Investigation on Improvements of Students’ Comprehensive Capability by Team Research Study

Team research study subvert the traditional lecture-style teaching model as it can help students to develop the ability of analyzing and solving problems, communication skills, sense of teamwork and creativity. So the students’ comprehensive capability has been greatly improved. This paper divided the team research study into three periods: preparation, research and results demonstration. It also elaborated the role of teachers in the three periods. On this basis, the paper analyzed two issues in depth when doing team research study, and point out that the quality of teachers should be improved as well as the problem of class-hour occupation should be well solved in order to ensure to carry out the team research study effectively

EFFECTS OF MONITORING SIGNAL HYSTERESIS ON SPEED REGULATION FOR THE AERO-DERIVATIVE GAS TURBINE

Sensor aging and sensor failure are the common phenomena due to the high temperature and pressure environment for gas turbines, which can lead to hysteresis of monitoring signals. In this paper, a kind of aero-derivative gas turbine is taken as the research object. The hysteresis effects of single monitoring signal and coupling of multiple monitoring signals on speed control are mainly studied, and the analysis is carried out from the perspective of adjustment time, overshoot, fuel quantity and fuel quantity regulation output. The analysis results show that the pressure signal hysteresis will lead to speed suspension. The speed signal hysteresis will change the speed regulation into a multi-step mode. When the monitoring signal hysteresis is coupled, the effect of pressure signal hysteresis is greater than that of speed signal hysteresis. The results of this paper can provide a reference for the optimal design of speed control of aero-derivative gas turbine

Numerical study of response behaviors of natural gas hydrate reservoir around wellbore induced by water jet slotting

The trial production of natural gas hydrate reservoirs remains poor. Reasonable reservoir reconstruction, which can improve formation permeability, is an important approach to increasing the efficiency and enhancing production. In this work, water jet slotting is proposed to reconstruct an natural gas hydrate reservoir near a wellbore. The spatial slots formed by water jet slotting not only directly constitute high-permeability channels, but also generate disturbances to the surrounding in-situ sediment. Water jet slotting disturbances to nearby sediment was investigated using a three dimensional flow-structure coupling model to evaluate the proposed reconstruction method. The reservoir at the SH2 site in the Shenhu area of the South China Sea was used as the reference. A horizontal slotting arrangement along the vertical well was adopted. The results demonstrate that water jet slotting can change the primary stress state of the sediment around the wellbore, and generate a dominant stress relaxation zone and small stress concentration zone. Within the stress relaxation zone, the in-situ compressive stress was remarkably reduced or even transformed into tensile stress, accompanied by sediment displacement and volumetric expansion strain. This is conducive to loosening the sediment around the wellbore and improving the permeability characteristics. In addition, the influence of the water jet slotting parameters including slot radius, spacing, and number on disturbances to the nearby sediment was studied. Reservoir responses to water jet slotting under balanced and unbalanced bottom-hole pressures were compared and analyzed. This study provides a reference for natural gas hydrate reservoir reconstruction using water jet slotting. Cited as: Huang, M., Su, D., Zhao, Z., Wu, L., Fang B., Ning, F.  Numerical study of response behaviors of natural gas hydrate reservoir around wellbore induced by water jet slotting. Advances in Geo-Energy Research, 2023, 7(2): 75-89. https://doi.org/10.46690/ager.2023.02.0

E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition

Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER systems in open environments. Evidential deep learning (EDL) has recently been proposed as a promising solution to explicitly model predictive uncertainty for classification tasks. However, directly applying EDL to NER applications faces two challenges, i.e., the problems of sparse entities and OOV/OOD entities in NER tasks. To address these challenges, we propose a trustworthy NER framework named E-NER by introducing two uncertainty-guided loss terms to the conventional EDL, along with a series of uncertainty-guided training strategies. Experiments show that E-NER can be applied to multiple NER paradigms to obtain accurate uncertainty estimation. Furthermore, compared to state-of-the-art baselines, the proposed method achieves a better OOV/OOD detection performance and better generalization ability on OOV entities.Comment: accepted by ACL Findings (2023

UVCPNet: A UAV-Vehicle Collaborative Perception Network for 3D Object Detection

With the advancement of collaborative perception, the role of aerial-ground collaborative perception, a crucial component, is becoming increasingly important. The demand for collaborative perception across different perspectives to construct more comprehensive perceptual information is growing. However, challenges arise due to the disparities in the field of view between cross-domain agents and their varying sensitivity to information in images. Additionally, when we transform image features into Bird's Eye View (BEV) features for collaboration, we need accurate depth information. To address these issues, we propose a framework specifically designed for aerial-ground collaboration. First, to mitigate the lack of datasets for aerial-ground collaboration, we develop a virtual dataset named V2U-COO for our research. Second, we design a Cross-Domain Cross-Adaptation (CDCA) module to align the target information obtained from different domains, thereby achieving more accurate perception results. Finally, we introduce a Collaborative Depth Optimization (CDO) module to obtain more precise depth estimation results, leading to more accurate perception outcomes. We conduct extensive experiments on both our virtual dataset and a public dataset to validate the effectiveness of our framework. Our experiments on the V2U-COO dataset and the DAIR-V2X dataset demonstrate that our method improves detection accuracy by 6.1% and 2.7%, respectively

Elevation Estimation-Driven Building 3D Reconstruction from Single-View Remote Sensing Imagery

Building 3D reconstruction from remote sensing images has a wide range of applications in smart cities, photogrammetry and other fields. Methods for automatic 3D urban building modeling typically employ multi-view images as input to algorithms to recover point clouds and 3D models of buildings. However, such models rely heavily on multi-view images of buildings, which are time-intensive and limit the applicability and practicality of the models. To solve these issues, we focus on designing an efficient DSM estimation-driven reconstruction framework (Building3D), which aims to reconstruct 3D building models from the input single-view remote sensing image. First, we propose a Semantic Flow Field-guided DSM Estimation (SFFDE) network, which utilizes the proposed concept of elevation semantic flow to achieve the registration of local and global features. Specifically, in order to make the network semantics globally aware, we propose an Elevation Semantic Globalization (ESG) module to realize the semantic globalization of instances. Further, in order to alleviate the semantic span of global features and original local features, we propose a Local-to-Global Elevation Semantic Registration (L2G-ESR) module based on elevation semantic flow. Our Building3D is rooted in the SFFDE network for building elevation prediction, synchronized with a building extraction network for building masks, and then sequentially performs point cloud reconstruction, surface reconstruction (or CityGML model reconstruction). On this basis, our Building3D can optionally generate CityGML models or surface mesh models of the buildings. Extensive experiments on ISPRS Vaihingen and DFC2019 datasets on the DSM estimation task show that our SFFDE significantly improves upon state-of-the-arts. Furthermore, our Building3D achieves impressive results in the 3D point cloud and 3D model reconstruction process

Fairness-guided Few-shot Prompting for Large Language Models

Large language models have demonstrated surprising ability to perform in-context learning, i.e., these models can be directly applied to solve numerous downstream tasks by conditioning on a prompt constructed by a few input-output examples. However, prior research has shown that in-context learning can suffer from high instability due to variations in training examples, example order, and prompt formats. Therefore, the construction of an appropriate prompt is essential for improving the performance of in-context learning. In this paper, we revisit this problem from the view of predictive bias. Specifically, we introduce a metric to evaluate the predictive bias of a fixed prompt against labels or a given attributes. Then we empirically show that prompts with higher bias always lead to unsatisfactory predictive quality. Based on this observation, we propose a novel search strategy based on the greedy search to identify the near-optimal prompt for improving the performance of in-context learning. We perform comprehensive experiments with state-of-the-art mainstream models such as GPT-3 on various downstream tasks. Our results indicate that our method can enhance the model's in-context learning performance in an effective and interpretable manner