Introductionmentioning

confidence: 99%

Retracted: Application of Migration Image Registration Algorithm Based on Improved SURF in Remote Sensing Image Mosaic

2020

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“…The proposed memetic GDE3 algorithm combines opposition-based learning, local search, and the differential evolution based strategy to get the benefits of each approach. Memetic algorithms [52] and differential evolution algorithms [53], [54] have been used in past studies for other problems. This study is the first attempt to use a memetic generalized differential evolution algorithm for this problem.…”

Section: B Optimization Techniques For Sequence Design Of Dna Problemmentioning

confidence: 99%

A Many-Objective Memetic Generalized Differential Evolution Algorithm for DNA Sequence Design

2020

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Designing reliable sequences of DNA (Deoxyribonucleic Acid) is a critical task in the fields of DNA computing, and nanotechnology. The quality and reliability of the DNA sequence can directly affect the accuracy of the processing of information stored in sequences. This problem of designing reliable sequences belongs to the NP-hard class of problems. It has many incompatible design criteria, which cannot be optimized at the same time. Many objective evolutionary algorithms can balance conflicting design criteria by using a diverse population of solutions. This paper proposes an opposition-based Memetic Generalized Differential Evolution (MGDE3) to handle four conflicting design criteria for reliable DNA sequence design. Opposition-based learning and local search strategies are suggested to strengthen the explorative and exploitative properties of the proposed MGDE3. The proposed algorithm is bench-marked with small, medium, and large data sets against 7 highly-cited many-objective and multi-objective algorithms. Experimental results and statistical analysis reveal that MGDE3 significantly outperforms the compared algorithms. The proposed method generates reliable real-life sequences of DNA that are substantially better than the DNA sequences generated by other considered algorithms.

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“…In recent years, new techniques and models have been developed by researchers worldwide [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. During the last 6 years, several studies have been individualized regarding the flash-flood susceptibility investigations, which were carried out through the integration of GIS techniques with bivariate statistical models such as: frequency ratio [ 36 ], weights of evidence [ 37 ], statistical index [ 38 ], evidential belief function [ 39 ], certainty factor [ 40 ], or index of entropy [ 41 ].…”

Section: Introductionmentioning

confidence: 99%

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors

Costache

Arabameri

Blaschke

et al. 2021

Sensors

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There is an evident increase in the importance that remote sensing sensors play in the monitoring and evaluation of natural hazards susceptibility and risk. The present study aims to assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS) databases which were involved as input data into a number of four ensemble models. In a first phase, with the help of high-resolution satellite images from the Google Earth application, 481 points affected by torrential processes were acquired, another 481 points being randomly positioned in areas without torrential processes. Seventy percent of the dataset was kept as training data, while the other 30% was assigned to validating sample. Further, in order to train the machine learning models, information regarding the 10 flash-flood predictors was extracted in the training sample locations. Finally, the following four ensembles were used to calculate the Flash-Flood Potential Index across the Bâsca Chiojdului river basin: Deep Learning Neural Network–Frequency Ratio (DLNN-FR), Deep Learning Neural Network–Weights of Evidence (DLNN-WOE), Alternating Decision Trees–Frequency Ratio (ADT-FR) and Alternating Decision Trees–Weights of Evidence (ADT-WOE). The model’s performances were assessed using several statistical metrics. Thus, in terms of Sensitivity, the highest value of 0.985 was achieved by the DLNN-FR model, meanwhile the lowest one (0.866) was assigned to ADT-FR ensemble. Moreover, the specificity analysis shows that the highest value (0.991) was attributed to DLNN-WOE algorithm, while the lowest value (0.892) was achieved by ADT-FR. During the training procedure, the models achieved overall accuracies between 0.878 (ADT-FR) and 0.985 (DLNN-WOE). K-index shows again that the most performant model was DLNN-WOE (0.97). The Flash-Flood Potential Index (FFPI) values revealed that the surfaces with high and very high flash-flood susceptibility cover between 46.57% (DLNN-FR) and 59.38% (ADT-FR) of the study zone. The use of the Receiver Operating Characteristic (ROC) curve for results validation highlights the fact that FFPIDLNN-WOE is characterized by the most precise results with an Area Under Curve of 0.96.

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Topology optimization against cascading failures on wireless sensor networks using a memetic algorithm

Cited by 157 publications

References 34 publications

Retracted: Application of Migration Image Registration Algorithm Based on Improved SURF in Remote Sensing Image Mosaic

Retracted: Application of Migration Image Registration Algorithm Based on Improved SURF in Remote Sensing Image Mosaic

A Many-Objective Memetic Generalized Differential Evolution Algorithm for DNA Sequence Design

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors

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