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

Estimating crop yield in large areas is essential for ensuring food security and sustainable development. Accounting for variations in the temporal cumulative growth of crops across regions (i.e., spatial heterogeneity of crop growth) can improve the accuracy of yield estimation in large areas. However, current spatial heterogeneity learning methods have limitations such as cutting off inherent correlations among regions, difficulty obtaining accurate prior knowledge, and high subjectivity. Therefore, this study proposed a novel deep learning adaptive crop model (DACM) to accomplish adaptive high-precision yield estimation in large areas, which emphasizes adaptive learning of the spatial heterogeneity of crop growth based on fully extracting crop growth information. Results showed that the DACM achieved an average root mean squared error (RMSE) of 4.406 bushels·acre−1 (296.304 kg ha−1), with an average coefficient of determination (R2) of 0.805. Compared with other state-of-the-art machine learning and deep learning methods, DACM improves the large-area yield estimation accuracy and performs more robustly in space. The analyses on attention values and estimation stability demonstrate that DACM can learn the spatial heterogeneity of crop growth and adopt adaptive strategies to optimize yield estimation. Considering both performance stability and interpretability, DACM provides a practical approach for estimating large-area crop yields by adaptively learning the spatial heterogeneity patterns of crop growth. 

A deep learning crop model for adaptive yield estimation in large areas

Sustainability research faces many challenges as respective environmental, urban and regional contexts are experiencing rapid changes at an unprecedented spatial granularity level, which involves growing massive data and the need for spatial relationship detection at a faster pace. Spatial join is a fundamental method for making data more informative with respect to spatial relations. The dramatic growth of data volumes has led to increased focus on high-performance large-scale spatial join. In this paper, we present Spatial Join with Spark (SJS), a proposed high-performance algorithm, that uses a simple, but efficient, uniform spatial grid to partition datasets and joins the partitions with the built-in join transformation of Spark. SJS utilizes the distributed in-memory iterative computation of Spark, then introduces a calculation-evaluating model and in-memory spatial repartition technology, which optimize the initial partition by evaluating the calculation amount of local join algorithms without any disk access. We compare four in-memory spatial join algorithms in SJS for further performance improvement. Based on extensive experiments with real-world data, we conclude that SJS outperforms the Spark and MapReduce implementations of earlier spatial join approaches. This study demonstrates that it is promising to leverage high-performance computing for large-scale spatial join analysis. The availability of large-sized geo-referenced datasets along with the high-performance computing technology can raise great opportunities for sustainability research on whether and how these new trends in data and technology can be utilized to help detect the associated trends and patterns in the human-environment dynamics.

A New Design of High-Performance Large-Scale GIS Computing at a Finer Spatial Granularity: A Case Study of Spatial Join with Spark for Sustainability

Traffic guidance, traffic management and emergency vehicle traffic all require keeping abreast of traffic status. Intelligent Transportation Systems (ITS) is highly expected to provide real-time traffic condition information service. To achieve this, the capability of handling dynamic data stream collected from multi traffic monitoring sources and serving the public with information timely is essential for ITS. With the wide spread of Internet of Things technology, not only the amount, but also the spatial and temporal resolutions of real-time traffic data have explosive growth, thereby enhancing the difficulty of real-time traffic data processing in ITS. Web pyramid map tiles is wide accepted for massive spatial data service, and the latency of tile generation significantly reduces the timeliness of information transmission and the reliability of services. A Flink-based method for dynamic pyramid tile generation and updating is proposed here. Take advantages of combining grid indexes, employing data partition and window selection mechanisms, and applying iterative computational characteristics for resampling, the distributed dynamic pyramid map tile generation algorithm (DPTG), can quickly visualize real-time spatial traffic data with digital map tiles. Taking the national highway road data from China as an example, the experimental results show that the Flink-based DPTG method has high efficiency and scalability in both batch processing and stream processing mode, which highlights the capability of the proposed method to support real-time traffic monitoring data processing for timely large-scale public service in ITS.

A Dynamic Pyramid Tilling Method for Traffic Data Stream Based on Flink

Cloud storage is an effective means to solve the storage and management challenges of the growing mass remote sensing data products. In order to settle the problems existing in the application of cloud storage for remote sensing data, a distributed storage module based on image blocks organization was put forward, and the inefficient problem of distributed file system in massive image blocks storage was solved. In the combination of the module and HDFS, the efficient distributed storage and retrieval of image data were implemented, and the ability of spatial data access of the remote sensing data cloud storage was enabled. As built upon distributed file system, the storage system has a good scalability to meet the requirements of data growing. The experiment and analysis showed that the storage system could maintain a high throughput and stability under multiple concurrent connections.

Zhenhong Du

Papers

A deep learning crop model for adaptive yield estimation in large areas

A New Design of High-Performance Large-Scale GIS Computing at a Finer Spatial Granularity: A Case Study of Spatial Join with Spark for Sustainability

A Dynamic Pyramid Tilling Method for Traffic Data Stream Based on Flink

Cloud storage of massive remote sensing data based on distributed file system

A Hybrid Semantic Similarity Measurement for Geospatial Entities