A tool for creating and visualizing semantic annotations on relational tables

Semantically annotating content from relational tables on the Web is a crucial task towards realizing the vision of the Semantic Web. However, there is a lack of open source, user-friendly tools to facilitate this. This paper describes an extension of the TableMiner+ system, an open source Semantic Table Interpretation system that automatically annotates Web tables using Linked Data in an effective and effi�cient approach. It adds a graphical user interface to TableMiner+, to facilitate the visualization and correction of automatically generated annotations. This makes TableMiner+ an ideal tool for the semi-automatic creation of high-quality semantic annotations on relational tables, which facilitates the publication of Linked Data on the Web

Visualizing semantic table annotations with TableMiner+

This paper describes an extension of the TableMiner+ sys- tem, an open source Semantic Table Interpretation system that annotates Web tables using Linked Data in an effective and e�fficient approach. It adds a graphical user interface to TableMiner+, to facilitate the visualization and correction of automatically generated annotations. This makes TableMiner+ an ideal tool for the semi-automatic creation of high-quality semantic annotations on tabular data, which facilitates the publication of Linked Data on the Web

Voxel-based Urban Vegetation Volume Analysis with LiDAR Point Cloud

The 3D volume and spatial distribution of urban vegetation are highly related to the delivery of multiple ecosystem services. However, due to the intricate vegetation structure, little research has been conducted to visualize and model the 3D spatial structure of urban vegetation. This study proposes an automated voxel-based modeling method to visualize and quantify the urban vegetation volume with LiDAR point cloud and performs a case study of the No.6 Middle School campus in Hengyang City, Hunan Province, China. The PointCNN model is used to perform semantic segmentation of the LiDAR data to extract the tree points. Then the points are voxelized into a 3D volume model with 1m×1m×1m cells. The result shows that the total vegetation volume of the area is 61,192m³, accounting for 37.28% of the total voxelized study area. The green space in front of the north teaching buildings has the largest proportion of vegetation volume, 19,366m³, accounting for 68.37% of the vegetation volume of the whole campus, due to the diverse vegetation and complex structure. The automated segmentation voxel modeling process could provide an efficient way to represent the spatial distribution of urban greenery. With an adjustable voxel size, the model could be adapted to various scales from regional to neighborhood. The model could also be used to analyze the green space structure at the human scale, as well as the interactions between green space and the surrounding environment, and to provide spatial data for the evaluation of multiple ecosystem services

Full-range Gate-controlled Terahertz Phase Modulations with Graphene Metasurfaces

Local phase control of electromagnetic wave, the basis of a diverse set of applications such as hologram imaging, polarization and wave-front manipulation, is of fundamental importance in photonic research. However, the bulky, passive phase modulators currently available remain a hurdle for photonic integration. Here we demonstrate full-range active phase modulations in the Tera-Hertz (THz) regime, realized by gate-tuned ultra-thin reflective metasurfaces based on graphene. A one-port resonator model, backed by our full-wave simulations, reveals the underlying mechanism of our extreme phase modulations, and points to general strategies for the design of tunable photonic devices. As a particular example, we demonstrate a gate-tunable THz polarization modulator based on our graphene metasurface. Our findings pave the road towards exciting photonic applications based on active phase manipulations

Calculation and finite element analysis of the temperature field for high-speed rail bearing based on vibrational characteristics

The complicated temperature environment of the high-speed rail bearing will generate the thermal stress and thermal deformation, which will change the vibrational characteristics of the bearing. If the vibration is serious, it will result in bearing failure and destructive accidents. Thus, the steady temperature field and the relationship between temperature field and the critical speed of the bearing were researched based on the vibrational characteristics in the paper. According to the specific work conditions and structure characteristics of the double row tapered roller bearing assembly, the heat transfer model of high-speed rail bearing was developed. The heat source and the external heat dissipation of the bearing were calculated, the reasonable boundary conditions of lubrication were set, and then the finite element model was established in ANSYS. According to four different distribution methods of heat source, the temperature field of the inner ring, outer ring and rollers were simulated and analyzed. Comparing the four different results, a reasonable distribution method of the heat source was put forward. Finally the effects of steady temperature field on critical speed of high-speed rail bearing were discussed. The simulation results showed that the bearing temperature distribution was basically consistent with the actual working conditions. The steady temperature field has stronger effect on vibration mode of low-order critical speed then high-order critical speed of bearing. The results of this study provide a basis of vibration characteristics for the use and optimal design of high-speed rail bearing

Artificial intelligence-based human–computer interaction technology applied in consumer behavior analysis and experiential education

In the course of consumer behavior, it is necessary to study the relationship between the characteristics of psychological activities and the laws of behavior when consumers acquire and use products or services. With the development of the Internet and mobile terminals, electronic commerce (E-commerce) has become an important form of consumption for people. In order to conduct experiential education in E-commerce combined with consumer behavior, courses to understand consumer satisfaction. From the perspective of E-commerce companies, this study proposes to use artificial intelligence (AI) image recognition technology to recognize and analyze consumer facial expressions. First, it analyzes the way of human–computer interaction (HCI) in the context of E-commerce and obtains consumer satisfaction with the product through HCI technology. Then, a deep neural network (DNN) is used to predict the psychological behavior and consumer psychology of consumers to realize personalized product recommendations. In the course education of consumer behavior, it helps to understand consumer satisfaction and make a reasonable design. The experimental results show that consumers are highly satisfied with the products recommended by the system, and the degree of sanctification reaches 93.2%. It is found that the DNN model can learn consumer behavior rules during evaluation, and its prediction effect is increased by 10% compared with the traditional model, which confirms the effectiveness of the recommendation system under the DNN model. This study provides a reference for consumer psychological behavior analysis based on HCI in the context of AI, which is of great significance to help understand consumer satisfaction in consumer behavior education in the context of E-commerce

Experimental investigation on the bamboo-concrete filled circular steel tubular stub columns

[EN] Concrete-filled steel tubes have been widely used all over the world due to their superior structural behaviour. To promote the use of ecofriendly materials and to reduce the use of concrete, this paper presents an innovative type of composite column, which can be referred as bamboo-concrete filled steel tubes. In this kind of column, concrete filled in the space between the external steel tube and the inner raw moso bamboo. Bamboo-concrete filled steel tubes inherit the merits of concrete-filled steel tubes such as high load-bearing capacity and ductility performance. Besides, global buckling behaviour of a bamboo column due to its relatively large slenderness can be significantly improved, and the bamboo column with nodes could provide confinement to the infilled concrete. This paper investigated the composite effect of bamboo-concrete filled steel tubular stub columns subjected to axial compression. In addition, concrete-filled double-skin steel tubular stub columns and hollow concrete-filled steel tubular stub columns were also tested for comparison. The main experimental parameter considered was the diameter-to-thickness ratio (D/t) of steel tube. Test results indicated that the composite columns with moso bamboo pipe as inner core elements showed better ductility than the hollow concrete-filled steel tubular stub columns. The bearing capacity and ductility visibly increased with decreasing of the D/t ratio.Gan, D.; Zhang, T.; Zhou, X.; He, Z. (2018). Experimental investigation on the bamboo-concrete filled circular steel tubular stub columns. En Proceedings of the 12th International Conference on Advances in Steel-Concrete Composite Structures. ASCCS 2018. Editorial Universitat Politècnica de València. 385-391. https://doi.org/10.4995/ASCCS2018.2018.7138OCS38539