Tech Science Press
Not a member yet
3972 research outputs found
Sort by
A Hybrid Quantum-Classical Simulation Study on Stress-Dependence of Li Diffusivity in Graphite
No full textUnderstanding the stress dependence of Li diffusivity in the Li-graphite intercalation compound (Li-GIC) that has been used in the Li-ion rechargeable battery as a negative electrode, is important to search for better conditions to improve the power performance of the battery. In the Li-GIC, the Li ion creates a long-ranged stress field around itself by expanding the inter-layer distance of the graphite. To take into account such a long-ranged stress field in the first-principles simulation of the Li diffusion, we develop the hybrid quantum (QM)-classical (CL) simulation code. In the hybrid code, the QM region selected adaptively around the Li ion following its motion is treated with the real-space density-functional theory. The rest of the total system is described with an empirical inter-atomic potential that includes a novel formula for the dispersion force between the C atoms that belong to different layers. A series of the hybrid QM-CL simulation runs for the dynamics of a single Li-ion in the graphite are performed at temperature 423 K for various values of the averaged inter-layer distance. We thereby find that the Li diffusivity is suppressed substantially when the inter-layer distance is compressed by a few percent from the equilibrium value. On the other hand, the Li diffusivity is unaffected by the stretching of the inter-layer distance up to a few percent. In the equilibrium and stretched cases, the diffusive motion of the Li ion is composed of ballistic and hopping modes. In the compressed case, the Li ion diffuses in the hopping mode only and is confined in a small area at long times. Separately the activation energy for the hopping diffusion is calculated at zero temperature to find that it is as small as 0.1 eV and that the substantial contribution comes from the deformation energy of the whole system. Based on the findings we propose a mechanism to explain the unique Li-density dependence of the Li diffusivity observed experimentally in the Li-GIC
Elastic Wave Propagation in Periodic Cellular Structures
No full textThe present work is devoted to a theoretical analysis and numerical modeling of elastic wave propagation firstly in a one-dimensional periodic elastic rod structure and then in two-dimensional periodic elastic beam structures by using Bloch wave theorem. The dispersion relation between Bloch wave vectors and eigen frequencies is obtained and its dependency upon the micro-structural characteristics of the periodic cellular structure is analyzed. Thanks to the Bloch wave transforms, only the primitive cell is considered theoretically or numerically and the phenomena of frequency band-gaps and the diffracted waves caused by the periodic cells are modeled and analyzed
Context-Based Intelligent Scheduling and Knowledge Push Algorithms for AR-Assist Communication Network Maintenance
Get PDFMaintenance is an important aspect in the lifecycle of communication network devices. Prevalent problems in the maintenance of communication networks include inconvenient data carrying and sub-optimal scheduling of work orders, which significantly restrict the efficiency of maintenance work. Moreover, most maintenance systems are still based on cloud architectures that slow down data transfer. With a focus on the completion time, quality, and load balancing of maintenance work, we propose in this paper a learning-based virus evolutionary genetic algorithm with multiple quality-of-service (QoS) constraints to implement intelligent scheduling in an edge network. The algorithm maintains the diversity of the population and improves the speed of convergence using a fitness function and a learning-based population generation mechanism. The test results demonstrate that the algorithm delivers good performance in terms of load balancing and QoS guarantee. We also propose a knowledge push algorithm based on a context model for intelligently pushing relevant knowledge according to the given conditions. The simulation results demonstrate that our scheme can improve the efficiency of on-site maintenance
The Analysis of Thermal-Induced Phase Transformation and Microstructural Evolution in Ni-Ti Based Shape Memory Alloys By Molecular Dynamics
Get PDFShape memory alloys has been widely applied on actuators and medical devices. The transformation temperature and microstructural evolution play two crucial factors and dominate the behavior of shape memory alloys. In order to understand the influence of the composition of the Ni-Ti alloys on the two factors, molecular dynamics was adopted to simulate the temperature-induced phase transformation. The results were post-processed by the martensite variant identification method. The method allows to reveal the detailed microstructural evolution of variants/phases in each case of the composition of Ni-Ti. Many features were found and having good agreement with those reported in the literature, such as the well-known Rank-2 herringbone structures; the X-interface; Ni-rich alloys have lower transformation temperature than Ti-rich alloys. In addition, some new features were also discovered. For example, the Ti-rich alloys enabled an easier martensitic transformation; the nucleated martensite pattern determined the microstructural evolution path, which also changed the atomic volume and temperature curves. The results generated in the current study are expected to provide the design guidelines for the applications of shape memory alloys
A Scalable Method of Maintaining Order Statistics for Big Data Stream
Get PDFRecently, there are some online quantile algorithms that work on how to analyze the order statistics about the high-volume and high-velocity data stream, but the drawback of these algorithms is not scalable because they take the GK algorithm as the subroutine, which is not known to be mergeable. Another drawback is that they can’t maintain the correctness, which means the error will increase during the process of the window sliding. In this paper, we use a novel data structure to store the sketch that maintains the order statistics over sliding windows. Therefore three algorithms have been proposed based on the data structure. And the fixed-size window algorithm can keep the sketch of the last W elements. It is also scalable because of the mergeable property. The time-based window algorithm can always keep the sketch of the data in the last T time units. Finally, we provide the window aggregation algorithm which can help extend our algorithm into the distributed system. This provides a speed performance boost and makes it more suitable for modern applications such as system/network monitoring and anomaly detection. The experimental results show that our algorithm can not only achieve acceptable performance but also can actually maintain the correctness and be mergeable
Network Embedding-Based Anomalous Density Searching for Multi-Group Collaborative Fraudsters Detection in Social Media
Get PDFDetecting collaborative fraudsters who manipulate opinions in social media is becoming extremely important in order to provide reliable information, in which, however, the diversity in different groups of collaborative fraudsters presents a significant challenge to existing collaborative fraudsters detection methods. These methods often detect collaborative fraudsters as the largest group of users who have the strongest relation with each other in the social media, consequently overlooking the other groups of fraudsters that are with strong user relation yet small group size. This paper introduces a novel network embedding-based framework NEST and its instance BEST to address this issue. NEST detects multiple groups of collaborative fraudsters by two steps. In the first step, to disclose user collaboration, it represents users according to their social relations. Then, in the second step, to identify the collaborative fraudsters, it detects the user groups with anomalous large group density in its representation space. BEST instantiates NEST by using a bipartite network embedding method to represent users and adopting a fast density group detection method based on the k-dimensional tree. Our experiments show BEST (i) performs significantly better in detecting fraudsters on four real-word social media data sets, and (ii) effectively detects multiple groups of collaborative fraudsters, compared to three state-of-the-art competitors
Relation Extraction for Massive News Texts
Get PDFWith the development of information technology including Internet technologies, the amount of textual information that people need to process daily is increasing. In order to automatically obtain valuable and user-informed information from massive amounts of textual data, many researchers have conducted in-depth research in the area of entity relation extraction. Based on the existing research of word vector and the method of entity relation extraction, this paper designs and implements an method based on support vector machine (SVM) for extracting English entity relationships from massive news texts. The method converts sentences in natural language into a form of numerical matrix that can be understood and processed by computers through word embedding and position embedding. Then the key features are extracted, and feature vectors are constructed and sent to the SVM classifiers for relation classification. In the process of feature extraction, we had two different models to finish the job, one by Principal Component Analysis (PCA) and the other by Convolutional Neural Networks (CNN). We designed experiments to evaluate the algorithm
DSA-Based Quantitative Assessment of Cerebral Hypoperfusion in Patients with Asymmetric Carotid Stenosis
Get PDFDigital subtraction angiography (DSA) is often used to evaluate the morphological and pathological changes of cerebral arteries in clinical practice. This study aims to explore the possibility of assessing cerebral hypoperfusion with DSA in patients with carotid stenosis. Thirty patients with a mild to severe stenosis on one side, and a mild stenosis on the other side of the carotid artery were recruited. Frontal, parietal, temporal and occipital lobes were chosen as regions of interest for measuring the quantitative perfusion parameters from their time-density curves (TDCs) of DSA images. The perfusion parameters were compared between the two hemispheres by using paired t-test. In addition, the bilateral asymmetry of these parameters was calculated and its correlation with the bilateral asymmetry in stenosis was analyzed. The parameters included mean transit time (MTT), time of contrast uptake (TU), time taken to the half peak value (1/2TMAX), area under the curve (AUC) were significantly prolonged at the severe stenosis side than those at the mild stenosis side in frontal lobe (P=0.013; P=0.041; P=0.009; P=0.027) and parietal lobe (P=0.008; P=0.041; P=0.002; P=0.012). The asymmetric ratios of MTT and AUC showed statistically significant correlations with stenosis asymmetry in all four lobes. MTT, TU, 1/2TMAX and AUC could reflect the bilateral asymmetry of the cerebral perfusion. These DSA parameters, therefore, may be used for the evaluation of cerebral hypoperfusion caused by carotid stenosis
Intramyocardial Injections to De-Stiffen the Heart: A Subject-Specific in Silico Approach
Get PDFWe hypothesized that minimally invasive injections of a softening agent at strategic locations in stiff myocardium could de-stiffen the left ventricle (LV) globally. Physics-based finite element models of the LV were created from LV echocardiography images and pressures recorded during experiments in four swine. Results confirmed animal models of LV softening by systemic agents. Regional de-stiffening of myocardium led to global de-stiffening of LV. The mathematical set up was used to design LV global de-stiffening by regional softening of myocardium. At an end diastolic pressure of 23 mmHg, when 8 ml of the free wall was covered by intramyocardial injections, end diastolic volume (EDV) increased by 15.0%, whereas an increase up to 11 ml due to intramyocardial injections in the septum and free wall led to a 26.0% increase in EDV. Although the endocardial intramyocardial injections occupied a lower LV wall volume, they led to an EDV (44 ml) that was equal compared to intramyocardial injections in the mid-wall (44 ml) and larger compared to intramyocardial injections in the epicardium (41 ml). Using an in silico set up, sites of regional myocardium de-stiffening could be planned in order to globally soften overly stiff LV in heart failure with preserved ejection fraction. This novel treatment is built on subject-specific data. Hypothesis-testing of these simulation findings in animal models is warranted
Delamination Identification for FRP Composites with Emphasis on Frequency-Based Vibration Monitoring-A Review
Get PDFFibre reinforced polymer (FRP) composite laminates are now commonly used in many structural applications, especially in the aerospace industry, where margins of safety are kept low in order to minimise weight. Timely detection and assessment of damage (in particular delaminations) in composite laminates are therefore critical, as they can cause loss of structural integrity affecting the safe operation of the composite structures. The current trend is towards implementation of structural health monitoring (SHM) systems which can monitor the structures in situ without down time. In this paper, first, the current available SHM techniques for delamination detection in FRP composites are briefly reviewed, including acoustic emission, fibre optic sensors, Lamb wave-, impedance- and vibration-based methods. Among different vibration-based methods, frequency monitoring is the simplest to implement, requiring only single point measurement, and is relatively accurate and reliable, thus it becomes the main focus of present paper. A comprehensive review of frequency-based vibration monitoring is conducted in terms of the various aspects of delamination identification in FRPs through frequency shifts, including review of theoretical models for free vibration of delaminated FRP beams, survey of finite element modelling of delaminated composite structures, summary of experimental modal analyses on FRP composites with delaminations, and inverse algorithms for frequency-based delamination assessment. This paper aims to help the readers to get an overview of the available SHM techniques for monitoring the integrity of FRP composites, with a special emphasis on delamination assessment through frequency-based vibration monitoring