TANK SIZING AND OPTIMIZING LOOP PLACEMENT IN A BRANCHED WATER DISTRIBUTION SYSTEM

More than eighteen percent of the world’s population lives without reliable access to clean water, forced to walk long distances to get small amounts of contaminated surface water. Carrying heavy loads of water long distances and ingesting contaminated water can lead to long-term health problems and even death. These problems affect the most vulnerable populations, women, children, and the elderly, more than anyone else. Water access is one of the most pressing issues in development today. Boajibu, a small village in Sierra Leone, where the author served in Peace Corps for two years, lacks access to clean water. Construction of a water distribution system was halted when a civil war broke out in 1992 and has not been continued since. The community currently relies on hand-dug and borehole wells that can become dirty during the dry season, which forces people to drink contaminated water or to travel a far distance to collect clean water. This report is intended to provide a design the system as it was meant to be built. The water system design was completed based on the taps present, interviews with local community leaders, local surveying, and points taken with a GPS. The design is a gravity-fed branched water system, supplied by a natural spring on a hill adjacent to Boajibu. The system’s source is a natural spring on a hill above Boajibu, but the flow rate of the spring is unknown. There has to be enough flow from the spring over a 24-hour period to meet the demands of the users on a daily basis, or what is called providing continuous flow. If the spring has less than this amount of flow, the system must provide intermittent flow, flow that is restricted to a few hours a day. A minimum flow rate of 2.1 liters per second was found to be necessary to provide continuous flow to the users of Boajibu. If this flow is not met, intermittent flow can be provided to the users. In order to aid the construction of a distribution system in the absence of someone with formal engineering training, a table was created detailing water storage tank sizing based on possible source flow rates. A builder can interpolate using the source flow rate found to get the tank size from the table. However, any flow rate below 2.1 liters per second cannot be used in the table. In this case, the builder should size the tank such that it can take in the water that will be supplied overnight, as all the water will be drained during the day because the users will demand more than the spring can supply through the night. In the developing world, there is often a problem collecting enough money to fund large infrastructure projects, such as a water distribution system. Often there is only enough money to add only one or two loops to a water distribution system. It is helpful to know where these one or two loops can be most effectively placed in the system. Various possible loops were designated for the Boajibu water distribution system and the Adaptive Greedy Heuristic Loop Addition Selection Algorithm (AGHLASA) was used to rank the effectiveness of the possible loops to construct. Loop 1 which was furthest upstream was selected because it benefitted the most people for the least cost. While loops which were further downstream were found to be less effective because they would benefit fewer people. Further studies should be conducted on the water use habits of the people of Boajibu to more accurately predict the demands that will be placed on the system. Further population surveying should also be conducted to predict population change over time so that the appropriate capacity can be built into the system to accommodate future growth. The flow at the spring should be measured using a V-notch weir and the system adjusted accordingly. Future studies can be completed adjusting the loop ranking method so that two users who may be using the water system for different lengths of time are not counted the same and vulnerable users are weighted more heavily than more robust users.

A novel adaptive weight selection algorithm for multi-objective multi-agent reinforcement learning

To solve multi-objective problems, multiple reward signals are often scalarized into a single value and further processed using established single-objective problem solving techniques. While the field of multi-objective optimization has made many advances in applying scalarization techniques to obtain good solution trade-offs, the utility of applying these techniques in the multi-objective multi-agent learning domain has not yet been thoroughly investigated. Agents learn the value of their decisions by linearly scalarizing their reward signals at the local level, while acceptable system wide behaviour results. However, the non-linear relationship between weighting parameters of the scalarization function and the learned policy makes the discovery of system wide trade-offs time consuming. Our first contribution is a thorough analysis of well known scalarization schemes within the multi-objective multi-agent reinforcement learning setup. The analysed approaches intelligently explore the weight-space in order to find a wider range of system trade-offs. In our second contribution, we propose a novel adaptive weight algorithm which interacts with the underlying local multi-objective solvers and allows for a better coverage of the Pareto front. Our third contribution is the experimental validation of our approach by learning bi-objective policies in self-organising smart camera networks. We note that our algorithm (i) explores the objective space faster on many problem instances, (ii) obtained solutions that exhibit a larger hypervolume, while (iii) acquiring a greater spread in the objective space.

Performance evaluation of adaptive context aware rate selection algorithm (ACARS) for road safety applications in vehicular network

One of the major drawbacks for mobile nodes in wireless networks is power management. Our goal is to evaluate the performance power control scheme to be used to reduce network congestion, improve quality of service and collision avoidance in vehicular network and road safety application. Some of the importance of power control (PC) are improving spatial reuse, and increasing network capacity in mobile wireless communications. In this simulation we have evaluated the performance of existing rate algorithms compared with context Aware Rate selection algorithm (ACARS) and also seen the performance of ACARS and how it can be applied to road safety, improve network control and power management. Result shows that ACARS is able to minimize the total transmit power in the presence of propagation processes and mobility of vehicles, by adapting to the fast varying channels conditions with the Path loss exponent values that was used for that environment which is shown in the network simulation parameter. Our results have shown that ACARS is a very robust algorithm which performs very well with the effect of propagation processes that is prone to every transmitted signal in mobile networks. © 2013 IEEE.

Adaptive Approach for a Maximum Entropy Algorithm in Ecological Niche Modeling

This paper presents an Adaptive Maximum Entropy (AME) approach for modeling biological species. The Maximum Entropy algorithm (MaxEnt) is one of the most used methods in modeling biological species geographical distribution. The approach presented here is an alternative to the classical algorithm. Instead of using the same set features in the training, the AME approach tries to insert or to remove a single feature at each iteration. The aim is to reach the convergence faster without affect the performance of the generated models. The preliminary experiments were well performed. They showed an increasing on performance both in accuracy and in execution time. Comparisons with other algorithms are beyond the scope of this paper. Some important researches are proposed as future works.

A forward constrained selection algorithm for probabilistic neural network

A new probabilistic neural network (PNN) learning algorithm based on forward constrained selection (PNN-FCS) is proposed. An incremental learning scheme is adopted such that at each step, new neurons, one for each class, are selected from the training samples arid the weights of the neurons are estimated so as to minimize the overall misclassification error rate. In this manner, only the most significant training samples are used as the neurons. It is shown by simulation that the resultant networks of PNN-FCS have good classification performance compared to other types of classifiers, but much smaller model sizes than conventional PNN.

Variable selection algorithm for the construction of MIMO operating point dependent neurofuzzy networks

An input variable selection procedure is introduced for the identification and construction of multi-input multi-output (MIMO) neurofuzzy operating point dependent models. The algorithm is an extension of a forward modified Gram-Schmidt orthogonal least squares procedure for a linear model structure which is modified to accommodate nonlinear system modeling by incorporating piecewise locally linear model fitting. The proposed input nodes selection procedure effectively tackles the problem of the curse of dimensionality associated with lattice-based modeling algorithms such as radial basis function neurofuzzy networks, enabling the resulting neurofuzzy operating point dependent model to be widely applied in control and estimation. Some numerical examples are given to demonstrate the effectiveness of the proposed construction algorithm.

Distribution system restoration in a DG environment using a heuristic constructive multi-start algorithm

This paper proposes a heuristic constructive multi-start algorithm (HCMA) to distribution system restoration in real time considering distributed generators installed in the system. The problem is modeled as nonlinear mixed integer and considers the two main goals of the restoration of distribution networks: minimizing the number of consumers without power and the number of switching. The proposed algorithm is implemented in C++ programming language and tested using a large real-life distribution system. The results show that the proposed algorithm is able to provide a set of feasible and good quality solutions in a suitable time for the problem. © 2011 IEEE.

Adaptív döntéshozatal a beszállítóválasztásban = Adaptive Decision-making in Supplier Selection

Herbert Simon, a korlátozott racionalitás elméletének megalkotója szerint a döntéshozatalunk hatékonyságát az határozza meg, hogy korlátozott kognitív kapacitásaink birtokában milyen stratégiákkal birkózunk meg a komplex környezet kihívásaival. Az erre az elméletre építő kutatások egyik alapvetése, hogy az egyéni problémamegoldási folyamat helyzetspecifikus és ez az idomulás kulcsfontosságú az eredményességben és hatékonyságban. A döntéshozó rendelkezik egy „adaptív szerszámosládával”, amelyből a megfelelő helyzetekben a megfelelő döntési eljárásokat választja. A tanulmányban a szerző egy kvalitatív kutatás eredményeire építve, a beszállítóválasztás példáján keresztül mutat be lehetséges válaszokat a keveset kutatott kérdésre: hogyan működik az adaptivitás folyamata? A tanulmány a döntési helyzethez való alkalmazkodás kialakulását vizsgálja a döntési folyamatok kognitív szintjén. ______ Herbert Simon, the author of theory of bounded rationality claimed that the results of our decision-making is defined by the approprietness of strategies with which we handle the complexity of the environment with our bounded cognitive capacities. One of the main issues or research programs building on this theory is that problem solving is situation-specific, and the adjustment of strategies to actual situational factors is crucial for the effectiveness and efficiency of decision-making. The decision-maker possesses an „adaptive toolbox”, from which he chooses the right decision tools in the right situations. The author, based on the findnings of a qualitative study, presents possible answers to the not well-elaborated question: how does the process of adaptivity work? Forming of an adaptive mechanism is in the focus.

The application of adaptive partitioned random search in feature selection problem

Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.

Adaptive approach heuristics for the generalized assignment problem

The Generalized Assignment Problem consists in assigning a setof tasks to a set of agents with minimum cost. Each agent hasa limited amount of a single resource and each task must beassigned to one and only one agent, requiring a certain amountof the resource of the agent. We present new metaheuristics forthe generalized assignment problem based on hybrid approaches.One metaheuristic is a MAX-MIN Ant System (MMAS), an improvedversion of the Ant System, which was recently proposed byStutzle and Hoos to combinatorial optimization problems, and itcan be seen has an adaptive sampling algorithm that takes inconsideration the experience gathered in earlier iterations ofthe algorithm. Moreover, the latter heuristic is combined withlocal search and tabu search heuristics to improve the search.A greedy randomized adaptive search heuristic (GRASP) is alsoproposed. Several neighborhoods are studied, including one basedon ejection chains that produces good moves withoutincreasing the computational effort. We present computationalresults of the comparative performance, followed by concludingremarks and ideas on future research in generalized assignmentrelated problems.

L’extraction de phrases en relation de traduction dans Wikipédia

Afin d'enrichir les données de corpus bilingues parallèles, il peut être judicieux de travailler avec des corpus dits comparables. En effet dans ce type de corpus, même si les documents dans la langue cible ne sont pas l'exacte traduction de ceux dans la langue source, on peut y retrouver des mots ou des phrases en relation de traduction. L'encyclopédie libre Wikipédia constitue un corpus comparable multilingue de plusieurs millions de documents. Notre travail consiste à trouver une méthode générale et endogène permettant d'extraire un maximum de phrases parallèles. Nous travaillons avec le couple de langues français-anglais mais notre méthode, qui n'utilise aucune ressource bilingue extérieure, peut s'appliquer à tout autre couple de langues. Elle se décompose en deux étapes. La première consiste à détecter les paires d’articles qui ont le plus de chance de contenir des traductions. Nous utilisons pour cela un réseau de neurones entraîné sur un petit ensemble de données constitué d'articles alignés au niveau des phrases. La deuxième étape effectue la sélection des paires de phrases grâce à un autre réseau de neurones dont les sorties sont alors réinterprétées par un algorithme d'optimisation combinatoire et une heuristique d'extension. L'ajout des quelques 560~000 paires de phrases extraites de Wikipédia au corpus d'entraînement d'un système de traduction automatique statistique de référence permet d'améliorer la qualité des traductions produites. Nous mettons les données alignées et le corpus extrait à la disposition de la communauté scientifique.

Submodular Optimization and Data Processing

Thesis (Ph.D.)--University of Washington, 2016-06

Social interaction as a heuristic for combinatorial optimization problems

We investigate the performance of a variant of Axelrod's model for dissemination of culture-the Adaptive Culture Heuristic (ACH)-on solving an NP-Complete optimization problem, namely, the classification of binary input patterns of size F by a Boolean Binary Perceptron. In this heuristic, N agents, characterized by binary strings of length F which represent possible solutions to the optimization problem, are fixed at the sites of a square lattice and interact with their nearest neighbors only. The interactions are such that the agents' strings (or cultures) become more similar to the low-cost strings of their neighbors resulting in the dissemination of these strings across the lattice. Eventually the dynamics freezes into a homogeneous absorbing configuration in which all agents exhibit identical solutions to the optimization problem. We find through extensive simulations that the probability of finding the optimal solution is a function of the reduced variable F/N(1/4) so that the number of agents must increase with the fourth power of the problem size, N proportional to F(4), to guarantee a fixed probability of success. In this case, we find that the relaxation time to reach an absorbing configuration scales with F(6) which can be interpreted as the overall computational cost of the ACH to find an optimal set of weights for a Boolean binary perceptron, given a fixed probability of success.