Decision Tree Classification Model In Water Supply Network

With the service life of water supply network (WSN) growth, the growing phenomenon of aging pipe network has become exceedingly serious. As urban water supply network is hidden underground asset, it is difficult for monitoring staff to make a direct classification towards the faults of pipe network by means of the modern detecting technology. In this paper, based on the basic property data (e.g. diameter, material, pressure, distance to pump, distance to tank, load, etc.) of water supply network, decision tree algorithm (C4.5) has been carried out to classify the specific situation of water supply pipeline. Part of the historical data was used to establish a decision tree classification model, and the remaining historical data was used to validate this established model. Adopting statistical methods were used to access the decision tree model including basic statistical method, Receiver Operating Characteristic (ROC) and Recall-Precision Curves (RPC). These methods has been successfully used to assess the accuracy of this established classification model of water pipe network. The purpose of classification model was to classify the specific condition of water pipe network. It is important to maintain the pipeline according to the classification results including asset unserviceable (AU), near perfect condition (NPC) and serious deterioration (SD). Finally, this research focused on pipe classification which plays a significant role in maintaining water supply networks in the future.

Optimization Of Groundwater Remediation With New Efficient Parallel Algorithm For Global Optimization

Application of optimization algorithm to PDE modeling groundwater remediation can greatly reduce remediation cost. However, groundwater remediation analysis requires a computational expensive simulation, therefore, effective parallel optimization could potentially greatly reduce computational expense. The optimization algorithm used in this research is Parallel Stochastic radial basis function. This is designed for global optimization of computationally expensive functions with multiple local optima and it does not require derivatives. In each iteration of the algorithm, an RBF is updated based on all the evaluated points in order to approximate expensive function. Then the new RBF surface is used to generate the next set of points, which will be distributed to multiple processors for evaluation. The criteria of selection of next function evaluation points are estimated function value and distance from all the points known. Algorithms created for serial computing are not necessarily efficient in parallel so Parallel Stochastic RBF is different algorithm from its serial ancestor. The application for two Groundwater Superfund Remediation sites, Umatilla Chemical Depot, and Former Blaine Naval Ammunition Depot. In the study, the formulation adopted treats pumping rates as decision variables in order to remove plume of contaminated groundwater. Groundwater flow and contamination transport is simulated with MODFLOW-MT3DMS. For both problems, computation takes a large amount of CPU time, especially for Blaine problem, which requires nearly fifty minutes for a simulation for a single set of decision variables. Thus, efficient algorithm and powerful computing resource are essential in both cases. The results are discussed in terms of parallel computing metrics i.e. speedup and efficiency. We find that with use of up to 24 parallel processors, the results of the parallel Stochastic RBF algorithm are excellent with speed up efficiencies close to or exceeding 100%.

Essays on decision sciences: exploring cognition, information processing, and complexity

This thesis provides three original contributions to the field of Decision Sciences. The first contribution explores the field of heuristics and biases. New variations of the Cognitive Reflection Test (CRT--a test to measure "the ability or disposition to resist reporting the response that first comes to mind"), are provided. The original CRT (S. Frederick [2005] Journal of Economic Perspectives, v. 19:4, pp.24-42) has items in which the response is immediate--and erroneous. It is shown that by merely varying the numerical parameters of the problems, large deviations in response are found. Not only the final results are affected by the proposed variations, but so is processing fluency. It seems that numbers' magnitudes serve as a cue to activate system-2 type reasoning. The second contribution explores Managerial Algorithmics Theory (M. Moldoveanu [2009] Strategic Management Journal, v. 30, pp. 737-763); an ambitious research program that states that managers display cognitive choices with a "preference towards solving problems of low computational complexity". An empirical test of this hypothesis is conducted, with results showing that this premise is not supported. A number of problems are designed with the intent of testing the predictions from managerial algorithmics against the predictions of cognitive psychology. The results demonstrate (once again) that framing effects profoundly affect choice, and (an original insight) that managers are unable to distinguish computational complexity problem classes. The third contribution explores a new approach to a computationally complex problem in marketing: the shelf space allocation problem (M-H Yang [2001] European Journal of Operational Research, v. 131, pp.107--118). A new representation for a genetic algorithm is developed, and computational experiments demonstrate its feasibility as a practical solution method. These studies lie at the interface of psychology and economics (with bounded rationality and the heuristics and biases programme), psychology, strategy, and computational complexity, and heuristics for computationally hard problems in management science.

Optimization of neural classifiers based on bayesian decision boundaries and idle neurons pruning

In this article we describe a feature extraction algorithm for pattern classification based on Bayesian Decision Boundaries and Pruning techniques. The proposed method is capable of optimizing MLP neural classifiers by retaining those neurons in the hidden layer that realy contribute to correct classification. Also in this article we proposed a method which defines a plausible number of neurons in the hidden layer based on the stem-and-leaf graphics of training samples. Experimental investigation reveals the efficiency of the proposed method. © 2002 IEEE.

Defects location in power systems components through a dedicated genetic algorithm

In this work the problem of defects location in power systems is formulated through a binary linear programming (BLP) model based on alarms historical database of control and protection devices from the system control center, sets theory of minimal coverage (AI) and protection philosophy adopted by the electric utility. In this model, circuit breaker operations are compared to their expected states in a strictly mathematical manner. For solving this BLP problem, which presents a great number of decision variables, a dedicated Genetic Algorithm (GA), is proposed. Control parameters of the GA, such as crossing over and mutation rates, population size, iterations number and population diversification, are calibrated in order to obtain efficiency and robustness. Results for a test system found in literature, are presented and discussed. © 2004 IEEE.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.

Application of an iterative method and an evolutionary algorithm in fuzzy optimization

This work develops two approaches based on the fuzzy set theory to solve a class of fuzzy mathematical optimization problems with uncertainties in the objective function and in the set of constraints. The first approach is an adaptation of an iterative method that obtains cut levels and later maximizes the membership function of fuzzy decision making using the bound search method. The second one is a metaheuristic approach that adapts a standard genetic algorithm to use fuzzy numbers. Both approaches use a decision criterion called satisfaction level that reaches the best solution in the uncertain environment. Selected examples from the literature are presented to compare and to validate the efficiency of the methods addressed, emphasizing the fuzzy optimization problem in some import-export companies in the south of Spain. © 2012 Brazilian Operations Research Society.

Software for calculation of reservoir active capacity with the sequent-peak algorithm

It is presented a software developed with Delphi programming language to compute the reservoir's annual regulated active storage, based on the sequent-peak algorithm. Mathematical models used for that purpose generally require extended hydrological series. Usually, the analysis of those series is performed with spreadsheets or graphical representations. Based on that, it was developed a software for calculation of reservoir active capacity. An example calculation is shown by 30-years (from 1977 to 2009) monthly mean flow historical data, from Corrente River, located at São Francisco River Basin, Brazil. As an additional tool, an interface was developed to manage water resources, helping to manipulate data and to point out information that it would be of interest to the user. Moreover, with that interface irrigation districts where water consumption is higher can be analyzed as a function of specific seasonal water demands situations. From a practical application, it is possible to conclude that the program provides the calculation originally proposed. It was designed to keep information organized and retrievable at any time, and to show simulation on seasonal water demands throughout the year, contributing with the elements of study concerning reservoir projects. This program, with its functionality, is an important tool for decision making in the water resources management.

Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning

Active machine learning algorithms are used when large numbers of unlabeled examples are available and getting labels for them is costly (e.g. requiring consulting a human expert). Many conventional active learning algorithms focus on refining the decision boundary, at the expense of exploring new regions that the current hypothesis misclassifies. We propose a new active learning algorithm that balances such exploration with refining of the decision boundary by dynamically adjusting the probability to explore at each step. Our experimental results demonstrate improved performance on data sets that require extensive exploration while remaining competitive on data sets that do not. Our algorithm also shows significant tolerance of noise.

Decision-directed feedback equalisation for QAM signals

Proposed is a symbol-based decision-directed algorithm for blind equalisation of quadrature amplitude modulation (QAM) signals using a decision feedback scheme. Independently of QAM order, it presents: (i) an error equal to zero when the equaliser output coincides with the transmitted signal; (ii) simultaneous recovery of the modulus and phase of the signal; (iii) a misadjustment close to that of the normalised least-mean squares algorithm; (iv) fast convergence; and (v) the avoidance of degenerative solutions. Additionally, its stability is ensured when the step-size is properly chosen.

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

Background: This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. Results: The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. Conclusions: We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.

Nonlinear estimation of neural processing time from BOLD signal with application to decision-making

The extraction of information about neural activity timing from BOLD signal is a challenging task as the shape of the BOLD curve does not directly reflect the temporal characteristics of electrical activity of neurons. In this work, we introduce the concept of neural processing time (NPT) as a parameter of the biophysical model of the hemodynamic response function (HRF). Through this new concept we aim to infer more accurately the duration of neuronal response from the highly nonlinear BOLD effect. The face validity and applicability of the concept of NPT are evaluated through simulations and analysis of experimental time series. The results of both simulation and application were compared with summary measures of HRF shape. The experiment that was analyzed consisted of a decision-making paradigm with simultaneous emotional distracters. We hypothesize that the NPT in primary sensory areas, like the fusiform gyrus, is approximately the stimulus presentation duration. On the other hand, in areas related to processing of an emotional distracter, the NPT should depend on the experimental condition. As predicted, the NPT in fusiform gyrus is close to the stimulus duration and the NPT in dorsal anterior cingulate gyrus depends on the presence of an emotional distracter. Interestingly, the NPT in right but not left dorsal lateral prefrontal cortex depends on the stimulus emotional content. The summary measures of HRF obtained by a standard approach did not detect the variations observed in the NPT. Hum Brain Mapp, 2012. (C) 2010 Wiley Periodicals, Inc.

A regional multirnodulus algorithm for blind equalization of QAM signals: Introduction and steady-state analysis

It is well known that constant-modulus-based algorithms present a large mean-square error for high-order quadrature amplitude modulation (QAM) signals, which may damage the switching to decision-directed-based algorithms. In this paper, we introduce a regional multimodulus algorithm for blind equalization of QAM signals that performs similar to the supervised normalized least-mean-squares (NLMS) algorithm, independently of the QAM order. We find a theoretical relation between the coefficient vector of the proposed algorithm and the Wiener solution and also provide theoretical models for the steady-state excess mean-square error in a nonstationary environment. The proposed algorithm in conjunction with strategies to speed up its convergence and to avoid divergence can bypass the switching mechanism between the blind mode and the decision-directed mode. (c) 2012 Elsevier B.V. All rights reserved.

Evolving decision trees with beam search-based initialization and lexicographic multi-objective evaluation

Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, traditional decision-tree induction algorithms implement a greedy approach for node splitting that is inherently susceptible to local optima convergence. Evolutionary algorithms can avoid the problems associated with a greedy search and have been successfully employed to the induction of decision trees. Previously, we proposed a lexicographic multi-objective genetic algorithm for decision-tree induction, named LEGAL-Tree. In this work, we propose extending this approach substantially, particularly w.r.t. two important evolutionary aspects: the initialization of the population and the fitness function. We carry out a comprehensive set of experiments to validate our extended algorithm. The experimental results suggest that it is able to outperform both traditional algorithms for decision-tree induction and another evolutionary algorithm in a variety of application domains.

A multi-objective genetic algorithm optimisation using variable speed pumps in water distribution systems

Due to its practical importance and inherent complexity, the optimisation of distribution networks for supplying drinking water has been the subject of extensive study for the past 30 years. The optimization is governed by sizing the pipes in the water distribution network (WDN) and / or optimises specific parts of the network such as pumps, tanks etc. or try to analyse and optimise the reliability of a WDN. In this thesis, the author has analysed two different WDNs (Anytown City and Cabrera city networks), trying to solve and optimise a multi-objective optimisation problem (MOOP). The main two objectives in both cases were the minimisation of Energy Cost (€) or Energy consumption (kWh), along with the total Number of pump switches (TNps) during a day. For this purpose, a decision support system generator for Multi-objective optimisation used. Its name is GANetXL and has been developed by the Center of Water System in the University of Exeter. GANetXL, works by calling the EPANET hydraulic solver, each time a hydraulic analysis has been fulfilled. The main algorithm used, was a second-generation algorithm for multi-objective optimisation called NSGA_II that gave us the Pareto fronts of each configuration. The first experiment that has been carried out was the network of Anytown city. It is a big network with a pump station of four fixed speed parallel pumps that are boosting the water dynamics. The main intervention was to change these pumps to new Variable speed driven pumps (VSDPs), by installing inverters capable to diverse their velocity during the day. Hence, it’s been achieved great Energy and cost savings along with minimisation in the number of pump switches. The results of the research are thoroughly illustrated in chapter 7, with comments and a variety of graphs and different configurations. The second experiment was about the network of Cabrera city. The smaller WDN had a unique FS pump in the system. The problem was the same as far as the optimisation process was concerned, thus, the minimisation of the energy consumption and in parallel the minimisation of TNps. The same optimisation tool has been used (GANetXL).The main scope was to carry out several and different experiments regarding a vast variety of configurations, using different pump (but this time keeping the FS mode), different tank levels, different pipe diameters and different emitters coefficient. All these different modes came up with a large number of results that were compared in the chapter 8. Concluding, it should be said that the optimisation of WDNs is a very interested field that has a vast space of options to deal with. This includes a large number of algorithms to choose from, different techniques and configurations to be made and different support system generators. The researcher has to be ready to “roam” between these choices, till a satisfactory result will convince him/her that has reached a good optimisation point.