skip to main content

Search for: All records

Award ID contains: 1823279

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. In this paper, we present the CET-LATS (Compressing Evolution of TINs from Location Aware Time Series) system, which enables testing the impacts of various compression approaches on evolving Triangulated Irregular Networks (TINs). Specifically, we consider the settings in which values measured in distinct locations and at different time instants, are represented as time series of the corresponding measurements, generating a sequence of TINs. Different compression techniques applied to location-specific time series may have different impacts on the representation of the global evolution of TINs - depending on the distance functions used to evaluate the distortion. CET-LATS users can view and analyze compression vs. (im)precision trade-offs over multiple compression methods and distance functions, and decide which method works best for their application. We also provide an option to investigate the impact of the choice of a compression method on the quality of prediction. Our prototype is a web-based system using Flask, a lightweight Python framework, relying on Apache Spark for data management and JSON files to communicate with the front-end, enabling extensibility in terms of adding new data sources as well as compression techniques, distance functions and prediction methods.
  2. Hydropower is the largest renewable energy source for electricity generation in the world, with numerous benefits in terms of: environment protection (near-zero air pollution and climate impact), cost-effectiveness (long-term use, without significant impacts of market fluctuation), and reliability (quickly respond to surge in demand). However, the effectiveness of hydropower plants is affected by multiple factors such as reservoir capacity, rainfall, temperature and fluctuating electricity demand, and particularly their complicated relationships, which make the prediction/recommendation of station operational output a difficult challenge. In this paper, we present DeepHydro, a novel stochastic method for modeling multivariate time series (e.g., water inflow/outflow and temperature) and forecasting power generation of hydropower stations. DeepHydro captures temporal dependencies in co-evolving time series with a new conditioned latent recurrent neural networks, which not only considers the hidden states of observations but also preserves the uncertainty of latent variables. We introduce a generative network parameterized on a continuous normalizing flow to approximate the complex posterior distribution of multivariate time series data, and further use neural ordinary differential equations to estimate the continuous-time dynamics of the latent variables constituting the observable data. This allows our model to deal with the discrete observations in the context of continuous dynamic systems,more »while being robust to the noise. We conduct extensive experiments on real-world datasets from a large power generation company consisting of cascade hydropower stations. The experimental results demonstrate that the proposed method can effectively predict the power production and significantly outperform the possible candidate baseline approaches.« less
  3. Flow super-resolution (FSR) enables inferring fine-grained urban flows with coarse-grained observations and plays an important role in traffic monitoring and prediction. The existing FSR solutions rely on deep CNN models (e.g., ResNet) for learning spatial correlation, incurring excessive memory cost and numerous parameter updates. We propose to tackle the urban flows inference using dynamic systems paradigm and present a new method FODE -- FSR with Ordinary Differential Equations (ODEs). FODE extends neural ODEs by introducing an affine coupling layer to overcome the problem of numerically unstable gradient computation, which allows more accurate and efficient spatial correlation estimation, without extra memory cost. In addition, FODE provides a flexible balance between flow inference accuracy and computational efficiency. A FODE-based augmented normalization mechanism is further introduced to constrain the flow distribution with the influence of external factors. Experimental evaluations on two real-world datasets demonstrate that FODE significantly outperforms several baseline approaches.

  4. Location-Based Services are often used to find proximal Points of Interest PoI - e.g., nearby restaurants and museums, police stations, hospitals, etc. - in a plethora of applications. An important recently addressed variant of the problem not only considers the distance/proximity aspect, but also desires semantically diverse locations in the answer-set. For instance, rather than picking several close-by attractions with similar features - e.g., restaurants with similar menus; museums with similar art exhibitions - a tourist may be more interested in a result set that could potentially provide more diverse types of experiences, for as long as they are within an acceptable distance from a given (current) location. Towards that goal, in this work we propose a novel approach to efficiently retrieve a path that will maximize the semantic diversity of the visited PoIs that are within distance limits along a given road network. We introduce a novel indexing structure - the Diversity Aggregated R-tree, based on which we devise efficient algorithms to generate the answer-set - i.e., the recommended locations among a set of given PoIs - relying on a greedy search strategy. Our experimental evaluations conducted on real datasets demonstrate the benefits of proposed methodology over the baselinemore »alternative approaches.« less
  5. The advances in the Internet of Things (IoT) paradigm have enabled generation of large volumes of data from multiple domains, capturing the evolution of various physical and social phenomena of interest. One of the consequences of such enormous data generation is that it needs to be stored, processed and queried – along with having the answers presented in an intuitive manner. A number of techniques have been proposed to alleviate the impact of the sheer volume of the data on the storage and processing overheads, along with bandwidth consumption – and, among them, the most dominant is compression. In this paper, we consider a setting in which multiple geographically dispersed data sources are generating data streams – however, the values from the discrete locations are used to construct a representation of continuous (time-evolving) surface. We have used different compression techniques to reduce the size of the raw measurements in each location, and we analyzed the impact of the compression on the quality of approximating the evolution of the shapes corresponding to a particular phenomenon. Specifically, we use the data from discrete locations to construct a TIN (triangulated irregular networks), which evolves over time as the measurements in each locations change.more »To analyze the global impact of the different compression techniques that are applied locally, we used different surface distance functions between raw-data TINs and compressed data TINs. We provide detailed discussions based on our experimental observations regarding the corresponding (compression method, distance function) pairs.« less
  6. We present a prototype system for effective management of a delivery fleet in the settings in which the traffic abnormalities may necessitate rerouting of (some of) the trucks. Unforeseen congestions (e.g., due to accidents) may affect the average speed along road segments that were used to calculate the routes of a particular truck. Complementary to the traditional (re)routing approaches where the main objective is to find the new shortest route to the same destination but under the changed traffic circumstances, we incorporate two additional constraints. Namely, we aim at striking a balance between minimizing the additional expenses due to drivers overtime pay and maximizing the delivery of the goods still available on the truck’s load, possibly by changing the original destinations. The project is developed with an actual industry partner with main business of managing supplies for office pantries, kitchens and caf´es.