RESEARCH ON UNBALANCED WEIGHING EXPERIMENT OF MULTI-POINT BRACED SWIVEL CABLE-STAYED BRIDGE

To guarantee the safety of the swivel process, the weighing experiment before the swivel is especially important. Based on this, this paper takes a twin-tower, double-cable prestressed concrete swivel cable-stayed bridge as the background and suggests a multi-point braced swivel weighing experiment involving the joint force of the arm-brace and the spherical hinge to solve problems such as a particular obstacle in the relying project's swivelling process. Firstly, the relevant weighing experiment formulas for various circumstances were theoretically derived. The field test results were then used to calculate the jacking force at the limit state during the jacking process, which was then substituted into the relevant formulae, and the relevant parameters of the weighing experiment were calculated. Finally, the counterweight is adjusted based on the weighing results to carry out the structural rotation. The angular velocity was stable during the swivelling process, and the structure was successfully swivelled. The successful practice of a multi-point braced swivel weighing experiment involving the joint force of the arm-brace, and the spherical hinge can provide a reference for the design and construction of similar bridges

STUDY ON MECHANICAL BEHAVIOR OF CABLE - STAYED BRIDGE SUPPORT SYSTEM IN MULTI - FULCRUM UNBALANCED ROTATION

With the maturity and wide application of the bridge rotation construction technology, the single-fulcrum spherical hinge balance rotation can not meet the need of crossing over the high-speed railway catenary and other obstacles, so the unbalanced rotation construction is often needed. In order to ensure the stability and safety of the unbalanced rotation process, a multi-pivot rotation method is proposed. In this paper, the railway cable-stayed bridge over Harbin West Avenue is taken as the research object, and the multi-fulcrum rotating construction method over the metal contact network is adopted. The Abaqus finite element model is established, the influence of different rotation angular velocity, friction coefficient of slideway and position of support foot on the force of support system in the course of rotation is studied. The results show that, compared with the traditional single-pivot rotation, the force on the multi-pivot rotation support foot becomes the main force component, and the force on the spherical hinge decreases. The rotation angular velocity is positively correlated with Mises stress of the support foot and the spherical hinge. The friction coefficient of the slideway has a great influence on the force of the support foot. When the friction coefficient of the slideway changes in order of 0.02,0.04,0.06,0.08 and 0.1, the friction stress of the outer edge of the support foot increases linearly. Considering the force of spherical hinge and support foot, the best position of supporting foot is 7.3 m from the center of spherical hinge. The research in this paper can be used for reference in the future multi-pivot unbalanced rotation construction

Controlling Class Layout for Deep Ordinal Classification via Constrained Proxies Learning

For deep ordinal classification, learning a well-structured feature space specific to ordinal classification is helpful to properly capture the ordinal nature among classes. Intuitively, when Euclidean distance metric is used, an ideal ordinal layout in feature space would be that the sample clusters are arranged in class order along a straight line in space. However, enforcing samples to conform to a specific layout in the feature space is a challenging problem. To address this problem, in this paper, we propose a novel Constrained Proxies Learning (CPL) method, which can learn a proxy for each ordinal class and then adjusts the global layout of classes by constraining these proxies. Specifically, we propose two kinds of strategies: hard layout constraint and soft layout constraint. The hard layout constraint is realized by directly controlling the generation of proxies to force them to be placed in a strict linear layout or semicircular layout (i.e., two instantiations of strict ordinal layout). The soft layout constraint is realized by constraining that the proxy layout should always produce unimodal proxy-to-proxies similarity distribution for each proxy (i.e., to be a relaxed ordinal layout). Experiments show that the proposed CPL method outperforms previous deep ordinal classification methods under the same setting of feature extractor.Comment: Accepted by AAAI 202

Unifying Token and Span Level Supervisions for Few-Shot Sequence Labeling

Few-shot sequence labeling aims to identify novel classes based on only a few labeled samples. Existing methods solve the data scarcity problem mainly by designing token-level or span-level labeling models based on metric learning. However, these methods are only trained at a single granularity (i.e., either token level or span level) and have some weaknesses of the corresponding granularity. In this paper, we first unify token and span level supervisions and propose a Consistent Dual Adaptive Prototypical (CDAP) network for few-shot sequence labeling. CDAP contains the token-level and span-level networks, jointly trained at different granularities. To align the outputs of two networks, we further propose a consistent loss to enable them to learn from each other. During the inference phase, we propose a consistent greedy inference algorithm that first adjusts the predicted probability and then greedily selects non-overlapping spans with maximum probability. Extensive experiments show that our model achieves new state-of-the-art results on three benchmark datasets.Comment: Accepted by ACM Transactions on Information System

Learning to Classify Open Intent via Soft Labeling and Manifold Mixup

Open intent classification is a practical yet challenging task in dialogue systems. Its objective is to accurately classify samples of known intents while at the same time detecting those of open (unknown) intents. Existing methods usually use outlier detection algorithms combined with K-class classifier to detect open intents, where K represents the class number of known intents. Different from them, in this paper, we consider another way without using outlier detection algorithms. Specifically, we directly train a (K+1)-class classifier for open intent classification, where the (K+1)-th class represents open intents. To address the challenge that training a (K+1)-class classifier with training samples of only K classes, we propose a deep model based on Soft Labeling and Manifold Mixup (SLMM). In our method, soft labeling is used to reshape the label distribution of the known intent samples, aiming at reducing model's overconfident on known intents. Manifold mixup is used to generate pseudo samples for open intents, aiming at well optimizing the decision boundary of open intents. Experiments on four benchmark datasets demonstrate that our method outperforms previous methods and achieves state-of-the-art performance. All the code and data of this work can be obtained at https://github.com/zifengcheng/SLMM.Comment: Accepted by IEEE/ACM Transactions on Audio Speech and Language Processin

Temporal Knowledge Graph Forecasting with Neural ODE

There has been an increasing interest in inferring future links on temporal knowledge graphs (KG). While links on temporal KGs vary continuously over time, the existing approaches model the temporal KGs in discrete state spaces. To this end, we propose a novel continuum model by extending the idea of neural ordinary differential equations (ODEs) to multi-relational graph convolutional networks. The proposed model preserves the continuous nature of dynamic multi-relational graph data and encodes both temporal and structural information into continuous-time dynamic embeddings. In addition, a novel graph transition layer is applied to capture the transitions on the dynamic graph, i.e., edge formation and dissolution. We perform extensive experiments on five benchmark datasets for temporal KG reasoning, showing our model's superior performance on the future link forecasting task.Comment: accepted at EMNLP 202

Field Measurements of Wind-induced Responses of Shanghai World Financial Center: Investigation of Amplitude-dependent Damping

This paper was reviewed and accepted by the APCWE-IX Programme Committee for Presentation at the 9th Asia-Pacific Conference on Wind Engineering, University of Auckland, Auckland, New Zealand, held from 3-7 December 2017

Controlling Class Layout for Deep Ordinal Classification via Constrained Proxies Learning

For deep ordinal classification, learning a well-structured feature space specific to ordinal classification is helpful to properly capture the ordinal nature among classes. Intuitively, when Euclidean distance metric is used, an ideal ordinal layout in feature space would be that the sample clusters are arranged in class order along a straight line in space. However, enforcing samples to conform to a specific layout in the feature space is a challenging problem. To address this problem, in this paper, we propose a novel Constrained Proxies Learning (CPL) method, which can learn a proxy for each ordinal class and then adjusts the global layout of classes by constraining these proxies. Specifically, we propose two kinds of strategies: hard layout constraint and soft layout constraint. The hard layout constraint is realized by directly controlling the generation of proxies to force them to be placed in a strict linear layout or semicircular layout (i.e., two instantiations of strict ordinal layout). The soft layout constraint is realized by constraining that the proxy layout should always produce unimodal proxy-to-proxies similarity distribution for each proxy (i.e., to be a relaxed ordinal layout). Experiments show that the proposed CPL method outperforms previous deep ordinal classification methods under the same setting of feature extractor

Technical-environmental assessment of CO2 conversion process to dimethyl carbonate/ethylene glycol

Utilization of CO2 to produce value-added chemicals is a promising approach to mitigate greenhouse gas emissions. In this work, a new process for the conversion of CO2 to dimethyl carbonate (DMC) and ethylene glycol (EG) was rigorously simulated and assessed in term of the technical performance and the environmental impact. The proposed model involves the conversion of CO2 catalyzed by ionic liquid-based catalysts, the reactive distillation with the reaction kinetics model, the pressure-swing distillation with rigorous phase equilibrium equations, and complex material-energy nexus between each unit. The results show that the carbon utilization efficiency of this process reaches 99% and the negative CO2 emission is 0.14 ton CO2/ton product achieving CO2 reduction. The green degree value of the entire process is 176.30 gd/h indicating that this new process can be evaluated as an environmental friendly process. Additionally, the retrofitted heat exchanger network designed via the pinch technique achieves 48.70% saving in heating utility consumption and increasing the green degree by 193.89 gd/h. (C) 2020 Elsevier Ltd. All rights reserved