There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The theory of affordances

We propose a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed. For obtaining a “flat” partition consisting of only the most significant clusters (possibly corresponding to different density thresholds), we propose a novel cluster stability measure, formalize the problem of maximizing the overall stability of selected clusters, and formulate an algorithm that computes an optimal solution to this problem. We demonstrate that our approach outperforms the current, state-of-the-art, density-based clustering methods on a wide variety of real world data.

Density-based clustering based on hierarchical density estimates

Integration of photovoltaics into power grids is difficult as solar energy is highly dependent on climate and geography; often fluctuating erratically. This causes penetrations and voltage surges, system instability, inefficient utilities planning and financial loss. Forecast models can help; however, time stamp, forecast horizon, input correlation analysis, data pre and post-processing, weather classification, network optimization, uncertainty quantification and performance evaluations need consideration. Thus, contemporary forecasting techniques are reviewed and evaluated. Input correlational analyses reveal that solar irradiance is most correlated with Photovoltaic output, and so, weather classification and cloud motion study are crucial. Moreover, the best data cleansing processes: normalization and wavelet transforms, and augmentation using generative adversarial network are recommended for network training and forecasting. Furthermore, optimization of inputs and network parameters, using genetic algorithm and particle swarm optimization, is emphasized. Next, established performance evaluation metrics MAE, RMSE and MAPE are discussed, with suggestions for including economic utility metrics. Subsequently, modelling approaches are critiqued, objectively compared and categorized into physical, statistical, artificial intelligence, ensemble and hybrid approaches. It is determined that ensembles of artificial neural networks are best for forecasting short term photovoltaic power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty. Additionally, convolutional neural network is found to excel in eliciting a model's deep underlying non-linear input-output relationships. The conclusions drawn impart fresh insights in photovoltaic power forecast initiatives, especially in the use of hybrid artificial neural networks and evolutionary algorithms.

A review and evaluation of the state-of-the-art in PV solar power forecasting:Techniques and optimization

Location monitoring is an important issue for real time management of mobile object positions. Significant research efforts have been dedicated to techniques for efficient processing of spatial continuous queries on moving objects in a centralized location monitoring system. Surprisingly, very few have promoted a distributed approach to real-time location monitoring. In this paper we present a distributed and scalable solution to processing continuously moving queries on moving objects and describe the design of MobiEyes, a distributed real-time location monitoring system in a mobile environment. Mobieyes utilizes the computational power at mobile objects, leading to significant savings in terms of server load and messaging cost when compared to solutions relying on central processing of location information at the server. We introduce a set of optimization techniques, such as Lazy Query Propagation, Query Grouping, and Safe Periods, to constrict the amount of computations handled by the moving objects and to enhance the performance and system utilization of Mobieyes. We also provide a simulation model in a mobile setup to study the scalability of the MobiEyes distributed location monitoring approach with regard to server load, messaging cost, and amount of computation required on the mobile objects.

MobiEyes: Distributed processing of continuously moving queries on moving objects in a mobile system

Geographically weighted regression (GWR) is a classic and widely used approach to model spatial non-stationarity. However, the approach makes no precise expressions of its weighting kernels and is ...

Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity

With the rapid development of mobile data acquisition technology, the volume of available spatial data is growing at an increasingly fast pace. The real-time processing of big spatial data has become a research frontier in the field of Geographic Information Systems (GIS). To cope with these highly dynamic data, we aim to reduce the time complexity of data updating by modifying the traditional spatial index. However, existing algorithms and data structures are based on single work nodes, which are incapable of handling the required high numbers and update rates of moving objects. In this paper, we present a distributed spatial index based on Apache Storm, an open-source distributed real-time computation system. Using this approach, we compare the range and K-nearest neighbor (KNN) query efficiency of four spatial indexes on a single dataset and introduce a method of performing spatial joins between two moving datasets. In particular, we build a secondary distributed index for spatial join queries based on the grid-partition index. Finally, a series of experiments are presented to explore the factors that affect the performance of the distributed index and to demonstrate the feasibility of the proposed distributed index based on Storm. As a real-world application, this approach has been integrated into an information system that provides real-time traffic decision support.

https://www.mdpi.com/2220-9964/5/10/178/pdf

Real-Time Spatial Queries for Moving Objects Using Storm Topology

In this research, factors that influence ecosystem health assessment (EHA) in coastal waters were grouped into three categories: natural causes, direct human causes, and indirect human causes. Statistical analysis based on previous researches was utilized to determine EHA indicators of the first two categories. For the third category, spatio-temporal patterns of potential EHA indicators are prevalent due to the variations of anthropogenic activities in their spatial and temporal distribution across seascape and over timescales. Various Geographic Information System (GIS) analysis methods were hence incorporated to translate and visualize anthropogenic activities into ecosystem-specific impacts. The most adequate indexes were identified to establish a holistic EHA framework. Case study in the northern coastal waters of Zhejiang Province from 2005 to 2014 excellently highlighted ecosystem health changes over the years and revealed the advantage of visualization efficiency and the superiority of considering intense human disturbance of our EHA method over traditional ones.

Ecosystem health assessment in coastal waters by considering spatio-temporal variations with intense anthropogenic disturbance

Geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR) are classic methods for estimating non-stationary relationships. Although these methods have be...

Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships

Multistep-ahead forecasting is essential to many practical problems, such as the early warning of disasters. However, existing studies mainly focus on current-time or one-step-ahead prediction since forecasting multiple steps continuously presents difficulties, such as accumulated errors and long-term time series modeling. In this paper, an effective multistep-ahead forecasting model wavelet nonlinear autoregressive network (WNARNet), which integrates the wavelet transform and a nonlinear autoregressive neural network (NAR), is proposed for the forecast of chlorophyll a concentration. The wavelet transform decreases the accumulative errors by dividing complicated time series into simpler ones. Simultaneously, the NAR maintains the dependencies between the time series. The buoy monitoring data of the Wenzhou coastal area obtained in 2014-2015 is used to verify the feasibility and effectiveness of WNARNet. The model performs well in predicting the dynamics of chlorophyll a and it is able to predict different horizons flexibly and accurately without training new models. Furthermore, experimental results demonstrate that WNARNet significantly outperforms other benchmark methods of multistep-ahead forecasting. When forecasting 20 steps ahead, the r of WNARNet is 0.08 higher and the RMSE is 0.04 lower than the values of the benchmark models. Therefore, the newly proposed approach represents a promising and effective method for the future prediction of chlorophyll a.

Zhenhong Du

Papers

Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity

Real-Time Spatial Queries for Moving Objects Using Storm Topology

Ecosystem health assessment in coastal waters by considering spatio-temporal variations with intense anthropogenic disturbance

Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships

Multistep-ahead forecasting of chlorophyll a using a wavelet nonlinear autoregressive network