Numerical Simulation of Fragment Separation during Rock Cutting Using a 3D Dynamic Finite Element Analysis Code

To predict fragment separation during rock cutting, previous studies on rock cutting interactions using simulation approaches, experimental tests, and theoretical methods were considered in detail. This study used the numerical code LS-DYNA (3D) to numerically simulate fragment separation. In the simulations, a damage material model and erosion criteria were used for the base rock, and the conical pick was designated a rigid material. The conical pick moved at varying linear speeds to cut the fixed base rock. For a given linear speed of the conical pick, numerical studies were performed for various cutting depths and mechanical properties of rock. The numerical simulation results demonstrated that the cutting forces and sizes of the separated fragments increased significantly with increasing cutting depth, compressive strength, and elastic modulus of the base rock. A strong linear relationship was observed between the mean peak cutting forces obtained from the numerical, theoretical, and experimental studies with correlation coefficients of 0.698, 0.8111, 0.868, and 0.768. The simulation results also showed an exponential relationship between the specific energy and cutting depth and a linear relationship between the specific energy and compressive strength. Overall, LS-DYNA (3D) is effective and reliable for predicting the cutting performance of a conical pick

Conditional Local Convolution for Spatio-temporal Meteorological Forecasting

Spatio-temporal forecasting is challenging attributing to the high nonlinearity in temporal dynamics as well as complex location-characterized patterns in spatial domains, especially in fields like weather forecasting. Graph convolutions are usually used for modeling the spatial dependency in meteorology to handle the irregular distribution of sensors' spatial location. In this work, a novel graph-based convolution for imitating the meteorological flows is proposed to capture the local spatial patterns. Based on the assumption of smoothness of location-characterized patterns, we propose conditional local convolution whose shared kernel on nodes' local space is approximated by feedforward networks, with local representations of coordinate obtained by horizon maps into cylindrical-tangent space as its input. The established united standard of local coordinate system preserves the orientation on geography. We further propose the distance and orientation scaling terms to reduce the impacts of irregular spatial distribution. The convolution is embedded in a Recurrent Neural Network architecture to model the temporal dynamics, leading to the Conditional Local Convolution Recurrent Network (CLCRN). Our model is evaluated on real-world weather benchmark datasets, achieving state-of-the-art performance with obvious improvements. We conduct further analysis on local pattern visualization, model's framework choice, advantages of horizon maps and etc.Comment: 14 page

Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification

Recent years have witnessed great success in handling graph-related tasks with Graph Neural Networks (GNNs). Despite their great academic success, Multi-Layer Perceptrons (MLPs) remain the primary workhorse for practical industrial applications. One reason for this academic-industrial gap is the neighborhood-fetching latency incurred by data dependency in GNNs, which make it hard to deploy for latency-sensitive applications that require fast inference. Conversely, without involving any feature aggregation, MLPs have no data dependency and infer much faster than GNNs, but their performance is less competitive. Motivated by these complementary strengths and weaknesses, we propose a Graph Self-Distillation on Neighborhood (GSDN) framework to reduce the gap between GNNs and MLPs. Specifically, the GSDN framework is based purely on MLPs, where structural information is only implicitly used as prior to guide knowledge self-distillation between the neighborhood and the target, substituting the explicit neighborhood information propagation as in GNNs. As a result, GSDN enjoys the benefits of graph topology-awareness in training but has no data dependency in inference. Extensive experiments have shown that the performance of vanilla MLPs can be greatly improved with self-distillation, e.g., GSDN improves over stand-alone MLPs by 15.54\% on average and outperforms the state-of-the-art GNNs on six datasets. Regarding inference speed, GSDN infers 75X-89X faster than existing GNNs and 16X-25X faster than other inference acceleration methods

Multiple-symmetry-protected lantern-like nodal walls in lithium-rich compound LiRuO2

Topological semimetals have attracted wide attention due to their potential applications, such as electronic devices and electrocatalysis. Herein, based on the first-principles calculations and symmetry analysis, we first report that ternary compound pnnm-type LiRuO2 is a typical lantern-like nodal wall semimetal. Specifically, without considering spin-orbit coupling (SOC), one-dimensional (1D) two-fold degenerate bands on the ki = ±π (i = x, y) planes form the two-dimensional (2D) topological state (namely, nodal surface) under the constraint of multiple symmetry operations. In addition, the symmetry-enforced nodal network is formed on the kz = ±π planes. Finally, these nodal networks and nodal surfaces are coupled together to form lantern-like nodal walls. Remarkably, these topological states are protected by multiple symmetries, namely, nonsymmorphic two-fold screw-rotational symmetry [S2i (i = x, y)], time-reversal symmetry (T), inversion symmetry (I), glide plane symmetry (σz), and two-fold rotational symmetry (C2x/y). In addition, we further discuss the effect of spin-orbit coupling on the lantern-like nodal walls. We find that even if LiRuO2 contains S2z and T symmetries, these nodal surfaces and nodal networks are still broken. Then, due to the existence of I and T symmetries, Dirac nodal lines and Dirac points are formed in the low-energy region. Therefore, our work indicates that LiRuO2 is an excellent material platform for researching multiple topological states

Revisiting the Temporal Modeling in Spatio-Temporal Predictive Learning under A Unified View

Spatio-temporal predictive learning plays a crucial role in self-supervised learning, with wide-ranging applications across a diverse range of fields. Previous approaches for temporal modeling fall into two categories: recurrent-based and recurrent-free methods. The former, while meticulously processing frames one by one, neglect short-term spatio-temporal information redundancies, leading to inefficiencies. The latter naively stack frames sequentially, overlooking the inherent temporal dependencies. In this paper, we re-examine the two dominant temporal modeling approaches within the realm of spatio-temporal predictive learning, offering a unified perspective. Building upon this analysis, we introduce USTEP (Unified Spatio-TEmporal Predictive learning), an innovative framework that reconciles the recurrent-based and recurrent-free methods by integrating both micro-temporal and macro-temporal scales. Extensive experiments on a wide range of spatio-temporal predictive learning demonstrate that USTEP achieves significant improvements over existing temporal modeling approaches, thereby establishing it as a robust solution for a wide range of spatio-temporal applications.Comment: Under revie

Dynamic characteristics analysis on shearer drum in condition of cutting coal with different distributed rocks

Dynamic characteristics analysis on shearer drum is an important aspect in shearer design and a well-structured shearer drum provides better performance in vibration reduction and service life extension. To make reference for structure improvement, many researchers have focused on the dynamic analysis on cutting pick. However, the simplified model such as single pick, linear motion and continuous coal in the previous study made it divorced from actual situations underground. On basis of this, the model of shearer drum with several arranged picks and the coal model with some distributed rock have been set up in order to consist with the real working conditions. Through simulation, it can be found that the mean force in X-direction is little influenced by rock distribution, but the peak force in X-direction fluctuates greatly under different rock distribution. During the analysis on the peak force, it can be found that this kind of force could be cut down effectively when the rock was distributed on the top of the shearer cutting range. Based on these conclusions, we can program the cutting strategy to lower the influence of cutting forces on the shearer and its drum in the future

Target-Driven Structured Transformer Planner for Vision-Language Navigation

Vision-language navigation is the task of directing an embodied agent to navigate in 3D scenes with natural language instructions. For the agent, inferring the long-term navigation target from visual-linguistic clues is crucial for reliable path planning, which, however, has rarely been studied before in literature. In this article, we propose a Target-Driven Structured Transformer Planner (TD-STP) for long-horizon goal-guided and room layout-aware navigation. Specifically, we devise an Imaginary Scene Tokenization mechanism for explicit estimation of the long-term target (even located in unexplored environments). In addition, we design a Structured Transformer Planner which elegantly incorporates the explored room layout into a neural attention architecture for structured and global planning. Experimental results demonstrate that our TD-STP substantially improves previous best methods' success rate by 2% and 5% on the test set of R2R and REVERIE benchmarks, respectively. Our code is available at https://github.com/YushengZhao/TD-STP