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

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.

APPLICATION OF K-MEANS ALGORITHM IN GROUPING OF CITY TOURISM CITY PAGAR ALAM

The purpose of this study was to analyze the application of the k-means algorithm in classifying tourist visits to the city of Pagar Alam. The k-means algorithm in grouping tourist objects begins by determining the number of clusters to be formed, determining the centroid value of each cluster, calculating the distance between the data, and calculating the minimum object distance calculated. There are 10 tourism objects that are superior from the data from the Tourism Office of the City of Pagar Alam. The research data used is the number of tourist visitors during the COVID-19 pandemic, namely 2020. The data are grouped into 4 clusters, namely C1 = high number of tourist visitors, C2 = moderate number of tourist visitors, C3 = low number of tourist visitors, C4 = number of visitors travel is very low. the centroid values ​​used are C1 = 92,494, Centroid C2 = 71,658, Centroid C3 = 26,981 and centroid C4 = 4,485. then we get the results of grouping C1=Green Paradise tourism, C2=Janang Orange Gardens,, C3=Curup Tujuh Kenangan, Curup Mangkok, Curup dew, Tegur Wangi Site, Pelang Kenidai Village, and C4= Lumai Site, Tebing Tinggi Site and Tanjung Aro Site . From the results of grouping for c4 it becomes a note for the government of the City of Pagar Alam in increasing the number of tourist visitors.

Development of laboratory devices for real-time measurement of object 3D position using stereo cameras

Measuring the 3D position at any time of a given object in real-time automatically and well documented to understand a physical phenomenon is essential. Exploring a stereo camera in developing 3D images is very intriguing since a 3D image perception generated by a stereo image may be reprojected back to generate a 3D object position at a specific time. This research aimed to develop a device and measure the 3D object position in real-time using a stereo camera. The device was constructed from a stereo camera, tripod, and a mini-PC. Calibration was carried out for position measurement in X, Y, and Z directions based on the disparity in the two images. Then, a simple 3D position measurement was carried out based on the calibration results. Also, whether the measurement was in real-time was justified. By applying template matching and triangulation algorithms on those two images, the object position in the 3D coordinate was calculated and recorded automatically. The disparity resolution characteristic of the stereo camera was obtained varied from 132 pixels to 58 pixels for an object distance to the camera from 30 cm to 70 cm. This setup could measure the 3D object position in real-time with an average delay time of less than 50 ms, using a notebook and a mini-PC. The 3D position measurement can be performed in real-time along with automatic documentation. Upon the stereo camera specifications used in this experiment, the maximum accuracy of the measurement in X, Y, and Z directions are ΔX = 0.6 cm, ΔY = 0.2 cm, and ΔZ = 0.8 cm at the measurement range of 30 cm–60 cm. This research is expected to provide new insights in the development of laboratory tools for learning physics, especially mechanics in schools and colleges.

CALIBRATION OF NON-METRIC UAV CAMERA USING DIFFERENT TEST FIELDS

The paper deals with the calibration of a non-metric digital camera Nikon EOS 6D with a 50 mm lens that could be adapted as a potential UAV sensor for the purposes of aerial inspections. The determination of the internal orientation parameters and the image errors of the non-metric digital camera involved self-calibration with Agisoft Metashape software solving the network of the images obtained from different test fields: a chessboard field, a professional laboratory field and a spatially diverse research area. The results of the control measurement for the examined object distance of 6 meters do not differ significantly. The RMSE from the control measurement for the second analyzed object distance of 15 meters was calculated on the basis of the internal orientation elements. The images from the laboratory field, the spatial test area and the chessboard field were used, and the obtained results amounted to 7.9, 9.9 and 11.5 mm, respectively. The conducted studies showed that in the case of very precise photogrammetric measurements performed by means of the Nikon EOS 6D camera equipped with a 50 mm lens, it is optimal to conduct calibration in a laboratory test field. The greatest RMSE errors were recorded for the control images with the elements of the internal camera orientation calculated on the basis of the chessboard area. The results of the experiments clearly show a relation between the accuracy of the Nikon EOS 6D camera calibrations and the percentage of the frame area filled with the test field. This explains why the weakest calibration results were obtained from the chessboard test field.

Roughness measurement of leaf surface based on shape from focus

Background Surface roughness has a significant effect on leaf wettability. Consequently, it influences the efficiency and effectiveness of pesticide application. Therefore, roughness measurement of leaf surface offers support to the relevant research efforts. To characterize surface roughness, the prevailing methods have drawn support from large equipment that often come with high costs and poor portability, which is not suitable for field measurement. Additionally, such equipment may even suffer from inherent drawbacks like the absence of relationship between pixel intensity and corresponding height for scanning electron microscope (SEM). Results An imaging system with variable object distance was created to capture images of plant leaves, and a method based on shape from focus (SFF) was proposed. The given space-variantly blurred images were processed with the proposed algorithm to obtain the surface roughness of plant leaves. The algorithm improves the current SFF method through image alignment, focus distortion correction, and the introduction of NaN values that allows it to be applied for precise 3d-reconstruction and small-scale surface roughness measurement. Conclusion Compared with methods that rely on optical three-dimensional interference microscope, the method proposed in this paper preserves the overall topography of leaf surface, and achieves superior cost performance at the same time. It is clear from experiments on standard gauge blocks that the RMSE of step was approximately 4.44 µm. Furthermore, according to the Friedman/Nemenyi test, the focus measure operator SML was expected to demonstrate the best performance.