Identification of customer electricity usage anomalies based on random matrix theoryZhou, Shuo; Wang, Qihui
doi: 10.1117/12.3014405pmid: N/A
A detection algorithm of maximum and minimum eigenvalues based on random matrix theory is proposed for the problem of abnormal detection of customer electricity consumption. Firstly, the data source matrix is constructed by time alignment and superimposed Gaussian white noise, and the sliding window method is used to obtain the window data indicating the operation status at each moment; secondly, the window data are standardized, feature extraction and other operations are performed, and the difference and the sum of the maximum and minimum eigenvalues are compared to construct the feature detection indexes and thresholds; finally, the algorithm is studied and verified by simulation. The results show that the algorithm does not depend on any model, can analyze the operation status of the system more comprehensively and adequately, and realizes the effective detection of abnormal data
Research on infrared point target recognition method based on space-based early warning systemOuyang, Yan; Shi, BinBin; Huang, XiaoBin; Lu, Li; Jiang, Yuan
doi: 10.1117/12.3014467pmid: N/A
The identification of launch event based on infrared image processing is an important machine learning technology for space-based early warning system. Due to the long-range detection and short duration of the powered phase of trajectory, launch event represents as a point target in the infrared image. Therefore, the main challenge is to address the issue of distinguishing the different types of infrared point target. To tackle this problem, we propose a novel method for recognizing images of rocket exhaust flame point targets at different temperatures. To validate our approach, we conducted experimental validation using simulated detecting images obtained from high-orbit infrared early warning satellites. These images are generated based on the EO/IR module in STK software. The experimental results verify the effectiveness of our proposed method in solving the challenge of infrared point target recognition.
Naming conventions-based multi-label and multi-task learning for fine-grained classificationZhou, Qinbang; Zhang, Kezhi; Yue, Feng; Zhang, Zhaoliang; Yu, Hui
doi: 10.1117/12.3014589pmid: N/A
This paper proposes a fine-grained image classification architecture using multi-task learning. The structure of the fine-grained classification network uses ResNest as the feature extraction layer of the multi-task hard parameter sharing mode with the fine-grained category label regression branch based on multi-hot naming conventions and classification branch based on cross-entropy loss with one-hot encoding. The coupling between the two branches enables multi-task classification through hyperparameter weighting. Subsequently, comparison and ablation experiments were performed on the public datasets of Stanford Cars, CUB-200-2011 and FGVC-Aircraft. The experimental result shows multi-label regression, multi-task learning and label smoothing can effectively improve the generalization of the model and increase the inter-class distance of the previous layer at the network output terminal, and reduces the intra-class distance.
Target allocation method based on multi-objective particle swarm optimization algorithmLiu, Qing; Liu, YunZheng; Zeng, Dexian
doi: 10.1117/12.3014410pmid: N/A
Aiming at the target distribution problem of anti-aircraft weapon firepower, a target allocation method based on multitarget particle swarm is proposed. The mathematical model of incoming target allocation constraint optimization is established, and the multi-target particle swarm algorithm is used to solve the target allocation model. The inertia weights in the velocity update formula of the particle swarm algorithm and the learning factor assignment method are improved. Compared with the simulation results and actual experience judgment, the designed algorithm solves the target problem in the air defense weapon system to a certain extent.
The application of target tracking algorithm in intelligent video system to flight supportPeng, Jianjun; Zhai, Jialei; Jin, Xiang; Hu, Chengshuang; Li, Zaigang
doi: 10.1117/12.3014375pmid: N/A
As the global pandemic gradually eases and the aviation transport industry continues to experience steady growth, highdensity flight operations are becoming the new normal. The intelligentization of flight support processes is a crucial avenue for enhancing both the safety and efficiency of flight operations. With the advancement of computer vision technology, video-based object tracking has shown significant potential in the context of flight support processes. However, in real airport environments, object tracking often encounters challenges such as occlusion, scale variations, rotation, and changes in lighting conditions, leading to a decrease in tracking accuracy and even target loss. In this paper, our focus is on overcoming tracking failures caused by occlusion, deformation, and lighting variations. We have conducted the following work, taking into consideration the unique characteristics of airport environments and the specific requirements of flight support processes: (i) We utilized features at three levels, namely, Histogram of Oriented Gradient (HOG), Color Names, and Convolutional Neural Networks (CNN), to describe the texture, color, and high-level semantics of video images, respectively. (ii) We employed a multi-feature fusion approach using a trilinear interpolation function to integrate information from various sources. (iii) We implemented improved ECO algorithms for the tracking of moving objects in the airport environment. Finally, we validated this object tracking system using real surveillance videos from the airport. Experimental results have demonstrated the effectiveness and practicality of the method under challenging conditions.
The automated segmentation and enhancement of cracks on airport pavements using three-dimensional imaging techniquesZhai, Shanshan; Xu, Yanna
doi: 10.1117/12.3014473pmid: N/A
Based on 3D images, this study aims to explore automatic segmentation and enhancement methods for airfield runway surface cracks. Firstly, a typical 2D Gaussian filter is used to remove noise from the road surface data. Then, Steerable Matched Filter (SMFB) is introduced to extract crack features. By constructing a set of 52 SMFB filters with different parameters, we are able to accurately capture cracks with different directions and sizes. After that, Tensor Voting (TV) technique is introduced to further enhance the continuity of the cracks. With this method, we are able to detect and segment the cracks in the airfield runway surface for a more accurate and comprehensive analysis. The experimental results show that the proposed method performs well in crack detection and segmentation, providing strong support for airport pavement maintenance and management.
Research on rule engine optimization algorithm in internet of things teaching platformLi, JianZhong; Wan, Qiang; Zhang, ZhiQiang
doi: 10.1117/12.3014592pmid: N/A
The rule engine is an important part of the industry-education integrated Internet of Things teaching platform, and it is the basis for realizing the dynamic configuration of business rules in the practical teaching function. Combined with the data characteristics of the Internet of Things application scenario, this paper proposes a rule engine optimization algorithm based on Rete, and designs a pre-sorting algorithm based on rule frequency, which pre-sorts the order of nodes according to the frequency of use of rule patterns, with priority Match frequently used patterns, increases the sharing rate of nodes, and reduce the memory usage of the inference network. Through experimental simulation, the improved algorithm is verified, and the experimental results prove the effectiveness of the algorithm.
Reversible data hiding in encrypted image based on adaptive difference prediction and block subdivisionZhang, Xuesheng; Wang, Jing
doi: 10.1117/12.3014480pmid: N/A
Reversible Data Hiding in Encrypted Images (RDHEI) embeds information while protecting the content of images from being leaked, allowing users to decrypt image content, extract embedded information, and losslessly recover the original content based on the key types they possess. It is a recent hot research area at the intersection of information hiding and encrypted computation, aiming to ensure both data security and the ability to hide information within images. However, inadequate utilization of image blocks in RDHEI results in a low embedding capacity of additional data. For this reason, this paper proposes a RDHEI based on adaptive difference prediction and block subdivision. At first, divide the image into equally sized blocks, and these blocks are encrypted to conceal the content of the image. For data hider, using adaptive the most significant bit(MSB) prediction to classify the available and unavailable blocks. Based on adaptive MSB prediction(AMP), adaptive difference prediction(ADP) is used to subdivide the unavailable blocks to vacate more room for data embedding. When receiver receives the embedded encrypted image, the embedded data or image can be decrypted according to its own key possession. Experimental results show that the proposed method has a significant effect on improving the embedding capacity.
Research on intelligent design algorithm of indoor space based on hybrid recommendation modelHe, Huaxue
doi: 10.1117/12.3014657pmid: N/A
Looking at the traditional interior space design industry, the traditional design method is mainly manual design and the use of interactive modeling software and its design process mainly relies on trial and error. This paper takes the interior space design software platform as the background to study the collocation recommendation algorithm of the 3D home model, aim at improve the efficiency of the intelligent design algorithm. The recommendation idea of collaborative filtering is simple to implement, does not need to consider the inherent attribute characteristics of three-dimensional home projects, and is fast to calculate. After constructing the image feature database, this article uses the similarity between images to measure the visual similarity of the indoor space model; uses similar home projects to predict the collocation data of adjacent projects, and densifies the sparse collocation data; constructs each image separately Feature database, and use this to build its similarity table. According to the similarity table corresponding to each item, the first simNum items of the same category that are similar to the current item can be found. The experimental results show that compared with the traditional algorithm, the algorithm in this paper has greatly improved the accuracy of collocation recommendation.
Rapid identification of adulterated rice using fusion of near-infrared spectroscopy and machine vision data: the combination of feature optimization and nonlinear modelingSong, Chenxuan; Liu, Jinming; Wang, Chunqi; Li, Zhijiang
doi: 10.1117/12.3014380pmid: N/A
Rice is susceptible to mold and mildew during storage. Metabolites such as aflatoxin produced during mildew will do great harm to consumers. To meet the need for rapid detection of normal rice adulterated with moldy rice, a rapid identification method of adulterated rice was established based on data fusion of near-infrared spectroscopy and machine vision. Using competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), and least angle regression (LARS) for spectral and image feature extraction, combined with support vector classification (SVC), random forest (RF), and gradient boosting tree (GBT) nonlinear discriminant models, and use Bayesian search to optimize modeling parameters. The results show that the GBT fusion data model established by LARS optimization of spectral and image feature variables has the highest discrimination accuracy, with recognition accuracy rates of 100.00% and 98.11% for its training and testing sets, respectively. The discrimination performance is significantly improved compared to single near-infrared spectroscopy and machine vision. The results indicate that rapid identification of adulterated rice based on near-infrared spectroscopy and machine vision data fusion technology is feasible, providing theoretical support for the development of online identification equipment for adulterated rice.