QoE-aware power management in vehicle-to-grid networks:a matching-theoretic approach

Frequency, time and places of charging and discharging have critical impact on the Quality of Experience (QoE) of using Electric Vehicles (EVs). EV charging and discharging scheduling schemes should consider both the QoE of using EV and the load capacity of the power grid. In this paper, we design a traveling plan-aware scheduling scheme for EV charging in driving pattern and a cooperative EV charging and discharging scheme in parking pattern to improve the QoE of using EV and enhance the reliability of the power grid. For traveling planaware scheduling, the assignment of EVs to Charging Stations (CSs) is modeled as a many-to-one matching game and the Stable Matching Algorithm (SMA) is proposed. For cooperative EV charging and discharging in parking pattern, the electricity exchange between charging EVs and discharging EVs in the same parking lot is formulated as a many-to-many matching model with ties, and we develop the Pareto Optimal Matching Algorithm (POMA). Simulation results indicates that the SMA can significantly improve the average system utility for EV charging in driving pattern, and the POMA can increase the amount of electricity offloaded from the grid which is helpful to enhance the reliability of the power grid.

Improving matching process in social network

Online dating networks, a type of social network, are gaining popularity. With many people joining and being available in the network, users are overwhelmed with choices when choosing their ideal partners. This problem can be overcome by utilizing recommendation methods. However, traditional recommendation methods are ineffective and inefficient for online dating networks where the dataset is sparse and/or large and two-way matching is required. We propose a methodology by using clustering, SimRank to recommend matching candidates to users in an online dating network. Data from a live online dating network is used in evaluation. The success rate of recommendation obtained using the proposed method is compared with baseline success rate of the network and the performance is improved by double.

Fast exact nearest neighbour matching in high dimensions using d-D Sort

Data structures such as k-D trees and hierarchical k-means trees perform very well in approximate k nearest neighbour matching, but are only marginally more effective than linear search when performing exact matching in high-dimensional image descriptor data. This paper presents several improvements to linear search that allows it to outperform existing methods and recommends two approaches to exact matching. The first method reduces the number of operations by evaluating the distance measure in order of significance of the query dimensions and terminating when the partial distance exceeds the search threshold. This method does not require preprocessing and significantly outperforms existing methods. The second method improves query speed further by presorting the data using a data structure called d-D sort. The order information is used as a priority queue to reduce the time taken to find the exact match and to restrict the range of data searched. Construction of the d-D sort structure is very simple to implement, does not require any parameter tuning, and requires significantly less time than the best-performing tree structure, and data can be added to the structure relatively efficiently.

Highly luminescent and thermally stable lanthanide coordination polymers designed from 4-(dipyridin-2-yl)aminobenzoate: efficient energy transfer from Tb3+ to Eu3+ in a mixed lanthanide coordination compound

Herein, a new aromatic carboxylate ligand, namely, 4-(dipyridin-2-yl)aminobenzoic acid (HL), has been designed and employed for the construction of a series of lanthanide complexes (Eu3+ = 1, Tb3+ = 2, and Gd3+ = 3). Complexes of 1 and 2 were structurally authenticated by single-crystal X-ray diffraction and were found to exist as infinite 1D coordination polymers with the general formulas {Eu(L)(3)(H2O)(2)]}(n) (1) and {Tb(L)(3)(H2O)]center dot(H2O)}(n) (2). Both compounds crystallize in monoclinic space group C2/c. The photophysical properties demonstrated that the developed 4-(dipyridin-2-yl)aminobenzoate ligand is well suited for the sensitization of Tb3+ emission (Phi(overall) = 64%) thanks to the favorable position of the triplet state ((3)pi pi*) of the ligand the energy difference between the triplet state of the ligand and the excited state of Tb3+ (Delta E) = (3)pi pi* - D-5(4) = 3197 cm(-1)], as investigated in the Gd3+ complex. On the other hand, the corresponding Eu3+ complex shows weak luminescence efficiency (Phi(overall) = 7%) due to poor matching of the triplet state of the ligand with that of the emissive excited states of the metal ion (Delta E = (3)pi pi* - D-5(0) = 6447 cm(-1)). Furthermore, in the present work, a mixed lanthanide system featuring Eu3+ and Tb3+ ions with the general formula {Eu0.5Tb0.5(L)(3)(H2O)(2)]}(n) (4) was also synthesized, and the luminescent properties were evaluated and compared with those of the analogous single-lanthanide-ion systems (1 and 2). The lifetime measurements for 4 strongly support the premise that efficient energy transfer occurs between Tb3+ and Eu3+ in a mixed lanthanide system (eta = 86%).

Probability-Matching Predictors for Extreme Extremes

A location- and scale-invariant predictor is constructed which exhibits good probability matching for extreme predictions outside the span of data drawn from a variety of (stationary) general distributions. It is constructed via the three-parameter {\mu, \sigma, \xi} Generalized Pareto Distribution (GPD). The predictor is designed to provide matching probability exactly for the GPD in both the extreme heavy-tailed limit and the extreme bounded-tail limit, whilst giving a good approximation to probability matching at all intermediate values of the tail parameter \xi. The predictor is valid even for small sample sizes N, even as small as N = 3. The main purpose of this paper is to present the somewhat lengthy derivations which draw heavily on the theory of hypergeometric functions, particularly the Lauricella functions. Whilst the construction is inspired by the Bayesian approach to the prediction problem, it considers the case of vague prior information about both parameters and model, and all derivations are undertaken using sampling theory.

Feature Matching for Object Localization in the Presence of Uncertainty

We consider the problem of matching model and sensory data features in the presence of geometric uncertainty, for the purpose of object localization and identification. The problem is to construct sets of model feature and sensory data feature pairs that are geometrically consistent given that there is uncertainty in the geometry of the sensory data features. If there is no geometric uncertainty, polynomial-time algorithms are possible for feature matching, yet these approaches can fail when there is uncertainty in the geometry of data features. Existing matching and recognition techniques which account for the geometric uncertainty in features either cannot guarantee finding a correct solution, or can construct geometrically consistent sets of feature pairs yet have worst case exponential complexity in terms of the number of features. The major new contribution of this work is to demonstrate a polynomial-time algorithm for constructing sets of geometrically consistent feature pairs given uncertainty in the geometry of the data features. We show that under a certain model of geometric uncertainty the feature matching problem in the presence of uncertainty is of polynomial complexity. This has important theoretical implications by demonstrating an upper bound on the complexity of the matching problem, an by offering insight into the nature of the matching problem itself. These insights prove useful in the solution to the matching problem in higher dimensional cases as well, such as matching three-dimensional models to either two or three-dimensional sensory data. The approach is based on an analysis of the space of feasible transformation parameters. This paper outlines the mathematical basis for the method, and describes the implementation of an algorithm for the procedure. Experiments demonstrating the method are reported.

The Matching of Doubly Ambiguous Stereograms

I have previously described psychophysical experiments that involved the perception of many transparent layers, corresponding to multiple matching, in doubly ambiguous random dot stereograms. Additional experiments are described in the first part of this paper. In one experiment, subjects were required to report the density of dots on each transparent layer. In another experiment, the minimal density of dots on each layer, which is required for the subjects to perceive it as a distinct transparent layer, was measured. The difficulties encountered by stereo matching algorithms, when applied to doubly ambiguous stereograms, are described in the second part of this paper. Algorithms that can be modified to perform consistently with human perception, and the constraints imposed on their parameters by human perception, are discussed.

Von Neumann-Morgenstern Stable Sets in Matching Problems

The following properties of the core of a one well-known: (i) the core is non-empty; (ii) the core is a lattice; and (iii) the set of unmatched agents is identical for any two matchings belonging to the core. The literature on two-sided matching focuses almost exclusively on the core and studies extensively its properties. Our main result is the following characterization of (von Neumann-Morgenstern) stable sets in one-to-one matching problem only if it is a maximal set satisfying the following properties : (a) the core is a subset of the set; (b) the set is a lattice; (c) the set of unmatched agents is identical for any two matchings belonging to the set. Furthermore, a set is a stable set if it is the unique maximal set satisfying properties (a), (b) and (c). We also show that our main result does not extend from one-to-one matching problems to many-to-one matching problems.

Matching Markets under (In)complete Information

We are the first to introduce incomplete information to centralized many-to-one matching markets such as those to entry-level labor markets or college admissions. This is important because in real life markets (i) any agent is uncertain about the other agents' true preferences and (ii) most entry-level matching is many-to-one (and not one-to-one). We show that for stable (matching) mechanisms there is a strong and surprising link between Nash equilibria under complete information and Bayesian Nash equilibria under incomplete information. That is,given a common belief, a strategy profile is a Bayesian Nash equilibrium under incomplete information in a stable mechanism if and only if, for any true profile in the support of the common belief, the submitted profile is a Nash equilibrium under complete information at the true profile in the direct preference revelation game induced by the stable mechanism. This result may help to explain the success of stable mechanisms in these markets.

A proposal to estimate the motion of an underwater vehicle through visual mosaicking

This thesis proposes a solution to the problem of estimating the motion of an Unmanned Underwater Vehicle (UUV). Our approach is based on the integration of the incremental measurements which are provided by a vision system. When the vehicle is close to the underwater terrain, it constructs a visual map (so called "mosaic") of the area where the mission takes place while, at the same time, it localizes itself on this map, following the Concurrent Mapping and Localization strategy. The proposed methodology to achieve this goal is based on a feature-based mosaicking algorithm. A down-looking camera is attached to the underwater vehicle. As the vehicle moves, a sequence of images of the sea-floor is acquired by the camera. For every image of the sequence, a set of characteristic features is detected by means of a corner detector. Then, their correspondences are found in the next image of the sequence. Solving the correspondence problem in an accurate and reliable way is a difficult task in computer vision. We consider different alternatives to solve this problem by introducing a detailed analysis of the textural characteristics of the image. This is done in two phases: first comparing different texture operators individually, and next selecting those that best characterize the point/matching pair and using them together to obtain a more robust characterization. Various alternatives are also studied to merge the information provided by the individual texture operators. Finally, the best approach in terms of robustness and efficiency is proposed. After the correspondences have been solved, for every pair of consecutive images we obtain a list of image features in the first image and their matchings in the next frame. Our aim is now to recover the apparent motion of the camera from these features. Although an accurate texture analysis is devoted to the matching pro-cedure, some false matches (known as outliers) could still appear among the right correspon-dences. For this reason, a robust estimation technique is used to estimate the planar transformation (homography) which explains the dominant motion of the image. Next, this homography is used to warp the processed image to the common mosaic frame, constructing a composite image formed by every frame of the sequence. With the aim of estimating the position of the vehicle as the mosaic is being constructed, the 3D motion of the vehicle can be computed from the measurements obtained by a sonar altimeter and the incremental motion computed from the homography. Unfortunately, as the mosaic increases in size, image local alignment errors increase the inaccuracies associated to the position of the vehicle. Occasionally, the trajectory described by the vehicle may cross over itself. In this situation new information is available, and the system can readjust the position estimates. Our proposal consists not only in localizing the vehicle, but also in readjusting the trajectory described by the vehicle when crossover information is obtained. This is achieved by implementing an Augmented State Kalman Filter (ASKF). Kalman filtering appears as an adequate framework to deal with position estimates and their associated covariances. Finally, some experimental results are shown. A laboratory setup has been used to analyze and evaluate the accuracy of the mosaicking system. This setup enables a quantitative measurement of the accumulated errors of the mosaics created in the lab. Then, the results obtained from real sea trials using the URIS underwater vehicle are shown.

Interactional infrastructure in rural communities : matching training needs and provision

This paper reports some of the main lessons learnt from a collaborative project titled Generating jobs in regional Tasmania: a social capital approach investigating how two small rural Tasmanian communities could better match local training needs with training provision. The project was conducted within the context of the wider social, economic and demographic changes affecting the two rural communities and their ongoing efforts to manage such change. The paper provides a profile of the two communities with particular attention to their local education, training and employment infrastructure. Three research questions in terms of improving the contributions of leadership, partnerships and social capital are addressed. Development and utilisation of social capital, particularly in the form of interactional infrastructure (defined as opportunities and structures for interaction in a community) that brought together the range of stakeholders, appeared the key to successfully matching needs with provision.

Operational research applied to regional healthcare system

In this thesis we focus on optimization and simulation techniques applied to solve strategic, tactical and operational problems rising in the healthcare sector. At first we present three applications to Emilia-Romagna Public Health System (SSR) developed in collaboration with Agenzia Sanitaria e Sociale dell'Emilia-Romagna (ASSR), a regional center for innovation and improvement in health. Agenzia launched a strategic campaign aimed at introducing Operations Research techniques as decision making tools to support technological and organizational innovations. The three applications focus on forecast and fund allocation of medical specialty positions, breast screening program extension and operating theater planning. The case studies exploit the potential of combinatorial optimization, discrete event simulation and system dynamics techniques to solve resource constrained problem arising within Emilia-Romagna territory. We then present an application in collaboration with Dipartimento di Epidemiologia del Lazio that focuses on population demand of service allocation to regional emergency departments. Finally, a simulation-optimization approach, developed in collaboration with INESC TECH center of Porto, to evaluate matching policies for the kidney exchange problem is discussed.

An Empirical Study of Non-Rigid Surface Feature Matching of Human from 3D Video

This paper presents an empirical study of affine invariant feature detectors to perform matching on video sequences of people with non-rigid surface deformation. Recent advances in feature detection and wide baseline matching have focused on static scenes. Video frames of human movement capture highly non-rigid deformation such as loose hair, cloth creases, skin stretching and free flowing clothing. This study evaluates the performance of six widely used feature detectors for sparse temporal correspondence on single view and multiple view video sequences. Quantitative evaluation is performed of both the number of features detected and their temporal matching against and without ground truth correspondence. Recall-accuracy analysis of feature matching is reported for temporal correspondence on single view and multiple view sequences of people with variation in clothing and movement. This analysis identifies that existing feature detection and matching algorithms are unreliable for fast movement with common clothing.

Sharpness Matching in Stereo Images

When stereo images are captured under less than ideal conditions, there may be inconsistencies between the two images in brightness, contrast, blurring, etc. When stereo matching is performed between the images, these variations can greatly reduce the quality of the resulting depth map. In this paper we propose a method for correcting sharpness variations in stereo image pairs which is performed as a pre-processing step to stereo matching. Our method is based on scaling the 2D discrete cosine transform (DCT) coefficients of both images so that the two images have the same amount of energy in each of a set of frequency bands. Experiments show that applying the proposed correction method can greatly improve the disparity map quality when one image in a stereo pair is more blurred than the other.