Accuracy in Determining Aircraft Position by Terrestrial Radio Navigation

The main objective of terrestrial radio navigation is position determination. In this study, the accuracy of the distance measurement, distance difference measurement, and integrated angle measurement/distance measurement terrestrial radio navigation methods is investigated. in order to calculate the position errors, simulations for the aircraft flight dynamics were carried out, and the obtained position values were compared with the actual values. The aircraft position determination methods were evaluated in the sense of accuracy. The position determination method with better accuracy was determined by comparing the absolute errors of the examined methods. Simulation and error analysis shows that the distance difference method is superior and gives more accurate position results. It was observed that the distance measurement method errors were smaller than the errors of the integrated angle measurement/distance measurement method.

Weighted Direct Position Determination via the Dimension Reduction Method for Noncircular Signals

This work studies the direct position determination (DPD) of noncircular (NC) signals with multiple arrays. Existing DPD algorithms of NC sources ignore the impact of path propagation loss on the performance of the algorithms. In practice, the signal-to-noise ratios (SNRs) of different observation stations are often different and unstable when the NC signal of the same radiation target strikes different observation locations. Besides, NC features of the target signals are applied not only to extend the virtual array manifold but also to bring high-dimensional search. For the sake of addressing the above problems, this study develops a DPD method of NC sources for multiple arrays combing weighted subspace data fusion (SDF) and dimension reduction (RD) search. First, NC features of the target signals are applied to extend the virtual array manifold. Second, we assign a weight to balance the error and obtain higher location accuracy with better robustness. Then, the RD method is used to eliminate the high computational complexity caused by the NC phase search dimension. Finally, the weighted fusion cost function is constructed by using the eigenvalues of the received signal covariance matrixes. It is verified by simulation that the proposed algorithm can effectively improve the location performance, get better robustness, and distinguish more targets compared with two-step location technology and SDF technology. In addition, without losing the estimation performance, the proposed algorithm can significantly reduce the complexity caused by the NC phase search dimension.

Fast and Accurate Position Determination of Multimodal Couplings of Steel Rolling Machinery and Equipment Combined with Multimedia Optimized Particle Filters

During the overhaul and maintenance of the transmission system of the modern rolling mill, a very common problem is the positioning of the motor and the reducer or speed increaser. The operation of the equipment and the service life of the equipment will be significantly affected by the accuracy of the multimodal coupling position determination. Therefore, precise positioning and rapid positioning must be achieved. This article intends to analyze how this type of equipment achieves rapid and accurate positioning and what factors will affect the location determination process.

Pose estimation for the case of prolonged under-ice based AUV

The technological operation of AUV docking is considered. An approach to its solution and modeling is proposed. A system for determining the relative position based on cameras and active-light markers is described. Active markers are offered to improve detection in dark deep water. A system of equations for modeling the dynamics of an underwater vehicle is described and a complete mathematical modeling of the whole pipeline of the positioning system is performed: image synthesis based on scene geometry, position determination based on the solution of the PnP problem, generating control based on synthesized measurements and modelling of the corresponding movement of the AUV. The presented model can be used to test various scenarios for the execution of a docking operation.

Artificial Intelligence in Positioning Between Tooth and Nerve on Panoramic Radiography

Determining the exact positional relationship between mandibular third molar (M3) and inferior alveolar nerve (IAN) is important for surgical extractions. Panoramic radiography is the most common dental imaging test. The purposes of this study were to develop an artificial intelligence (AI) model to determine two positional relationships (true contact and bucco-lingual position) between M3 and IAN when they were overlapped in panoramic radiographs and compare its performance with that of oral and maxillofacial surgery (OMFS) specialists. A total of 571 panoramic images of M3 from 394 patients was used for this study. Among the images, 202 were classified as true contact, 246 as intimate, 61 as IAN buccal position, and 62 as IAN lingual position. A deep convolutional neural network model with ResNet-50 architecture was trained for each task. We randomly split the dataset into 75% for training and validation and 25% for testing. Model performance was superior in bucco-lingual position determination (accuracy 0.76, precision 0.83, recall 0.67, and F1 score 0.73) to true contact position determination (accuracy 0.63, precision 0.62, recall 0.63, and F1 score 0.61). AI exhibited much higher accuracy in both position determinations compared to OMFS specialists. In determining true contact position, OMFS specialists demonstrated an accuracy of 52.68% to 69.64%, while the AI showed an accuracy of 72.32%. In determining bucco-lingual position, OMFS specialists showed an accuracy of 32.26% to 48.39%, and the AI showed an accuracy of 80.65%. Moreover, Cohen’s kappa exhibited a substantial level of agreement for the AI (0.61) and poor agreements for OMFS specialists in bucco-lingual position determination. Determining the position relationship between M3 and IAN is possible using AI, especially in bucco-lingual positioning. The model could be used to support clinicians in the decision-making process for M3 treatment.

Displacement of the Greater Tuberosity in Humeral Head Fractures Does Not only Depend on Rotator Cuff Status

It is assumed that dorsocranial displacement of the greater tuberosity in humeral head fractures is caused by rotator cuff traction. The purpose of this study was to investigate the association between rotator cuff status and displacement characteristics of the greater tuberosity in four-part humeral head fractures. Computed tomography scans of 121 patients with Neer type 4 fractures were analyzed. Fatty infiltration of the supra- and infraspinatus muscles was classified according to Goutallier. Position determination of the greater tuberosity fragment was performed in both coronary and axial planes to assess the extent of dorsocranial displacement. Considering non-varus displaced fractures, the extent of the dorsocranial displacement was significantly higher in patients with mostly inconspicuous posterosuperior rotator cuff status compared to advanced fatty degenerated cuffs (cranial displacement: Goutallier 0–1: 6.4 mm ± 4.6 mm vs. Goutallier 2–4: 4.2 mm ± 3.5 mm, p = 0.020; dorsal displacement: Goutallier 0–1: 28.4° ± 32.3° vs. Goutallier 2–4: 13.1° ± 16.1°, p = 0.010). In varus displaced humeral head fractures, no correlation between the displacement of the greater tuberosity and the condition of the posterosuperior rotator cuff could be detected (p ≥ 0.05). The commonly accepted theory of greater tuberosity displacement in humeral head fractures by rotator cuff traction cannot be applied to all fracture types.

Direct Position Determination of Noncircular Sources with Multiple Nested Arrays: Reduced Dimension Subspace Data Fusion

Direct position determination (DPD) of noncircular (NC) sources with multiple nested arrays (NA) is investigated in this paper. Noncircular sources are used to expand the dimension of the received signal matrix, so the number of identifiable information sources and the accuracy of direct position determination are improved. Furthermore, nested array increases spatial degree of freedom. In this paper, the high-dimensional search problem of noncircular sources is investigated. Therefore, we propose algorithm dimension reduction subspace data fusion (RD-SDF) to reduce complexity and increase positioning accuracy. Simulation results show that the proposed RD-SDF algorithm for multiple nested arrays with noncircular sources has improved positioning accuracy with higher spatial degree of freedom than SDF, Capon, and two-step algorithms with uniform linear array and circular sources (CS).