Implementation and Field Testing of Characteristics-Based Intersection Queue Estimation Model

This paper proposes and implements a dynamic queue length estimation model at signalized intersections. Based on the continuum principle, this model describes the formation and dissipation of traffic queues at an intersection and the interactions with the signals. The use of a general speeddensity relationship in the model leads to the development of a general mathematical formulation for intersection queue length studies, which allows the model to adapt to changes in traffic and geometric conditions through parameter updating. Field testing of the model has demonstrated initial success. Experience suggests that use of headway and average speed data in density derivation lead to more stable and accurate results than using other traffic data.     

Contents,Volume 12, 2000

Volume Contents

Contents, Volume 12, 2000     

Group decision-making model with incomplete fuzzy preference relations based on additive consistency.

In decision-making problems there may be cases in which experts do not have an in-depth knowledge of the problem to be solved. In such cases, experts may not put their opinion forward about certain aspects of the problem, and as a result they may present incomplete preferences, i.e., some preference values may not be given or may be missing. In this paper, we present a new model for group decision making in which experts' preferences can be expressed as incomplete fuzzy preference relations. As part of this decision model, we propose an iterative procedure to estimate the missing information in an expert's incomplete fuzzy preference relation. This procedure is guided by the additive-consistency (AC) property and only uses the preference values the expert provides. The AC property is also used to measure the level of consistency of the information provided by the experts and also to propose a new induced ordered weighted averaging (IOWA) operator, the AC-IOWA operator, which permits the aggregation of the experts' preferences in such a way that more importance is given to the most consistent ones. Finally, the selection of the solution set of alternatives according to the fuzzy majority of the experts is based on two quantifier-guided choice degrees: the dominance and the nondominance degree.     

Full-Duplex DOCSIS/WirelessDOCSIS Fiber–Radio Network Employing Packaged AFPMs as Optical/Electrical Transducers

A hybrid fiber-radio access network architecture for simultaneous wireline and wireless transmissions of data-over-cable service interface specification (DOCSIS) signals is presented. An all-optical harmonic up-conversion technique using a dual-drive Mach-Zehnder modulator provides the downstream optical signal modulated not only at the intermediate frequency in the 600- to 900-MHz band for wireline transmission but also at the up-converted frequency in the 5.45- to 5.75-GHz band for wireless transmission. An InGaAsP/InGaAsP multiple-quantum-well asymmetric Fabry-Perot modulator/detector has been designed, fabricated, and packaged and has been employed in the base station (BS) as an optical/electrical transducer, simultaneously providing the functions of optical intensity modulation and photodetection. At the BS, the DOCSIS signals are recovered at the wireline and wireless frequencies for the respective feeding of a cable access network or a fixed wireless access network in a highly flexible approach. Full-duplex operation has been demonstrated for both access types in an indoor laboratory environment. In a subsequent small-scale field trial, real-life Internet traffic provided by a local community antenna television system operator has been transported over the present hybrid fiber-radio access network architecture, and simultaneous transmission of both DOCSIS and digital television signals has also been performed.     

EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling

Classification with imbalanced data-sets has become one of the most challenging problems in Data Mining. Being one class much more represented than the other produces undesirable effects in both the learning and classification processes, mainly regarding the minority class. Such a problem needs accurate tools to be undertaken; lately, ensembles of classifiers have emerged as a possible solution. Among ensemble proposals, the combination of Bagging and Boosting with preprocessing techniques has proved its ability to enhance the classification of the minority class.In this paper, we develop a new ensemble construction algorithm (EUSBoost) based on RUSBoost, one of the simplest and most accurate ensemble, which combines random undersampling with Boosting algorithm. Our methodology aims to improve the existing proposals enhancing the performance of the base classifiers by the usage of the evolutionary undersampling approach. Besides, we promote diversity favoring the usage of different subsets of majority class instances to train each base classifier. Centered on two-class highly imbalanced problems, we will prove, supported by the proper statistical analysis, that EUSBoost is able to outperform the state-of-the-art methods based on ensembles. We will also analyze its advantages using kappa-error diagrams, which we adapt to the imbalanced scenario.     

First intercomparison between the aerosols optical depth data obtained by the NOAA satellite sensors and the Optronic OL752 spectroradiometer in the Canary Islands

The aerosol optical depth data obtained by two methods are compared. The first method uses solar radiance measurements with the spectroradiometer Optronic OL752 installed inside the Santa Cruz de Tenerife Harbour (28.48 N, 16.24 W). The second one takes the solar radiation scattered by the atmosphere using the Advanced Very High Resolution Radiometer (AVHRR) radiometer channels on board the NOAA meteorological satellites. A 91 per cent correlation between the two procedures was obtained for channel 1 (630nm).     

Finding multiple solutions in job shop scheduling by niching genetic algorithms

The interest in multimodal optimization methods is increasing in the last years. The objective is to find multiple solutions that allow the expert to choose the solution that better adapts to the actual conditions. Niching methods extend genetic algorithms to domains that require the identification of multiple solutions. There are different niching genetic algorithms: sharing, clearing, crowding and sequential, etc. The aim of this study is to study the applicability and the behavior of several niching genetic algorithms in solving job shop scheduling problems, by establishing a criterion in the use of different methods according to the needs of the expert. We will experiment with different instances of this problem, analyzing the behavior of the algorithms from the efficacy and diversity points of view.     

Diet Prevents Social Stress-Induced Maladaptive Neurobehavioural and Gut Microbiota Changes in a Histamine-Dependent Manner

Exposure to repeated social stress may cause maladaptive emotional reactions that can be reduced by healthy nutritional supplementation. Histaminergic neurotransmission has a central role in orchestrating specific behavioural responses depending on the homeostatic state of a subject, but it remains to be established if it participates in the protective effects against the insults of chronic stress afforded by a healthy diet. By using C57BL/6J male mice that do not synthesize histamine (Hdc^−/−) and their wild type (Hdc^+/+) congeners we evaluated if the histaminergic system participates in the protective action of a diet enriched with polyunsaturated fatty acids and vitamin A on the deleterious effect of chronic stress. Behavioural tests across domains relevant to cognition and anxiety were performed. Hippocampal synaptic plasticity, cytokine expression, hippocampal fatty acids, oxylipins and microbiota composition were also assessed. Chronic stress induced social avoidance, poor recognition memory, affected hippocampal long-term potentiation, changed the microbiota profile, brain cytokines, fatty acid and oxylipins composition of both Hdc^−/− and Hdc^+/+ mice. Dietary enrichment counteracted stress-induced deficits only in Hdc^+/+ mice as histamine deficiency prevented almost all the diet-related beneficial effects. Interpretation: Our results reveal a previously unexplored and novel role for brain histamine as a mediator of many favorable effects of the enriched diet. These data present long-reaching perspectives in the field of nutritional neuropsychopharmacology.     

Quantum Entanglement and Correlations in Superconducting Flux Qubits

We report on the quantum correlations dissipative dynamics followed by coupled superconducting flux qubits. The coupling between the superconducting quantum register and the reservoir is described by two different mechanisms: collective and independent decoherence. By means of the Bloch?CRedfield formalism, we solve the quantum master equation and show that coupling under collective quantum noise is more robust to decoherence. This result is demonstrated for different flux qubit initial preparations, taking into account the influence due to external fields and temperature. Furthermore, we compute the entanglement and the quantum discord dissipative dynamics as controlled by external parameters. We show that the discord is more robust against decoherence effects. This fact could be harnessed in the realization of quantum computing tasks that do not need to invoke entanglement in their implementation.     

Dynamic classifier selection for One-vs-One strategy: Avoiding non-competent classifiers

The One-vs-One strategy is one of the most commonly used decomposition technique to overcome multi-class classification problems; this way, multi-class problems are divided into easier-to-solve binary classification problems considering pairs of classes from the original problem, which are then learned by independent base classifiers.The way of performing the division produces the so-called non-competence. This problem occurs whenever an instance is classified, since it is submitted to all the base classifiers although the outputs of some of them are not meaningful (they were not trained using the instances from the class of the instance to be classified). This issue may lead to erroneous classifications, because in spite of their incompetence, all classifiers' decisions are usually considered in the aggregation phase.In this paper, we propose a dynamic classifier selection strategy for One-vs-One scheme that tries to avoid the non-competent classifiers when their output is probably not of interest. We consider the neighborhood of each instance to decide whether a classifier may be competent or not. In order to verify the validity of the proposed method, we will carry out a thorough experimental study considering different base classifiers and comparing our proposal with the best performer state-of-the-art aggregation within each base classifier from the five Machine Learning paradigms selected. The findings drawn from the empirical analysis are supported by the appropriate statistical analysis.