Unloading deterioration and mechanism of rock under different loading and unloading stress paths

In order to study the failure mechanism of deep coal seam floor under unloading, pseudo-triaxial mechanical tests were carried out under different loading and unloading stress paths. The relationship between rock elastic modulus, generalized Poisson ratio and confining pressure under different stress paths was analyzed and fitted. The rock fracture mechanics model was constructed, and the stress concentration at the end of the branch crack under different loading and unloading paths was analyzed. The deterioration mechanism of rock mechanical parameters during unloading was analyzed from the aspects of deviatoric stress, energy and acoustic emission events. ① The research showed that the degree of deterioration of the elastic modulus of the sample and the change range of Poisson ratio under different axial compression loading methods were in the order of axial compression> axial compression remain unchanged> unloading axial pressure. Under the same axial pressure and different confining pressure unloading rates, the elastic modulus deterioration degree and Poisson ratio change of the sample were in a positive correlation with the unloading rate. ② Compared with the increase of the deviator stress at the unloading point, it changed in a positive correlation with the deterioration degree of the elastic modulus of the rock sample and the change of Poisson's ratio. During the unloading process of the confining pressure, the energy accumulated in the path that the axial load or remain unchanged was larger than the stress path of axial unloading. When the confining pressure was unloaded to a certain extent, the internal closed micro-cracks and primary fissures were reopened, and the energy used for crack propagation increased rapidly, and the ratio of change to elastic modulus and the Poisson’s ratio increased significantly, and the number of acoustic emission event ringing showed a characteristic of non-linear growth. ③ Under different loading and unloading paths, the deviatoric stress was the fundamental cause of rock instability and failure. The faster the deviatoric stress increased, the more obvious the degradation degree of rock elastic modulus and Poisson ratio changed. Under the action of loading and unloading, the deterioration degree of rock mass was the most serious under the influence of advanced abutment pressure, which was consistent with the fact that the water inrush accidents in the floor of working face mostly occur near the working face

Two-Stream Retentive Long Short-Term Memory Network for Dense Action Anticipation

Analyzing and understanding human actions in long-range videos has promising applications, such as video surveillance, automatic driving, and efficient human-computer interaction. Most researches focus on short-range videos that predict a single action in an ongoing video or forecast an action several seconds earlier before it occurs. In this work, a novel method is proposed to forecast a series of actions and their durations after observing a partial video. This method extracts features from both frame sequences and label sequences. A retentive memory module is introduced to richly extract features at salient time steps and pivotal channels. Extensive experiments are conducted on the Breakfast data set and 50 Salads data set. Compared to the state-of-the-art methods, the method achieves comparable performance in most cases

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI

Influenced by the great success of deep learning via cloud computing and the rapid development of edge chips, research in artificial intelligence (AI) has shifted to both of the computing paradigms, i.e., cloud computing and edge computing. In recent years, we have witnessed significant progress in developing more advanced AI models on cloud servers that surpass traditional deep learning models owing to model innovations (e.g., Transformers, Pretrained families), explosion of training data and soaring computing capabilities. However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed. In this survey, we conduct a systematic review for both cloud and edge AI. Specifically, we are the first to set up the collaborative learning mechanism for cloud and edge modeling with a thorough review of the architectures that enable such mechanism. We also discuss potentials and practical experiences of some on-going advanced edge AI topics including pretraining models, graph neural networks and reinforcement learning. Finally, we discuss the promising directions and challenges in this field.Comment: 20 pages, Transactions on Knowledge and Data Engineerin

A two-level hierarchical discrete-device control method for power networks with integrated wind farms

Abstract Power systems depend on discrete devices, such as shunt capacitors/reactors and on-load tap changers, for their long-term reliability. In transmission systems that contain large wind farms, we must take into account the uncertainties in wind power generation when deciding when to operate these devices. In this paper, we describe a method to schedule the operation of these devices over the course of the following day. These schedules are designed to minimize wind-power generation curtailment, bus voltage violations, and dynamic reactive-power deviations, even under the worst possible conditions. Daily voltage-control decisions are initiated every 15 min using a dynamic optimization algorithm that predicts the state of the system over the next 4-hour period. For this, forecasts updated in real-time are employed, because they are more precise than forecasts for the day ahead. Day-ahead schedules are calculated using a two-stage robust mixed-integer optimization algorithm. The proposed control strategies were tested on a Chinese power network with wind power sources; the control performance was also validated numerically

Fair Federated Learning for Digital Healthcare

Datasets, models, and codes for paper Unified Fair Federated Learning for Digital Healthcare

Mechanical Analysis of the Failure Characteristics of Stope Floor Induced by Mining and Confined Aquifer

Mining above confined aquifer has become an important task for water inrush prevention in China. To study the failure characteristics of stope floor along the strike, a mechanical model under combined action of mining and confined aquifer was constructed, and the distribution of vertical stress, horizontal stress, and shear stress was obtained. Based on the Mohr–Coulomb criterion, the failure range of the floor is determined and verified by the in situ test. The results indicate the following. (1) Both vertical stress and horizontal stress in the stope floor take the junction of stress increasing area and stress decreasing area as the dividing line, forming two groups of “convex arches” at the solid coal side and the goaf side, respectively. (2) The vertical stress gradient in the solid coal side is significantly higher than that in the goaf side, while the horizontal stress gradient in the solid coal side is similar to that in the goaf side. The shear stress distribution is divided into three regions by the boundary between positive and negative shear stress, which makes the stope floor in this area to show compression shear or tension shear failure. (3) According to the in situ test, the maximum floor failure depth of 41503 working face is 11.38 m, which is quite close to the theoretical calculation result of 9.68 m. (4) Applying the mechanical model to five other coal mines with different mining conditions and stress states, the maximum absolute error between the measured and theoretical values of floor failure depth is 1.1 m, the average absolute error is 0.8 m, the maximum relative error is 8.2%, and the average relative error is 6.5%. The study provides a certain mechanical basis and reference for the floor failure mechanism induced by mining and confined aquifer