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As a common vegetable variety, asparagus is rich in B vitamins, vitamin A, and trace elements such as folate, selenium, iron, manganese, and zinc. With the increasing market demand, China has become the world's largest cultivated area for asparagus production and product exportation. However, traditional asparagus grading mostly relies on manual visual judgment and needs a lot of manpower input to carry out the classification operation, which cannot meet the needs of large-scale production. To address the high labor cost and labor-intensive production process resulting from the large amount of manpower input and low accuracy of existing asparagus grading devices, this study proposed an improved asparagus grading system and method based on TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) objective evaluation and SVM (support vector machine) prediction. The key structure of classification device was analyzed first, the key components were designed, and the structural parameters were determined by theoretical calculation. Through analysis of the factors affecting asparagus quality, three key attributes were determined: length, diameter, and bruises, which were used as reference attributes to conduct experimental analysis. Then, the graded control groups were set up, combining the TOPSIS principle with weighting, and a score for each asparagus sample was determined. These scores were compared with those of a graded control group to derive the grade of each asparagus, and these subsets of the dataset were used as the training set and the test set, excluding the error caused by the subjectivity of the manual judgment. Based on a comparison of the accuracies of different machine learning models, the support vector machine (SVM) was determined to be the most accurate, and four SVM methods were used to evaluate the test set: linear SVM, quadratic SVM, cubic SVM, and medium Gaussian SVM. The test results showed that the grading device was feasible for asparagus. The bruises had a large influence on asparagus quality. The training accuracy of the medium Gaussian SVM method was high (96%), whereas its test accuracy was low (86.67%). The training accuracies and test accuracy of the quadratic and cubic SVM methods were 93.34%. The quadratic SVM and cubic SVM were demonstrated to have better generalization ability than the medium Gaussian SVM method for predicting unknown grades of asparagus and meeting the operational requirements of the asparagus grading. [ABSTRACT FROM AUTHOR]

Boosting of the ensemble learning model has made great progress, but most of the methods are Boosting the single mode. For this reason, based on the simple multiclass enhancement framework that uses local similarity as a weak learner, it is extended to multimodal multiclass enhancement Boosting. First, based on the local similarity as a weak learner, the loss function is used to find the basic loss, and the logarithmic data points are binarized. Then, we find the optimal local similarity and find the corresponding loss. Compared with the basic loss, the smaller one is the best so far. Second, the local similarity of the two points is calculated, and then the loss is calculated by the local similarity of the two points. Finally, the text and image are retrieved from each other, and the correct rate of text and image retrieval is obtained, respectively. The experimental results show that the multimodal multi-class enhancement framework with local similarity as the weak learner is evaluated on the standard data set and compared with other most advanced methods, showing the experience proficiency of this method. [ABSTRACT FROM AUTHOR]

With rapid urbanization, the disposal and management of urban construction waste have become the main concerns of urban management. The distribution of urban construction waste is characterized by its wide range, irregularity, and ease of confusion with the surrounding ground objects, such as bare soil, buildings, and vegetation. Therefore, it is difficult to extract and identify information related to urban construction waste by using the traditional single spectral feature analysis method due to the problem of spectral confusion between construction waste and the surrounding ground objects, especially in the context of very-high-resolution (VHR) remote sensing images. Considering the multi-feature analysis method for VHR remote sensing images, we propose an optimal method that combines morphological indexing and hierarchical segmentation to extract the information on urban construction waste in VHR images. By comparing the differences between construction waste and the surrounding ground objects in terms of the spectrum, geometry, texture, and other features, we selected an optimal feature subset to improve the separability of the construction waste and other objects; then, we established a classification model of knowledge rules to achieve the rapid and accurate extraction of construction waste information. We also chose two experimental areas of Beijing to validate our algorithm. By using construction waste separability quality evaluation indexes, the identification accuracy of construction waste in the two study areas was determined to be 96.6% and 96.2%, the separability indexes of the construction waste and buildings reached 1.000, and the separability indexes of the construction waste and vegetation reached 1.000 and 0.818. The experimental results show that our method can accurately identify the exposed construction waste and construction waste covered with a dust screen, and it can effectively solve the problem of spectral confusion between the construction waste and the bare soil, buildings, and vegetation. [ABSTRACT FROM AUTHOR]

• We analyze the limitation of the classification head in one-stage detectors, which fills the gap in the literature. • We explain the classifier's limitation by visualizing its representations and analyzing its robustness to the scene context. • The findings give insights to design location-aware multi-dilation module (LAMD) in the classifiers for robust detection. • Experiments on MS COCO across various detectors with different backbones show that our method can achieve higher performance. Recent progress on one-stage detectors focuses on improving the quality of bounding boxes, while they pay less attention to the classification head. In this work, we focus on investigating the influence of the classification head. To understand the behavior of the classifier in one-stage detectors, we resort to the methods of the Explainable deep learning area. We visualize its learned representations via activation maps and analyze its robustness to image scene context. Based on the analysis, we observe that the classifier limits the performance of the detector due to its limited receptive field and the lack of object locations. Then, we design a simple but efficient location-aware multi-dilation module (LAMD) to enhance the weak classifier. We conduct extensive experiments on the COCO benchmark to validate the effectiveness of LAMD. The results suggest that our LAMD can achieve consistent improvements and leads to robust detection across various one-stage detectors with different backbones. [ABSTRACT FROM AUTHOR]

In recent years, with the development of unconventional oil and gas, the significance of microfractures for oil and gas exploration and development has become increasingly prominent. Scholars at home and abroad have made extensive and profound discussions on the definition, classification, origin, controlling factors and prediction methods of microfractures in different reservoirs. The upper limit value for defining the length of microcracks is 50 mm, and the upper limit value for aperture varies in different reservoirs. Genesis-based classification scheme is superior to other classification schemes and is currently most widely applied. The formation of microfractures in different rocks is the superposition of single or multiple factors including tectonism, diagenesis and abnormal high pressure. The main controlling factors of different types of microfractures are quite different, and the main controlling factors of the same type of microfractures in different reservoirs are also different. The prediction of microcracks is still at an early stage, and the existing methods have problems of poor accuracy and reliability, high data requirements and high cost. Quantitative analysis techniques based on fractal and mercury intrusion curves are the main means for quantitative characterization of microcracks. The study of microfractures is of great significance to the prediction of macrofractures, the study of sedimentary diagenetic evolution and the oil and gas development. The key points of next study is the comparison of microfractures in different reservoirs, the relationship between microfractures and sedimentation and diagenesis, the prediction and quantitative characterization of microfractures, combination relationship between microfractures of different origins and pore space, and the contribution of microfractures of different origins to reservoir permeability. [ABSTRACT FROM AUTHOR]

Natural scene statistic (NSS) has been widely used in no reference (NR) image quality assessment (IQA). Most IQA criteria operate upon the luminance component only, and color statistic has not been properly utilized. This paper focuses mainly on the natural color statistic (NCS) that can accurately describe image quality. Through experimental analysis, we study the characteristics of different color channels to find proper channels for IQA. We explore the possibility of using NCS in IQA by modeling color coefficients. Compared with other distributions, generalized Gaussian distribution (GGD) is more suitable for NCS. Additionally, we experiment the best description of different distortion types that fit different color channels. Therefore, we design a NR-IQA framework utilizing color statistic. This method can be divided into two stages: classification of distortion type and evaluation utilizing distortion-specific methods based on the most suitable color channel. Experimental results demonstrate that the proposed framework can achieve satisfactory results, and outperform IQA methods that have been broadly accepted and used in the quality community. Besides, our framework has the versatility for used in NR-IQA. [ABSTRACT FROM AUTHOR]