Based on the engineering practice of large cross-section highway tunnel, this paper reveals the space-time coordinated evolution law of the construction mechanical characteristics and deformation ...distribution of the support structure in the construction by half bench CD method through field test. At the same time, the mechanical response calculation model of the supporting structure in the partial excavation is constructed, and the mechanical characteristics of the support structure in the partial excavation process are analyzed by above mechanical calculation model. Then, the mechanical and deformation distribution of the feet-reinforcement bolt in the steel frame-foot-reinforcement bolt combined support system is analyzed under different levels of surrounding rock load and different structural parameters of the feet-reinforcement bolt. The research results show that: (1) The internal force of the supporting structure changes most obviously during the excavation of Part â , Part II and Part â¢, and the internal force of the support structure gradually tends to be stable after a slight increase in the excavation of Part ⣠and Part â¤; (2) The horizontal deformation and vertical deformation of the support structure mainly occur in the excavation process of Part â , Part II and Part â¢, and the excavation of Part ⣠and ⤠has little effect on the deformation response of the structure. The vertical displacement of the supporting structure is larger than the horizontal displacement, and the dynamic response of the temporary diaphragm structure during tunnel excavation is shrinkage-expansion-shrinkage-expansion; (3) The bending strain of each measuring point decreases with the increase of the distance from the loading point, and the bending strain of section 1 and section 2 is much larger than that of the other three sections; (4) With the increase of the angle, the section position with strain close to 0 gradually moves to the deeper position of the bolt, and the axial strain of each section on the bolt gradually changes from positive strain to negative strain.
One key issue in text mining and natural language processing is how to effectively represent documents using numerical vectors. One classical model is the Bag-of-Words (BoW). In a BoW-based vector ...representation of a document, each element denotes the normalized number of occurrence of a basis term in the document. To count the number of occurrence of a basis term, BoW conducts exact word matching, which can be regarded as a hard mapping from words to the basis term. BoW representation suffers from its intrinsic extreme sparsity, high dimensionality, and inability to capture high-level semantic meanings behind text data. To address the aforementioned issues, we propose a new document representation method named fuzzy Bag-of-Words (FBoW) in this paper. FBoW adopts a fuzzy mapping based on semantic correlation among words quantified by cosine similarity measures between word embeddings. Since word semantic matching instead of exact word string matching is used, the FBoW could encode more semantics into the numerical representation. In addition, we propose to use word clusters instead of individual words as basis terms and develop fuzzy Bag-of-WordClusters (FBoWC) models. Three variants under the framework of FBoWC are proposed based on three different similarity measures between word clusters and words, which are named as <inline-formula><tex-math notation="LaTeX">\text{FBoWC}_{\rm mean}</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">\text{FBoWC}_{\rm max}</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">\text{FBoWC}_{\rm min}</tex-math></inline-formula>, respectively. Document representations learned by the proposed FBoW and FBoWC are dense and able to encode high-level semantics. The task of document categorization is used to evaluate the performance of learned representation by the proposed FBoW and FBoWC methods. The results on seven real-word document classification datasets in comparison with six document representation learning methods have shown that our methods FBoW and FBoWC achieve the highest classification accuracies.
In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring ...approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redefine representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data. CBLSTM firstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks(LSTMs) are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods.
Herein we reported a direct synthesis series of indolines through cyclization of tertiary aryl amines with iodonium ylides. This cyclization reaction occurred under sole visible‐light irradiation at ...room temperature without the addition of any photocatalysts and additives. Preliminary mechanism studies revealed that an electron donor‐acceptor (EDA) complex between iodonium ylides and tertiary aryl amines should be formed during the reaction.
Person Re-Identification by Saliency Learning Zhao, Rui; Oyang, Wanli; Wang, Xiaogang
IEEE transactions on pattern analysis and machine intelligence,
2017-Feb.-1, 2017-02-00, 2017-2-1, 20170201, Letnik:
39, Številka:
2
Journal Article
Recenzirano
Human eyes can recognize person identities based on small salient regions, i.e., person saliency is distinctive and reliable in pedestrian matching across disjoint camera views. However, such ...valuable information is often hidden when computing similarities of pedestrian images with existing approaches. Inspired by our user study result of human perception on person saliency, we propose a novel perspective for person re-identification based on learning person saliency and matching saliency distribution. The proposed saliency learning and matching framework consists of four steps: (1) To handle misalignment caused by drastic viewpoint change and pose variations, we apply adjacency constrained patch matching to build dense correspondence between image pairs. (2) We propose two alternative methods, i.e., K-Nearest Neighbors and One-class SVM, to estimate a saliency score for each image patch, through which distinctive features stand out without using identity labels in the training procedure. (3) saliency matching is proposed based on patch matching. Matching patches with inconsistent saliency brings penalty, and images of the same identity are recognized by minimizing the saliency matching cost. (4) Furthermore, saliency matching is tightly integrated with patch matching in a unified structural RankSVM learning framework. The effectiveness of our approach is validated on the four public datasets. Our approach outperforms the state-of-the-art person re-identification methods on all these datasets.
•Both external and internal short circuit tests were performed on Li-ion batteries.•An electrochemical–thermal model with an additional nail site heat source is presented.•The model can accurately ...simulate the temperature variations of non-venting batteries.•The model is reliable in predicting the occurrence and start time of thermal runaway.•A hydrogel cooling system proves its strength in preventing battery thermal runaway.
Safety is the first priority in lithium ion (Li-ion) battery applications. A large portion of electrical and thermal hazards caused by Li-ion battery is associated with short circuit. In this paper, both external and internal short circuit tests are conducted. Li-ion batteries and battery packs of different capacities are used. The results indicate that external short circuit is worse for smaller size batteries due to their higher internal resistances, and this type of short can be well managed by assembling fuses. In internal short circuit tests, higher chance of failure is found on larger capacity batteries. A modified electrochemical–thermal model is proposed, which incorporates an additional heat source from nail site and proves to be successful in depicting temperature changes in batteries. Specifically, the model is able to estimate the occurrence and approximate start time of thermal runaway. Furthermore, the effectiveness of a hydrogel based thermal management system in suppressing thermal abuse and preventing thermal runaway propagation is verified through the external and internal short tests on batteries and battery packs.
In modern industries, machine health monitoring systems (MHMS) have been applied wildly with the goal of realizing predictive maintenance including failures tracking, downtime reduction, and assets ...preservation. In the era of big machinery data, data-driven MHMS have achieved remarkable results in the detection of faults after the occurrence of certain failures (diagnosis) and prediction of the future working conditions and the remaining useful life (prognosis). The numerical representation for raw sensory data is the key stone for various successful MHMS. Conventional methods are the labor-extensive as they usually depend on handcrafted features, which require expert knowledge. Inspired by the success of deep learning methods that redefine representation learning from raw data, we propose local feature-based gated recurrent unit (LFGRU) networks. It is a hybrid approach that combines handcrafted feature design with automatic feature learning for machine health monitoring. First, features from windows of input time series are extracted. Then, an enhanced bidirectional GRU network is designed and applied on the generated sequence of local features to learn the representation. A supervised learning layer is finally trained to predict machine condition. Experiments on three machine health monitoring tasks: tool wear prediction, gearbox fault diagnosis, and incipient bearing fault detection verify the effectiveness and generalization of the proposed LFGRU.
China is experiencing a stage of rapid urban development. The energy consumption and related carbon dioxide emissions of households continue to increase. This paper calculates direct and indirect ...carbon dioxide emissions of households based on the input–output method in China from 1996 to 2012. The results reveal that there were more total carbon dioxide emissions from urban households than from rural households, far more indirect emissions from urban households than from rural households, slightly more direct emissions from urban households than from rural households, and differences in direct carbon dioxide emissions from various fuels and in indirect emissions from various sectors between urban and rural households. To examine the causal relationship between urbanization and the carbon dioxide emissions of households, cointegration and Granger causality tests are applied. A unidirectional causal relation was found running from urbanization to both direct and indirect household carbon dioxide emissions, and the direct and indirect carbon dioxide emissions of households would increase 2.9% and 1.1%, respectively, for every increase of one percent in urbanization. We discuss the reasons of why the development of urbanization will lead to more household direct and indirect carbon dioxide emissions, and suggest certain policy implications for urbanization and carbon dioxide emissions based on the results of this study.
•Direct and indirect carbon emissions of households are calculated.•Differences in carbon emissions between urban and rural households are identified.•A unidirectional causal relation was found running from urbanization to emissions.•Reasons of why urbanization leads to more household carbon emissions are discussed.•Policy implications for urbanization and carbon emissions are suggested.
Summary
Increasing demand of electricity and severer concerns to environment call for green energy sources as well as efficient energy conversion systems. SCO2 power cycles integrated with ...concentrating solar power (CSP) are capable of enhancing the competitiveness of thermal solar electricity. This article makes a comprehensive review of supercritical CO2 power cycles integrated with CSP. A detailed comparison of four typical CSP technologies is conducted, and the cost challenge of currently CSP technologies is pointed out. The thermophysical properties of sCO2 and the corresponding two real gas effects are analyzed elaborately to express the features of sCO2 power cycles. An extensive review of sCO2 layouts relevant for CSP including 12 single layouts and 1 combined layout is implemented logically. Strengths and weaknesses of sCO2 power cycles over traditional steam‐Rankine cycle generally adopted in current CSP plants are concluded, followed by metal material degration summary in CSP relevant temperature sCO2 environment, which shows that the nickel‐based alloy is a proper structural material candidate for sCO2‐CSP integration. Thermodynamic analyses of sCO2 power cycles when integrated with CSP are divided into three level of which design‐point analysis and off‐design modeling are conducted and compared, more researches into the off‐design point analysis, dynamic modeling, especially the transient behavior are suggested. Economic analysis of the integrated system is concluded and presents a considerable levelized cost of electricity reduction of 15.6% to 67.7% compared to that of state of art CSP. Taking the thermodynamic and economic analysis into consideration, target designs of sCO2 power cycles for CSP are summarized in three aspects. Finally, current theoretical and experimental researches of sCO2 power cycles integrated with CSP for market penetration are introduced. The strengths, weaknesses, and potential solutions to the gaps of three potential pathways (molten salt pathway, particle pathway, and gas phase pathway) to realize the integration of sCO2 power cycles in the next CSP generation plants up to 700°C are reviewed. In general, the integration of sCO2 power cycles with CSP technologies exhibits promising expectations for facilitating the competitiveness of thermal solar electricity.
Person Re-identification by Salience Matching Zhao, Rui; Ouyang, Wanli; Wang, Xiaogang
2013 IEEE International Conference on Computer Vision,
12/2013
Conference Proceeding, Journal Article
Human salience is distinctive and reliable information in matching pedestrians across disjoint camera views. In this paper, we exploit the pair wise salience distribution relationship between ...pedestrian images, and solve the person re-identification problem by proposing a salience matching strategy. To handle the misalignment problem in pedestrian images, patch matching is adopted and patch salience is estimated. Matching patches with inconsistent salience brings penalty. Images of the same person are recognized by minimizing the salience matching cost. Furthermore, our salience matching is tightly integrated with patch matching in a unified structural Rank SVM learning framework. The effectiveness of our approach is validated on the VIPeR dataset and the CUHK Campus dataset. It outperforms the state-of-the-art methods on both datasets.
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