Search Results (89)

We propose and demonstrate a tunable and switchable multi-wavelength fiber ring laser configuration based on a Mach–Zehnder interferometer (MZI) filter. The MZI was fabricated using a core-offset splicing technique, with a 2 cm piece of thin-core erbium-doped fiber (TCEDF), with a core diameter of 2.90 µm, coupled in the central region of the MZI between two segments of single-mode fiber (SMF). By applying curvature to the MZI filter, we generated lasing single-, double-, triple-, and quadruple-emission lines with a curvature range from 2.3452 m⁻¹ to 6.0495 m⁻¹. A single-emission lasing line can be tuned from 1556.63 nm to 1564.25 nm with a tuning span of 7.62 nm and an SMSR of 49.80 dB. The laser emission can be switched to quadruple- and triple-emission lasing signals, with SMSR values of 39.96 dB and 36.83 dB, respectively. The dual-narrow emission lasing signal can be tuned from 1564.56 nm to 1561.34 nm, with an SMSR of 40.46 dB. Another lasing dual-emission signal can be tuned from 1585.69 nm to 1576.89 nm, producing an 8.8 nm tuning range, and from 1572.53 nm to 1563.66 nm, producing an 8.87 nm range, with the best SMSR of 42.35 dB. Full article

The continuous monitoring of tall industrial buildings is necessary to ensure safe operation. With technological advances in terrestrial laser scanning and other non-contact measurement methods, the methods and techniques for assessing the stability of tall industrial chimneys are evolving. This paper presents a method for determining the non-verticality and straightness of chimneys that offers significant advantages over existing methods. Narrow bands of scanned point clouds are processed at selected height intervals. Using the RANSAC method, points that do not belong to the chimney shell are filtered and the centre of the circle or ellipse is adjusted using the least squares method. The proposed method enables the efficient filtering of point clouds due to frequent obstructions on the chimney shell, the determination of the regularity of the chimney shell shape, a mathematical analysis of the chimney axis curvature, and an intuitive graphical representation of chimney non-verticality. The comparison of the results with other studies confirms the efficiency of the method. Full article

Over the past 30 years, researchers have developed X-ray-focusing telescopes by employing the principle of total reflection in thin metal films. The Wolter-I focusing mirror with variable-curvature surfaces demands high precision. However, there has been limited investigation into the removal mechanisms for variable-curvature X-ray mandrels, which are crucial for achieving the desired surface roughness and form accuracy, especially in reducing mid-spatial frequency (MSF) errors. It is essential to incorporate flexible control in deterministic small-tool polishing to improve the tool’s adaptability to curvature variations and achieve stable, Gaussian-like tool influence functions (TIFs). In this paper, we introduce a curvature-adaptive prediction model for compliance figuring, based on the Preston hypothesis, using a compliant shaping tool with high slurry absorption and retention capabilities. This model predicts the compliance figuring process of variable-curvature symmetrical mandrels for X-ray grazing incidence mirrors by utilizing planar tool influence functions. Initially, a variable-curvature pressure model was developed to account for the parabolic and hyperbolic optical surfaces’ curvature characteristics. By introducing time-varying removal functions for material removal, the model establishes a variable-curvature factor function, which correlates actual downward pressure with parameters such as contact radius and contact angle, thus linking the variable-curvature surface with a planar reference. Subsequently, through analysis of the residence time distribution across different TIF models, hierarchical filtering, and PSD distribution, real-time correction of the TIFs was achieved to enable customized variable-curvature polishing. Furthermore, by applying a time-varying deconvolution algorithm, multiple rounds of flexible polishing iterations were conducted on the mandrels of a rotationally symmetric variable-curvature optical component, and the experimental results demonstrate a significant improvement in form accuracy, surface quality, and the optical performance of the mirror. Full article

In response to the saturated attacks by low, slow, and small UAV swarms, there is currently a lack of effective countermeasures. Counter-UAV swarm technology is an important issue that urgently requires breakthroughs. This paper conducts research on a mid–short-range hard-kill counter-swarm scenario where fewer swarms confront multiple swarms and stronger swarms confront weaker swarms. The requirement is for counter-swarm UAVs to quickly penetrate the swarm at mid–short range and collide with as many incoming UAVs as possible to destroy them. To address the sparse solution space problem, an improved genetic algorithm that integrates multiple strategies is adopted to calculate the spatial density distribution of the incoming swarm. A baseline is identified through gradient descent that maximizes the density integral in a straight-line direction. Based on this baseline, the solution space for single strikes on the swarm is filtered. During the solution process, an elite strategy is introduced to prevent the overall degradation of the population performance. Additionally, the feasibility of the flight trajectory needs to be assessed. A piecewise cubic spline interpolation method is used to optimize the flight trajectory, minimizing the maximum curvature. Ultimately, multiple counter-swarm UAV targets within the swarm and their corresponding trajectories are obtained. Full article

This paper presents a tunable, switchable multi-wavelength emission from an erbium-doped fiber ring laser, enabled by adjusting the spectral fringe visibility of a fiber interferometer filter. The filter is formed with specially designed concatenated tapered fibers to configure a Mach-Zehnder fiber interferometer (MZFI). The laser emission is highly flexible and reconfigurable, allowing for tuning between single- and dual-wavelength operation. The laser can switch sequentially from one up to six wavelengths by fixing the curvature and adjusting the polarization state. The lasing emission is generated over a stable wavelength range between 1559.59 nm and 1563.54 nm, exhibiting an optical signal-to-noise ratio (OSNR) exceeding ~35 dB. The performance of amplitude and wavelength fluctuations were evaluated, indicating an appropriate stability of ~3 dB and a shift less than 0.1 nm within a 45 min period at room temperature. A detailed comparison with the literature is given. Full article

To enhance the safety of lane changes for connected autonomous vehicles in an intelligent transportation environment, this study draws from potential field theory to analyze variations in the risks that vehicles face under different traffic conditions. The safe minimum vehicle distance is dynamically adjusted, and a comprehensive vehicle risk potential field model is developed. This model systematically quantifies the risks encountered by connected autonomous vehicles during the driving process, providing a more accurate assessment of safety conditions. Subsequently, vehicle motion is decoupled into lateral and longitudinal components within the Frenet coordinate system, with quintic polynomials employed to generate clusters of potential trajectories. To improve computational efficiency, trajectory evaluation metrics are developed based on vehicle dynamics, incorporating factors such as acceleration, jerk, and curvature. An initial filtering process is applied to these trajectories, yielding a refined set of candidates. These candidate trajectories are further assessed using a minimum safety distance model derived from potential field theory, with optimization focusing on safety, comfort, and efficiency. The algorithm is tested in a three-lane curved simulation environment that includes both constant-speed and variable-speed lane change scenarios. Results show that the collision risk between the target vehicle and surrounding vehicles remains below the minimum safety distance threshold throughout the lane change process, ensuring a high level of safety. Furthermore, across various driving conditions, the target vehicle’s acceleration, jerk, and trajectory curvature remained well within acceptable limits, demonstrating that the proposed lane change trajectory planning algorithm successfully balances safety, comfort, and smoothness, even in complex traffic environments. Full article

In recent years, artificial intelligence technology has seen increasingly widespread application in the field of intelligent manufacturing, particularly with deep learning offering novel methods for recognizing geometric shapes with specific features. In traditional CNC machining, computer-aided manufacturing (CAM) typically generates G-code for specific machine tools based on existing models. However, the tool paths for most CNC machines consist of a series of collinear motion commands (G01), which often result in discontinuities in the curvature of adjacent tool paths, leading to machining defects. To address these issues, this paper proposes a method for CNC system machining trajectory feature recognition and path optimization based on intelligent agents. This method employs intelligent agents to construct models and analyze the key geometric information in the G-code generated during CNC machining, and it uses the MCRL deep learning model incorporating linear attention mechanisms and multiple neural networks for recognition and classification. Path optimization is then carried out using mean filtering, Bézier curve fitting, and an improved novel adaptive coati optimization algorithm (NACOA) according to the degree of unsmoothness of the path. The effectiveness of the proposed method is validated through the optimization of process files for gear models, pentagram bosses, and maple leaf models. The research results indicate that the CNC system machining trajectory feature recognition and path optimization method based on intelligent agents can significantly enhance the smoothness of CNC machining paths and reduce machining defects, offering substantial application value. Full article

Among the various types of polymeric materials, geosynthetics deserve special attention. A geosynthetic is a product made from synthetic polymers that is embedded in soils for various purposes. There are some basic functions of geosynthetics, namely, erosion control, filtration, drainage, separation, reinforcement, containment, barrier, and protection. Geosynthetics for erosion control are very effective in preventing or limiting soil loss by water erosion on slopes or river/channel banks. Where the current line runs through the undercut area of the slope, the curvature of the arch is increased. If this phenomenon is undesirable, the meander arch should be protected from erosion processes. The combination of geosynthetics provides the best resistance to erosion. In addition to external erosion, internal erosion of soils is also a negative phenomenon. Internal erosion refers to any process by which soil particles are eroded from within or beneath a water-retaining structure. Geosynthetics, particularly geotextiles, are used to prevent internal erosion of soils in contact with the filters. Therefore, the main objective of this review paper is to address the many ways in which geosynthetics are used for erosion control (internal and external). Many examples of hydrotechnical and civil engineering applications of geosynthetics will be presented. Full article

Farmland boundary data, an important basic data for the operation of agricultural automation equipment, has been widely studied by scholars from all over the world. However, the common methods of farmland boundary acquisition through sensors such as LiDAR and vision cameras combined with complex algorithms suffer from problems such as serious data drift, difficulty in eliminating noise, and inaccurate plot boundary data. In order to solve this problem, this study proposes a method for estimating the orientation dimensions of farmland based on the seeding trajectory. The method firstly calculates the curvature of the discrete data of the seeding trajectory; secondly, we innovatively use a low-pass filter and integral clustering to filter the curvature values and distinguish between straight lines and curves; and finally, the straight-line portion located at the edge of the seeding trajectory is fitted with a univariate linear fit to calculate the estimation of the farmland size orientation. As verified by the field experiments, the minimum linear error of the vertices is only 0.12m, the average error is 0.315m, and the overlapping rate of the plot estimation is 98.36% compared with the real boundary of the plot. Compared with LiDAR mapping, the average linear error of the vertices’ position is reduced by 50.2%, and the plot estimation overlap rate is increased by 2.21%. The experimental results show that this method has the advantage of high accuracy, fast calculation speed, and small calculation volume, which provides a simple and accurate method for constructing farmland maps, provides the digital data support for the operation of agricultural automation equipment, and has significance for farm digital mapping. Full article

Accurate arbor extraction is an important element of forest surveys. However, the presence of shrubs can interfere with the extraction of arbors. Addressing the issues of low accuracy and weak generalizability in existing Terrestrial Laser Scanning (TLS) arbor point clouds extraction methods, this study proposes a trunk axis fitting (TAF) method for arbor extraction. After separating the point cloud data by upper and lower, slicing, clustering, fitting circles, obtaining the main central axis, filtering by distance, etc. The canopy point clouds are merged with the extracted trunk point clouds to precisely separate arbors and shrubs. The advantage of the TAF method proposed in this study is that it is not affected by point cloud density or the degree of trunk curvature. This study focuses on a natural forest plot in Shangri-La City, Yunnan Province, and a plantation plot in Kunming City, using manually extracted data from a standardized dataset of samples to test the accuracy of the TAF method and validate the feasibility of the proposed method. The results showed that the TAF method proposed in this study has high extraction accuracy. It can effectively avoid the problem of trunk point cloud loss caused by tree growth curvature. The experimental accuracy for both plots reached over 99%. This study can provide certain technical support for arbor parameter extraction and scientific guidance for forest resource investigation and forest management decision-making. Full article

Aiming at the problem of the difficult segmentation of adherent images due to the not fully convex shape of peanut pods, their complex surface texture, and their diverse structures, a multimodal fusion algorithm is proposed to achieve a 2D segmentation of adherent peanut images with the assistance of 3D point clouds. Firstly, the point cloud of a running peanut is captured line by line using a line structured light imaging system, and its three-dimensional shape is obtained through splicing and combining it with a local surface-fitting algorithm to calculate a normal vector and curvature. Seed points are selected based on the principle of minimum curvature, and neighboring points are searched using the KD-Tree algorithm. The point cloud is filtered and segmented according to the normal angle and the curvature threshold until achieving the completion of the point cloud segmentation of the individual peanut, and then the two-dimensional contour of the individual peanut model is extracted by using the rolling method. The search template is established, multiscale feature matching is implemented on the adherent image to achieve the region localization, and finally, the segmentation region is optimized by an opening operation. The experimental results show that the algorithm improves the segmentation accuracy, and the segmentation accuracy reaches 96.8%. Full article

As a new type of aftertreatment technology, non-thermal plasma (NTP) can effectively decompose the particulate matter (PM) deposited in diesel particulate filters (DPFs). In this paper, a regeneration test of a DPF loaded with carbon black was carried out using an NTP injection system, and the changes of oxidative activity, elemental content, and occurrence state, microstructure and graphitization degree of carbon black were analyzed to reveal the evolution of the physicochemical properties of carbon black at different regeneration stages of the DPF regenerated by NTP. As the regeneration stage of the DPF advanced, Ti, Tmax, and Te of the carbon black at the bottom of the DPF decreased, which were higher than those at the regeneration interface. After the NTP reaction, the proportion of C element decreased to less than 80%, while the proportion of O element increased to more than 20%; C-O was converted to C=O and the relative content of C=O increased. The average microcrystalline length and average spacing decreased, while the average microcrystalline curvature increased. The I_D1/I_G (relative peak intensities) of carbon black samples decreased from 3.31 to 3.10, and the R3 (relative peak intensities, R3 = I_D3/(I_G + I_D2 + I_D3)) increased from 0.41 to 0.58. The content of carbon clusters had a great influence on the disorder of the microcrystalline structure, so the graphitization degree of carbon black decreased and the oxidation activity increased. Full article

Polar format algorithm (PFA) is a widely used high-resolution SAR imaging algorithm that can be implemented in advanced widefield staring synthetic aperture radar (WFS-SAR). However, existing algorithms have limited analysis in wavefront curvature error (WCE) and are challenging to apply to WFS-SAR with high-resolution and large-swath scenes. This paper proposes an extended polar format algorithm for joint envelope and phase error correction in WFS-SAR imaging with maneuvering trajectory. The impact of the WCE and residual acceleration error (RAE) are analyzed in detail by deriving the specific wavenumber domain signal based on the mapping relationship between the geometry space and wavenumber space. Subsequently, this paper improves the traditional WCE compensation function and introduces a new range cell migration (RCM) recalibration function for joint envelope and phase error correction. The 2D precisely focused SAR image is acquired based on the spatially variant inverse filtering in the final. Simulation experiments validate the effectiveness of the proposed method. Full article

This study introduces an advanced lateral control strategy for autonomous vehicles using a clothoid-based approach integrated with an adaptive lookahead mechanism. The primary focus is on enhancing lateral stability and path-tracking accuracy through the application of Euler spirals for smooth curvature transitions, thereby reducing passenger discomfort and the risk of vehicle rollover. An innovative aspect of our work is the adaptive adjustment of lookahead distance based on real-time vehicle dynamics and road geometry, which ensures optimal path following under varying conditions. A quasi-feedback control algorithm constructs optimal clothoids at each time step, generating the appropriate steering input. A lead filter compensates for the vehicle’s lateral dynamics lag, improving control responsiveness and stability. The effectiveness of the proposed controller is validated through a comprehensive co-simulation using TruckSim^® and Simulink^®, demonstrating significant improvements in lateral control performance across diverse driving scenarios. Future directions include scaling the controller for higher-speed applications and further optimization to minimize off-track errors, particularly for articulated vehicles. Full article

Multiple reflection is a common interference wave in offshore petroleum and gas exploration, and the Radon-based filtering method is a frequently used approach for multiple removal. However, the filtering parameter setting is crucial in multiple suppression and relies heavily on the experience of processors. To reduce the dependence on human intervention, we introduce the geometric mode decomposition (GMD) and develop a novel processing flow that can automatically separate primaries and multiples, and then accomplish the suppression of multiples. GMD leverages the principle of the Wiener filtering to iteratively decompose the data into modes with varying curvature and intercept. By exploiting the differences in curvature, GMD can separate primary modes and multiple modes. Then, we propose a novel sparse Radon transform (RT) constrained with the elastic half (EH) norm. The EH norm contains a l1/2 norm and a scaled l2 norm, which is added to overcome the numerical oscillation problem of the l1/2 norm. With the help of the EH norm, the estimated Radon model can reach a remarkable level of sparsity. To solve the optimization problem of the proposed sparse RT, an efficient alternating multiplier iteration algorithm is employed. Leveraging the high sparsity of the Radon model obtained from the proposed transform, we improve the GMD-based multiple removal framework. The high-sparsity Radon model obtained from the proposed Radon transform can not only simplify the separation of primary and multiple modes but also accelerate the convergence of GMD, thus improving the processing efficiency of the GMD method. The performance of the proposed GMD-based framework in multiple elimination is validated through synthetic and field data tests. Full article