TrajStat: GIS-based software that uses various trajectory statistical analysis methods to identify potential sources from long-term air pollution measurement data

Statistical analysis of air mass back trajectories combined with long-term ambient air pollution measurements are useful tools for source identification. Using these methods, the geographic information system (GIS) based software, TrajStat, was developed to view, query, and cluster the trajectories and compute the potential source contribution function (PSCF) and concentration weighted trajectory (CWT) analyses when measurement data are included.     

A strategic classification support system for brownfield redevelopment

Brownfield redevelopment (BR) is an ongoing issue for governments, communities, and consultants around the world. It is also an increasingly popular research topic in several academic fields. Strategic decision support that is now available for BR is surveyed and assessed. Then a dominance-based rough-set approach is developed and used to classify cities facing BR issues according to the level of two characteristics, BR effectiveness and BR future needs. The data for the classification are based on the widely available results of a survey of US cities. The unique features of the method are its reduced requirement for preference information, its ability to handle missing information effectively, and the easily understood linguistic decision rules that it generates, based on a training classification provided by experts. The resulting classification should be a valuable aid to cities and governments as they plan their BR projects and budgets.     

AVCOL: Availability-aware information aggregation in large distributed systems under uncollaborative behavior

Aggregation of system-wide information in large-scale distributed systems, such as p2p systems and Grids, can be unfairly influenced by nodes that are selfish, colluding with each other, or are offline most of the time. We present AVCOL, which uses probabilistic and gossip-style techniques to provide availability-aware aggregation. Concretely, AVCOL is the first aggregation system that: (1) implements any (arbitrary) global predicate that explicitly specifies any node’s probability of inclusion in the global aggregate, as a mathematical function of that node’s availability (i.e., percentage time online); (2) probabilistically tolerates large numbers of selfish nodes and large groups of colluders; and (3) scales well with hundreds to thousands of nodes. AVCOL uses several unique design decisions: per-aggregation tree construction where nodes are allowed a limited but flexible probabilistic choice of parents or children, probabilistic aggregation along trees, and auditing of nodes both during aggregation as well as in gossip-style (i.e., periodically). We have implemented AVCOL, and we experimentally evaluated it using real-life churn traces. Our evaluation and our mathematical analysis show that AVCOL satisfies arbitrary predicates, scales well, and withstands a variety of selfish and colluding attacks.     

Exponential stability for uncertain neutral systems with Markov jumps

This paper deals with the global exponential stability problems for stochastic neutral Markov jump systems （MJSs） with uncertain parameters and multiple time-delays. The delays are respectively considered as constant and time varying cases, and the uncertainties are assumed to be norm bounded. By selecting appropriate Lyapunov-Krasovskii functions, it gives the sufficient condition such that the uncertain neutral MJSs are globally exponentially stochastically stable for all admissible uncertainties. The stability criteria are formulated in the form of linear matrix inequalities （LMIs）, which can be easily checked in practice. Finally, two numerical examples are exploited to illustrate the effectiveness of the developed techniques.     

考虑数据分类的建筑电能耗集成预测方法

建筑侧各类可再生能源的应用日益普及,建筑电能耗预测在用能供需平衡、电网稳定运行、尖峰需求响应等方面发挥越来越重要作用。尽管诸多数据驱动模型在能耗预测方面获得广泛应用,当前仍缺乏预测精度高、泛化能力强的短期预测模型。针对该问题,提出一种基于建筑物能耗特点并结合数据挖掘技术的分类集成式能耗预测方法。首先,采用递归特征消除法对数据进行特征筛选,并用模糊C均值聚类算法对训练集数据进行聚类,使用K最邻近法对验证集和测试集数据进行归类;选择5种结合智能优化算法的混合数据驱动模型作为子学习器,分别对每类数据做预测,最后使用多元线性回归法进行结果集成。经3个建筑电力用能案例验证,此集成预测模型精度均优于单个子模型,具有适用不同建筑类型和用能尺度的预测潜力。     

Siamese transformer network-based similarity metric learning for cross-source remote sensing image retrieval

Neural Computing and Applications - As a fundamental technique for mining and analysis of remote sensing (RS) big data, content-based remote sensing image retrieval (CBRSIR) has received a lot of...     

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,...     

Volt-VAR control for distribution networks with high penetration of DGs: An overview

Continuously improving the ability to accept distributed renewable energies is the trend of future grid development, and a large number of papers have been published in recent years to study the problem of Volt-VAR control (VVC) for distribution networks with high penetration of distributed generations. This paper summarizes the relevant modeling and solution methods for VVC problems, mainly including VVC based on multiple time scales, hierarchical partitioning, multi-stage and network reconstruction, in conjunction with the operational characteristics of distribution networks containing distributed renewable energies; meanwhile, it analyzes the advantages and disadvantages of traditional optimization methods, heuristic intelligent algorithms and random variable processing methods used to solve VVC problems, and then introduces the application of model-free deep reinforcement learning as a latest decision method in VVC of distribution networks. Most of the models and methods compiled in this article are from the research results of the last three years and have some reference value.     

Orthonormal Bernoulli polynomials for space–time fractal-fractional modified Benjamin–Bona–Mahony type equations

Engineering with Computers - In this study, a new fractal-fractional (FF) derivative is defined by coupling the local conformable derivative and non-local Caputo fractional derivative. Using the...