FractalAD: A simple industrial anomaly detection method using fractal anomaly generation and backbone knowledge distillation

Although industrial anomaly detection (AD) technology has made significant progress in recent years, generating realistic anomalies and learning priors of normal remain challenging tasks. In this study, we propose an end-to-end industrial anomaly detection method called FractalAD. Training samples are obtained by synthesizing fractal images and patches from normal samples. This fractal anomaly generation method is designed to sample the full morphology of anomalies. Moreover, we designed a backbone knowledge distillation structure to extract prior knowledge contained in normal samples. The differences between a teacher and a student model are converted into anomaly attention using a cosine similarity attention module. The proposed method enables an end-to-end semantic segmentation network to be used for anomaly detection without adding any trainable parameters to the backbone and segmentation head, and has obvious advantages over other methods in training and inference speed.. The results of ablation studies confirmed the effectiveness of fractal anomaly generation and backbone knowledge distillation. The results of performance experiments showed that FractalAD achieved competitive results on the MVTec AD dataset and MVTec 3D-AD dataset compared with other state-of-the-art anomaly detection methods.Comment: 12 pages, 5 figure

Spectrum and Image Texture Features Analysis for Early Blight Disease Detection on Eggplant Leaves

This study investigated both spectrum and texture features for detecting early blight disease on eggplant leaves. Hyperspectral images for healthy and diseased samples were acquired covering the wavelengths from 380 to 1023 nm. Four gray images were identified according to the effective wavelengths (408, 535, 624 and 703 nm). Hyperspectral images were then converted into RGB, HSV and HLS images. Finally, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) based on gray level co-occurrence matrix (GLCM) were extracted from gray images, RGB, HSV and HLS images, respectively. The dependent variables for healthy and diseased samples were set as 0 and 1. K-Nearest Neighbor (KNN) and AdaBoost classification models were established for detecting healthy and infected samples. All models obtained good results with the classification rates (CRs) over 88.46% in the testing sets. The results demonstrated that spectrum and texture features were effective for early blight disease detection on eggplant leaves

An experimental and numerical study of leader development in rod-rod gaps under positive switching impulse voltage

The main form of electrical discharge in long air gaps is broken down by leader mechanism. The leader development in rod-rod gaps under positive switching impulse was investigated by using two high-speed CCD cameras. The clearer and more particular colorized physical morphology of the leader propagation process than the traditional image converter cameras was recorded and the leader development parameters were also obtained. As a comparative study, an improved simplified model for rod-rod configuration was performed to simulate the leader development. The numerical simulations of leader development characteristics were in a close agreement with that of experimental measurements

Color measurement of tea leaves at different drying periods using hyperspectral imaging technique.

This study investigated the feasibility of using hyperspectral imaging technique for nondestructive measurement of color components (ΔL*, Δa* and Δb*) and classify tea leaves during different drying periods. Hyperspectral images of tea leaves at five drying periods were acquired in the spectral region of 380-1030 nm. The three color features were measured by the colorimeter. Different preprocessing algorithms were applied to select the best one in accordance with the prediction results of partial least squares regression (PLSR) models. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to identify the effective wavelengths, respectively. Different models (least squares-support vector machine [LS-SVM], PLSR, principal components regression [PCR] and multiple linear regression [MLR]) were established to predict the three color components, respectively. SPA-LS-SVM model performed excellently with the correlation coefficient (rp) of 0.929 for ΔL*, 0.849 for Δa*and 0.917 for Δb*, respectively. LS-SVM model was built for the classification of different tea leaves. The correct classification rates (CCRs) ranged from 89.29% to 100% in the calibration set and from 71.43% to 100% in the prediction set, respectively. The total classification results were 96.43% in the calibration set and 85.71% in the prediction set. The result showed that hyperspectral imaging technique could be used as an objective and nondestructive method to determine color features and classify tea leaves at different drying periods

Statistical information of calibration and prediction sets.

<p>Statistical information of calibration and prediction sets.</p

Rockburst prediction and prevention in underground space excavation

The technical challenges associated with deep underground space activities have become increasingly significant. Among these challenges, one major concern is the assessment of rockburst risks and the instability of rock masses. Extensive research has been conducted by numerous scholars to mitigate the risks and prevent occurrences of rockburst through various assessment methods. Rockburst incidents commonly occur during the excavation of hard rock in underground environments, posing severe threats to personnel safety, equipment integrity, and operational continuity. Thus, it is crucial to systematically document real cases of rockburst, allowing for a comprehensive understanding of the underlying mechanisms and triggering conditions. This understanding will contribute to the advancement of rockburst prediction and prevention methods. Proper selection of an appropriate rockburst assessment method is a fundamental aspect in underground operations. However, there is a limited number of studies that summarize and compare different prediction and prevention methods of rockburst. This paper aims to address this gap by analyzing global trends using CiteSpace software since 1990. It discusses rockburst classification and characteristics, comprehensively reviews research findings related to rockburst prediction, including empirical, simulation, mathematical modeling, and microseismic monitoring methods. Additionally, the paper presents a compilation of current rockburst prevention measures. Notably, the paper emphasizes the significance of control strategies, which provide key insights into the effective utilization of stored energy within rock. Finally, the paper concludes by suggesting six directions for implementing intelligent management techniques to mitigate hazards during underground operations and reduce the probability of rockburst incidents

Effective wavelengths suggested by CARS.

<p>Effective wavelengths suggested by CARS.</p