scholarly journals Positioning Localities for Vague Spatial Location Description: A Supervaluation Semantics Approach

2022 ◽  
Vol 11 (1) ◽  
pp. 68
Author(s):  
Peng Ye ◽  
Xueying Zhang ◽  
Chunju Zhang ◽  
Yulong Dang

In the big data era, spatial positioning based on location description is the foundation to the intelligent transformation of location-based-services. To solve the problem of vagueness in location description in different contexts, this paper proposes a positioning method based on supervaluation semantics. Firstly, through combing the laws of human spatial cognition, the types of elements that people pay attention to in location description are clarified. On this basis, the source of vagueness in the location description and its embodiment in the expression form of each element are analyzed from multiple levels. Secondly, the positioning model is constructed from the following three aspects: spatial object, distance relation and direction relation. The contexts of multiple location description are super-valued, respectively, while the threshold of observations is obtained from the context semantics. Thus, the precisification of location description is realized for positioning. Thirdly, a question-answering system is designed to the collect contexts of location description, and a case study on the method is conducted. The case can verify the transformation of a set of users’ viewpoints on spatial cognition into the real-world spatial scope, to realize the representation of vague location description in the geographic information system. The result shows that the method proposed in the paper breaks through the traditional vagueness modeling, which only focuses on spatial relationship, and enhances the interpretability of semantics of vague location description. Moreover, supervaluation semantics can obtain the precisification results of vague location description in different situations, and the positioning localities are more suitable to individual subjective cognition.

2021 ◽  
Vol 10 (7) ◽  
pp. 437
Author(s):  
Hongxia Qi ◽  
Yunjia Wang ◽  
Jingxue Bi ◽  
Hongji Cao ◽  
Shenglei Xu

Floor positioning is an important aspect of indoor positioning technology, which is closely related to location-based services (LBSs). Currently, floor positioning technologies are mainly based on radio signals and barometric pressure. The former are impacted by the multipath effect, rely on infrastructure support, and are limited by different spatial structures. For the latter, the air pressure changes with the temperature and humidity, the deployment cost of the reference station is high, and different terminal models need to be calibrated in advance. In view of these issues, here, we propose a novel floor positioning method based on human activity recognition (HAR), using smartphone built-in sensor data to classify pedestrian activities. We obtain the degree of the floor change according to the activity category of every step and determine whether the pedestrian completes floor switching through condition and threshold analysis. Then, we combine the previous floor or the high-precision initial floor with the floor change degree to calculate the pedestrians’ real-time floor position. A multi-floor office building was chosen as the experimental site and verified through the process of alternating multiple types of activities. The results show that the pedestrian floor position change recognition and location accuracy of this method were as high as 100%, and that this method has good robustness and high universality. It is more stable than methods based on wireless signals. Compared with one existing HAR-based method and air pressure, the method in this paper allows pedestrians to undertake long-term static or round-trip activities during the process of going up and down the stairs. In addition, the proposed method has good fault tolerance for the misjudgment of pedestrian actions.


2021 ◽  
Author(s):  
Paulo Bala ◽  
Valentina Nisi ◽  
Mara Dionisio ◽  
Nuno Jardim Nunes ◽  
Stuart James

AI Magazine ◽  
2016 ◽  
Vol 37 (1) ◽  
pp. 63-72 ◽  
Author(s):  
C. Lawrence Zitnick ◽  
Aishwarya Agrawal ◽  
Stanislaw Antol ◽  
Margaret Mitchell ◽  
Dhruv Batra ◽  
...  

As machines have become more intelligent, there has been a renewed interest in methods for measuring their intelligence. A common approach is to propose tasks for which a human excels, but one which machines find difficult. However, an ideal task should also be easy to evaluate and not be easily gameable. We begin with a case study exploring the recently popular task of image captioning and its limitations as a task for measuring machine intelligence. An alternative and more promising task is Visual Question Answering that tests a machine’s ability to reason about language and vision. We describe a dataset unprecedented in size created for the task that contains over 760,000 human generated questions about images. Using around 10 million human generated answers, machines may be easily evaluated.


2011 ◽  
Vol 411 ◽  
pp. 388-392 ◽  
Author(s):  
Yun Long Li ◽  
Jian Min Gao ◽  
Lei Shi ◽  
Song Wang

In order to improve the efficiency of assembly modeling and provide a complete assembly model for assembly sequence planning (ASP), a method of assembly modeling based on polychromatic sets (PS) is proposed. Firstly, the assembly information of 3D Component is obtained by API function in SolidWorks. In addition, assembly incident matrix, information matrix and mapping matrix are built. On the basis of these matrixes, a method of location modeling is explored. The interference relation matrix is developed by judging the spatial location and analyzing dynamic interference relation among parts. Finally, a case study is given to verify the method.


2022 ◽  
Author(s):  
Sami Ryan Yousif

Mental representations are the essence of cognition. Yet, to understand how the mind works, we must understand not just the content of mental representations (i.e., what information is stored), but also the format of those representations (i.e., how that information is stored). But what does it mean for representations to be formatted? How many formats are there? Is it possible that the mind represents some pieces of information in multiple formats at once? To address these questions, I discuss a ‘case study’ of representational format: the representation of spatial location. I review work (a) across species and across development, (b) across spatial scales, and (c) across levels of analysis (e.g., high-level cognitive format vs. low-level neural format). Along the way, I discuss the possibility that the same information may be organized in multiple formats simultaneously (e.g., that locations may be represented in both Cartesian and polar coordinates). Ultimately, I argue that seemingly ‘redundant’ formats may support the flexible spatial behavior observed in humans, and that we should approach the study of all mental representations with this possibility in mind.


2018 ◽  
Vol 64 (2) ◽  
pp. 249-259 ◽  
Author(s):  
Sota Inoue ◽  
Shinya Yamamoto ◽  
Monamie Ringhofer ◽  
Renata S. Mendonça ◽  
Carlos Pereira ◽  
...  

2020 ◽  
Vol 7 (2-3) ◽  
pp. 261-279
Author(s):  
Ellen Larson

Between 2015 and 2020, artist Cao Fei occupied the former Beijing-based Hongxia Theatre, transforming the space into a vehicle for creative research, production and exhibition. This article will examine Cao Fei’s engagement with multiple temporalities as directly shaped by her spatial position within the theatre. Research related to the Hongxia Theatre and surrounding former People’s Republic of China (PRC)-era factory neighbourhood informs her understanding of not only China’s industrial history but also resurging connections to themes that exist across, within and beyond traditional temporal frameworks. The following narrative will employ Asia One (2018), the first full-length film made by Cao Fei since moving into the Hongxia Theatre, as a case study, highlighting strategies in which Hongxia fosters a pivotal spatial relationship between the artist’s new work and intersecting affinities towards time, memory and nostalgia. Drawing on China’s utopian past and dreams of a fully automated future, Asia One demonstrates a temporally nonlinear yearning to record both remembered and imagined emotional attachments, despite both globalizing and domestic conditions, which engender the urge to forget.


Author(s):  
Myungwoo Lee ◽  
Aemal J. Khattak

Traffic crash hot spot analyses allow identification of roadway segments that may be of safety concern. Understanding geographic patterns of existing motor vehicle crashes is one of the primary steps for geostatistical-based hot spot analysis. Much of the current literature, however, has not paid particular attention to differentiating among cluster types based on crash severity levels. This study aims at building a framework for identifying significant spatial clustering patterns characterized by crash severity and analyzing identified clusters quantitatively. A case study using an integrated method of network-based local spatial autocorrelation and the Kernel density estimation method revealed a strong spatial relationship between crash severity clusters and geographic regions. In addition, the total aggregated distance and the density of identified clusters obtained from density estimation allowed a quantitative analysis for each cluster. The contribution of this research is incorporating crash severity into hot spot analysis thereby allowing more informed decision making with respect to highway safety.


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