VS-TransGRU: A Novel Transformer-GRU-based Framework Enhanced by Visual-Semantic Fusion for Egocentric Action Anticipation

Egocentric action anticipation is a challenging task that aims to make advanced predictions of future actions from current and historical observations in the first-person view. Most existing methods focus on improving the model architecture and loss function based on the visual input and recurrent neural network to boost the anticipation performance. However, these methods, which merely consider visual information and rely on a single network architecture, gradually reach a performance plateau. In order to fully understand what has been observed and capture the dependencies between current observations and future actions well enough, we propose a novel visual-semantic fusion enhanced and Transformer GRU-based action anticipation framework in this paper. Firstly, high-level semantic information is introduced to improve the performance of action anticipation for the first time. We propose to use the semantic features generated based on the class labels or directly from the visual observations to augment the original visual features. Secondly, an effective visual-semantic fusion module is proposed to make up for the semantic gap and fully utilize the complementarity of different modalities. Thirdly, to take advantage of both the parallel and autoregressive models, we design a Transformer based encoder for long-term sequential modeling and a GRU-based decoder for flexible iteration decoding. Extensive experiments on two large-scale first-person view datasets, i.e., EPIC-Kitchens and EGTEA Gaze+, validate the effectiveness of our proposed method, which achieves new state-of-the-art performance, outperforming previous approaches by a large margin.Comment: 12 pages, 7 figure

Heat Transfer Correlations for Supercritical Water in Vertically Upward Tubes

Supercritical pressure water (SCW) has been widely used in many engineering fields and industries, such as fossil fuel-fired power plants, newly developed Gen-IV nuclear power plants and so forth. Heat transfer characteristics of SCW are of great importance for both design and safe operation of the related systems. Many heat transfer correlations have been developed in the history for predicting the heat transfer characteristics of SCW. However, the prediction accuracy of the existing correlations is less than satisfactory, especially in the cases with deteriorated heat transfer (DHT) because of the severe and quick variation in thermal physical properties of SCW in the vicinity of the fluids’ pseudo critical point. It is very necessary to develop new correlations for the heat transfer of SCW to meet the engineering requirements for satisfactory prediction of the heat transfer behavior of SCW. In this chapter, experimental data on heat transfer of SCW are extensively collected from published literatures, and the performance of the existing heat transfer correlations for SCW are reviewed and quantitatively evaluated against the collected experimental data, and then a new heat transfer correlation for SCW with high prediction accuracy is proposed

An improved method for calculating slope length (λ) and the LS parameters of the Revised Universal Soil Loss Equation for large watersheds

The Universal Soil Loss Equation (USLE) and its revised version (RUSLE) are often used to estimate soil erosion at regional landscape scales. USLE/RUSLE contain parameters for slope length factor (L) and slope steepness factor (S), usually combined as LS. However a major limitation is the difficulty in extracting the LS factor. Methods to estimate LS based on geographic information systems have been developed in the last two decades. L can be calculated for large watersheds using the unit contributing area (UCA) or the slope length (λ) as input parameters. Due to the absence of an estimation of slope length, the UCA method is insufficiently accurate. Improvement of the spatial accuracy of slope length and LS factor is still necessary for estimating soil erosion. The purpose of this study was to develop an improved method to estimate the slope length and LS factor. We combined the algorithm for multiple-flow direction (MFD) used in the UCA method with the LS-TOOL (LS-TOOLSFD) algorithms, taking into account the calculation errors and cutoff conditions for distance, to obtain slope length (λ) and the LS factor. The new method, LS-TOOLMFD, was applied and validated in a catchment with complexly variable slopes. The slope length and LS calculated by LS-TOOLMFD both agreed better with field data than with the calculations using the LS-TOOLSFD and UCA methods, respectively. We then integrated the LS-TOOLMFD algorithm into LS-TOOL developed in Microsoft's.NET environment using C# with a user-friendly interface. The method can automatically calculate slope length, slope steepness, L, S, and LS factor, providing the results as ASCII files that can be easily used in GIS software and erosion models. This study is an important step forward in conducting accurate large-scale erosion evaluation

Quality of terrestrial data derived from UAV photogrammetry : A case study of Hetao irrigation district in northern China

Most crops in northern China are irrigated, but the topography affects the water use, soil erosion, runoff and yields. Technologies for collecting high-resolution topographic data are essential for adequately assessing these effects. Ground surveys and techniques of light detection and ranging have good accuracy, but data acquisition can be time-consuming and expensive for large catchments. Recent rapid technological development has provided new, flexible, high-resolution methods for collecting topographic data, such as photogrammetry using unmanned aerial vehicles (UAVs). The accuracy of UAV photogrammetry for generating high-resolution Digital Elevation Model (DEM) and for determining the width of irrigation channels, however, has not been assessed. A fixed-wing UAV was used for collecting high-resolution (0.15 m) topographic data for the Hetao irrigation district, the third largest irrigation district in China. 112 ground checkpoints (GCPs) were surveyed by using a real-time kinematic global positioning system to evaluate the accuracy of the DEMs and channel widths. A comparison of manually measured channel widths with the widths derived from the DEMs indicated that the DEM-derived widths had vertical and horizontal root mean square errors of 13.0 and 7.9 cm, respectively. UAV photogrammetric data can thus be used for land surveying, digital mapping, calculating channel capacity, monitoring crops, and predicting yields, with the advantages of economy, speed and ease.</p

Bayesian Regularization Algorithm Based Recurrent Neural Network Method and NSGA-II for the Optimal Design of the Reflector

The optical-mechanical system of a space camera is composed of several complex components, and the effects of several factors (weight, gravity, modal frequency, temperature, etc.) on its system performance need to be considered during ground tests, launch, and in-orbit operation. In order to meet the system specifications of the optical camera system, the dimensional parameters of the optical camera structure need to be optimized. There is a highly nonlinear functional relationship between the dimensional parameters of the optical machine structure and the design indexes. The traditional method takes a significant amount of time for finite element calculation and is less efficient. In order to improve the optimization efficiency, a recurrent neural network prediction model based on the Bayesian regularization algorithm is proposed in this paper, and the NSGA-II is used to globally optimize multiple prediction objectives of the prediction model. The reflector of the space camera is used as an example to predict the weight, first-order modal frequency, and gravitational mirror deformation root mean square of the reflector, and to complete the lightweight design. The results show that the prediction model established by BR-RNN-NSGA-II offers high prediction accuracy for the design indexes of the reflector, which all reach over 99.6%, and BR-RNN-NSGA-II can complete the multi-objective optimization search efficiently and accurately. This paper provides a new idea of optimization of optical machine structure, which enriches the theory of complex structure design

Bayesian Regularization Algorithm Based Recurrent Neural Network Method and NSGA-II for the Optimal Design of the Reflector

The optical-mechanical system of a space camera is composed of several complex components, and the effects of several factors (weight, gravity, modal frequency, temperature, etc.) on its system performance need to be considered during ground tests, launch, and in-orbit operation. In order to meet the system specifications of the optical camera system, the dimensional parameters of the optical camera structure need to be optimized. There is a highly nonlinear functional relationship between the dimensional parameters of the optical machine structure and the design indexes. The traditional method takes a significant amount of time for finite element calculation and is less efficient. In order to improve the optimization efficiency, a recurrent neural network prediction model based on the Bayesian regularization algorithm is proposed in this paper, and the NSGA-II is used to globally optimize multiple prediction objectives of the prediction model. The reflector of the space camera is used as an example to predict the weight, first-order modal frequency, and gravitational mirror deformation root mean square of the reflector, and to complete the lightweight design. The results show that the prediction model established by BR-RNN-NSGA-II offers high prediction accuracy for the design indexes of the reflector, which all reach over 99.6%, and BR-RNN-NSGA-II can complete the multi-objective optimization search efficiently and accurately. This paper provides a new idea of optimization of optical machine structure, which enriches the theory of complex structure design

Obtaining Land Cover Type for Urban Storm Flood Model in UAV Images Using MRF and MKFCM Clustering Techniques

With the accelerated urbanization process, cities are suffering from extremely heavy rain and urban storm water logging disasters in recent years. To provide reliable and effective information for urban management and emergency decision-making, the accuracy of basic data must be guaranteed in any urban rainwater model. This paper presents a novel MKFCM-MRF (Multiple Kernel Fuzzy C Means-Markov Random Field) model to segment high-resolution Unmanned Aerial Vehicle (UAV) images. The core ideas of MKFCM-MRF model are as follows. Firstly, in order to increase the correlation information between pixels, multiple-kernel functions are introduced into Fuzzy C Means (FCM) clustering algorithm, which automatically filters out the optimal weight combination among kernel functions according to the distribution characteristics of pixels in feature space. Secondly, in order to better segment the texture and edge of the image, the clustering results of multiple-kernel FCM clustering algorithm are introduced into Markov Random Field (MRF) model, a novel spatial energy function integrating fuzzy local information is constructed. Finally, based on the total of data and spatial energies, the raw clustering results are regularized by a global minimization of the energy function using its iterated conditional modes (ICM). The effectiveness of MKFCM-MRF is performed by high-resolution UAV images data. The experimental results indicate MKFCM-MRF can refine the classification map in homogeneous areas, while reducing most of the edge blurring artifact, and improving the classification accuracy compared with FCM clustering algorithm. In addition, the validation of the urban storm flood model shows that the trend of the two clustering results is the same, but the runoff producing time and the peak time of FCM clustering results are advanced, the peak flow and the total runoff are larger; the simulation results corresponding to MKFCM-MRF clustering results are more realistic

Lane Change Analysis and Prediction Using Mean Impact Value Method and Logistic Regression Model

The analysis and estimation of lane change (LC) behavior are essential for autonomous vehicles (AVs) to predict other vehicles\u27 intentions and avoid accidents. Since the LC intention is easily affected by various features, the feature selection and LC modeling greatly influence the prediction accuracy and interpretability. Therefore, a binary logistic regression LC model with a mean impact value (MIV) method to select features is proposed for accurate prediction. First, the related features are classified as individual, microscopic, and macroscopic levels. Then they are ranked and analyzed by the MIV method. Next, the closely related features are selected and used as input to the logistic regression model for LC intention prediction. As a result, a highly interpretable LC model is built with a prediction performance of around 80%. This paper benefits the quantification and explanation of the influences of different levels\u27 features on LC intention and lays a solid foundation for the AVs to predict the LC behavior

Design of Compact Mid-Infrared Cooled Echelle Spectrometer Based on Toroidal Uniform-Line-Spaced (TULS) Grating

A traditional flat-panel spectrometer does not allow high-resolution observation and miniaturization simultaneously. In this study, a compact, high-resolution cross-dispersion spectrometer was designed based on the theoretical basis of echelle grating for recording an infrared spectrum. To meet the high-resolution observation and miniaturization design requirements, a reflective immersion grating was used as the primary spectroscopic device. To compress the beam aperture of the imaging system, the order-separation device of the spectrometer adopted toroidal uniform line grating, which had both imaging and dispersion functions in the spectrometer. The aberration balance condition of the toroidal uniform line grating was analyzed based on the optical path difference function of the concave grating, and dispersion characteristics of the immersed grating and thermal design of the infrared lens were discussed based on the echelle grating. An immersion echelle spectrometer optical system consisting of a culmination system, an immersed echelle grating, and a converged system was used. The spectrometer was based on the asymmetrical Czerny-Turner and Littrow mount designs, and it was equipped with a 320 &times; 256 pixel detector array. The designed wavelength range was 3.7&ndash;4.8 &mu;m, the F-number was 4, and the central wavelength resolution was approximately 30,000. An infrared cooling detector was used. The design results showed that, in the operating band range, the root implied that the square diameter of the spectrometer spot diagram was less than 30 &mu;m, the energy was concentrated in a pixel size range, and the spectrometer system design met the requirements

Nonlinear buckling of steel cone-segmented cylinders under external pressure:numerical and experimental evaluations

The nonlinear buckling of steel cone-segmented cylinders under external pressure has been investigated in this study. The cone-segmented cylinders in focus include four nominally identical cones closed by thick flat plates and their slant angles ranged from 0°–20° in every 1° increments. Note that the cylinder with a 0° slant angle forms a circular cylinder that is used for comparisons. Two nominally identical cone-segmented and circular cylinders have been fabricated, manufactured, measured, and tested. The numerical simulations for these cylinders are in good agreement with their experimental counterparts. Both linear and nonlinear buckling behaviors of the cylinders (both with and without imperfections) have also been evaluated, and the optimal slant angle can then be identified to be 6°–8°. These optimized imperfect cylinders yield buckling loads 1.7–2.8 times greater than those of an imperfect equivalent circular cylinder. Our results reveal a new finding that the average buckling load of the cone-segmented cylinders is approximately 2.3 times that of the equivalent circular cylinders