Ontology for pixel processing

For all kinds of output devices, such as monitors, printers etc, the most important thing is to show the right information to the user. Pixel is the basic element both on screen and materials printed with. And, as a result pixel processing is the basic technique to make the output correct, precise, and suitable to use on different occasions. Pixel processing solves operations on each pixel of the image, which is for the pixel matrices of that image, so that the image would have different appearance. Ontology is about the exact description of things and their relationships. It is an old study of philosophy from ancient Greece. As the study of artificial intelligence keeps growing, the concept of ontology has been in use more and more in the formalization of knowledge in terms of classes, properties, instances and relations [1]. This paper mainly discusses how to build ontology of pixel processing with OWL. Actually, it is focused on how to describe pixel processing and its functions or operations in an understandable way by computer. With such description, it is possible to improve the development of pixel processing and the sharing of its knowledge both between people and machines. That is from the Natural Language Processing point of view. And also, in the future, it provides a base for intelligent agent to implement pixel processing by understanding such kind of definition and description directly through its knowledge base built up with such ontology. In other words, that may realize the automatic program or program analysis

Distilling Inter-Class Distance for Semantic Segmentation

Knowledge distillation is widely adopted in semantic segmentation to reduce the computation cost.The previous knowledge distillation methods for semantic segmentation focus on pixel-wise feature alignment and intra-class feature variation distillation, neglecting to transfer the knowledge of the inter-class distance in the feature space, which is important for semantic segmentation. To address this issue, we propose an Inter-class Distance Distillation (IDD) method to transfer the inter-class distance in the feature space from the teacher network to the student network. Furthermore, semantic segmentation is a position-dependent task,thus we exploit a position information distillation module to help the student network encode more position information. Extensive experiments on three popular datasets: Cityscapes, Pascal VOC and ADE20K show that our method is helpful to improve the accuracy of semantic segmentation models and achieves the state-of-the-art performance. E.g. it boosts the benchmark model("PSPNet+ResNet18") by 7.50% in accuracy on the Cityscapes dataset.Comment: IJCAI-ECAI2022 Long Ora

Mapping theme trends and recognizing hot spots in postmenopausal osteoporosis research: a bibliometric analysis

Background This study aimed to draw a series of scientific maps to quantitatively and qualitatively evaluate hot spots and trends in postmenopausal osteoporosis research using bibliometric analysis. Methods Scientific papers published on postmenopausal osteoporosis were extracted from the Web of Science Core Collection and PubMed database. Extracted information was analyzed quantitatively with bibliometric analysis by CiteSpace, the Online Analysis Platform of Literature Metrology and Bibliographic Item Co-Occurrence Matrix Builder (BICOMB). To explore the hot spots in this field, co-word biclustering analysis was conducted by gCLUTO based on the major MeSH terms/MeSH subheading terms-source literatures matrix. Results We identified that a total of 5,247 publications related to postmenopausal osteoporosis were published between 2013 and 2017. The overall trend decreased from 1,071 literatures in 2013 to 1,048 literatures in 2017. Osteoporosis International is the leading journal in the field of postmenopausal osteoporosis research, both in terms of impact factor score (3.819) and H-index value (157). The United States has retained a top position and has exerted a pivotal influence in this field. The University of California, San Francisco was identified as a leading institution for research collaboration, and Professors Reginster and Kanis have made great achievements in this area. Eight research hot spots were identified. Conclusions Our study found that in the past few years, the etiology and drug treatment of postmenopausal osteoporosis have been research hot spots. They provide a basis for the study of the pathogenesis of osteoporosis and guidelines for the drug treatment of osteoporosis

A 2D Cd(II) Coordination Polymer Constructed From 1,3-di(4-pyridyl)propane and 2,7-naphthalenedisulfonate

<div><p>A new coordination polymer [Cd(2,7-nds)(bpp)₂]·2DMA·4H₂O (<b>1</b>) (2,7-nds = 2,7-naphthalene disulfonic acid, bpp = 1,3-di(4-pyridyl)propane, DMA = N,N-Dimethylacetamide) has been synthesized by assembly of Cd(II) with 2,7-nds and bpp ligands and characterized by elemental analysis, IR, TGA, and single-crystal X-ray diffraction. The X-ray diffraction analysis reveals that <b>1</b> crystallizes in the monoclinic crystal system, space group <i>C2</i>. Topological analysis of <b>1</b> discloses an uninodal 4-connected net with the sql topology.</p></div

A 3D Cd(II) Coordination Polymer With SRA Topology

<div><p>One new cadmium(II) coordination polymer, [Cd(pzdc)(H₂O)] (<b>1</b>) (H₂pzdc = pyrazine-2,3-dicarboxylic acid) has been synthesized by the reaction of Cd(II) ions with H₂pzdc under the solvothermal condition and characterized by elemental analysis, IR, TG, and single-crystal X-ray diffraction. The X-ray diffraction analysis reveals that <b>1</b> (C₆H₄CdN₂O₅) crystallizes in the orthorhombic crystal system, space group <i>Pnma</i>. In compound <b>1</b>, the pzdc ligands link Cd(II) ions into a 3D 4-connected {4².6³.8}(SRA) topological net.</p></div

Research of Flow Characteristics Hybrid Model of Steam Turbine Stage Based on the Improved PSO Algorithm

Power station steam turbine stage flow characteristics show the Corresponding relationship between pressure and flow rate, which is the important research foundation for analysis of steam turbine performance and the further optimization analysis of unit. Based on strict theory analysis, this article obtained two important key characteristic coefficients such as the capacity of flow coefficient and the level of group critical pressure ratio which mainly influenced the turbine characteristics. And then the secondary flow calculation model was imposed combining with the massive actual data, adapting the method of improved PSO algorithm. The practical results show that, the obtained model not only ensured good regularity and ductility, but also has higher calculation precision. DOI: http://dx.doi.org/10.11591/telkomnika.v11i11.350