POLYVINYL ALCOHOL (PVA) FIBER-REINFORCED RUBBER CONCRETE AND RUBBERIZED SELF-COMPACTING CONCRETE

This study experimentally investigates the mechanical performance and durability of Polyvinyl Alcohol (PVA) fiber-reinforced rubber concrete and the rubberized self-compacting concrete. The waste rubber particles were introduced as a partial replacement of fine aggregate in the plain concrete. In addition, the waste tire rubbers were pre-treated with alkali surface treatment method to enhance the performance. The PVA fibers were added to the concrete mixes to enhance the post-failure resistance and thus fracture energy. Rubberized fiber concrete samples were prepared with different fine aggregate replacement ratios and the optimum fiber content. At the same time, the rubber particles had been used to partially replace the fine aggregate in normal self-compacting concrete (SCC). The rubberized self-compacting concrete (RSCC) had also been prepared with different rubber contents. The effects of NaOH treatment method had been evaluated in the self-compacting concrete. For these samples, the mechanical performance including compressive strength, indirect tensile strength, and flexural behavior was measured to compare with control samples. The transport property was also detected by electrical resistivity test. The durability performance such as alkali-silica reaction (ASR) expansion and drying shrinkage were evaluated and compared with control samples. The test results of the PVA-fiber reinforced rubber concrete showed that it could achieve a high fracture energy and maintain xvi a high mechanical performance after addition of recycled rubber and PVA-fiber, furthermore, the modified specimens showed a better performance in durability than control samples. At the same time, the results from rubberized self-compacting concrete (RSCC) also indicated that after using of NaOH surface treated rubbers can successfully achieve high-strength requirement and improve durability performance. Overall, the polyvinyl alcohol (PVA) fiber could be considered to improve the mechanical performance and durability in normal rubberized concrete. In addition, the NaOH surface treatment method for rubber particles could improve the performance of rubberized self-compacting concrete (RSCC), thus achieve a high-strength and good durability with the recycled tire aggregate

Detrended Fluctuation Analysis and Hough Transform Based Self-Adaptation Double-Scale Feature Extraction of Gear Vibration Signals

This paper presents the analysis of the vibration time series of a gear system acquired by piezoelectric acceleration transducer using the detrended fluctuation analysis (DFA). The experimental results show that gear vibration signals behave as double-scale characteristics, which means that the signals exhibit the self-similarity characteristics in two different time scales. For further understanding, the simulation analysis is performed to investigate the reasons for double-scale of gear’s fault vibration signal. According to the analysis results, a DFA double logarithmic plot based feature vector combined with scale exponent and intercept of the small time scale is utilized to achieve a better performance of fault identification. Furthermore, to detect the crossover point of two time scales automatically, a new approach based on the Hough transform is proposed and validated by a group of experimental tests. The results indicate that, comparing with the traditional DFA, the faulty gear conditions can be identified better by analyzing the double-scale characteristics of DFA. In addition, the influence of trend order of DFA on recognition rate of fault gears is discussed

Magnetic Crosstalk Suppression and Probe Miniaturization of Coupled Core Fluxgate Sensors

This paper demonstrates the probe structure optimization of coupled core fluxgate magnetic sensors through finite element analysis. The obtained modelling results have been used to optimize the probe structures from horizontal- to vertical- arrangements for magnetic crosstalk suppression and probe miniaturization. The finite element analysis show that with the same distance between each adjacent fluxgate elements, the magnetic crosstalk is suppressed by 6 times and the volume is reduced by 2 times after the optimization. Furthermore, the miniaturized probes with low magnetic crosstalk have been designed and implemented. The experimental results which showed more than 5 times suppression of magnetic crosstalk verified the simulation results. Therefore, the results provide detailed reference to cope with the contradiction between volume miniaturization and magnetic crosstalk suppression in magnetic sensor-array design

MAPS-KB: A Million-scale Probabilistic Simile Knowledge Base

The ability to understand and generate similes is an imperative step to realize human-level AI. However, there is still a considerable gap between machine intelligence and human cognition in similes, since deep models based on statistical distribution tend to favour high-frequency similes. Hence, a large-scale symbolic knowledge base of similes is required, as it contributes to the modeling of diverse yet unpopular similes while facilitating additional evaluation and reasoning. To bridge the gap, we propose a novel framework for large-scale simile knowledge base construction, as well as two probabilistic metrics which enable an improved understanding of simile phenomena in natural language. Overall, we construct MAPS-KB, a million-scale probabilistic simile knowledge base, covering 4.3 million triplets over 0.4 million terms from 70 GB corpora. We conduct sufficient experiments to justify the effectiveness and necessity of the methods of our framework. We also apply MAPS-KB on three downstream tasks to achieve state-of-the-art performance, further demonstrating the value of MAPS-KB.Comment: Accepted to AAAI 202

Language Models as Knowledge Embeddings

Knowledge embeddings (KE) represent a knowledge graph (KG) by embedding entities and relations into continuous vector spaces. Existing methods are mainly structure-based or description-based. Structure-based methods learn representations that preserve the inherent structure of KGs. They cannot well represent abundant long-tail entities in real-world KGs with limited structural information. Description-based methods leverage textual information and language models. Prior approaches in this direction barely outperform structure-based ones, and suffer from problems like expensive negative sampling and restrictive description demand. In this paper, we propose LMKE, which adopts Language Models to derive Knowledge Embeddings, aiming at both enriching representations of long-tail entities and solving problems of prior description-based methods. We formulate description-based KE learning with a contrastive learning framework to improve efficiency in training and evaluation. Experimental results show that LMKE achieves state-of-the-art performance on KE benchmarks of link prediction and triple classification, especially for long-tail entities.Comment: This revision corrects some texts after fixing a data leakage issu

ConcEPT: Concept-Enhanced Pre-Training for Language Models

Pre-trained language models (PLMs) have been prevailing in state-of-the-art methods for natural language processing, and knowledge-enhanced PLMs are further proposed to promote model performance in knowledge-intensive tasks. However, conceptual knowledge, one essential kind of knowledge for human cognition, still remains understudied in this line of research. This limits PLMs' performance in scenarios requiring human-like cognition, such as understanding long-tail entities with concepts. In this paper, we propose ConcEPT, which stands for Concept-Enhanced Pre-Training for language models, to infuse conceptual knowledge into PLMs. ConcEPT exploits external taxonomies with entity concept prediction, a novel pre-training objective to predict the concepts of entities mentioned in the pre-training contexts. Unlike previous concept-enhanced methods, ConcEPT can be readily adapted to various downstream applications without entity linking or concept mapping. Results of extensive experiments show the effectiveness of ConcEPT in four tasks such as entity typing, which validates that our model gains improved conceptual knowledge with concept-enhanced pre-training.Comment: 12pages. Work completed in 2023.0

Performance degradation effect countermeasures in residence times difference (RTD) fluxgate magnetic sensors

This paper aims to explore the detection defect of residence times difference (RTD) fluxgate working in low-power mode and present the countermeasures for sensor resolution improvement and linearity enhancement. The main defects are amplitude and symmetry changes induced in the output spikes of fluxgate probe due to the magnetic field. These defects lead to thresholds deviation and asymmetry, then cause severe performance degradation especially on detection resolution and linearity according to the RTD theory. To overcome such effects, the optimized RTD method based on voltage extraction and feedback technology is proposed to implement magnetic field compensation and achieve a zero-field running regime of the RTD fluxgate. In this regard, the sensor linearity is improved by a factor of 38, and the resolution degradation effect is suppressed more than 6 times, verified by the laboratory experiments. The optimized detection method proposed in this paper demonstrated a great potential to achieve lower power consumption and will make the RTD fluxgate more promising technology among bio-magnetic applications