Irregular Field Research Articles

Temperature Distribution of Frozen Wall Formed by Irregular Hole Arrangement During In Situ Repair of Underground Shield Machine

In order to study the development law of the irregular hole freezing temperature field, combined with the shield solution project of the Nanjing Water Supply Corridor, the distribution characteristics and influencing factors of the irregular freezing temperature field of the river bottom shield machine are studied by numerical simulation. The following conclusions are obtained: (1) The extension length of the outer ring pipe is correlated approximately positively with the thickness and average temperature of the freezing wall at the bottom of the cup. The thickness increases by 0.25 m, and the average temperature decreases by 1.25 °C for every 1 m increase in the extension length. (2) The intersection time decreases logarithmically with the increase in the extension length of the outer ring tube. (3) As the ratio of the axial angle between the two adjacent tubes in the weak area of the outer ring tube becomes larger, the temperature of the weak point in the center of the two tubes increases approximately linearly. The midpoint temperature of the two tubes increases by 3.3 °C for every 1 increase in the angle coefficient. (4) With the increase in the opening angle of the inner ring hole, the thickness and average temperature change, respectively, at 150 d are not more than 0.15 m and 0.6 °C. The results show that under the irregular freezing form, the angle and length of the outer ring pipe have a great influence on the temperature field, and the angle of the inner ring pipe has little influence on the final distribution of the temperature field. The average temperature and the temperature distribution of the weak points show a trend of decreasing first and then increasing along the shield advancing direction, reaching a minimum near the cutterhead.

Manipulation of d-Orbital Electron Configurations in Nonplanar Fe-Based Electrocatalysts for Efficient Oxygen Reduction.

Manipulation of the spin state holds great promise to improve the electrochemical activity of transition metal-based catalysts. However, the underlying relationship between the nonplanar metal coordination environment and spin states remains to be explored. Herein, we report the precise regulation of nonplanar Fe atomic d-orbital energy level into an irregular tetrahedral crystal field configuration by introducing P atoms. With the increase of P coordination number, the spin magnetic moment decreases linearly from 3.8 μB to 0.2 μB, and the high spin content decreases linearly from 31% to 5%. Significantly, a volcanic curve between the spin states of Fe-based catalysts (Fe-NxPy) and oxygen reduction reaction (ORR) activity has been unequivocally established based on the thermodynamic results. Thus, the Fe-N3P1 catalyst with a 19% medium spin state experimentally exhibits the optimal reaction activity with a high half-wave potential of 0.92 V. These findings indicate that regulating electron spin moments through coordination engineering is a promising catalyst design strategy, providing important insights into spin catalysis.

Fine classification of rice fields in high-resolution remote sensing images

Fine-grained management of rice fields can enhance the yield and quality of rice crops. Challenges in achieving fine classification include interference from similar vegetation, the irregularity of natural field shapes, and complex scale variations. This paper introduces Rice Attention Cascade Network (RACNet), for the fine classification of rice fields in high-resolution satellite remote sensing imagery. The network employs the Hybrid Task Cascade network as the base framework and uses spectral and indices mixed multimodal data as input to reinforce the feature differentiation of similar vegetation. Initially, a Channel Attention Deformable-ResNet (CAD-ResNet) was designed to enhance the feature representation of rice on different channels. Deformable convolution improves the ability of CAD-ResNet to capture irregular field shapes. Then, to address the issue of complex scale changes, the multi-scale features extracted by the CAD-ResNet are progressively fused using an Asymptotic Feature Pyramid, reducing the loss of scale information between non-adjacent layers. Experiments on the Meishan rice dataset show that the proposed method is capable of accurate instance segmentation for fragmented or irregularly shaped rice fields. The evaluation metric AP50 of RACNet reaches 50.8%.

Multi-constraint improved RS path planning method for unmanned rice direct seeding machine

Path planning is one of the key technologies that determines the efficiency and quality of field operations using autonomous agricultural machinery. To date, there has been extensive research on global coverage path planning for unmanned agricultural machinery within the working area; however, the issue of “encircling and edging” left by the machinery during headland turns is often overlooked. This study focuses on the operational conditions of unmanned agricultural machinery in the rice fields of southern China. The path planning problem is abstracted under physical constraints, such as the space at the field headland, the agricultural environment (e.g., types of field ridges), the turning characteristics of the machinery, turning radius, working width, and minimum safety distance, into mathematical constraints. This yields an optimized path planning model and sequence combination. Based on the improved Reeds–Shepp curve, an en-circling and edging path planning algorithm is designed. The planned path for the unmanned agricultural machinery from start to finish consists only of arcs and straight lines, suitable for the right-angle turning, reversing, and 180° U-turn operations required for rice field machinery, thus significantly improving the coverage of field edges and missed areas at the headland. A rice direct-seeding machine was used as a test subject for encircling and edging field trials, with tests conducted in rectangular, trapezoidal, and irregular quadrilateral rice fields. The results show that, for rectangular plots, the full-field coverage rate of rice direct-seeding operations can reach 96.44%; for trapezoidal plots, the coverage rate is 95.47%; and for irregular quadrilateral fields, the coverage rate is 95.69%. These results are promising, indicating that the encircling and edging path planning based on the improved Reeds–Shepp curve can effectively increase the full-field coverage rate of unmanned agricultural machinery, improving work quality and meeting the needs of modernized, intelligent agricultural machinery operations.

Proprioceptive swarms for celestial body exploration

The exploration of small celestial bodies has been a very active field ever since the dawn of space exploration. However, traditional approaches based on monolithic landers allow only the area in the immediate vicinity to the landing spot to be examined. Using mobile systems like rovers allow for better coverage, but at considerable cost and complexity. In this work, we propose an approach based on swarms of small nonmaneuverable agents, equipped with a sensor package, that are launched from a base station and made to land across the entire surface of the asteroid or comet. The methodology is based on multilateration using small range-finding radio frequency sensors aboard the agents; this enables position determination relative to the base station. Through dynamics simulation, we show that this approach is feasible even in highly irregular gravity fields where closed-form solutions for orbital mechanics are not available, such as the case of comet 67P/Churyumov–Gerasimenko. We show a sensitivity analysis of the absolute position error of the agents which originated from range-measurement error. The surface coverage is evaluated against the numerosity of the swarm. Finally, we show that allowing for a subset of agents to follow a long-period orbit around the object enables better localization of the landed agents, thus increasing the overall performance.

Linear and nonlinear transport equations with coordinate-wise increasing velocity fields

We consider linear and nonlinear transport equations with irregular velocity fields, motivated by models coming from mean field games. The velocity fields are assumed to increase in each coordinate, and the divergence therefore fails to be absolutely continuous with respect to the Lebesgue measure in general. For such velocity fields, the well-posedness of first- and second-order linear transport equations in Lebesgue spaces is established, as well as the existence and uniqueness of regular ODE and SDE Lagrangian flows. These results are then applied to the study of certain nonconservative, nonlinear systems of transport type, which are used to model mean field games in a finite state space. A notion of weak solution is identified for which unique minimal and maximal solutions exist, which do not coincide in general. A selection-by-noise result is established for a relevant example to demonstrate that different types of noise can select any of the admissible solutions in the vanishing noise limit.

An integrated DEM code for tracing the entire regolith mass movement on asteroids

ABSTRACT This paper presents a general strategy for tracking the scale-span movement process of asteroid regolith materials. It achieves the tracking of the mass movement on the asteroid at a realistic scale, under conditions of high-resolution asteroid surface topography (submeter level) and actual regolith particle sizes. To overcome the memory exponential expansion caused by the enlarged computational domain, we improved the conventional cell-linked list method so that it can be applied to arbitrarily large computational domains around asteroids. An efficient contact detection algorithm for particles and polyhedral shape models of asteroids is presented, which avoids traversing all surface triangles and thus allows us to model high-resolution surface topography. A parallel algorithm based on Compute Unified Device Architecture for the gravitational field of the asteroid is presented. Leveraging heterogeneous computing features, further architectural optimization overlaps computations of the long-range and short-range interactions, resulting in an approaching doubling of computational efficiency compared to the code lacking architectural optimizations. Using the above strategy, a specific high-fidelity discrete element method code that integrates key mechanical models, including the irregular gravitational field, the interparticle and particle-surface interactions, and the coupled dynamics between the particles and the asteroid, is developed to track the asteroid regolith mass movement. As tests, we simulated the landslide of a sand pile on the asteroid’s surface during spin-up. The simulation results demonstrate that the code can track the mass movement of the regolith particles on the surface of the asteroid from local landslides to mass leakage with good accuracy.

A two‐dimensional, reach‐scale implementation of space‐time image velocimetry (STIV) and comparison to particle image velocimetry (PIV)

AbstractImage‐based algorithms have become a powerful tool for estimating flow velocities in rivers. In this study, we generalize the space‐time image velocimetry (STIV) framework for reach‐scale application rather than along a cross section. The new algorithm provides information on both the magnitude and orientation of velocity vectors, and we refer to the algorithm as two‐dimensional STIV, or 2D‐STIV. The workflow involves setting up a grid, using centreline tangent vectors as initial estimates of flow direction, and then extracting space‐time images (STIs) along search lines radiating from each grid node. The autocorrelation function is used to infer the inclination of streak lines present in STIs, which represents the advection of water surface features. Information on flow direction is obtained by evaluating various candidate search lines and identifying that which yields the highest velocity. This search can be performed exhaustively or via optimization. We applied the new 2D‐STIV algorithm to three test cases, one simulated data set and two natural channels, and compared image‐derived velocities to modelled or measured values. We also applied two established particle image velocimetry (PIV) algorithms to the same data sets. 2D‐STIV performed as well as the two PIV algorithms for simulated images. For a natural river with distinct water surface features, 2D‐STIV was effective for much of the channel but also led to a more patchy, irregular velocity field than the two PIV algorithms. For a site lacking obvious surface features, exhaustive 2D‐STIV led to velocity estimates uncorrelated with field data while the optimization‐based version produced erratic flow directions. 2D‐STIV also required greater image sequence durations, higher frame rates, and generally longer computational run times. Overall, ensemble PIV was the most reliable algorithm.

Applications of the landscape function for Schrödinger operators with singular potentials and irregular magnetic fields

We resolve both a conjecture and a problem of Z. Shen from the 90's regarding non-asymptotic bounds on the eigenvalue counting function of the magnetic Schrödinger operator La,V=−(∇−ia)2+V with a singular or irregular magnetic field B on Rn, n≥3. We do this by constructing a new landscape function for La,V, and proving its corresponding uncertainty principle, under certain directionality assumptions on B, but with no assumption on ∇B. These results arise as applications of our study of the Filoche-Mayboroda landscape function u, a solution to the equation LVu=−divA∇u+Vu=1, on unbounded Lipschitz domains in Rn, n≥1, and 0≤V∈Lloc1, under a mild decay condition on the Green's function. For LV, we prove a priori exponential decay of Green's function, eigenfunctions, and Lax-Milgram solutions in an Agmon distance with weight 1/u, which may degenerate. Similar a priori results hold for La,V. Furthermore, when n≥3 and V satisfies a scale-invariant Kato condition and a weak doubling property, we show that 1/u is pointwise equivalent to the Fefferman-Phong-Shen maximal function m(⋅,V) (also known as Shen's critical radius function); in particular this gives a setting where the Agmon distance with weight 1/u is not too degenerate. Finally, we extend results from the literature for La,V regarding exponential decay of the fundamental solution and eigenfunctions, to the situation of irregular magnetic fields with directionality assumptions.

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In this paper we construct a new type of noise of fractional nature that has a strong regularizing effect on differential equations. We consider an equation driven by a highly irregular vector field and study the effect of this noise on such dynamical systems. We employ a new method to prove existence and uniqueness of global strong solutions, where classical methods fail because of the “roughness” and non-Markovianity of the driving process. In addition, we prove the rather remarkable property that such solutions are infinitely many times classically differentiable with respect to the initial condition in spite of the vector field being discontinuous. The technique used in this article corresponds, in a certain sense, to the Nash–Moser iterative scheme in combination with a new concept of “higher order averaging operators along highly fractal stochastic curves”. This approach may provide a general principle for the study of regularization by noise effects in connection with important classes of partial differential equations.

Thermal-vibration correlation study for high-temperature superconducting maglev intelligent monitoring based on back propagation neural network analysis

The internal temperature rise inside the high-temperature superconducting (HTS) superconductor (SC) resulting from irregular magnetic field (MF) above the permanent magnet guideway is a major factor contributing to the decline of levitation performance. Real-time monitoring of the temperature rise inside YBCO SC is an important issue for the safe operation of the maglev train systems. However, the existing temperature rise testing method involves destructive intrusion less or more, easily affected by strong MF, occupying limited space and sensors prone to detachment. This paper innovatively proposes a non-contact internal temperature rise testing method combining artificial intelligence (AI) methods. Vibration is common signal of a maglev train system, which inspires to establish a fundamental thermal-dynamic levitation force synchronous testing device for YBCO SC. Then, a set of temperature rise-vibration dataset exposed to different alternating MF frequencies is created. The wavelet transform is chosen to extract the frequency band energy of vibration, and the backpropagation neural network is used to identify the corresponding temperature rise. The recognition accuracy can reach over 99.9%, which firstly proves the effectiveness of AI algorithms in the thermal-vibration correlation analysis for the HTS maglev system. The results can provide the foundation reference for the intelligent monitoring and fault diagnosis of thermal-dynamics stabilities of HTS maglev train in the future.

Irregular Agricultural Field Delineation Using a Dual-Branch Architecture From High-Resolution Remote Sensing Images

Irregular Agricultural Field Delineation Using a Dual-Branch Architecture From High-Resolution Remote Sensing Images

Satellite Imagery Analysis for Crop Type Segmentation Using U-Net Architecture

Analyzing satellite images plays a critical role in surveying and monitoring agricultural areas, enabling various applications such as precision agriculture, land use planning, and yield estimation. One of the crucial steps in these applications is accurate crop-type segmentation from satellite imagery. This task becomes challenging in the scenarios of smallholder farms due to their irregular field shapes, frequent cloud cover, small plot sizes, and a severe shortage of training data, which make it difficult to apply conventional machine learning methods effectively. In this research work, a technique for segmenting the crop types from the difficult scenario of smallholder farms based on fully convolutional encoder-decoder semantic segmentation architecture, U-Net, has been proposed and its performance has been compared with the traditional machine learning techniques. To evaluate the proposed approach, experimental assessments were conducted on the Kenya satellite imagery crop dataset. The proposed technique achieved an accuracy, precision, recall, and F1 score of 95.3%, 80.2%, 68.1%, and 73.6%, respectively. The results demonstrate that the U-Net model surpasses conventional image classification methods in accurately segmenting different crop types.

Symmetric and asymmetric buckling of rotating eccentric annular plate

The thermo-induced buckling, including the symmetric and asymmetric buckling, of rotating eccentric annular plate is investigated. And the splitting phenomenon in buckling caused by structural eccentricity is at the core of the present paper. To overcome the computational difficulty resulted from the irregular physical field of the eccentric annular plate, the transformed differential quadrature method is adopted to solve the governing equations. The governing equations and boundary conditions are discretized directly in the physical domain by utilizing the conformal mapping. The combined effects of eccentricity, boundary conditions, geometric dimensions, and rotational motion on the buckling of annular plate are discussed through parameter studies. The eccentricity leads to the splitting of asymmetric buckling of eccentric annular plate. With the increase of eccentricity, this phenomenon becomes more obvious. Meanwhile, the variation tendency of critical buckling temperature rise is closely related to the number of pitch diameter in buckling mode. The sequence of symmetric and asymmetric buckling appears is affected by the boundary conditions. To a certain extent, the rotational motion inhibits the splitting phenomenon in buckling arose from the eccentricity.

Solar energetic particles propagation during solar events at the beginning of the 25th solar cycle

Space weather is induced by solar events like solar flares, solar wind, the solar cycle, and coronal mass ejections. These solar events have the potential to affect various aspects of Earth, including radio communications, electric power failures, and navigation signals, and pose risks to spacecraft and astronauts. Solar activity is more or less intense according to the solar cycle. We are currently in the 25th solar cycle, which began in 2019. This research studies the space weather at the beginning of the 25th solar cycle using the distribution data obtained from spacecraft including the explosion on the Sun’s surface, by analyzing the solar energetic particles (SEPs) propagation. The distribution of SEPs with flux; F is given by a power law for solar events on November 29, 2020, July 3, 2021, and April 22, 2022, with indices 6.2, 4.5, and 1.8 depending on energy. The propagation of the SEPs was studied using Ruffolo’s transport equation, which was solved using the Finite-differences method. We found that solar flares with higher X-ray intensity have a longer movement along the irregular magnetic field and arrive at Earth more quickly than those with lower X-ray intensity. The SEPs are weaker and have no impact on Earth, although both events generated coronal mass ejection. The M-class and X-class solar flares occurred during the beginning of the 25th solar cycle, the position of explosion and injection time of particles resulted in space weather with little impact on Earth.

A new probability density function for the surface elevation in irregular seas

To date, the predominant means for computing the probability density function (p.d.f.) for the free surface elevation of a nonlinear, irregular water wave field, free of assumptions involving narrow-bandedness and small directionality, is the approximate Gram–Charlier series solution of Longuet-Higgins (J. Fluid Mech., vol. 17, 1963, pp. 459–480, hereafter LH63). In this paper we re-visit the derivation of this p.d.f. to second order in the wave steepness, utilizing both moment and cumulant generating functions. We show that LH63's approximate solution based on the cumulant generating function, in fact, matches that derived from the moment generating function. Moreover, through a change of variables coupled with complex analysis, it is shown that the approximation employed by LH63 is unnecessary, and the second-order p.d.f. stemming from the cumulant generating function can be represented exactly in terms of the Airy function. The new second-order p.d.f. predicts increased probability of extreme positive surface elevations typical of e.g. rogue waves, relative to both second- and third-order solutions of LH63. This heavy positive tail is inherent, and is explained through comparison of the asymptotic limits of the p.d.f.s for large surface elevations. A semi-theoretical method is also proposed for remedying non-physical spurious oscillations that arise in the negative tail, based on the envelope of the Airy function with negative arguments. This modified negative tail is valid for irregular wave fields having skewness less than or equal to 0.2. The new p.d.f.s are compared against those based on data sets generated from second-order irregular wave theory as well as a fully nonlinear, spectrally accurate numerical wave model. Good accuracy is collectively demonstrated for directionally spread irregular seas in both finite and infinite water depths for a range of directional spreading.

Signal Pre-processing Method for Condition Monitoring based on Vibration Signals from On-Site Manipulators

Handling irregular and noisy field data is challenging in condition monitoring. In contrast to refined lab data, where external influences are kept to a minimum, acquired signal from accelerometer attached to mechanical devices involves a great deal of uncontrollable variables. Especially, irregular operation cycles of the process make difficult to specify significant vibration signals for monitoring without mechanical expertise and information of the manipulators' motion. In this study, we distinguish motion signals from noisy raw signal using Shannon Energy Envelope (SEE). The extracted individual motion signals are algorithmically clustered through the signal graph characteristics for each robot motion. Clusters are evaluated for the effectiveness of monitoring, and it enables users to obtain a reference whose signal can perform the same accuracy for condition monitoring with expert knowledge.

The effect of the tongue and groove of the multileaf collimator on dose distribution: examination of results of measurements and treatment planning system

Multileaf collimators play an important role in the field of radiotherapy and are widely used today. The effect of multileaf collimator structure on dose distribution should be well investigated and whether treatment planning systems reflect these effects on dose distribution should be examined. In our study, we examined the effect of tongue-and-groove on irregular fields and intensity-modulated radiotherapy plan. We also investigated the depth dependence of the tongue-and-groove effect. All fields are created in RayStation treatment planning system. Data obtained from treatment planning systems were compared with data obtained with 2-dimensional dose-measuring devices. We found the tongue-and-groove effect consistent in both measurements in single field measurements. We calculated dose reductions due to tongue-and-groove effect between 6% and 14%. When we examined the tongue-and-groove effect at different depths, we observed that the tongue-and-groove effect decreased as the depth increased. Although the tongue-and-groove effect did not create significant results on the dose distribution in multi-field use, we observed that this effect could occur at the edge of the field. Dose differences due to multileaf collimator structure should be examined by clinics. In addition, the extent to which treatment planning system takes these effects into account should be checked with some measurements. Otherwise, undesirable dose differences may be seen in the patient.

A Real-Time Measurement Method and System for the Harvesting Area of a Grain Combine Harvester

A real-time system for measuring harvesting area is crucial for cross-regional operation. In response to the current situation where significant errors often occur in measuring the areas of small and irregular fields, we designed a low-cost and high-precision online real-time measurement system for the harvesting area of grain combines. The real-time measurement hardware system for measuring area was designed using an STM32 microcontroller as the core unit. The system was developed using three modes: intelligent measurement, circular measurement, and fusion measurement. Simulation and field experiments were conducted to verify the performance of the system. The experimental results showed that in the field simulation experiment with four differently shaped operating areas, the average accuracy of the intelligent mode reached 93.76%, and the average accuracy of the circular mode reached 95.48%. In the field experiments, the average accuracy of the intelligent mode reached 95.09%, the average accuracy of the circular mode reached 95.74%, and the average accuracy of the fusion measurement mode reached 98.01%. Therefore, the real-time measurement system for the harvesting area of a grain combine harvester designed in this study has high accuracy when measuring the area of small and irregular farmland, hence meeting the requirements of the design objectives.

Hierarchical extraction of cropland boundaries using Sentinel-2 time-series data in fragmented agricultural landscapes

Accurately extracting cropland field parcels from satellite data in fragmented agricultural landscapes presents huge challenges due to small field sizes and irregular field shapes. Here, we developed a hierarchical framework for agricultural field boundary extraction from Sentinel-2 satellites based on the concept of the degree in field fragmentation. Our framework focused on three tasks, including a core task for agricultural field extraction and two auxiliary tasks to address special situations in two different fragmented regions, namely the Plains-Basin scenes with relatively regular shapes and diverse crop types, and the Plateau-Hilly scenes, with irregular shapes and small field sizes. First, the field boundaries were delineated using a modified Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net), and post-processed using morphological filtering and the Douglas-Peucker algorithm (DPA). A rule-based decision fusion strategy assisted with a classification method was developed to address the under-segmentation problem in the Plains-Basin scenes, and an adaptive generalized buffer algorithm (AGBA) was used to address the incomplete-segmentation problem in the Plateau-Hilly scenes. We tested the proposed methods in Yangming County, Heilongjiang Province of China, using time series Sentinel-2 imageries with 10-m spatial resolution. Compared with the results from using R2U-Net or a single-temporal image alone, the fragmented field boundaries show an outstanding performance, with an overall accuracy of 86.42% and 84.15% and F1 score of 0.85 and 0.83 in Plains-Basin scenes and Plateau-Hilly scenes, respectively. Our proposed methods provide a feasible solution to finely extract field boundaries in highly fragmented and heterogeneous agricultural landscapes.