Rotated Method Research Articles

TSAF-Net: a rotated two-stage Cnaphalocrocis medinalis damage detection method based on anchor-free arbitrary-oriented proposal network.

Cnaphalocrocis medinalis (C.medinalis) is an agricultural pest with recurrent outbreaks. The investigation into automated pest and disease detection technology holds significant value for in-field surveys. Current generic detection methods are inadequate due to arbitrary orientations and a wide range of aspect ratios in damage symptoms. To tackle these issues, we put forward a rotated two-stage detection method for in-field C.medinalis surveys. This method relies on an anchor-free rotated region proposal network (AF-R2PN), bypassing the need for hyper-parameter optimization induced by predefined anchor boxes. An in-field C.medinalis dataset is constructed during on-site pest surveys to validate the effectiveness of our method. The experimental results show that our method can accomplish 80% average precision (AP), surpassing the corresponding horizontal detector by 2.3%. The visualization results of our work showcase its exceptional localization capability over generic detection methods, facilitating inspection by plant protectors. Meanwhile, our proposed method outperforms other state-of-the-art rotated detection algorithms. The AF-R2PN module can generate superior arbitrary-oriented proposals even with a decreased number of proposals, balancing inference speed and detection performance among other rotated two-stage methods. The proposed method exhibits superiority in detecting C. medinalis damage under complex field conditions. It provides greater practical applicability during in-field surveys, enhancing their efficiency and coverage. The findings hold significance for pest and disease monitoring, providing important technical support for agricultural production. © 2024 Society of Chemical Industry.

Traffic state estimation from vehicle trajectories with anisotropic Gaussian processes

Accurately monitoring road traffic state is crucial for various applications, including travel time prediction, traffic control, and traffic safety. However, the lack of sensors often results in incomplete traffic state data, making it challenging to obtain reliable information for decision-making. This paper proposes a novel method for imputing traffic state data using Gaussian processes (GP) to address this issue. We propose a kernel rotation re-parametrization scheme that transforms a standard isotropic GP kernel into an anisotropic kernel, which can better model the congestion propagation in traffic flow data. The model parameters can be estimated by statistical inference using data from sparse probe vehicles or loop detectors. Moreover, the rotated GP method provides statistical uncertainty quantification for the imputed traffic state, making it more reliable. We also extend our approach to a multi-output GP, which allows for simultaneously estimating the traffic state for multiple lanes. We evaluate our method using real-world traffic data from the Next Generation simulation (NGSIM) and HighD programs, along with simulated data representing a traffic bottleneck scenario. Considering current and future mixed traffic of connected vehicles (CVs) and human-driven vehicles (HVs), we experiment with the traffic state estimation (TSE) scheme from 5% to 50% available trajectories, mimicking different CV penetration rates in a mixed traffic environment. We also test the traffic state estimation when traffic flow information is obtained from loop detectors. The results demonstrate the adaptability of our TSE method across different CV penetration rates and types of detectors, achieving state-of-the-art accuracy in scenarios with sparse observations.

Assessing the potential impact of assimilating total surface current velocities in the Met Office’s global ocean forecasting system

Accurate prediction of ocean surface currents is important for marine safety, ship routing, tracking of pollutants and in coupled forecasting. Presently, velocity observations are not routinely assimilated in global ocean forecasting systems, largely due to the sparsity of the observation network. Several satellite missions are now being proposed with the capability to measure Total Surface Current Velocities (TSCV). If successful, these would substantially increase the coverage of ocean current observations and could improve accuracy of ocean current forecasts through data assimilation. In this paper, Observing System Simulation Experiments (OSSEs) are used to assess the impact of assimilating TSCV in the Met Office’s global ocean forecasting system. Synthetic observations are generated from a high-resolution model run for all standard observation types (sea surface temperature, profiles of temperature and salinity, sea level anomaly and sea ice concentration) as well as TSCV observations from a Sea surface KInematics Multiscale monitoring (SKIM) like satellite. The assimilation of SKIM like TSCV observations is tested over an 11 month period. Preliminary experiments assimilating idealised single TSCV observations demonstrate that ageostrophic velocity corrections are not well retained in the model. We propose a method for improving ageostrophic currents through TSCV assimilation by initialising Near Inertial Oscillations with a rotated incremental analysis update (IAU) scheme. The OSSEs show that TSCV assimilation has the potential to significantly improve the prediction of velocities, particularly in the Western Boundary Currents, Antarctic Circumpolar Current and in the near surface equatorial currents. For global surface velocity the analysis root-mean-square-errors (RMSEs) are reduced by 23% and there is a 4-day gain in forecast RMSE. There are some degradations to the subsurface in the tropics, generally in regions with complex vertical salinity structures. However, outside of the tropics, improvements are seen to velocities throughout the water column. Globally there are also improvements to temperature and sea surface height when TSCV are assimilated. The TSCV assimilation largely corrects the geostrophic ocean currents, but results using the rotated IAU method show that the energy at inertial frequencies can be improved with this method. Overall, the experiments demonstrate significant potential benefit of assimilating TSCV observations in a global ocean forecasting system.

A rotated shift-splitting method for complex symmetric linear systems

A rotated shift-splitting method for complex symmetric linear systems

An adaptable rotated bounding box method for automatic detection of arbitrary-oriented cracks

Concrete crack detection is a crucial task for the safety and durability of engineering structures. Extensive research has been conducted on deep-learning methods employing horizontal bounding boxes (HBBs) for crack detection. However, due to the inherently random distribution of concrete cracks, HBB-based methods often produce excessive overlaps and encompass extensive background regions, obstructing the effective interpretation and adaptation of the detection results. To address this issue and achieve efficient utilization of bounding box space for detecting cracks at any orientation, a rotated bounding box (RBB)-based method, that is, Rotated Faster R-CNN with a post processing strategy (RFR-P), was proposed. To realize this method, an RBB-based crack annotation strategy was introduced to standardize the annotation baseline for the evolutionarily established RBB-based crack detection dataset. Then, an RBB-based post-processing strategy was inventively developed to quantify the patterns of cracks with their corresponding rotation angles encompassing longitudinal cracks, transverse cracks, and diagonal cracks. Subsequently, experimental results showed that the RFR-P method provides more reasonable and elaborate detection results in terms of crack distribution patterns when compared to HBB-based methods. Based on the comprehensive consideration of evaluation metrics and detected results, it can be concluded that the RFR-P is aptly designed for detecting cracks at any rotation angle with relatively high accuracy. Finally, an RBB-based concrete crack detection platform was established to automatically detect in situ concrete bridge cracks for real-world applications. The proposed RFR-P model introduces a new perspective on crack detection methods and offers practical references for structural condition evaluation.

A new lizard-like reptile with unusual mandibular neurovasculature from the Upper Triassic of Virginia

ABSTRACT Here, we report a new small-bodied reptile taxon, Idiosaura virginiensis gen. et sp. nov., from the Upper Triassic (Carnian) Vinita Formation of the Richmond basin (Newark Supergroup) in east-central Virginia, U.S.A. The material consists of a fragmentary dentary bearing numerous tall cylindrical teeth implanted in a pleurodont fashion into spongy alveolar tissue. Micro-CT scan data reveal an unusual and complex network of interconnected neurovascular canals within the dentary with connections to the alveolar tissue and pulp cavities of the teeth. The external anatomy of the dentary is consistent with that of Triassic kuehneosaurid reptiles, suggesting affinities to that group; the results of a phylogenetic analysis were inconclusive but do not exclude kuehneosaurid relationships. Alongside the recently described Micromenodon pitti (Rhynchocephalia) and Vinitasaura lizae (Lepidosauromorpha), the new taxon adds to the relatively diverse assemblage of small-bodied reptiles known from the Tomahawk microvertebrate bonebed (USNM locality 39981). Relative tooth complexities of I. virginiensis and V. lizae were analyzed using the Orientation Patch Count Rotated method, and results suggest that the rise of lepidosauromorph ecomorphologies specializing for feeding on invertebrates occurred in Triassic communities alongside non-lepidosauromorph taxa with similar mandibulo-dental features, as in kuehneosaurs.

Improvement of rotated object detection and instance segmentation in warship satellite remote sensing images based on convolutional neural network

ABSTRACT Object detection and instance segmentation networks are improved to realise the accurate detection and instance segmentation of rotated warship objects in satellite remote sensing images. An adaptive threshold generation scheme and segmentation annotation information are applied used to improve a rotated label generation method to obtain high-precision rotated object labels. The original RPN is combined with the bbox head with improved output dimensions to obtain a rotated RPN to generate rotated region proposals. Rotated RoIAlign is used to solve the problem of mismatch between rotated region proposals and dimensions of subsequent feature maps. A rotated detection frame is used to correct the output of the network, which alleviates false detection and omission. In addition, this removes the pixels outside the rotated detection frame that are incorrectly classified as objects. The improved networks can achieve high-precision detection and instance segmentation of rotated warship objects, and the methods used in this study have good generalisability.

Finite-difference modeling of 3D frequency-domain elastic wave equation using an affine mixed-grid method

In seismic frequency-domain finite-difference modeling, the numerical accuracy depends on how mass and partial differential terms are discretized in the acoustic/elastic wave equation. For the mass term(s), a combination of consistent and lumped mass methods is widely accepted. For the partial differential terms, there exist many discretization technologies, such as the average-derivative method (ADM) and the rotated mixed-grid method (RMM). In comparison with the ADM, which focuses on the media interior, the RMM is excellent at handling the media interior and free-surface boundary simultaneously. However, the existing RMMs are based on the orthogonal coordinate transformation and suffer from the restriction of cube-grid sampling. Affine coordinate systems do not restrict the axes to be orthogonal. By introducing the 3D affine coordinate transformations to the RMM, we have developed an affine mixed-grid method and generated an optimal 27-point scheme for the 3D elastic wave equation. Our scheme removes the sampling restriction of cubic mesh, thus working for general cuboid mesh. In addition, it provides consistent expressions in the whole computational domain, thus improving the accuracies of the internal and free-surface grid points simultaneously. Dispersion analysis and numerical examples demonstrate that our scheme provides acceptable accuracy with five points per minimal S-wavelength for the internal and free-surface points with arbitrary spatial grid intervals.

ESRTMDet: An End-to-End Super-Resolution Enhanced Real-Time Rotated Object Detector for Degraded Aerial Images

The degradation of image resolution reduces the detection performance in aerial imagery because it generates a large number of small objects, and accurately detecting these small objects remains a challenge. Existing methods mostly use a superresolution (SR) model to first obtain the SR image of the low-resolution degraded image ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$I^{\text{LR}}$</tex-math></inline-formula> ) and then use this image as the input of the object detection (OD) network to solve this problem. However, this architecture that involves executing a complex SR network before the detector is time-consuming and makes it hard to achieve real-time model inference. To address this challenge, we propose a simple and effective rotated small OD method, named end-to-end superresolution enhanced real-time rotated object detector (ESRTMDet). First, we design a lightweight embedded feature map superresolution module (ESRM) embedded in the detection model to enhance and amplify the backbone output features, making the detection heads detect small objects more easily. Furthermore, we train a parallel SR network branch (PSRB) simultaneously that uses the backbone feature to restore a high-resolution image. Through our proposed feature alignment loss and feature affinity layer, our PSRB effectively guides the feature map enhancement of ESRM. Finally, through end-to-end joint optimization of the detector and PSRB, the detection performance on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$I^{\text{LR}}$</tex-math></inline-formula> is significantly improved. Extensive experiments over DOTA and UCAS-AOD demonstrate that our method can achieve state-of-the-art results. In addition, we discard our PSRB and use <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$I^{\text{LR}}$</tex-math></inline-formula> as the input during inference, reducing the inference time-consuming of our model. Therefore, our ESRTMDet-X not only achieves 77.11% mean of average precision on the degraded DOTA dataset, but also achieves an amazing inference speed of 337 FPS, thus obtaining the best speed–accuracy tradeoff.

Determining Hansen Solubility Parameters by a Rotated Ellipsoid-Based Method

Determining Hansen Solubility Parameters by a Rotated Ellipsoid-Based Method

Development of an ultrasound guided focused ultrasound system for 3D volumetric low energy nanodroplet-mediated histotripsy

Low pressure histotripsy is likely to facilitate current treatments that require extremely high pressures. An ultrasound guided focused ultrasound system was designed to accommodate a rotating imaging transducer within a low frequency therapeutic transducer that operates at a center frequency of 105 kHz. The implementation of this integrated system provides real-time therapeutic and volumetric imaging functions, that are used here for low-cost, low-energy 3D volumetric ultrasound histotripsy using nanodroplets. A two-step approach for low pressure histotripsy is implemented with this dual-array. Vaporization of nanodroplets into gaseous microbubbles was performed via the 1D rotating imaging probe. The therapeutic transducer is then used to detonate the vaporized nanodroplets and trigger potent mechanical effects in the surrounding tissue. Rotating the imaging transducer creates a circular vaporized nanodroplet shape which generates a round lesion upon detonation. This contrasts with the elongated lesion formed when using a standard 1D imaging transducer for nanodroplet activation. Optimization experiments show that maximal nanodroplet activation can be achieved with a 2-cycle excitation pulse at a center frequency of 3.5 MHz, and a peak negative pressure of 3.4 MPa (a mechanical index of 1.84). Vaporized nanodroplet detonation was achieved by applying a low frequency treatment at a center frequency of 105 kHz and mechanical index of 0.9. In ex-vivo samples, the rotated nanodroplet activation method yielded the largest lesion area, with a mean of 4.7 ± 0.5 mm2, and a rounded shape. In comparison, standard fixed transducer nanodroplet activation resulted in an average lesion area of 2.6 ± 0.4 mm2, and an elongated shape. This hybrid system enables to achieve volumetric low energy histotripsy, and thus facilitates the creation of precise, large-volume mechanical lesions in tissues, while reducing the pressure threshold required for standard histotripsy by over an order of magnitude.

Poro-acoustoelasticity finite-difference simulation of elastic wave propagation in prestressed porous media

Insights into wave propagation in prestressed porous media are important in geophysical applications, such as monitoring changes in geo-pressure. This can be addressed by poro-acoustoelasticity theory, which extends the classical acoustoelasticity of solids to porous media. The relevant poro-acoustoelasticity equations can be derived from anisotropic poroelasticity equations by replacing the poroelastic stiffness matrix with an acoustoelastic stiffness matrix consisting of second-order and third-order elastic constants. The theory considers the poroelasticity equations to be nonlinear due to the cubic strain-energy function with linear strains under finite-magnitude prestresses. A rotated staggered-grid finite-difference method with an unsplit convolutional perfectly matched layer absorbing boundary is used to solve a first-order velocity-stress formulation of poro-acoustoelasticity equations for elastic wave propagation in prestressed porous media. Numerical solutions are partially verified by computing the velocities of fast P wave, slow P wave, and S wave as a function of hydrostatic prestress and are compared with the exact values. Numerical simulations of wave propagation are carried out for the model of poro-acoustoelastic homogeneous space under three states—prestress confining (hydrostatic), uniaxial, and pure shear—and for the model of two poro-acoustoelastic homogeneous half-spaces in the planar contact under confining (hydrostatic) prestress. The resulting wavefield snapshots show fast P-wave, slow P-wave, and S-wave propagations in poro-acoustoelastic media under loading prestresses, which illustrate that the stress-induced velocity anisotropy is of orthotropy strongly related to the orientation of prestresses. These examples demonstrate the significant impact of prestressing conditions on seismic responses in velocity and anisotropy.

Individual wood board tracing method using oriented fast and rotated brief method in the wood traceability system

Individual wood board tracing method using oriented fast and rotated brief method in the wood traceability system

Inverse stereographic hyperbolic secant distribution: a new symmetric circular model by rotated bilinear transformations

The inverse stereographic projection (ISP), or equivalently, bilinear transformation, is a method to produce a circular distribution based on an existing linear model. By the genesis of the ISP method, many important circular models have been provided by many researchers. In this study, we propose a new symmetric unimodal/bimodal circular distribution by the rotated ISP method considering the hyperbolic secant distribution as a baseline distribution. Rotation means that fixing the origin and rotating all other points the same amount counterclockwise. Considering the effect of rotation on the circular distribution to be obtained with the bilinear transformation, it is seen that it actually induces a location parameter in the obtained circular probability distribution. We analyze some of the stochastic properties of the proposed distribution. The methods for the parameter estimation of the new circular model and the simulation-based compare results of these estimators are extensively provided by the paper. Furthermore, we compare the fitting performance of the new model according to its well-known symmetric alternatives, such as Von-Misses, and wrapped Cauchy distributions, on a real data set. From the information obtained by the analysis on the real data, we say that the fitting performance of the new distribution is better than its alternatives according to the criteria frequently used in the literature.

Enhanced Rotated Mask R-CNN for Chromosome Segmentation.

Karyotyping is an important process for finding chromosome abnormalities that could cause genetic disorders. This process first requires cytogeneticists to arrange each chromosome from the metaphase image to generate the karyogram. In this process, chromosome segmentation plays an important role and it is directly related to whether the karyotyping can be achieved. The key to achieving accurate chromosome segmentation is to effectively segment the multiple touching and overlapping chromosomes at the same time identify the isolated chromosomes. This paper proposes a method named Enhanced Rotated Mask R-CNN for automatic chromosome segmentation and classification. The Enhanced Rotated Mask R-CNN method can not only accurately segment and classify the isolated chromosomes in metaphase images but also effectively alleviate the problem of inaccurate segmentation for touching and overlapping chromosomes. Experiments show that the proposed approach achieves competitive performances with 49.52 AP on multi-class evaluation and 69.96 AP on binary-class evaluation for chromosome segmentation.

Quantitative Examination of the Inclusion and the Rotated Bending Fatigue Behavior of SAE52100

This study investigated the effect of maximum inclusion on the life of SAE52100 bearing steel processed by two different melting routes, vacuum induction melting plus electroslag remelting (VIM + ESR), and basic oxygen furnace plus ladle furnace plus vacuum degassing process (BOF + LF + RH) by the metallographic method, Aspex explorer, and rotated bending fatigue test. The rotated bending method was applied to examine the maximum inclusion size in a satisfactory manner, whereas both the metallographic method and Aspex explorer underestimated the result. Regardless of the characterization methods, the results show that the total number of inclusions in VIM + ESR melted steel is significantly higher than that in BOF + LF + RH processed steel, but the maximum inclusion size of VIM + ESR melted steel is significantly smaller than that of the BOF + LF + RH degassed steel. The distribution of the maximum inclusion size could be well fitted by the inverse Weibull distribution and could be well applied to reveal the different inclusion size distribution based on the data examined by the rotated bending fatigue method. Finally, a new equation was proposed to establish the relationship among the loading stress amplitude, rotated bending fatigue number, and the maximum inclusion size.

Simulation and characteristics analysis of a wavefield in a thermoelastic medium adopting the rotated staggered-grid pseudo-spectral method and L-S theory

Simulation and characteristics analysis of a wavefield in a thermoelastic medium adopting the rotated staggered-grid pseudo-spectral method and L-S theory

Boundary layer structure characteristics under objective classification of persistent pollution weather types in the Beijing area

Abstract. Different types of pollution boundary layer structures form via the coupling of different synoptic systems and local mesoscale circulation in the boundary layer; this coupling contributes toward the formation and continuation of haze pollution. In this study, we objectively classify the 32 heavy haze pollution events using integrated meteorological and environmental data and ERA-Interim analysis data based on the rotated empirical orthogonal function method. The thermodynamic and dynamic structures of the boundary layer for different pollution weather types are synthesized, and the corresponding three-dimensional boundary layer conceptual models for haze pollution are constructed. The results show that four weather types mainly influence haze pollution events in the Beijing area: (a) type 1 – southerly transport, (b) type 2 – easterly convergence, (c) type 3 – sinking compression, and (d) type 4 – local accumulation. The explained variances in the four pollution weather types are 43.69 % (type 1), 33.68 % (type 2), 16.51 % (type 3), and 3.92 % (type 4). In persistent haze pollution events, type 1 and type 2 surpass 80 % on the first and second days, while the other types are present alternately in later stages. The atmospheric structures of type 1, type 2, and type 3 have typical baroclinic characteristics at mid–high latitudes, indicating that the accumulation and transport of pollutants in the boundary layer are affected by coupled structures in synoptic-scale systems and local circulation. The atmospheric structure of type 4 has typical barotropic characteristics, indicating that the accumulation and transport of pollutants is primarily affected by local circulation. In type 1, southerly winds with a specific thickness and intensity prevail in the boundary layer, which is favorable for the accumulation of pollutants in plain areas along the Yan and Taihang Mountains, whereas haze pollution levels in other areas are relatively low. Due to the interaction between weak easterly winds and the western mountains, pollutants accumulate mainly in the plain areas along the Taihang Mountains in type 2. The atmospheric vertical structure is not conducive to upward pollutant diffusion. In type 3, the heights of the inversion and boundary layers are the lowest due to a weak sinking motion while relative humidity is the highest among the four types. The atmosphere has a small capacity for pollutant dispersion and is favorable to particulate matter hygroscopic growth; as a result, type 3 has the highest PM2.5 concentration. In type 4, the boundary layer is the highest among the four types, the relative humidity is the lowest, and the PM2.5 concentration is relatively lower under the influence of local mountain–plain winds. Different weather types will shape significantly different structures of the pollution boundary layer. The findings of this study allow us to understand the inherent difference among heavy pollution boundary layers; in addition, they reveal the formation mechanism of haze pollution from an integrated synoptic-scale and boundary layer structure perspective. We also provide scientific support for the scientific reduction of emissions and air quality prediction in the Beijing–Tianjin–Hebei region of China.

Elemental Characteristics of Respirable Particulate Matter in the Urban Atmosphere of Dehradun, Uttrakhand, India

To evaluate the ambient air quality of the Dehradun city, respirable particulate matter was collected using respirable dust sampler (RDS) and analysed for the heavy metal content using atomic absorption spectroscopy (AAS). The morphology of particulates were determined using scanning electron microscope (SEM) and the elemental composition was determined through SEM- energy dispersive spectroscopy (EDS). Particulate matter mass concentration ranged from 65.00 µg m-3 to 337.33 µg m-3. Quantified heavy metals in particulate matter were Copper (Cu), Zinc (Zn), Cobalt (Co), Manganese (Mn), Iron (Fe), Nickel (Ni), Chromium (Cr), Lead (Pb) and Cadmium (Cd). The order of concentration of heavy metals were found to be in the trend of Fe&gt;Zn&gt;Cu&gt;Pb&gt;Cr&gt;Ni&gt;Mn&gt;Co&gt;Cd. Maximum concentration of PM10 was found at commercial site during summer, winter and monsoon season. Enrichment factor analysis showed substantial contribution of anthropogenic activities on PM10. Source apportionment (varimax rotated factor analysis method) showed dominance of incineration and uncontrolled burning of waste and refuses, resuspended dust with vehicular emission and crustal sources as the dominant sources in Dehradun. Plantation drive strategy have major role in ambient particulate matter mitigation measures and carbon sequestration from climate change and global problem worldwide. This study will be help to mitigate or decrease the load of air pollution by the using of various trees for sustainable human development on the marvellous earth planet.

Simulation of thermoelastic waves based on the Lord-Shulman theory

Thermoelasticity is important in seismic propagation due to the effects related to wave attenuation and velocity dispersion. We have applied a novel finite-difference (FD) solver of the Lord-Shulman thermoelasticity equations to compute synthetic seismograms that include the effects of the thermal properties (expansion coefficient, thermal conductivity, and specific heat) compared with the classic forward-modeling codes. We use a time splitting method because the presence of a slow quasistatic mode (the thermal mode) makes the differential equations stiff and unstable for explicit time-stepping methods. The spatial derivatives are computed with a rotated staggered-grid FD method, and an unsplit convolutional perfectly matched layer is used to absorb the waves at the boundaries, with an optimal performance at the grazing incidence. The stability condition of the modeling algorithm is examined. The numerical experiments illustrate the effects of the thermoelasticity properties on the attenuation of the fast P-wave (or E-wave) and the slow thermal P-wave (or T-wave). These propagation modes have characteristics similar to the fast and slow P-waves of poroelasticity, respectively. The thermal expansion coefficient has a significant effect on the velocity dispersion and attenuation of the elastic waves, and the thermal conductivity affects the relaxation time of the thermal diffusion process, with the T mode becoming wave-like at high thermal conductivities and high frequencies.