Cluster’s Competitiveness of Photoelectron Industry of Optics Valley of Wuhan Based on the GEM Model

Wuhan East Lake High-tech Zone was called Optics Valley of Wuhan ratified by the Ministry of Science and Technology of China in 2001, which became a national photoelectron industry base now. As time goes by, Optics Valley of Wuhan photoelectron industry cluster become more and more powerful, and it has become a major form of regional economy in gaining competitive advantages. This paper establishes a GEM model of optics Valley photoelectron industry Cluster, and creates its competitiveness evaluation system. At the same time, not only do we measure the cluster’s competitiveness by distributing questionnaires, but also preliminary analyze and evaluate the measurement results

Conflating point of interest (POI) data: A systematic review of matching methods

Point of interest (POI) data provide digital representations of places in the real world, and have been increasingly used to understand human-place interactions, support urban management, and build smart cities. Many POI datasets have been developed, which often have different geographic coverages, attribute focuses, and data quality. From time to time, researchers may need to conflate two or more POI datasets in order to build a better representation of the places in the study areas. While various POI conflation methods have been developed, there lacks a systematic review, and consequently, it is difficult for researchers new to POI conflation to quickly grasp and use these existing methods. This paper fills such a gap. Following the protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we conduct a systematic review by searching through three bibliographic databases using reproducible syntax to identify related studies. We then focus on a main step of POI conflation, i.e., POI matching, and systematically summarize and categorize the identified methods. Current limitations and future opportunities are discussed afterwards. We hope that this review can provide some guidance for researchers interested in conflating POI datasets for their research

MP2: A Momentum Contrast Approach for Recommendation with Pointwise and Pairwise Learning

Binary pointwise labels (aka implicit feedback) are heavily leveraged by deep learning based recommendation algorithms nowadays. In this paper we discuss the limited expressiveness of these labels may fail to accommodate varying degrees of user preference, and thus lead to conflicts during model training, which we call annotation bias. To solve this issue, we find the soft-labeling property of pairwise labels could be utilized to alleviate the bias of pointwise labels. To this end, we propose a momentum contrast framework (MP2) that combines pointwise and pairwise learning for recommendation. MP2 has a three-tower network structure: one user network and two item networks. The two item networks are used for computing pointwise and pairwise loss respectively. To alleviate the influence of the annotation bias, we perform a momentum update to ensure a consistent item representation. Extensive experiments on real-world datasets demonstrate the superiority of our method against state-of-the-art recommendation algorithms.Comment: This paper was accepted at SIGIR 202

Research on surface movement and deformation characteristics of loess gully landform in Northern Shaanxi

In order to study the surface movement and deformation characteristics of the collapsible loess gully landform in the northern Shaanxi mining area in the middle reaches of the Yellow River Basin, the N1212 working face in the loess gully area of the Ningtiaota Mine has been systematically monitored for surface subsidence to analyze the high-intensity mining conditions Deformation characteristics of the ground surface subsidence, determine the maximum surface subsidence speed and the maximum subsidence speed lag angle, surface movement time and dynamic surface movement parameters. The results of the study show that the discontinuous deformation and destruction of the surface in high-strength coal mining in the collapsible loess layer in northern Shaanxi are severe, and the loess surface is easily affected by the combined effects of movement and deformation and topographic conditions, resulting in uneven settlement. Under high-strength mining conditions, the surface movement and deformation are severely developed , The maximum surface subsidence value is 5255 mm, the maximum horizontal movement value is 2680 mm, the maximum subsidence speed is 187.4 mm/d, the maximum subsidence coefficient of single coal seam mining is 0.63, the maximum subsidence coefficient of oblique repeated mining is 0.84, the active period is about 55 d, and the period of subsidence is about 55 d. The amount accounts for 97% of the total subsidence, the maximum lagging distance of the down-town velocity is 74 m, and the maximum lagging angle of the sinking velocity is 67°. The above results verify that in the high-intensity mining of shallow coal seams, the surface subsidence is proportional to the geological mining factors when the ground subsidence is severe, the activity period is short, and the mining is repeated. The surface deformation of high-intensity mining in the valley terrain has the characteristics of fast speed, large collapse and heavy damage

Classification of coal gangue pile vegetation based on UAV remote sensing

The accurate classification of vegetation species is the basis for the evaluation of vegetation restoration effect of coal gangue pile. In this paper, the visible image of coal gangue pile in different seasons was obtained by UAV remote sensing technology. The color space conversion and texture filtering were used to adequately explore the rich features of color, structure and texture in the visible image. Then, the traditional artificial feature selection method was improved, which could quickly, simply and efficiently screen features information to obtain the optimal classification features, and the optimized results were fused with RGB images to obtain multi-feature fusion images. Finally, based on two stages of RGB images and multi-feature fusion images, the vegetation of coal gangue pile was classified by three supervised classification methods, including support vector machine (SVM), maximum likelihood (ML) and neural network (NN). Meanwhile, the accuracy of classification results was evaluated by confusion matrix and the dynamic changes of vegetation were analyzed. The results showed that the improved artificial feature selection method could screen out the optimal classification features of coal gangue pile vegetation in different seasons. The selected classification features can not only effectively reflect the differences of various ground features, but also reduce the redundancy of feature information to improve the accuracy and efficiency of image classification. The classification result based on Support Vector Machine Classification (SVM) combined with multi-feature fusion image had highest classification accuracy, and the overall classification accuracy could reach 90.60%, and the corresponding Kappa coefficient is 0.8780, which was 9.74% and 0.1265 higher than that of RGB image of the same period, respectively. And, the accuracy of MLC and NNC classification methods was less improved. Compared with the RGB images of the same period, the overall classification accuracy could be improved by 6.95% and 3.93%, respectively, and the corresponding Kappa coefficient could be improved by 0.0845 and 0.0541, respectively. At the same time, based on the result of optimal classification, this paper evaluated the vegetation restoration effect of coal gangue pile in Changcun from the perspectives of vegetation coverage and vegetation allocation pattern. The results showed that a variety of different vegetation allocation patterns were adopted by the coal gangue pile, and the vegetation coverage in autumn and summer is higher than 75%. The overall effect of vegetation restoration was better. This study could provide reference for the identification and classification of coal gangue piles vegetation information based on UAV visible light image, and meanwhile provide opinions or suggestions for the later management and maintenance of coal gangue piles vegetation restoration

Research on extraction method of ground fissures caused by mining through UAV image in coal mine areas

In order to promptly and exactly identify the mining ground fissures in coal mining areas, and avoid the secondary geological disasters, as well as restore the land ecological environment in the coal mining areas, this study focused on the extraction method of surface mining induced fissures, with the fissure development zone of coal mining face of Ningtiaota Coal Mine as the study area, which was located in the northwest of Shenmu County, Yulin City, Shaanxi Province. Meanwhile, the smooth execution of this research was based on low-altitude UAV remote sensing images, field surveys, and the construction of an object-oriented supervision classified model method. The images acquisition process was shown as follows: Firstly, the candidate segmentation parameters were obtained utilizing the ESP（Estimation of scale parameter）optimal segmentation scale evaluation tool, and then the optimal segmentation parameters were determined immediately combining visual interpretation, finally the image objects such as fissures and vegetation were obtained. 15 optimized feature parameters were determined from 24 initial feature sets to construct the optimized feature set with the feature space optimization tool. On this basis, a variety of machine learning classifier models were combined, such as Support Vector Machine, K Nearest Neighbor, Random Forest, Naive Bayes, etc. The experimental analysis results presented that the classification effect and accuracy of the land features were consistent. The SVM classification method had the best overall effect, performing best in the four erroneously partitioned domains, with the least number of misclassified small patches. The overall classification accuracy achieved 88.97%, and the Kappa coefficient attained 0.849. In addition, the F1 value of crack extraction accuracy reached 87.87%, with the Kappa coefficient amount to 0.848. The overall classification accuracy of the four classification methods was above 80%. The optimal model method accurately extracted 10 main fissures in the research area, which was more efficient than traditional manual vectorization. The surface mining fissures could be effectively extracted by the aid of low-altitude drone remote sensing images and object-oriented methods. This research could provide technical support for the investigation and monitoring of geological disasters caused by coal mining subsidence and land ecological restoration