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.     

ArcFVS: An integration of a Geographic Information System and Forest Vegetation Simulator

This paper presents the design and implementation of an integrated application program called ArcFVS that links the Forest Vegetation Simulator (FVS) and a Geographic Information System (GIS) to realize spatial selection of input files and graphic display of modeling output. Data for testing and running the model came from the U.S. Forest Service’s Forest Inventory and Analysis (FIA) Database and were also collected in field surveys in north-central Indiana. ArcFVS 1.0 is designed using the ArcGIS software from the Environmental Systems Research Institute (ESRI) and the Visual Basic for Applications (VBA) programming environment to manipulate ESRI’s ArcObjects. The resulting product offers custom functions as commands in a new menu or as tools on a new toolbar. They are used to: select spatially or by attribute the forest plots to be projected by FVS, create the FVS input files for the selected plots and display in a geospatial environment different types of FVS output (text output files, tables with variables of interest and visualization image files). Advantages of ArcFVS 1.0 include the new GIS capabilities, enhanced format translation functions and the standardized programming environment.     

滑步与再同步过程的微观结构分析

按照定常两机电力系统平衡点的属性，将其轨迹分类为稳定的结点、不稳定的结点、稳定的焦点、不稳定的焦点和鞍点这5种不同运动模式。利用互补群惯量中心(CCCOI)：R^n→R^2保稳映射将实际多机受扰轨迹映射为时变的两机等值轨迹，进而分析后者如何由对应于上述运动模式的多个轨迹段构成，并用符号序列反映其微观结构。以电力系统从一连串的滑步进入再同步为例，研完了多机电力系统的后期动态行为会随着参数的细微变化而显著改变的现象。如果一段具有鞍点特性的相轨迹的起始点从稳定流形的一侧变到另一侧，那么不论系统参数的变化多么细微，其后继轨迹将有完全不同的动态。     

间接蒸汽加热时二元连续精馏塔提馏段操作线方程

提出了间接蒸汽加热时二元连续精馏塔以ｘＦ，ｘＤ，ｘＷ，ｑ，Ｒ表达的提馏段操作线方程。