Search Results (67)

Regarding the distortion correction problem of large field of view wide-angle cameras commonly used in railway visual inspection systems, this paper proposes a novel online calibration method for non-specially made cooperative calibration objects. Based on the radial distortion divisor model, first, the spatial coordinates of natural spatial landmark points are constructed according to the known track gauge value between two parallel rails and the spacing value between sleepers. By using the image coordinate relationships corresponding to these spatial coordinates, the coordinates of the distortion center point are solved according to the radial distortion fundamental matrix. Then, a constraint equation is constructed based on the collinear constraint of vanishing points in railway images, and the Levenberg–Marquardt algorithm is used to found the radial distortion coefficients. Moreover, the distortion coefficients and the coordinates of the distortion center are re-optimized according to the least squares method (LSM) between points and the fitted straight line. Finally, based on the above, the distortion correction is carried out for the distorted railway images captured by the camera. The experimental results show that the above method can efficiently and accurately perform online distortion correction for large field of view wide-angle cameras used in railway inspection without the participation of specially made cooperative calibration objects. The whole method is simple and easy to implement, with high correction accuracy, and is suitable for the rapid distortion correction of camera images in railway online visual inspection. Full article

The present review discusses the solution of the Heun, confluent, biconfluent, double confluent, and triconfluent equations in terms of polynomial expansions, and applies the results to generate exactly solvable Schrödinger potentials. Although there are more general approaches to solve these differential equations in terms of the expansions of certain special functions, the importance of polynomial solutions is unquestionable, as most of the known potentials are solvable in terms of the hypergeometric and confluent hypergeometric functions; i.e., Natanzon-class potentials possess bound-state solutions in terms of classical orthogonal polynomials, to which the (confluent) hypergeometric functions can be reduced. Since some of the Heun-type equations contain the hypergeometric and/or confluent hypergeometric differential equations as special limits, the potentials generated from them may also contain Natanzon-class potentials as special cases. A power series expansion is assumed around one of the singular points of each differential equation, and recurrence relations are obtained for the expansion coefficients. With the exception of the triconfluent Heun equations, these are three-term recurrence relations, the termination of which is achieved by prescribing certain conditions. In the case of the biconfluent and double confluent Heun equations, the expansion coefficients can be obtained in the standard way, i.e., after finding the roots of an (N + 1)th-order polynomial in one of the parameters, which, in turn, follows from requiring the vanishing of an (N + 1) × (N + 1) determinant. However, in the case of the Heun and confluent Heun equations, the recurrence relation can be solved directly, and the solutions are obtained in terms of rationally extended X₁-type Jacobi and Laguerre polynomials, respectively. Examples for solvable potentials are presented for the Heun, confluent, biconfluent, and double confluent Heun equations, and alternative methods for obtaining the same potentials are also discussed. These are the schemes based on the rational extension of Bochner-type differential equations (for the Heun and confluent Heun equation) and solutions based on quasi-exact solvability (QES) and on continued fractions (for the biconfluent and double confluent equation). Possible further lines of investigations are also outlined concerning physical problems that require the solution of second-order differential equations, i.e., the Schrödinger equation with position-dependent mass and relativistic wave equations. Full article

In the field of autonomous driving, the accurate identification of drivable areas on roads is the key to ensuring the safe driving of vehicles. However, unstructured roads lack clear lane lines and regular road structures, and they have fuzzy edges and rutting marks, which greatly increase the difficulty of identifying drivable areas. To address the above challenges, this paper proposes a drivable area detection method for unstructured roads based on the MRNet model. To address the problem that unstructured roads lack clear lane lines and regular structures, the model dynamically captures local and global context information based on the self-attention mechanism of a Transformer, and it combines the input of image and LiDAR data to enhance the overall understanding of complex road scenes; to address the problem that detailed features such as fuzzy edges and rutting are difficult to identify, a multi-scale dilated convolution module (MSDM) is proposed to capture detailed information at different scales through multi-scale feature extraction; to address the gradient vanishing problem in feature fusion, a residual upsampling module (ResUp Block) is designed to optimize the spatial resolution recovery process of the feature map, correct errors, and further improve the robustness of the model. Experiments on the ORFD dataset containing unstructured road data show that MRNet outperforms other common methods in the drivable area detection task and achieves good performance in segmentation accuracy and model robustness. In summary, MRNet provides an effective solution for drivable area detection in unstructured road environments, supporting the environmental perception module of autonomous driving systems. Full article

This paper explores how competing interactions in the intermolecular potential of fluids affect their structural transitions. This study employs a versatile potential model with a hard core followed by two constant steps, representing wells or shoulders, analyzed in both one-dimensional (1D) and three-dimensional (3D) systems. Comparing these dimensionalities highlights the effect of confinement on structural transitions. Exact results are derived for 1D systems, while the rational function approximation is used for unconfined 3D fluids. Both scenarios confirm that when the steps are repulsive, the wavelength of the oscillatory decay of the total correlation function evolves with temperature either continuously or discontinuously. In the latter case, a discontinuous oscillation crossover line emerges in the temperature–density plane. For an attractive first step and a repulsive second step, a Fisher–Widom line appears. Although the 1D and 3D results share common features, dimensionality introduces differences: these behaviors occur in distinct temperature ranges, require deeper wells, or become attenuated in 3D. Certain features observed in 1D may vanish in 3D. We conclude that fluids with competing interactions exhibit a rich and intricate pattern of structural transitions, demonstrating the significant influence of dimensionality and interaction features. Full article

2D-3D registration is increasingly being applied in various scientific and engineering scenarios. However, due to appearance differences and cross-modal discrepancies, it is demanding for image and point cloud registration methods to establish correspondences, making 2D-3D registration highly challenging. To handle these problems, we propose a novel and automatic solution for 2D-3D registration in Manhattan world based on line primitives, which we denote as VPPnL. Firstly, we derive the rotation matrix candidates by establishing the vanishing point coordinate system as the link of point cloud principal directions to camera coordinate system. Subsequently, the RANSAC algorithm, which accounts for the clustering of parallel lines, is employed in conjunction with the least-squares method for translation vectors estimation and optimization. Finally, a nonlinear least-squares graph optimization method is carried out to optimize the camera pose and realize the 2D-3D registration and point colorization. Experiments on synthetic data and real-world data illustrate that our proposed algorithm can address the problem of 2D-3D direct registration in the case of Manhattan scenes where images are limited and sparse. Full article

The purpose of this article is to provide a novel approach and justification of the idea that classical physics and quantum physics can neither function nor even be conceived without the other—in line with ideas attributed to, e.g., Niels Bohr or Lev Landau. Though this point of view may contradict current common wisdom, we will show that it perfectly fits with empirical evidence, and can be maintained without giving up physical realism. In order to place our arguments in a convenient historical perspective, we will proceed as if we were following the path of a scientific investigation about the demise, or vanishing, of some valuable properties of the two electrons in the helium atom. We will start from experimentally based evidence in order to analyze and explain the physical facts, moving cautiously from a classical to a quantum description, without mixing them up. The overall picture will be that the physical properties of microscopic systems are quantized, as initially shown by Planck and Einstein, and that they are also contextual, i.e., they can be given a physical sense only by embedding a microscopic system within a macroscopic measurement context. Full article

In this study, we explore the sweeping surfaces in Euclidean 3-space, utilizing the modified orthogonal frames with non-zero curvature and torsion, which allows us to consider the spine curves even if their second differentiations vanish. If the curvature of the spine curve of a sweeping surface has discrete zero points, the Frenet frame might undergo a discontinuous change in orientation. Therefore, the conventional parametrization with the Frenet frame of such a surface cannot be given. Thus, we introduce two types of modified sweeping surfaces by considering two types of spine curves; the first one’s curvature is not identically zero and the second one’s torsion is not identically zero. Then, we determine the criteria for classifying the coordinate curves of these two types of modified sweeping surfaces as geodesic, asymptotic, or curvature lines. Additionally, we delve into determining criteria for the modified sweeping surfaces to be minimal, developable, or Weingarten. Through our analysis, we aim to clarify the characteristics defining these surfaces. We present graphical representations of sample modified sweeping surfaces to enhance understanding and provide concrete examples that showcase their properties. Full article

This research aims to overcome three major challenges in foreign object detection on power transmission lines: data scarcity, background noise, and high computational costs. In the improved YOLOv8 algorithm, the newly introduced lightweight GSCDown (Ghost Shuffle Channel Downsampling) module effectively captures subtle image features by combining 1 × 1 convolution and GSConv technology, thereby enhancing detection accuracy. CSPBlock (Cross-Stage Partial Block) fusion enhances the model’s accuracy and stability by strengthening feature expression and spatial perception while maintaining the algorithm’s lightweight nature and effectively mitigating the issue of vanishing gradients, making it suitable for efficient foreign object detection in complex power line environments. Additionally, PAM (pooling attention mechanism) effectively distinguishes between background and target without adding extra parameters, maintaining high accuracy even in the presence of background noise. Furthermore, AIGC (AI-generated content) technology is leveraged to produce high-quality images for training data augmentation, and lossless feature distillation ensures higher detection accuracy and reduces false positives. In conclusion, the improved architecture reduces the parameter count by 18% while improving the mAP@0.5 metric by a margin of 5.5 points when compared to YOLOv8n. Compared to state-of-the-art real-time object detection frameworks, our research demonstrates significant advantages in both model accuracy and parameter size. Full article

Including motivational variables such as interest in the cognitive load framework is an ongoing process. Of particular interest is the question of how motivational variables influence the investment of mental effort. In this study, we investigated how topic interest affects the investment of mental effort in simple tasks. A total of 1543 students’ judgments regarding invested mental effort, perceived task difficulty, and topic interest for 32 tasks of a chemistry test were analyzed at the task level based on item response theory parameters. Additionally, objective task difficulty was calculated. The Rasch parameters were used for correlation and moderated regression analyses. The results indicated that when perceived task difficulty was low, students invested more mental effort in solving tasks of low topic interest compared to tasks of high topic interest. With increasing perceived task difficulty, the amount of invested mental effort rose for tasks of low as well as high topic interest. However, the difference between tasks of low and high topic interest in the amount of invested mental effort decreased as perceived task difficulty increased and even vanished when perceived task difficulty roughly corresponded to students’ performance capability. These results are in line with flow theory and the expectancy-value-cost model of motivation. When solving tasks that match their performance capability, students can experience a flow situation. However, when solving rather easy tasks of low interest, students can experience motivational costs in terms of additional effort, such as an increased need for motivational self-regulation. The results of this study provide a basis for systematically investigating and better understanding the relationship between interest, task difficulty, invested mental effort, flow experience, and emotional costs. Full article

High-resolution (2 km) high-frequency (hourly) SST data from 2015 to 2021 provided by the Advanced Himawari Imager (AHI) onboard the Japanese Himawari-8 geostationary satellite were used to study spatial and temporal variability of the China Coastal Front (CCF) in the South China Sea. The SST data were processed with the Belkin and O’Reilly (2009) algorithm to generate monthly maps of the CCF’s intensity (defined as SST gradient magnitude GM) and frontal frequency (FF). The horizontal structure of the CCF was investigated from cross-frontal distributions of SST along 11 fixed lines that allowed us to determine inshore and offshore boundaries of the CCF and calculate the CCF’s strength (defined as total cross-frontal step of SST). Combined with the results of Part 1 of this study, where the CCF was documented in the East China Sea, the new results reported in this paper allowed the CCF to be traced from the Yangtze Bank to Hainan Island. The CCF is continuous in winter, when its intensity peaks at 0.15 °C/km (based on monthly data). In summer, when the Guangdong Coastal Current reverses and flows eastward, the CCF’s intensity is reduced to 0.05 °C/km or less, especially off western Guangdong, where the CCF vanishes almost completely. Owing to its breadth (50–100 km, up to 200 km in the Taiwan Strait), the CCF is a very strong front, especially in winter, when the total SST step across the CCF peaks at 9 °C in the Taiwan Strait. The CCF’s strength decreases westward to 6 °C off eastern Guangdong, 5 °C off western Guangdong, and 2 °C off Hainan Island, all in mid-winter. Full article

As the carrier of electric energy transmission, transmission lines undertake the important task of electric energy distribution and transfer. However, with the increasing frequency of construction using large machinery such as tower cranes and excavators under the transmission channels, transmission line accidents occur frequently. Therefore, this paper proposes a refined identification method for the hidden dangers of external damage in transmission lines based on the generation of effective regions driven by vanishing points. The comprehensive and accurate perception of external damage targets through the perception model of scene elements based on slicing-aided hyperinference was realized. Secondly, the accuracy and robustness of the calculation of the transmission line’s vanishing point were improved based on Canny edge detection and Hough linear detection. The effective region on the visual images was generated by combining the vanishing point and the bottom of transmission tower coordinates. Finally, the relative position relationship between areas with hidden dangers of external damage and the effective warning regions were compared, and the refined identification of hidden dangers was realized. The experimental data show that the proposed method realized a perception accuracy of 82.9% in identifying hidden dangers of external damage caused by ground- and aerial-moving targets, which shows better detection performance and practical value compared with the existing method. Full article

A monocular ranging method for forward vehicles in intelligent driving is proposed. This method measures vehicle distance more accurately under the condition of a single camera and can estimate camera attitude in real-time. For the estimation of camera pitch and yaw angles, it is achieved using road vanishing points. The images collected by the camera are sequentially processed through the Roberts operator amplitude calculation, feature point extraction, feature line segment generation, road vanishing point voting, and estimation of camera attitude to obtain pitch and yaw angles. A distance estimation network was designed, which is divided into multiple levels based on image size and incorporates image feature, integrating vehicle grounding point, and vehicle width information, effectively improving ranging accuracy. Finally, validation was conducted on KITTI data, with a relative error (AbsRel) of 8.3%. Additionally, the TuSimple dataset and continuous driving scenarios were also validated, resulting in improved performance compared to previous algorithms. Full article

We discuss the concept and realization of a heat bath in solid state quantum systems. We demonstrate that, unlike a true resistor, a finite one-dimensional Josephson junction array or analogously a transmission line with non-vanishing frequency spacing, commonly considered as a reservoir of a quantum circuit, does not strictly qualify as a Caldeira–Leggett type dissipative environment. We then consider a set of quantum two-level systems as a bath, which can be realized as a collection of qubits. We show that only a dense and wide distribution of energies of the two-level systems can secure long Poincare recurrence times characteristic of a proper heat bath. An alternative for this bath is a collection of harmonic oscillators, for instance, in the form of superconducting resonators. Full article

Background: Our study endeavors to elucidate the clinical implications of PD-L1 positivity in individuals afflicted with advanced urothelial carcinoma of the bladder (UCB). Methods: Patients with advanced UCB were prospectively enrolled following a radical cystectomy (RC) performed within January 2017 to December 2022 at our tertiary referral center. The clinical outcome, defined as the progression-free survival (PFS) and overall survival (OS) on systemic treatment, was analyzed using an χ²-test, Mann–Whitney U-test, the Kaplan–Meier method, and a log-rank test. Results: A total of 648 patients were included following an RC performed within January 2017 to December 2022. Their PD-L1 status was analyzed with the primary PD-L1-specific antibody (clone SP263, Ventana) and defined both by the CPS and IC-score in 282 patients (43.5%) with a high risk (pT3–pT4 and/or lymph node involvement) or metastatic UCB. While the median PFS was significantly prolonged 5-fold in PD-L1+ patients, we found no difference in OS, regardless of PD-L1 status, or treatment regimen. Conclusions: While PD-L1 positivity indicates prolonged PFS, the presence of PD-L1 does not influence OS rates, suggesting its limited usefulness as a prognostic biomarker in bladder cancer. However, the positive correlation between an PD-L1 status and a sustained response to ICI treatments indicates its potential role as a predictive biomarker. Further research is required to understand how the predictive value of PD-L1 positivity may extend to the use of ICIs in combination with antibody-drug conjugates. Full article

In conventional point-line visual–inertial odometry systems in indoor environments, consideration of spatial position recovery and line feature classification can improve localization accuracy. In this paper, a monocular visual–inertial odometry based on structured and unstructured line features of vanishing points is proposed. First, the degeneracy phenomenon caused by a special geometric relationship between epipoles and line features is analyzed in the process of triangulation, and a degeneracy detection strategy is designed to determine the location of the epipoles. Then, considering that the vanishing point and the epipole coincide at infinity, the vanishing point feature is introduced to solve the degeneracy and direction vector optimization problem of line features. Finally, threshold constraints are used to categorize straight lines into structural and non-structural features under the Manhattan world assumption, and the vanishing point measurement model is added to the sliding window for joint optimization. Comparative tests on the EuRoC and TUM-VI public datasets validated the effectiveness of the proposed method. Full article