高精度・最小バイアス座標変換

Integrating GPS with national networks by collocation method

Fast algorithm for point pattern matching: Invariant to translations, rotations and scale changes

Ground station receivers or networks’ location uses different geodetic datums. The geodetic datum defines size and shape of the Earth model and origin of orientation of coordinate systems used to map the Earth. They are modified to converge to the International Terrestrial Reference Frame (ITRF). The Thailand geodetic reference frame has been maintained by the Royal Thai Survey Department (RTSD); currently, it is based on GNSS permanent networks. Precise orbit and clock solutions, earth orientation parameters and tropospheric gradients are applied as fixing parameters to estimate station positions using a network approach where all station coordinates are simultaneously adjusted; defined as a zero-order network. They are determined with respected to ITRF with centimetre precisions and accuracies. The shape of the Earth varies over time due to oceanic tides and plate movements. Changing are more gradually when earthquakes and volcanic eruptions happen. ITRF and other geodetic datums are changing continually; therefore, frame transformations are required. The latest adjusted coordinates are based on ITRF2005, 2008 and 2014 frame and velocities are set at the epoch 2008.83, 2013.81 and 2021.93 respectively. Constraints are cast in form of transformation parameters. The grid shift is then generated to transform from global coordinate systems to local geocentric reference frames. GNSS measurements obtained from another set of ground stations are used as check coordinates. Transformed coordinates are at 1 centimetre-level of accuracy both horizontally and vertically at 95 percent confidence level. Coordinate transformations can be applied to map Thailand based on one map on one datum policy.

An often neglected but important role is played by differential scale changes in transforming geodetic datums. A rigorous account of scale variations in any transformation involving reference ellipsoids and its effects on geodetic heights is essential. This role provides a plausible explanation for the reported z-shift between the Doppler defined terrestrial systems and the satellite laser ranging frames.

Effective and efficient low-altitude flying target tracking in the field of visual tracking is challenging due to factors such as background interference, a small target imaging area, scale changes, and in-plane/out-of-plane rotation. Fast compressive tracking is an effective algorithm that combines compressive sensing theory and the naive Bayes classifier to track targets in real-time. Since the target motion information is not used in the tracking process and a fixed learning rate is adopted, the target may be lost during tracking, especially when the background interference is considerable or when in-plane/out-of-plane rotation exists. To solve this problem, first, target motion information was introduced to reduce the search area for predicting the target position. Then, the confidence calculation was optimized by comprehensively considering the posterior probability of the candidate region and the positive sample membership value. Finally, the learning rate was dynamically adjusted according to the target velocity and optimized confidence. Experimental results verified that the proposed method could effectively improve the efficiency, accuracy, and robustness of target tracking.

Formation of features of microstructure images for their subsequent classification often encounters difficulties due to the high variability of images caused by changes in lighting, angle, and scale. Classifying images after feature discovery is also complicated by intraclass variability, where objects of the same class can differ greatly from each other, and interclass similarity, where objects of different classes are similar to each other, which also complicates the task. This paper proposes the formation of image features based on their topological decomposition. The use of the proposed approach makes it possible to avoid the problems described above since the generated features do not depend on the variability associated with changes in the brightness and geometric properties of images. The study was conducted on two datasets: images of metals containing 6 classes and images of polyvinyl alcohol cryogels containing 20 classes. The conducted testing showed that the classification accuracy when using the proposed approach is 94.11% on metals and 80.61% on polyvinyl alcohol cryogels. When using neural networks, the accuracy was 92.16 and 55.45%, respectively. Experiments have shown a preference for using the proposed approach when there is interclass similarity in the dataset.

Real-time human activity recognition is essential for human-robot interactions for assisted healthy independent living. Most previous work in this area is performed on traditional two-dimensional (2D) videos and both global and local methods have been used. Since 2D videos are sensitive to changes of lighting condition, view angle, and scale, researchers begun to explore applications of 3D information in human activity understanding in recently years. Unfortunately, features that work well on 2D videos usually don't perform well on 3D videos and there is no consensus on what 3D features should be used. Here we propose a model of human activity recognition based on 3D movements of body joints. Our method has three steps, learning dictionaries of sparse codes of 3D movements of joints, sparse coding, and classification. In the first step, space-time volumes of 3D movements of body joints are obtained via dense sampling and independent component analysis is then performed to construct a dictionary of sparse codes for each activity. In the second step, the space-time volumes are projected to the dictionaries and a set of sparse histograms of the projection coefficients are constructed as feature representations of the activities. Finally, the sparse histograms are used as inputs to a support vector machine to recognize human activities. We tested this model on three databases of human activities and found that it outperforms the state-of-the-art algorithms. Thus, this model can be used for real-time human activity recognition in many applications.

The PTRF is based on 43 sites with 64 SSC collocation points with the optimum geographic distribution, which were selected from all stations of the ITRF89 according to the criterion of the minimum value of the errors of 7 parameters of transformation. The ITRF89 was computed by the IERS Terrestrial Frame Section in Institut Geographique National - IGN and contains 192 VLBI and SLR stations (points) with 119 collocation ones. The PTRF has been compared with the ITRF89. The errors of the 7 parameters of transformation between the PTRF and 18 individual SSC as well as the mean square errors of station coordinates are of the same order as those for the ITRF89. The transformation parameters between the ITRF89 and the PTRF are negligible and their errors are of the order of 3 mm.

Structure-property relationships in the lead-free piezoceramic system K0.5Bi0.5TiO3 - BiMg0.5Ti0.5O3

Based on strain parameters, a weight function method is proposed to determine the critical plane under variable amplitude axial-torsion loading. The critical plane is determined by averaging the maximum absolute shear strain plane during the time for one block loading. The averaging process is conducted through a weight function, which is proposed based on the maximum shear strain. Then, combined with the life prediction model and Wang-Brown’s cycle counting method, the proposed method is used to predict the fatigue life under variable amplitude loading. The experimental data of En15R steel and 7050-T7451 aluminum alloy are used to verify the proposed method, and a good result is obtained.

Precision positioning is very important for automatic assembly and inspection in the electronic manufacturing process. In this paper, we propose a fast image alignment method using the expectation–maximization (E–M) technique. The proposed algorithm is especially applied to positioning and defect inspection of printed circuit boards (PCBs). It can well handle deformed or incomplete object shapes with translation, rotation, and scale changes. The Canny edge detector is used to generate the edge maps of images. The E-step of the E–M procedure finds mutual edge points in both compared images by assigning weights to individual edge points. The mutual edge points give larger weights, while the foreign edge points in two images have smaller weights. The M-step then calculates the geometric transformation parameters using the weighted edge points in individual images. For an edge point in one image, a fast spiral search is proposed to find its corresponding edge point with the shortest distance in the other image. The spiral search is carried out by a predetermined lookup table, and no computation is involved in the search process. The weight of each edge point is inversely proportional to the neighboring distance. Experimental results indicate that the proposed E–M positioning method can achieve a translation error less than 1 pixel and a rotation error smaller than 1° for PCB positioning.

Secular crustal deformation and interplate coupling of the Japanese Islands as deduced from continuous GPS array, 1996–2001

Computational evaluation of altered biomechanics related to articular cartilage lesions observed in vivo.

Surface deformations resulting from tectonic movements can be detected through statistical analyses. Deformations occurring on a surface can be computed independently of time using a model of static deformation. Besides, deformations can be obtained independently of the datum with the strain analyses and interpreted geometrically. Strain is defined as the ratio of the displacements at station points caused by a force to the initial coordinate values. Dilation and shear strains are used for the geometrical interpretation of the deformations. While dilation affects perpendicular to a surface, shear strain affects parallel to a surface. In any geodetic network, strains can emerge due to both the internal structure of the network and the external factors. Strains related to the internal structure of the network are affected by the observation plan and the weights of the observations. Besides, strains caused by external factors occur due to tectonic movements. In this study, the deformations caused by the earthquakes with magnitudes of Mw 7.7 and Mw 7.6 in Kahramanmaraş on February 6, 2023, have been analyzed. For this purpose, three different geodetic networks have been generated using Turkish National Permanent GNSS Network-Active (TNPGN-Active) stations. Observations obtained before and after earthquakes have been evaluated in two different epochs. In each epoch, the observation plan obtained by the Delaunay triangulation, the number of stations, and the initial coordinates are constant. In the geodetic networks, the deformations in the triangular surfaces between two epochs were analyzed using the theta-square criterion in the static deformation model. Consequently, comparing the first and third networks reveals that adding new stations to the network plan reduces the deformation test values. On the other hand, the second network has the larger deformation test values and the maximum shear strain on the triangular surfaces that are ANTE-EKZ1-MAR1, MLY1-GURU-EKZ1, EKZ1-TUF1-MAR1, TUF1-EKZ1-GURU, MLY1-EKZ1-ANTE among the three networks. Besides, the triangular surface of MLY1-EKZ1-ANTE has the largest deformation test value. Additionally, 2D strain analyses have been used to geometrical interpretation of the deformation on the triangular surfaces. Shear strains have been calculated using the strain matrices for each triangular surface. Accordingly, the maximum shear strain on the triangular surface, e.g., EKZ1-TUF1-MAR1, exceeds 9000 microstrains in each network plan. Furthermore, the location of the maximum shear strain is the same in three network plans.

A previously validated, highly detailed three‐dimensional finite element (FE) rat brain model was used to correlate FE model‐predicted intracranial responses with experimentally measured brain contusions. In the published in vivo experimental study, the animals were injured via a controlled cortical impact device at two different severities and the location and volume of contusion measured at 24 h post injury. Brain internal responses, including the maximum principal strain (MPS), maximum shear strain (MSS), shear strain (SS) in the coronal plane, strain energy density (SED), and intracranial pressure (ICP), were investigated through FE simulations. Distributions of MPS, MSS, and SED were comparable with the shapes of contusion observed experimentally. However, the regions with high positive ICP were not found to correlate with the regions of brain contusion. Locations of high SS were in the vicinity of the experimental contusion region, but the shallow shape of the SS distribution differed greatly from contusion observed experimentally. This study suggests that MPS, MSS, and SED are candidate injury mechanisms for brain contusion, and the corresponding threshold for predicting the contusion volume measured at 24 h post injury is 0.265 MPS, 0.281 MSS, or 1.72E −5 (J/mm3) SED based on the current FE model. Copyright © 2010 John Wiley & Sons, Ltd.