TecDEM: A MATLAB based toolbox for tectonic geomorphology, Part 1: Drainage network preprocessing and stream profile analysis

We present TecDEM, a software shell implemented in MATLAB that applies tectonic geomorphologic tasks to digital elevation models (DEMs). The first part of this paper series describes drainage partitioning schemes and stream profile analysis. The graphical user interface of TecDEM provides several options: determining flow directions, stream vectorization, watershed delineation, Strahler order labeling, stream profile generation, knickpoints selection, Concavity, Steepness and Hack indices calculations. The knickpoints along selected streams as well as stream profile analysis, and Hack index per stream profile are computed using a semi-automatic method. TecDEM was used to extract and investigate the stream profiles in the Kaghan Valley (Northern Pakistan). Our interpretations of the TecDEM results correlate well with previous tectonic evolution models for this region. TecDEM is designed to assist geoscientists in applying complex tectonic geomorphology tasks to global DEM data.     

View-invariant motion trajectory-based activity classification and recognition

Motion trajectories provide rich spatio-temporal information about an object's activity. The trajectory information can be obtained using a tracking algorithm on data streams available from a range of devices including motion sensors, video cameras, haptic devices, etc. Developing view-invariant activity recognition algorithms based on this high dimensional cue is an extremely challenging task. This paper presents efficient activity recognition algorithms using novel view-invariant representation of trajectories. Towards this end, we derive two Affine-invariant representations for motion trajectories based on curvature scale space (CSS) and centroid distance function (CDF). The properties of these schemes facilitate the design of efficient recognition algorithms based on hidden Markov models (HMMs). In the CSS-based representation, maxima of curvature zero crossings at increasing levels of smoothness are extracted to mark the location and extent of concavities in the curvature. The sequences of these CSS maxima are then modeled by continuous density (HMMs). For the case of CDF, we first segment the trajectory into subtrajectories using CDF-based representation. These subtrajectories are then represented by their Principal Component Analysis (PCA) coefficients. The sequences of these PCA coefficients from subtrajectories are then modeled by continuous density hidden Markov models (HMMs). Different classes of object motions are modeled by one Continuous HMM per class where state PDFs are represented by GMMs. Experiments using a database of around 1750 complex trajectories (obtained from UCI-KDD data archives) subdivided into five different classes are reported.     

Multivariable integral linear quadratic Gaussian robust control of islanded microgrid to mitigate voltage oscillation for improving transient response

A novel robust integral linear quadratic Gaussian (ILQG) controller is presented in this paper to control the voltage of islanded microgrid and improves its transient response. Microgrid is a small grid that consists of number of distributed generator units, power‐electronic components with inductor‐capacitor (LC) filters and loads. The loads are parametrically uncertain and unknown that produces the voltage or power oscillation. The ILQG controller is capable to compensate for the voltage oscillation and exhibits the tracking of grid voltage against the different load dynamics. The design of ILQG controller is carried out by augmenting the plant dynamics with an integrator. The robustness of the ILQG controller is studied by considering a number of uncertainties within the plant model. The performance of ILQG controller is compared with linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG) controller in terms of rise time, settling time, bandwidth and tracking error. The comparison results ensure the high bandwidth and tracking performance of ILQG controller as compared to other controllers.     

Quantifying explainable discrimination and removing illegal discrimination in automated decision making

Recently, the following discrimination-aware classification problem was introduced. Historical data used for supervised learning may contain discrimination, for instance, with respect to gender. The question addressed by discrimination-aware techniques is, given sensitive attribute, how to train discrimination-free classifiers on such historical data that are discriminative, with respect to the given sensitive attribute. Existing techniques that deal with this problem aim at removing all discrimination and do not take into account that part of the discrimination may be explainable by other attributes. For example, in a job application, the education level of a job candidate could be such an explainable attribute. If the data contain many highly educated male candidates and only few highly educated women, a difference in acceptance rates between woman and man does not necessarily reflect gender discrimination, as it could be explained by the different levels of education. Even though selecting on education level would result in more males being accepted, a difference with respect to such a criterion would not be considered to be undesirable, nor illegal. Current state-of-the-art techniques, however, do not take such gender-neutral explanations into account and tend to overreact and actually start reverse discriminating, as we will show in this paper. Therefore, we introduce and analyze the refined notion of conditional non-discrimination in classifier design. We show that some of the differences in decisions across the sensitive groups can be explainable and are hence tolerable. Therefore, we develop methodology for quantifying the explainable discrimination and algorithmic techniques for removing the illegal discrimination when one or more attributes are considered as explanatory. Experimental evaluation on synthetic and real-world classification datasets demonstrates that the new techniques are superior to the old ones in this new context, as they succeed in removing almost exclusively the undesirable discrimination, while leaving the explainable differences unchanged, allowing for differences in decisions as long as they are explainable.     

An open-book watershed model for prototyping space-borne flood monitoring systems in International River Basins

A new era involving both simple and complex hydrologic modeling of un-gauged river basins may now emerge with the anticipated global availability of high resolution satellite rainfall data from the proposed Global Precipitation Measurement (GPM) mission. This era of application pertains to rapid prototyping of GPM-based flood monitoring systems for downstream nations in International River Basins (IRBs) where basin-wide in-situ rainfall data is unavailable due to lack of either an infrastructure or a treaty for real-time data sharing with upstream riparian nations. In this paper, we develop, verify and apply an open-book watershed model for demonstrating the value of a parsimonious modeling scheme in quick prototyping of satellite rainfall-based flood monitoring systems for lowermost nations in flood-prone IRBs. The open-book watershed modeling concept was first formulated by Yen and Chow [1969. A laboratory study of surface runoff due to moving rainstorms. Water Resources Research 5(5), 989–1006] more than 30 years ago as a convenient and pragmatic framework to understand the underlying physics behind surface hydrologic phenomena. Our developed model is based on first principles of conservation of mass and momentum that parsimoniously represents the static geophysical features of a basin with minimum calibration. Such a generic and parsimonious representation has the added potential to supplement complex hydrologic models for stakeholder involvement and conflict management in transboundary river basins, among many additional applications. We first demonstrate the physical consistency of our model through sensitivity analysis of some geophysical basin parameters pertinent to the rainfall-runoff transformation. Next, we simulate the stream-flow hydrograph for a 4-month long period using basin-wide radar (WSR-88D) rainfall data over Oklahoma assuming an open-book river basin configuration. Finally, using the radar-simulated hydrograph as the benchmark, and assuming a two-nation hypothetical IRB over Oklahoma, we explored the impact of assimilating NASA's real-time satellite rainfall data (IR-3B41RT) over the upstream nation on the flow monitoring accuracy for the downstream nation. We developed a relationship defining the improvement in flow monitoring that can be expected from assimilating IR-3B41RT over transboundary regions as a function of the relative area occupied by the downstream nation for a semi-arid region. The relative improvement in flow monitoring accuracy for the downstream nation was found to be clearly high (over 35% reduction in root mean squared error) when more than 90% of the basin is transboundary. However, flow monitoring accuracy reduces considerably and even becomes negative when 60% or less of the basin area is transboundary to the downstream nation. Our findings, although hypothetical and very regime-specific, illustrate very clearly the feasibility of utilizing anticipated GPM data to alleviate the current flood monitoring limitations experienced by many nations in IRBs through the application of a generic and parsimonious model.     

Crown Structures for Vertex Cover Kernelization

Crown structures in a graph are defined and shown to be useful in kernelization algorithms for the classic vertex cover problem. Two vertex cover kernelization methods are discussed. One, based on linear programming, has been in prior use and is known to produce predictable results, although it was not previously associated with crowns. The second, based on crown structures, is newer and much faster, but produces somewhat variable results. These two methods are studied and compared both theoretically and experimentally with each other and with older, more primitive kernelization algorithms. Properties of crowns and methods for identifying them are discussed. Logical connections between linear programming and crown reductions are established. It is shown that the problem of finding an induced crown-free subgraph, and the problem of finding a crown of maximum size in an arbitrary graph, are solvable in polynomial time.     

Extending SPARQL with regular expression patterns (for querying RDF)

RDF is a knowledge representation language dedicated to the annotation of resources within the framework of the semantic web. Among the query languages for RDF, SPARQL allows querying RDF through graph patterns, i.e., RDF graphs involving variables. Other languages, inspired by the work in databases, use regular expressions for searching paths in RDF graphs. Each approach can express queries that are out of reach of the other one. Hence, we aim at combining these two approaches. For that purpose, we define a language, called PRDF (for “Path RDF”) which extends RDF such that the arcs of a graph can be labeled by regular expression patterns. We provide PRDF with a semantics extending that of RDF, and propose a correct and complete algorithm which, by computing a particular graph homomorphism, decides the consequence between an RDF graph and a PRDF graph. We then define the PSPARQL query language, extending SPARQL with PRDF graph patterns and complying with RDF model theoretic semantics. PRDF thus offers both graph patterns and path expressions. We show that this extension does not increase the computational complexity of SPARQL and, based on the proposed algorithm, we have implemented a correct and complete PSPARQL query engine.     

Interference management in dense inband D2D network using spectral clustering & dynamic resource allocation

Device-to-device (D2D) communication has emerged as a promising concept to improve resource utilization in fifth generation cellular networks. D2D network’s architectural capability to offload traffic from the backhaul network to direct links enables it to be used for internet of things (IoT) services. In a densely deployed setting of IoT devices, D2D network may experience critical interferences due to a limited number of spectral resources. To increase the overall signal-to-interference-plus-noise ratio (SINR) of the network while reducing the computational load on a macro base station, a novel decentralized interference management methodology is proposed for dense in-band D2D underlay LTE-A network. The proposed interference management scheme can decouple interference in a network into cross-cluster and intra-cluster interference and tackle with them separately. To mitigate the cross-cluster interference in a dense D2D network we propose dividing the densely deployed D2D user equipments (UEs) network into well-separated clusters using spectral clustering with modified kernel weights. The proposed spectral clustering scheme obtains well-separated clusters with regards to cross-cluster interference, that is, the UEs that offer maximum interference to each other are grouped into the same cluster. Thereafter, a dynamic resource allocation algorithm is proposed within each cluster to reduce the intra-cluster interference. The proposed dynamic resource allocation algorithm uses graph coloring to allocate resources in such a manner that after each spectrum allocation, a small cell base station updates the interference graph and assigns the next largest interference affected UE a spectrum resource that minimizes the overall intra-cluster interference the most. In conventional graph coloring, the adjacent UEs are allocated different spectrum resources without taking into consideration if the allocated spectrum resource might result in increased interference in the cluster. The simulation results show that the proposed clustering strategy considerably reduces the average cross-cluster interference as compared to other benchmark clustering algorithms such as K-means and KPCA. Moreover, the proposed resource allocation algorithm decreases the intra-cluster interference in the network resulting in the overall SINR maximization of the network.

     

Optimization Study of Residuum Hydrotreating Processes

This paper presents an optimization study of catalytic hydrotreating reactors processing heavy residuum feedstock. The focus is on conversion, throughput and catalyst life. The core of the proposed optimization model is a cost function representing the essential economical parameters of hydrotreating processes and accounts for additional costs imposed by deeper desulfurization in addition to the monetary benefit of lower sulfur products. Operational variables are estimated using a mathematical model, which accounts for catalyst deactivation. Simulation results are presented to illustrate the effect of various operating variables on the process performance. An industrial scale atmospheric residue desulfurization process has been selected as a typical hydrotreating unit to demonstrate the capabilities of the optimization model. Optimization results were found quite reliable and consistent with actual industrial practices.