On the Role of “Fuzzy Language” in Cultivating “Core Accomplishment”: Based on the Cross-Cultural Communication

Fuzzy language has something to do with beauty, techniques, competence and accomplishment. On the way from "core knowledge era" to "core accomplishment era", the author tries to focus on the role of fuzzy language in cultivating students’ core accomplishment by cultivating aesthetic taste, promoting international understanding, enriching humanistic accomplishment and enhancing practical ability

Three Phase Erosion Testing and Vibration Analysis of an Electrical Submersible Pump

Electrical Submersible Pump (ESP) has been recognized as an excellent artificial lifting method in industry due to its high liquid flow rate in both onshore and offshore applications. As oil exploration goes deep into water, ESP equipment is facing a crucial problem of slurry erosion which may affect life and cost significantly. The wear caused by slurry erosion may bring the issue such as unbalanced side loads, severe vibration and decreased pressure head. Eventually, this phenomenon will lead to a complete system failure. In present work, a systematic study on the erosion wear has been carried out in order to give better understanding. The WJE-1000 ESP pump manufactured by Baker Hughes has been employed in this study. During the whole procedure, 117 hours two-phase (water-sand) testing has been performed and is followed by 68 hours three-phase (watersand- air) testing. A combined analysis by combining components erosion wear measurement, pump performance testing and vibration signal process has clearly indicated the trend of erosion process on each component. Furthermore, the correlation between vibration signals collected by proximity probe and remote 3D accelerometer provided a future direction for monitoring inaccessible downhole equipment. Finally, the conclusion that air could further accelerate ESP erosion has been found by comparing the erosion rate and vibration signals in two-phase test and three-phase test

Instance Segmentation in the Dark

Existing instance segmentation techniques are primarily tailored for high-visibility inputs, but their performance significantly deteriorates in extremely low-light environments. In this work, we take a deep look at instance segmentation in the dark and introduce several techniques that substantially boost the low-light inference accuracy. The proposed method is motivated by the observation that noise in low-light images introduces high-frequency disturbances to the feature maps of neural networks, thereby significantly degrading performance. To suppress this ``feature noise", we propose a novel learning method that relies on an adaptive weighted downsampling layer, a smooth-oriented convolutional block, and disturbance suppression learning. These components effectively reduce feature noise during downsampling and convolution operations, enabling the model to learn disturbance-invariant features. Furthermore, we discover that high-bit-depth RAW images can better preserve richer scene information in low-light conditions compared to typical camera sRGB outputs, thus supporting the use of RAW-input algorithms. Our analysis indicates that high bit-depth can be critical for low-light instance segmentation. To mitigate the scarcity of annotated RAW datasets, we leverage a low-light RAW synthetic pipeline to generate realistic low-light data. In addition, to facilitate further research in this direction, we capture a real-world low-light instance segmentation dataset comprising over two thousand paired low/normal-light images with instance-level pixel-wise annotations. Remarkably, without any image preprocessing, we achieve satisfactory performance on instance segmentation in very low light (4~\% AP higher than state-of-the-art competitors), meanwhile opening new opportunities for future research.Comment: Accepted by International Journal of Computer Vision (IJCV) 202

Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication

It is anticipated that 6G wireless networks will accelerate the convergence of the physical and cyber worlds and enable a paradigm-shift in the way we deploy and exploit communication networks. Machine learning, in particular deep learning (DL), is expected to be one of the key technological enablers of 6G by offering a new paradigm for the design and optimization of networks with a high level of intelligence. In this article, we introduce an emerging DL architecture, known as the transformer, and discuss its potential impact on 6G network design. We first discuss the differences between the transformer and classical DL architectures, and emphasize the transformer's self-attention mechanism and strong representation capabilities, which make it particularly appealing for tackling various challenges in wireless network design. Specifically, we propose transformer-based solutions for various massive multiple-input multiple-output (MIMO) and semantic communication problems, and show their superiority compared to other architectures. Finally, we discuss key challenges and open issues in transformer-based solutions, and identify future research directions for their deployment in intelligent 6G networks.Comment: 9 pages, 6 figures. The current version has been accepted by IEEE Wireless Communications Magzin

The impact of different sentiment in investment decisions: evidence from China’s stock markets IPOs

In this study, we used data on China’s initial public offerings (IPOs), market volatility and macro environment before and after two stock crashes during 2006–2016 to investigate how different investor sentiment affects IPO first-day flipping. The empirical results show that the expected returns of allocated investors are affected by sentiment, with allocated investors having higher psychological expectations of future returns during an optimistic bull market and their optimism discouraging first-day flipping, while higher risk-free interest rate levels and rising broad market indices also discourage first-day flipping and tend to sell in the future. The pessimistic bear market during which allocated investors have lower psychological expectations of future returns, their pessimism will promote first-day flipping, and the increase in the risk-free rate level will also promote first-day flipping, which is the opposite of the optimistic bull market, indicating that their risk aversion has increased and they tend to sell on the same day. We also found an anomaly that the greater the decline in the broad market index during a pessimistic bear market, the more inclined the allocated investors are to sell in the future when the broad market index rises in an attempt to gain higher returns. These findings help explain and understand the impact of market and macro index fluctuations on investor behavior under different investor sentiments

IJTC2010-41127 SURFACE DAMAGE UNDER EXTREME CONDITIONS EXISTED IN AIRCRAFT BEARINGS

ABSTRACT The extreme conditions of aircraft bearing steel M50 have been simulated by a two-disk test rig for investigating the surface damage of the ball/raceway contact surfaces. The slide/roll ratio are 0.12 and 0.15, correspondingly, the rolling speed are 43.2m/s and 49.5m/s. Aircraft engine oil 4050 as the supplied oil has been maintained at approximately 80℃ in the tests. The ultimate Hertzian contact stresses of the surface damage obtained from the experiments are 3.8GPa in 0.12 slide/roll ratio and 3.5GPa in 0.15 slide/roll ratio. The damage mode is scuffing in 0.12 slide/roll ratio and it is oxidation, thermal fatigue and scuffing in 0.15 slide/roll ratio. Cracks in the contact areas originate from surface layer in the two slide/roll ratios. INTRODUCTION The bearings used in aircraft engines operate in extreme conditions such as high speed, heavy load and high temperature. The combination of speed, load, and temperature which exist in aircraft bearings will exceed the capability of conventional synthetic lubricants and materials. The contact surfaces of the parts always occur fatigue, scuffing and many other damage modes, these damages would result in severe wear and even catastrophe M50 steel is a main material used in aircraft bearings, its tribological behavior in extreme conditions is related to the reliability and life of bearings. Rolling contact fatigue experiments of M50 steel have shown that the orientation of surface micro-crack is related to the friction direction and asperity-scale micro-cracks as well as micro-spalls may evolve into macroscopic spalling under heavy load and rolling/sliding speed conditions The study simulated the extreme conditions of aircraft bearings, the ultimate parameters and the damage modes o

Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification

Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern (CSP) as preprocessing step before classification. The CSP method is a supervised algorithm. Therefore a lot of time-consuming training data is needed to build the model. To address this issue, one promising approach is transfer learning, which generalizes a learning model can extract discriminative information from other subjects for target classification task. To this end, we propose a transfer kernel CSP (TKCSP) approach to learn a domain-invariant kernel by directly matching distributions of source subjects and target subjects. The dataset IVa of BCI Competition III is used to demonstrate the validity by our proposed methods. In the experiment, we compare the classification performance of the TKCSP against CSP, CSP for subject-to-subject transfer (CSP SJ-to-SJ), regularizing CSP (RCSP), stationary subspace CSP (ssCSP), multitask CSP (mtCSP), and the combined mtCSP and ssCSP (ss + mtCSP) method. The results indicate that the superior mean classification performance of TKCSP can achieve 81.14%, especially in case of source subjects with fewer number of training samples. Comprehensive experimental evidence on the dataset verifies the effectiveness and efficiency of the proposed TKCSP approach over several state-of-the-art methods

Quasi-Synchronous Random Access for Massive MIMO-Based LEO Satellite Constellations

Low earth orbit (LEO) satellite constellation-enabled communication networks are expected to be an important part of many Internet of Things (IoT) deployments due to their unique advantage of providing seamless global coverage. In this paper, we investigate the random access problem in massive multiple-input multiple-output-based LEO satellite systems, where the multi-satellite cooperative processing mechanism is considered. Specifically, at edge satellite nodes, we conceive a training sequence padded multi-carrier system to overcome the issue of imperfect synchronization, where the training sequence is utilized to detect the devices' activity and estimate their channels. Considering the inherent sparsity of terrestrial-satellite links and the sporadic traffic feature of IoT terminals, we utilize the orthogonal approximate message passing-multiple measurement vector algorithm to estimate the delay coefficients and user terminal activity. To further utilize the structure of the receive array, a two-dimensional estimation of signal parameters via rotational invariance technique is performed for enhancing channel estimation. Finally, at the central server node, we propose a majority voting scheme to enhance activity detection by aggregating backhaul information from multiple satellites. Moreover, multi-satellite cooperative linear data detection and multi-satellite cooperative Bayesian dequantization data detection are proposed to cope with perfect and quantized backhaul, respectively. Simulation results verify the effectiveness of our proposed schemes in terms of channel estimation, activity detection, and data detection for quasi-synchronous random access in satellite systems.Comment: 38 pages, 16 figures. This paper has been accepted by IEEE JSAC SI on 3GPP Technologies: 5G-Advanced and Beyond. Copyright may be transferred without notice, after which this version may no longer be accessibl

A Multi-objective Optimization Algorithm for Multiple Home Users Intelligent Power Management and Control Based on Pareto and Nash Equilibrium Game

A multi-objective optimization model for multiple home users intelligent power management and control is proposed. A photovoltaic power model, an electric vehicle battery model and a load model are developed first, and then a strategy of home intelligent power management is presented based on battery operation and PV spontaneous self-use. Secondly, a multi-objective optimization model of multiple home users intelligent power management, including the user comfort, economy and optimization of load curve, is provided under the constraints. Then using a multi-objective optimization algorithm and Nash equilibrium game theory to solve the multi-objective problem. Finally, the 100-home power management and control simulation case show that the presented algorithm can improve the comfort and the economy of users effectively, but also help the power grid to peak load shifting