A new crack diagnosis method on box structure based on empirical mode decomposition

A new crack diagnosis method on a two-story box structure based on empirical mode decomposition is proposed in this paper. According to the simulation analysis, it turns out that the model of the structure can be barely influenced by the crack. Response signals of swept sine vibration test are empirical mode decomposed into a set of intrinsic mode functions, from which tag vectors are constructed, then tag angles are defined to dignose the failure of the board. Combined with the load direction to the structure, the position and direction of the crack can be deduced using tag angles

Electrical transport across metal/two-dimensional carbon junctions: Edge versus side contacts

Metal/two-dimensional carbon junctions are characterized by using a nanoprobe in an ultrahigh vacuum environment. Significant differences were found in bias voltage (V) dependence of differential conductance (dI/dV) between edge- and side-contact; the former exhibits a clear linear relationship (i.e., dI/dV \propto V), whereas the latter is characterized by a nonlinear dependence, dI/dV \propto V3/2. Theoretical calculations confirm the experimental results, which are due to the robust two-dimensional nature of the carbon materials under study. Our work demonstrates the importance of contact geometry in graphene-based electronic devices

Predicting Transition Temperature of Superconductors with Graph Neural Networks

Predicting high temperature superconductors has long been a great challenge. The difficulty lies in how to predict the transition temperature (Tc) of superconductors. Although recent progress in material informatics has led to a number of machine learning models predicting Tc, prevailing models have not shown adequate generalization ability and physical rationality to find new high temperature superconductors, yet. In this work, a bond sensitive graph neural network (BSGNN) was developed to predict the Tc of various superconductors. In BSGNN, communicative message passing and graph attention methods were utilized to enhance the model's ability to process bonding and interaction information in the crystal lattice, which is crucial for the superconductivity. Consequently, our results revealed the relevance between chemical bond attributes and Tc. It indicates that shorter bond length is favored by high Tc. Meanwhile, some specific chemical elements that have relatively large van der Waals radius is favored by high Tc. It gives a convenient guidance for searching high temperature superconductors in materials database, by ruling out the materials that could never have high Tc

A competitive swarm optimizer-based technoeconomic optimization with appliance scheduling in domestic PV-battery hybrid systems

hybrid energy system is investigated. It incorporates the appliance time scheduling with appliance-specific power dispatch. The optimization is aimed at minimizing energy cost, maximizing renewable energy penetration, and increasing user satisfaction over a finite horizon. Nonlinear objective functions and constraints, as well as discrete and continuous decision variables, are involved. To solve the proposed mixed-integer nonlinear programming problem at a large scale, a competitive swarm optimizer-based numerical solver is designed and employed. The effectiveness of the proposed approach is verified by simulation results.The National Nature Science Foundation of China and the Fundamental Research Funds for the Central Universities.https://www.hindawi.com/journals/complexityam2020Electrical, Electronic and Computer Engineerin

iTRAQ-Based Differential Proteomic Analysis Reveals the Pathways Associated with Tigecycline Resistance in Acinetobacter baumannii

Background/Aims: Acinetobacter baumannii is an aerobic and Gram-negative bacterial pathogen with high morbidity and mortality. It remains a serious public health problem arising from its multidrug-resistant and extensive antibiotic resistance spectrum. Methods: In the present study, iTRAQ coupled with 2D LC-MS/MS was used to evaluate the proteome in standard Acinetobacter baumannii standard strains and tigecycline-resistant strains. Results: A total of 3639 proteins were identified and 961 proteins were identified to be differentially expressed in tigecycline-resistant Acinetobacter baumannii strains compared to the standard strains. 506 (52.6%) proteins were up-regulated and 455 (47.4%) proteins were down-regulated. Based on the GO enrichment analysis and KEGG pathway analysis, we concluded that most differentially expressed proteins were associated with stress responses, cellular component organization, proteins synthesis, degradation and function. Moreover, β-lactam resistance, the longevity regulating pathway and other related pathways were also involved in the regulation of tigecycline-resistant Acinetobacter baumannii. The differential expression of key proteins were evaluated by transcript analysis using quantitative RT-PCR. Conclusion: These results may provide new insights into the mechanisms of drug resistance in Acinetobacter baumannii

Discriminative Fusion Correlation Learning for Visible and Infrared Tracking

Discriminative correlation filter- (DCF-) based trackers are computationally efficient and achieve excellent tracking in challenging applications. However, most of them suffer low accuracy and robustness due to the lack of diversity information extracted from a single type of spectral image (visible spectrum). Fusion of visible and infrared imaging sensors, one of the typical multisensor cooperation, provides complementarily useful features and consistently helps recognize the target from the background efficiently in visual tracking. Therefore, this paper proposes a discriminative fusion correlation learning model to improve DCF-based tracking performance by efficiently combining multiple features from visible and infrared images. Fusion learning filters are extracted via late fusion with early estimation, in which the performances of the filters are weighted to improve the flexibility of fusion. Moreover, the proposed discriminative filter selection model considers the surrounding background information in order to increase the discriminability of the template filters so as to improve model learning. Extensive experiments showed that the proposed method achieves superior performances in challenging visible and infrared tracking tasks

Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

International audiencePhoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, backpropagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 − 8.47% comparing to state-of-the-arts with different data training data density

Data Management of System-of-Systems Requirements Modeling

info:eu-repo/semantics/publishe