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In this paper, tests and finite element analysis are used to study the shear resistance of cold-formed steel stud walls in low-rise residential structures. Firstly, the shear resistance of cold-formed steel stud walls under monoto...
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In this paper, tests and finite element analysis are used to study the shear resistance of cold-formed steel stud walls in low-rise residential structures. Firstly, the shear resistance of cold-formed steel stud walls under monotonic loading is tested. The test models, including walls with single-sided gypsum sheathing, walls with single-sided oriented strand board sheathing, and walls with gypsum sheating on the back and oriented strand board on the face are made in full scale of engineering project. The test apparatus and test method and the failure process of specimens are introduced in detail. Then, the finite element analysis model of cold-formed steel stud walls considering geometric large deformation and materials nonlinear is presented to study their shear resistance. Walls were simulated as shell elements. The studs and tracks are simply connected. The screws connecting the sheathings to the frame are modeled by coupling methods. The solution method of equations is selected by ANSYS program automatically. Finite element analysis results in this paper are close to that of experiment. The results of test and finite element analysis show that sheathing materials influences the wall's shear resistance more greatly. The strength of steel has a less influence on the shear resistance of walls. As the decrease of stud spacing, height of wall and screw spacing at the perimeter, the walls' load ability increases obviously.
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摘要 :
In this paper, tests and finite element analysis are used to study the shear resistance of cold-formed steel stud walls in low-rise residential structures. Firstly, the shear resistance of cold-formed steel stud walls under monoto...
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In this paper, tests and finite element analysis are used to study the shear resistance of cold-formed steel stud walls in low-rise residential structures. Firstly, the shear resistance of cold-formed steel stud walls under monotonic loading is tested. The test models, including walls with single-sided gypsum sheathing, walls with single-sided oriented strand board sheathing, and walls with gypsum sheathing on the back and oriented strand board on the face are made in full scale of engineering project. The test apparatus and test method and the failure process of specimens are introduced in detail. Then, the finite element analysis model of cold-formed steel stud walls considering geometric large deformation and materials nonlinear is presented to study their shear resistance. Walls were simulated as shell elements. The studs and tracks are simply connected. The screws connecting the sheathings to the frame are modeled by coupling methods. The solution method of equations is selected by ANSYS program automatically. Finite element analysis results in this paper are close to that of experiment. The results of test and finite element analysis show that sheathing materials influences the wall's shear resistance more greatly. The strength of steel has a less influence on the shear resistance of walls. As the decrease of stud spacing, height of wall and screw spacing at the perimeter, the walls' load ability increases obviously.
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False data injection attacks modify the meter measurements to mislead the control center into estimating inaccurate system states and thus affect the reliable operation of smart grids. In this paper, we propose a deep reinforcemen...
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False data injection attacks modify the meter measurements to mislead the control center into estimating inaccurate system states and thus affect the reliable operation of smart grids. In this paper, we propose a deep reinforcement learning based vulnerability analysis scheme for smart grids that enables the control center to construct an attack vector from the attacker's view to identify the vulnerable meters. The control center chooses the attack vector based on power system states, meter measurements, the previous number of analyzed meters, and injected errors without knowing the power system topology. This scheme designs an actor-critic architecture that applies an actor network to output the policy probability distribution to handle the continuous and high-dimensional vulnerability analysis policy and contains a critic network to guide the weights update of the actor network. We also analyze the computational complexity and perform simulations to verify the efficacy of this scheme in terms of the vulnerability detection rate, the number of analyzed meters and the utility.
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Weakly supervised methods estimate the labels for a dataset using the predictions of several noisy supervision sources. Many machine learning practitioners have begun using weak supervision to more quickly and cheaply annotate dat...
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Weakly supervised methods estimate the labels for a dataset using the predictions of several noisy supervision sources. Many machine learning practitioners have begun using weak supervision to more quickly and cheaply annotate data compared to traditional manual labeling. In this paper, we focus on the specific problem of weakly supervised named entity recognition (NER) and propose an end-to-end model to learn optimal assignments of latent NER tags using observed tokens and weak labels provided by labeling functions. To capture the sequential dependencies between the latent and observed variables, we propose a sequential graphical model where the components are approximated using neural networks. State-of-the-art contextual embeddings are used to further discriminate the quality of noisy weak labels in various contexts. Results of experiments on four public weakly supervised named entity recognition datasets show a significant improvement in F1 score over recent approaches.
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Weakly supervised methods estimate the labels for a dataset using the predictions of several noisy supervision sources. Many machine learning practitioners have begun using weak supervision to more quickly and cheaply annotate dat...
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Weakly supervised methods estimate the labels for a dataset using the predictions of several noisy supervision sources. Many machine learning practitioners have begun using weak supervision to more quickly and cheaply annotate data compared to traditional manual labeling. In this paper, we focus on the specific problem of weakly supervised named entity recognition (NER) and propose an end-to-end model to learn optimal assignments of latent NER tags using observed tokens and weak labels provided by labeling functions. To capture the sequential dependencies between the latent and observed variables, we propose a sequential graphical model where the components are approximated using neural networks. State-of-the-art contextual embeddings are used to further discriminate the quality of noisy weak labels in various contexts. Results of experiments on four public weakly supervised named entity recognition datasets show a significant improvement in F1 score over recent approaches.
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Timely identification of dissatisfied customers would provide corporations and other customer serving enterprises the opportunity to take meaningful interventions. This work describes a fully operational system we have developed a...
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Timely identification of dissatisfied customers would provide corporations and other customer serving enterprises the opportunity to take meaningful interventions. This work describes a fully operational system we have developed at a large US insurance company for predicting customer satisfaction following all incoming phone calls at our call center. To capture call relevant information, we integrate signals from multiple heterogeneous data sources including: speech-to-text transcriptions of calls, call metadata (duration, waiting time, etc.), customer profiles and insurance policy information. Because of its ordinal, subjective, and often highly-skewed nature, self-reported survey scores presents several modeling challenges. To deal with these issues we introduce a novel modeling workflow: First, a ranking model is trained on the customer call data fusion. Then, a convolutional fitting function is optimized to map the ranking scores to actual survey satisfaction scores. This approach produces more accurate predictions than standard regression and classification approaches that directly fit the survey scores with call data, and can be easily generalized to other customer satisfaction prediction problems. Source code and data are available at https://github.com/cyberyu/ecml2017.
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Timely identification of dissatisfied customers would provide corporations and other customer serving enterprises the opportunity to take meaningful interventions. This work describes a fully operational system we have developed a...
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Timely identification of dissatisfied customers would provide corporations and other customer serving enterprises the opportunity to take meaningful interventions. This work describes a fully operational system we have developed at a large US insurance company for predicting customer satisfaction following all incoming phone calls at our call center. To capture call relevant information, we integrate signals from multiple heterogeneous data sources including: speech-to-text transcriptions of calls, call metadata (duration, waiting time, etc.), customer profiles and insurance policy information. Because of its ordinal, subjective, and often highly-skewed nature, self-reported survey scores presents several modeling challenges. To deal with these issues we introduce a novel modeling workflow: First, a ranking model is trained on the customer call data fusion. Then, a convolutional fitting function is optimized to map the ranking scores to actual survey satisfaction scores. This approach produces more accurate predictions than standard regression and classification approaches that directly fit the survey scores with call data, and can be easily generalized to other customer satisfaction prediction problems. Source code and data are available at https://github.com/cyberyu/ecml2017.
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To obtain correlated and complementary information contained in text mining and bibliometrics, hybrid clustering to incorporate textual content and citation information has become a popular strategy. In this paper, we propose a ne...
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To obtain correlated and complementary information contained in text mining and bibliometrics, hybrid clustering to incorporate textual content and citation information has become a popular strategy. In this paper, we propose a new computational framework of integrating text mining and bibliometrics to provide a mapping of journal sets. Two different approaches of hybrid clustering methods are applied in this paper. The first category is ensemble clustering, which combines different clustering results obtained from individual data into a consolidated clustering result. The second category is kernel fusion, which maps heterogeneous data sets into the kernel space and combines the kernel matrices for clustering. Kernels can be combined either averagely, or by an optimized weighted linear combination model. In this paper, we propose a novel adaptive kernel K-means clustering algorithm to combine textual content and citation information for clustering. The proposed algorithm is systematically compared with other methods on a clustering problem of 1869 journals published in 2002-2006. Based on several validation indices, the experimental results demonstrate that our hybrid clustering strategy is able to provide clustering result as well as the best individual data source.
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