AGING BEHAVIOUR OF PARTICULATE REINFORCED ALUMINUM ALLOY MATRIX COMPOSITES

The process of aging precipitation in SiC_p/6061 and Al_2O_3/6061 composites were investigated. Hardness testing, differential scanning calorimetry(DSC) and transmission electron microscopy were employed. Results showed that the precipitation phase form directly along dislocation lines in the composites because the particles produce high densities of dislocations which makes vacancy densities in the composites decrease, and the main precipitation phase at peak hardness was β′ phase.     

齿轮系统噪声预估及声诊断方法研究

通过对正常齿轮系统和故障齿轮系统振动信号的试验测试,提取了正常与故障齿轮的信号特征,以声学理论为基础,在系统运转的情况下分别在时域和频域对系统振动所产生的声压进行了模拟计算,并对正常情况、齿根裂纹故障情况(深度为3mm和5mm)、分度圆裂纹故障(深度为2mm和4mm)及齿面磨损情况下齿轮系统的辐射声压进行了比较分析,并与试验结果进行了对比验证。结果表明,利用声诊断方法不仅可以较好地对几种典型故障(类型,大小)进行初步定征和识别,在故障诊断领域有良好的应用前景,而且可以降低声压级测量的成本,有效避免环境噪声的影响。     

Herfindahl index applied to Fourier analysis

Since the harmonic components in the signal to be analyzed are uncertain and diverse, it is difficult to make the sampling frequency and the signal length be harmonically related to the component frequencies included in the signal, and the leakage is produced in the discrete Fourier transform. Based on the definition of the Herfindahl index in economics, the spectrum concentration can be defined and computed. The appropriate number of points can be decided according to the peak location of the spectrum concentration. The method and the examples are specified in detail in the paper.     

Semi-supervised transformable architecture search for feature distillation

Pattern Analysis and Applications - The designed method aims to perform image classification tasks efficiently and accurately. Different from the traditional CNN-based image classification methods,...     

Joint Classification and Regression for Visual Tracking with Fully Convolutional Siamese Networks

International Journal of Computer Vision - Visual tracking of generic objects is one of the fundamental but challenging problems in computer vision. Here, we propose a novel fully convolutional...     

Container-Based Internet of Vehicles Edge Application Migration Mechanism

Internet of Vehicles (IoV) applications integrating with edge computing will significantly drive the growth of IoV. However, the contradiction between the high-speed mobility of vehicles, the delay sensitivity of corresponding IoV applications and the limited coverage and resource capacity of distributed edge servers will pose challenges to the service continuity and stability of IoV applications. IoV application migration is a promising solution that can be supported by application containerization, a technology forseamless cross-edge-server application migration without user perception. Therefore, this paper proposes the container-based IoV edge application migration mechanism, consisting of three parts. The first is the migration trigger determination algorithm for cross-border migration and service degradation migration, respectively, based on trajectory prediction and traffic awareness to improve the determination accuracy. The second is the migration target decision calculation model for minimizing the average migration time and maximizing the average service time to reduce migration times and improve the stability and adaptability of migration decisions. The third is the migration decision algorithm based on the improved artificial bee colony algorithm to avoid local optimal migration decisions. Simulation results show that the proposed migration mechanism can reduce migration times, reduce average migration time, improve average service time and enhance the stability and adaptability of IoV application services.     

二溴对甲基偶氮甲磺光度法测定钢中微量锶

研究了二溴对甲基偶氮甲磺与锶生成2:1兰色络合物进行钢中微量锶的测定.该法λ_(max)=638nm,摩尔吸光系数E_(638)=5.01×10~4,锶量在0.02～0.34μg/L范围内服从比尔定律.方法已用于钢中微量锶的测定,结果满意.     

Batch Nearest Neighbor Search for Video Retrieval

To retrieve similar videos to a query clip from a large database, each video is often represented by a sequence of high- dimensional feature vectors. Typically, given a query video containing m feature vectors, an independent nearest neighbor (NN) search for each feature vector is often first performed. After completing all the NN searches, an overall similarity is then computed, i.e., a single content-based video retrieval usually involves m individual NN searches. Since normally nearby feature vectors in a video are similar, a large number of expensive random disk accesses are expected to repeatedly occur, which crucially affects the overall query performance. Batch nearest neighbor (BNN) search is stated as a batch operation that performs a number of individual NN searches. This paper presents a novel approach towards efficient high-dimensional BNN search called dynamic query ordering (DQO) for advanced optimizations of both I/O and CPU costs. Observing the overlapped candidates (or search space) of a pervious query may help to further reduce the candidate sets of subsequent queries, DQO aims at progressively finding a query order such that the common candidates among queries are fully utilized to maximally reduce the total number of candidates. Modelling the candidate set relationship of queries by a candidate overlapping graph (COG), DQO iteratively selects the next query to be executed based on its estimated pruning power to the rest of queries with the dynamically updated COG. Extensive experiments are conducted on real video datasets and show the significance of our BNN query processing strategy.     

Predicting dam failure risk for sustainable flood retention basins: A generic case study for the wider Greater Manchester area

This study aims to provide a rapid screening tool for assessment of sustainable flood retention basins (SFRBs) to predict corresponding dam failure risks. A rapid expert-based assessment method for dam failure of SFRB supported by an artificial neural network (ANN) model has been presented. Flood storage was assessed for 110 SFRB and the corresponding Dam Failure Risk was evaluated for all dams across the wider Greater Manchester study area. The results show that Dam Failure Risk can be estimated by using the variables Dam Height, Dam Length, Maximum Flood Water Volume, Flood Water Surface Area, Mean Annual Rainfall (based on Met Office data), Altitude, Catchment Size, Urban Catchment Proportion, Forest Catchment Proportion and Managed Maximum Flood Water Volume. A cross-validation R² value of 0.70 for the ANN model signifies that the tool is likely to predict variables well for new data sets. Traditionally, dams are considered safe because they have been built according to high technical standards. However, many dams that were constructed decades ago do not meet the current state-of-the-art dam design guidelines. Spatial distribution maps show that dam failure risks of SFRB located near cities are higher than those situated in rural locations. The proposed tool could be used as an early warning system in times of heavy rainfall.     

Least squares recursive projection twin support vector machine for classification

In this paper we formulate a least squares version of the recently proposed projection twin support vector machine (PTSVM) for binary classification. This formulation leads to extremely simple and fast algorithm, called least squares projection twin support vector machine (LSPTSVM) for generating binary classifiers. Different from PTSVM, we add a regularization term, ensuring the optimization problems in our LSPTSVM are positive definite and resulting better generalization ability. Instead of usually solving two dual problems, we solve two modified primal problems by solving two systems of linear equations whereas PTSVM need to solve two quadratic programming problems along with two systems of linear equations. Our experiments on publicly available datasets indicate that our LSPTSVM has comparable classification accuracy to that of PTSVM but with remarkably less computational time.