Weighted Information Entropy Research Articles

Acoustic Emission Monitoring of Bond Deterioration in GFRP-Reinforced Concrete Members: An Entropy-Based Approach

The primary objective of acoustic emission (AE) monitoring is to accurately detect imminent critical damage, preventing premature failure by analysing the dynamic evolution of AE parameters. This study introduces AE entropy, a distinctive parameter derived from Shannon's entropy, employed to monitor the bond degradation between glass fibre-reinforced polymer (GFRP) rebars and the concrete interface in GFRP-reinforced concrete elements. Characteristic AE parameters have been synchronously collected during flexural bond tests, and information entropy and weighted information entropy, composed of typical AE characteristic parameters based on Shannon’s information entropy theory in the time domain, have been defined. Findings indicate that, with increasing bond stress, the specimens exhibit a trend characterized by "decreasing, stabilizing, and then increasing" in both AE characteristic parameters and weighted information entropy. The information entropy value shows fluctuations during any damage associated with GFRP-concrete bond deterioration. At peak bond stress values, information entropy values experience a rapid surge, indicating significant deterioration. The evolution process of debonding between GFRP rebar and concrete, switching between a chaotic state that develops randomly and an organized state that produces in order, follows specific laws (disordered–ordered–disordered), corresponding well to AE characteristic parameters weighted information entropy. Moreover, entropy values, both individual and weighted, when correlated with bond stress and slip variations in the test specimens, distinctly outline and predict various stress transfer mechanisms between the GFRP bar and concrete. In summary, this research underscores the utility of AE entropy as a valuable tool for damage monitoring in GFRP-reinforced structures. Its ability to capture microstructural deformations positions AE entropy as a promising candidate for improving critical damage identification and preventing premature failure

Predictive models on potential energies of zeolite ZK-5 using bond weighted information entropy measures

Zeolites are hydrated aluminosilicate mineral characterised by a uniform pore structure. Zeolite ZK-5 or KFI framework was synthesized due to it's important physicochemical characteristics. So far, computational material development has improved efficiency by identifying novel zeolites with desirable properties including system equilibrium and stability. Topological indices, which characterise KFI zolite's complex structure, can help researchers develop new techniques for studying the complex structure. Shannon's entropy modelling help us prototype and understand our zeolites element changes and complexity characteristics. Modelling and understanding these changes and relationships can help us improve our zeolite in the field of the microporous material by QSAR/QSPR studies. We have also studied accuracy of the cubic regression model between the derived degree type entropies of KFI zeolite and it's potential energy. Therefore, for investigating the property of large-shaped KFI zeolite, the anticipated cubic correlation analysis reveals a significant correlation with electrostatic, dispersion energies.

MSPIF: Multi-stage progressive visible and infrared image fusion with structures preservation

Infrared and visible image fusion is one of the hottest research in computer vision to improve image quality. Traditional fusion methods suffer from detail loss, low resolution, and application scenarios limitation. To address these problems, this paper proposes a multi-stage progressive visible and infrared image fusion strategy (MSPIF), including the following key stages: Firstly, the visible image is enhanced using a weighted fusion visible image enhancement algorithm. Secondly, the infrared and enhanced visible images are both decomposed by a pre-trained network, namely Retinex_Net, to obtain their respective illumination and reflectance components. Thirdly, the reflectance components are decomposed by discrete wavelet transform (DWT). The low-frequency components are fused by a weighted information entropy fusion strategy while the high-frequency components are fused by a local energy fusion strategy. Such two fused parts contribute to the final fused reflectance component with inverse DWT (IDWT). Meanwhile, the illumination components are fused using a multi-scale fusion strategy based on the Laplace pyramid. Finally, the improved result is generated by the fused reflectance and illumination components based on Retinex theory. Quantitative and qualitative results of experiments on the TNO, KAIST, and ROAD datasets show that the proposed MSPIF is effective and achieves good results with structures preservation, even is superior to existing methods.

Pythagorean Fuzzy Sets to Solve Z-Numbers in Decision-Making Model

Since the concept of Z-numbers was proposed, it has attracted the attention of scholars. In this article, we are committed to solving Z-numbers from the perspective of coordinate system and q-rung orthopedic fuzzy sets (q-ROFSs). First, a new layout coordinate system is defined to map Z-numbers to q-ROFSs. For convenience, Pythagorean fuzzy set (PFS), a special form of q-ROFSs, is employed in this article. In this way, the parameters <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$A$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$B$</tex-math></inline-formula> in Z-numbers are transformed into the corresponding membership and nonmembership. At the same time, the genetic algorithm is adopted to derive the potential probability distribution. Next, an approach for calculating the weighted information entropy of Z-numbers is proposed, and the theorem proves the rationality. To integrate multiple Z-information, the linguistic Z-PFS weighted aggregation operator ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$LZPFSPWAO$</tex-math></inline-formula> ) is presented. Also, a score function is defined with the help of curve length calculation formula in the coordinate system. Finally, a decision-making model is constructed based on the new solution. Two examples, UAV firefighting and energy investment selection, are adopted to demonstrate the validity. The comparison results show that the proposed method is effective and beneficial in solving Z-information.

An adaptive false-color enhancement algorithm for super-8-bit high grayscale X-ray defect image of solid rocket engine shell

The solid rocket engine is the main power unit of various missile weapons today, and it also has a wide range of applications in the aerospace field. However, in the process of welding and long-term operation, micro-invisible defects such as cracks and pores will inevitably occur inside the shell, which directly affects the performance of the solid rocket motor. This paper takes the solid engine shell X-ray film as the object. Most of the existing false-color methods are to quantize the 256-level grayscale in enhancing the image’s details. In high-level grayscale image enhancement, the grayscale loss is serious and the adaptive adjustment is not satisfactory. An adaptive false color enhancement algorithm for high-level gray-scale (super 8-bit) images is proposed. In addition, for the adaptive enhancement problem, a new method of weld locating based on the Difference of Gaussian statistics is proposed. Moreover, a new method based on the fitness function of image quality evaluation is integrated with weighted information entropy, in which PSO is improved for fast optimization. To verify the effectiveness of the method in this paper, it is applied to the visual enhancement of the low-contrast and fuzzy defect information of the solid engine shell X-ray film. The experimental results show that our design obtains the best performance on the high-level X-ray film of a solid rocket engine shell, the algorithm in this paper has greatly improved various indicators compared with the existing algorithms. The high-level false-color enhancement method is used to process the Steel Pipe Weld X-ray in small sample dataset (3408), and apply the YOLOV4 mixed training to obtain the best detection results. The method in this paper can adaptively realize the false-color enhancement of high grayscale X-ray film, with a better visual effect, which can enhance the definition of the image detail of the welding defects of the solid engine shell, and display the high grayscale image on the ordinary monitor.

A Personalized Recommendation Algorithm Based on Weighted Information Entropy and Particle Swarm Optimization

Similarity calculation is the most important basic algorithm in collaborative filtering recommendation. It plays an important role in calculating the similarity between users (items), finding nearest neighbors, and predicting scores. However, the existing similarity calculation is affected by over reliance on item scores and data sparsity, resulting in low accuracy of recommendation results. This paper proposes a personalized recommendation algorithm based on information entropy and particle swarm optimization, which takes into account the similarity of users’ score and preference characteristics. It uses random particle swarm optimization to optimize their weights to obtain the comprehensive similarity value. Experimental results on public data sets show that the proposed method can effectively improve the accuracy of recommendation results on the premise of ensuring recommendation coverage.

Target Detection in High-Resolution SAR Image via Iterating Outliers and Recursing Saliency Depth

In dealing with the problem of target detection in high-resolution Synthetic Aperture Radar (SAR) images, segmenting before detecting is the most commonly used approach. After the image is segmented by the superpixel method, the segmented area is usually a mixture of target and background, but the existing regional feature model does not take this into account, and cannot accurately reflect the features of the SAR image. Therefore, we propose a target detection method based on iterative outliers and recursive saliency depth. At first, we use the conditional entropy to model the features of the superpixel region, which is more in line with the actual SAR image features. Then, through iterative anomaly detection, we achieve effective background selection and detection threshold design. After that, recursing saliency depth is used to enhance the effective outliers and suppress the background false alarm to realize the correction of superpixel saliency value. Finally, the local graph model is used to optimize the detection results. Compared with Constant False Alarm Rate (CFAR) and Weighted Information Entropy (WIE) methods, the results show that our method has better performance and is more in line with the actual situation.

Prediction of Breast Cancer images Classification Using Bidirectional Long Short Term Memory and Two-Dimensional Convolutional Neural network

Breast cancer is most prevalent among women around the world and Nigeria is no exception in this menace. The increased in survival rate is due to the dramatic advancement in the screening methods, early diagnosis, and discovery in cancer treatments. There is an improvement in different strategies of breast cancer classification. A model for training deep neural networks for classification of breast cancer in histopathological images was developed in this study. However, this images are affected by data unbalance with the support of active learning. The output of the neural network on unlabeled samples was used to calculate weighted information entropy. It is utilized as uncertainty score for automatic selecting both samples with high and low confidence. A threshold that decays over iteration number is used to decide which high confidence samples should be concatenated with manually labeled samples and then used infine-tuning of convolutional neural network. The neural network was optionally trained using weighted cross-entropy loss to better cope with bias towards the majority class. The developed model was compared with the existing model. The accuracy level of 98.3% was achieved for the developed model while the existing model 93.97%. The accuracy gain of 4.33%. was achieved as performance in the prediction of breast cancer .&#x0D;

Unsupervised band selection based on weighted information entropy and 3D discrete cosine transform for hyperspectral image classification

ABSTRACTBand selection is an effective means of reducing the dimensionality of the hyperspectral image by selecting the most informative and distinctive bands. Bands are usually selected by adopting information theoretic measures, such as, the information entropy or mutual information. However, directly using these information theoretic measures for selection of informative bands is not enough. Therefore, in this paper, the Three Dimensional Discrete Cosine Transform (3D DCT)-based information entropy is used for band selection from a high-dimensional data space. DCT has excellent energy compaction properties for highly correlated images such as hyperspectral images, which reduces the complexity of the separation significantly. First, DCT is applied to extract the complementary features from the raw hyperspectral cube. The weighted information entropy is introduced for measurement of the information about the discrimination ability of each band and reduction of the redundancy among them. An efficient search strategy is then used to select the optimal bands set. The selected bands are fed to a Support Vector Machine (SVM) to carry out the classification task. The proposed band selection approach is tested using three standard hyperspectral datasets such as, Indian Pines, Pavia University and Salinas dataset. The experimental results demonstrate the promising discriminant capability of the DCT features. The proposed method achieves maximum overall classification accuracy of about 84.61% for Indian Pines, 92.83% for Pavia University, and 94.14% for Salinas dataset. When compared to the other competitive band selection methods, the proposed approach shows the significant increase in the overall classification accuracy, when the number of selected bands ranges from 20 to 50.

Weighted Oblique Decision Trees

Decision trees have attracted much attention during the past decades. Previous decision trees include axis-parallel and oblique decision trees; both of them try to find the best splits via exhaustive search or heuristic algorithms in each iteration. Oblique decision trees generally simplify tree structure and take better performance, but are always accompanied with higher computation, as well as the initialization with the best axis-parallel splits. This work presents the Weighted Oblique Decision Tree (WODT) based on continuous optimization with random initialization. We consider different weights of each instance for child nodes at all internal nodes, and then obtain a split by optimizing the continuous and differentiable objective function of weighted information entropy. Extensive experiments show the effectiveness of the proposed algorithm.

Analysis of the ship target detection in high-resolution SAR images based on information theory and Harris corner detection

In order to make up the shortcomings of some existing ship target detection algorithms for high-resolution synthetic aperture radar (SAR) images, a ship target detection algorithm based on information theory and Harris corner detection for SAR images is proposed in this paper. Firstly, the SAR image is pretreated, and next, it is divided into superpixel patches by using the improved simple linear iterative clustering (SLIC) superpixel generation algorithm. Then, the self-information value of the superpixel patches is calculated, and the threshold T1 is set to select the candidate superpixel patches. And then, the extended neighborhood weighted information entropy growth rate threshold T2 is set to eliminate the false alarm candidate superpixel patches. Finally, the Harris corner detection algorithm is used to process the detection result and the number of the corner threshold T3 is set to filter out the false alarm patches, and the final SAR image target detection result is obtained. The effectiveness and superiority of the proposed algorithm are verified by comparing the proposed method with the results of constant false alarm rate (CFAR) detection algorithm combined with morphological processing algorithm and other ship target detection algorithms.

Adaptive Ship Detection for Single-Look Complex SAR Images Based on SVWIE-Noncircularity Decomposition.

In this paper, we present an adaptive ship detection method for single-look complex synthetic aperture radar (SAR) images. First, noncircularity is analyzed and adopted in ship detection task; besides, similarity variance weighted information entropy (SVWIE) is proposed for clutter reduction and target enhancement. According to the analysis of scattering of SVWIE and noncircularity, SVWIE-noncircularity (SN) decomposition is developed. Based on the decomposition, two components, the high-noncircularity SVWIE amplitude (h) and the low-noncircularity SVWIE amplitude (l), are obtained. We demonstrate that ships and clutter in SAR images are different for h detector and h detector can be effectively used for ship detection. Finally, to extract ships from the background, the generalized Gamma distribution (GD) is used to fit h statistics of clutter and the constant false alarm rate (CFAR) is utilized to choose an adaptive threshold. The performance of the proposed method is demonstrated on HH polarization of Alos-2 images. Experimental results show that the proposed method can accurately detect ships in complex background, i.e., ships are close to small islands or with strong noise.

Ship Detection from Ocean SAR Image Based on Local Contrast Variance Weighted Information Entropy

Ship detection from synthetic aperture radar (SAR) images is one of the crucial issues in maritime surveillance. However, due to the varying ocean waves and the strong echo of the sea surface, it is very difficult to detect ships from heterogeneous and strong clutter backgrounds. In this paper, an innovative ship detection method is proposed to effectively distinguish the vessels from complex backgrounds from a SAR image. First, the input SAR image is pre-screened by the maximally-stable extremal region (MSER) method, which can obtain the ship candidate regions with low computational complexity. Then, the proposed local contrast variance weighted information entropy (LCVWIE) is adopted to evaluate the complexity of those candidate regions and the dissimilarity between the candidate regions with their neighborhoods. Finally, the LCVWIE values of the candidate regions are compared with an adaptive threshold to obtain the final detection result. Experimental results based on measured ocean SAR images have shown that the proposed method can obtain stable detection performance both in strong clutter and heterogeneous backgrounds. Meanwhile, it has a low computational complexity compared with some existing detection methods.

An Improved Superpixel-Level CFAR Detection Method for Ship Targets in High-Resolution SAR Images

To achieve efficient ship detection in high-resolution synthetic aperture radar images, an improved superpixel-level constant false alarm rate (CFAR) detection method is proposed with three modifications. First, the weighted information entropy (WIE) describes the statistical characteristics of superpixels, yielding a better distinction between target and clutter superpixels. Second, a two-stage CFAR detection scheme is proposed to detect target superpixels, including global detection and local detection. Specifically, the WIE-based global detector is utilized to prescreen candidate target superpixels (CTSs) and then the local CFAR detector is adaptively conducted over the selected CTSs to refine target superpixels. Third, the superpixels selected by the global detection can be excluded as outliers for clutter estimation, thus the disturbance from adjacent targets in multitarget situations can be reduced and more accurate detection results can be obtained. Compared with CFAR methods implemented with the sliding window technique, the computational burden of the proposed method is significantly reduced without loss of detection performance. Experiments of ship detection on three TerraSAR-X datasets are presented to validate the effectiveness of the proposed method.

Ship Detection for Complex Background SAR Images Based on a Multiscale Variance Weighted Image Entropy Method

Ship detection from complex background synthetic aperture radar (SAR) images is a challenging task. Due to varying local clutter and low signal-to-clutter ratio, the most conventional methods fail to yield satisfactory results. An effective ship detection approach for complex background is developed in this letter. The approach measures the local dissimilarity between target and its neighborhood by using the variance weighted information entropy (VWIE) method. In order to enhance targets, an optimal window selection mechanism based on the multiscale local contrast measure is used in the local VWIE calculation. Experiments indicate that the proposed approach can effectively detect ship targets from the complex background SAR images by using the optimal selection mechanism.

Adaptive ship detection in SAR images using variance WIE-based method

Automatic detection of ship targets from synthetic aperture radar (SAR) images is an important and challenging problem. Given the different nature of target returns in homogeneous and heterogeneous regions in SAR imagery, conventional detection algorithms fail to yield automatic and robust results. In support of automatic vessel monitoring, an adaptive detection framework designed for detecting ships from SAR imagery is proposed in this paper, and the variance weighted information entropy is introduced into the framework construction. Experimental results indicate that the proposed method can effectively detect the ship targets from various circumstances without any prior knowledge.

Infrared Target Extraction Using Weighted Information Entropy and Adaptive Opening Filter

In infrared (IR) images, near targets have a transient distribution at the boundary region, as opposed to a steady one at the inner region. Based on this fact, this paper proposes a novel IR target extraction method that uses both a weighted information entropy (WIE) and an adaptive opening filter to extract near finely shaped targets in IR images. Firstly, the boundary region of a target is detected using a local variance WIE of an original image. Next, a coarse target region is estimated via a labeling process used on the boundary region of the target. From the estimated coarse target region, a fine target shape is extracted by means of an opening filter having an adaptive structure element. The size of the structure element is decided in accordance with the width information of the target boundary and mean WIE values of windows of varying size. Our experimental results show that the proposed method obtains a better extraction performance than existing algorithms.

SAR image target detection in complex environments based on improved visual attention algorithm

A novel target detection algorithm for synthetic aperture radar (SAR) images based on an improved visual attention method is proposed in this paper. With the development of SAR technology, target detection algorithms are confronted with many difficulties such as a complicated environment and scarcity of target information. Visual attention of the human visual system can make humans easily focus on key points in a complex picture, and the visual attention algorithm has been used in many fields. However, existing algorithms based on visual attention models cannot obtain satisfactory results for SAR image target detection under complex environmental conditions. After analysing the existing visual attention models, we combine the pyramid model of visual attention with singular value decomposition to simulate the human retina, which can make the visual attention model more suitable to the characteristics of SAR images. We introduce variance weighted information entropy into the model to optimize the detection results. The results obtained by the existing visual attention algorithm for target detection in SAR images yield a large number of false alarms and misses. However, the proposed algorithm can improve both the efficiency and accuracy of target detection in a complicated environment and under weak-target conditions. The experimental results validate the performance of our method.

Fast target detection method for high-resolution SAR images based on variance weighted information entropy

Since the traditional CFAR algorithm is not suitable for high-resolution target detection of synthetic aperture radar (SAR) images, a new two-stage target detection method based on variance weighted information entropy is proposed in this paper. On the first stage, the regions of interest (ROIs) in SAR image is extracted based on the variance weighted information entropy (WIE), which has been proved to be a simple and effective quantitative description index for the complex degree of infrared image background. Considering that SAR images are nonuniform, an experiment is conducted ahead, in which the value of the variance WIE from a real SAR image in three areas with significant different uniform levels are tested and compared. The results preliminarily verified that the variance WIE is able to measure the complex degree of SAR images. After that, in order to make the segmentation efficient, the rough ROIs are further processed with a series of methods which adjust ROIs into regular pieces. On the second stage, for each of the ROIs, a variational segmentation algorithm based on the Split-Bregman algorithm is adopted to extract the target. In our experiment, the proposed method is tested on two kinds of SAR images, and its effectiveness is successfully demonstrated.

Detection of Small Targets in Infrared Images based on Weighted Information Entropy

본 논문에서는 다양한 배경을 갖는 적외선 영상에서 소형 표적을 효과적으로 검출하기 위하여 가중치 정보 엔트로피 기반의 표적 검출 방법을 제안한다. 제안된 방법은 기존의 가중치 정보 엔트로피를 이용한 소형 표적 검출 방법의 단점을 개선하기 위하여 전처리 과정을 수행한다. 전처리 과정에서는 수평/수직 방향의 1차원 전역 및 지역 평균을 이용하여 배경 영역의 밝기가 제거된 소형 표적 후보 영상을 획득한다. 그리고 소형 표적 후보 영상에 대하여 가중치 정보 엔트로피를 계산하고 이에 따라 적절한 컷-오프 주파수를 결정한다. 최종적으로 결정된 컷-오프 주파수를 이용하여 적응적 고주파 통과 필터를 적용한다. 실험 결과를 통하여 제안한 방법이 기존 방법보다 다양한 배경 환경에 강건하고 정확하게 소형 표적을 검출할 수 있음을 확인하였다.