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GIS-Based Decision Support System for Improved Operations and Efficiency Conservation in Large-Scale Irrigation Systems

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JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING 2012年第10期138卷 857-867页

作者： Triana, Enrique Labadie, John W. Colorado State Univ Dept Civil & Environm Engn Ft Collins CO 80523 USA MWH Amer Inc Ft Collins CO 80525 USA

The long-term sustainability of irrigated agriculture is contingent on application of cost-effective water conservation measures and improved management in the face of intense competition for limited freshwater resources from municipal, industrial, and environmental/ecological interests. A geographic information system (GIS)-based decision support system (GeoDSS) is presented for maximizing water savings from efficiency improvements and conservation measures through integrated management of large-scale irrigation systems. The GeoDSS incorporates the GeoMODSIM network flow optimization model as implemented within ArcGIS Desktop (ESRI, Inc.) and is applied to the large-scale, complex Imperial Irrigation District (IID) water delivery system in California in support of the IID/San Diego County Water Authority (SDCWA) Water Transfer Agreement. IID GeoDSS automatically constructs a GeoMODSIM flow network within an ESRI geodatabase composed of feature classes for IID canals, drains, reservoirs, farm gates, interceptors, and pumpback locations. Following calibration of the baseline network, GeoMODSIM simulations predict substantial water savings from mixtures of strategies of varying cost for improved water management and increased delivery system efficiency and on-farm water conservation, without the need for fallowing or retirement of IID irrigated lands. DOI: 10.1061/(ASCE)IR.1943-4774.0000481. (C) 2012 American Society of Civil Engineers.

关键词： Canals Decision support system Geographic information system Irrigation efficiency network flow optimization Water conservation Water delivery systems

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(MP)²T: Multiple People Multiple Parts Tracker

(MP)<SUP>2</SUP>T: Multiple People Multiple Parts Tracker

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12th European Conference on Computer Vision (ECCV)

作者： Izadinia, Hamid Saleemi, Imran Li, Wenhui Shah, Mubarak Univ Cent Florida Comp Vis Lab Orlando FL 32816 USA

ISBN: (纸本)9783642337826

We present a method for multi-target tracking that exploits the persistence in detection of object parts. While the implicit representation and detection of body parts have recently been leveraged for improved human detection, ours is the first method that attempts to temporally constrain the location of human body parts with the express purpose of improving pedestrian tracking. We pose the problem of simultaneous tracking of multiple targets and their parts in a network flow optimization framework and show that parts of this network need to be optimized separately and iteratively, due to inter-dependencies of node and edge costs. Given potential detections of humans and their parts separately, an initial set of pedestrian tracklets is first obtained, followed by explicit tracking of human parts as constrained by initial human tracking. A merging step is then performed whereby we attempt to include part-only detections for which the entire human is not observable. This step employs a selective appearance model, which allows us to skip occluded parts in description of positive training samples. The result is high confidence, robust trajectories of pedestrians as well as their parts, which essentially constrain each other's locations and associations, thus improving human tracking and parts detection. We test our algorithm on multiple real datasets and show that the proposed algorithm is an improvement over the state-of-the-art.

关键词： multi-target tracking pedestrians humans body part tracking network flow optimization k-shortest paths

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Supervised feature selection in graphs with path coding penalties and network flows

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2013年第1期14卷

作者： Kevin Murphy Bernhard Schölkopf Julien Mairal Bin Yu Google MPI for Intelligent Systems INRIA Grenoble Rhône-Alpes France and Department of Statistics University of California Berkeley CA Department of Statistics University of California Berkeley CA and Department of Electrical Engineering & Computer Science

We consider supervised learning problems where the features are embedded in a graph, such as gene expressions in a gene network. In this context, it is of much interest to automatically select a subgraph with few connected components; by exploiting prior knowledge, one can indeed improve the prediction performance or obtain results that are easier to interpret. Regularization or penalty functions for selecting features in graphs have recently been proposed, but they raise new algorithmic challenges. For example, they typically require solving a combinatorially hard selection problem among all connected subgraphs. In this paper, we propose computationally feasible strategies to select a sparse and well-connected subset of features sitting on a directed acyclic graph (DAG). We introduce structured sparsity penalties over paths on a DAG called "path coding" penalties. Unlike existing regularization functions that model long-range interactions between features in a graph, path coding penalties are tractable. The penalties and their proximal operators involve path selection problems, which we efficiently solve by leveraging network flow optimization. We experimentally show on synthetic, image, and genomic data that our approach is scalable and leads to more connected subgraphs than other regularization functions for graphs.

关键词： convex and non-convex optimization graph sparsity network flow optimization

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Escape from the Delta: preparation and evacuation for catastrophic flooding in California Emergency Management Agency Region IV

Escape from the Delta: preparation and evacuation for catast...

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作者： Yuhas, Timothy C. Naval Postgraduate School

学位级别：硕士

We model the regional highway system of Central California and consider the challenge of evacuating a highly populated region from the threat of catastrophic flood. Specifically, we build a minimum cost network flow problem to represent the movement of more than one million people within Yolo, Sacramento, San Joaquin, and Stanislaus Counties. Our model solves for "best case" evacuation routes and clearing times assuming perfect knowledge of flood inundation and road conditions. Our model is large but efficient, solving 35 separate scenarios in less than 45 minutes. We develop two basic evacuation scenarios, each having many variations, resulting in 490 total scenarios. For these, we analyze model assumptions and the effect of interruptions to evacuation behavior for a range of "what-if" situations.

关键词： Evacuation network flow optimization Minimum Cost network flow

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Applying Mathematical Programming Food Supply Model for Improving Japan's Food Self-Sufficiency Ratio

Applying Mathematical Programming Food Supply Model for Impr...

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第十届运筹学及其应用国际研讨会

作者： Kunihisa Yoshii Tatsuo Oyama Ministry of Agriculture ForestryAnd Fishery Central GovernmentBldgNo. 4 3-1-1Tokyo 100-0013Japan National Graduate Institute for Policy Studies 7-22-1Roppongi Minato-ku Tokyo 106-8677Japan

As the largest net import country of agricultural products in the world, Japan's food self-sufficiency ratio (SSR) on a calorie basis has significantly decreased from 73% in FY 1965 to about 40% in recent years. Improving the SSR has been or will be a very important policy issue for Japan. The latest Basic Plan for Food, Agriculture and Rural Areas has set a target of 50% as the SSR in FY 2020. Using the mathematical programming food supply model, we investigate the policy strategies for improving the SSR. Our model is structurally a linear programming model aiming at a network flow optimization in order to maximize Japan's SSR. We analyze four scenarios on the policy strategies for improving the SSR;ⅰ)promoting consumption of rice, ⅱ)expanding the production of rice for feed, ⅲ)expanding the production of wheat and soybeans, and ⅳ)combination of the above-mentioned three strategies. Numerical results show that Japan's SSR could get to only 48% even if perfect import substitution of domestic product for imported one holds, given the constraint of calories consumed per day per capita per food item.

关键词： food self-sufficiency ratio linear programming model network flow optimization optimal food supply policy strategy

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An adaptive latency mitigation scheme for massively multiuser virtual environments

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JOURNAL OF network AND COMPUTER APPLICATIONS 2009年第5期32卷 1049-1063页

作者： Hariri, Behnoosh Shirmohammadi, Shervin Pakravan, Mohammad Reza Alavi, Mohammad Hossein Univ Ottawa Sch Informat Technol & Engn Distributed Collaborat Virtual Environm Res DISCO Ottawa ON K1N 6N5 Canada Sharif Univ Technol Dept Elect Engn ACRI Tehran Iran

As massively multiuser virtual environments (MMVEs) expand in terms of size and user population, they tend toward using P2P architectures as a way to provide scalability without the need for large centralized resources. Distributed hash table (DHT)-based networks have been introduced as a promising option for overlay-based distributed massively multiuser virtual environment applications. However, overlay latency stretch seriously affects MMVE performance where QoS is crucial for real-time User collaboration. This work includes a series of efforts in the alleviation of such undesired latency. Our approach to latency mitigation consists of two phases. First, we propose a position-based ID assignment approach to minimize message hop-count by exploiting the clustered pattern of traffic exchange among MMVE Users. Second, we introduce a new ant-based distributed neighbor selection scheme that can be used by MMVE Users to select the best neighbors within their areas of interest. In order to evaluate the performance of this heuristic approach, we model the neighbor selection problem in the form of a network flow problem and use its solution as an optimality bound to compare the results. Simulation results demonstrate that the proposed algorithms will compensate for DHT latency stretch to a high extent and the performance of the resulting System would closely follow the optimal bound while communication overhead is negligible. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Massively multiuser virtual environments Hilbert curve Proximity neighbor selection network flow optimization Ant routing

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A Novel Approach for Providing QoS with network Coding

A Novel Approach for Providing QoS with Network Coding

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International Symposium on Telecommunications

作者： Salavati, Amir Hesam Khalaj, Babak Hosein Aref, Mohammad Reza Sharif Univ Technol Sch Elect Engn Tehran Iran

ISBN: (纸本)9781424427505

In this paper, we present a decentralized algorithm that computes minimum cost QoS flow sub-graphs in network coded multicast networks. These subgraphs are minimum cost solutions that also satisfy user-specified QoS constraints, specifically handling elastic rate and delay demands. Although earlier network coding algorithms in this area have only demonstrated QoS improvements, the proposed QoS network coding is clearly different in the sense that it guarantees that given QoS constraints are met over the network.

关键词： network coding Quality of service network flow optimization

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Optimal information extraction in energy-limited wireless sensor networks

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2004年第6期22卷 1121-1129页

作者： Ordónez, F Krishnamachari, B Univ So Calif Dept Ind & Syst Engn Los Angeles CA 90036 USA Univ So Calif Dept Elect Engn Los Angeles CA 90036 USA

The current practice in wireless sensor networks (WSNs) is to develop functional system designs and protocols for information extraction using intuition and heuristics, and validate them through simulations and implementations. We address the need for a complementary formal methodology by developing nonlinear optimization models of static WSN that yield fundamental performance bounds and optimal designs. We present models both for maximizing the total information gathered subject to energy constraints (on sensing, transmission, and reception), and for minimizing the energy usage subject to:information constraints. Other constraints in these models correspond to fairness and channel capacity (assuming noise but no interference). We also discuss extensions of these models that can handle data aggregation, interference, and even node mobility. We present results and illustrations from computational experiments using these models that show how the, optimal solution varies as a function of the energy/information constraints, network size, fairness constraints, and reception power. We also compare the performance of some simple heuristics with respect to the optimal solutions.

关键词： maximum information extraction network flow optimization wireless sensor networks (WSNs)

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DISTRIBUTION-BASED OPTION PRICING ON LATTICE ASSET DYNAMICS MODELS

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International Journal of Theoretical and Applied Finance 2002年第6期5卷 599-618页

作者： YUJI YAMADA JAMES A. PRIMBS Graduate School of Business Sciences University of Tsukuba 3-29-1 Otsuka Bunkyo-ku Tokyo 112-0012 Japan Management Science and Engineering Stanford University Terman Engineering Center 700-327 CA 94305-4026 USA

In this paper, we propose a numerical option pricing method based on an arbitrarily given stock distribution. We first formulate a European call option pricing problem as an optimal hedging problem by using a lattice based incomplete market model. A dynamic programming technique is then applied to solve the mean square optimal hedging problem for the discrete time multi-period case by assigning suitable probabilities on the lattice, where the underlying stock price distribution is derived directly from empirical stock price data which may possess "heavy tails". We show that these probabilities are obtained from a network flow optimization which can be solved efficiently by quadratic programming. A computational complexity analysis demonstrates that the number of iterations for dynamic programming and the number of parameters in the network flow optimization are both of square order with respect to the number of periods. Numerical experiments illustrate that our methodology generates the implied volatility smile.

关键词： Incomplete markets Lattice models Minimum mean square hedges Dynamic programming network flow optimization

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Multiobjective real-time reservoir operation with a network flow algorithm

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JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION 2001年第4期37卷 837-852页

作者： Braga, B Barbosa, PSF Univ Sao Paulo Escola Politecn BR-05508900 Sao Paulo Brazil UNICAMP Dept Civil Engn BR-13083970 Campinas SP Brazil

A network flow algorithm has been developed for the optimization of real-time operation of a multiple reservoir system. Two purposes have been considered in the operation: flood control and hydropower generation. A special network structure was developed which allows the consideration of river routing. A multiobjective formulation is utilized thus allowing generation of a non-dominated curve. The effect of imperfect forecast on the performance of the real-time operation model is also evaluated. An application is made to a subsystem of the Brazilian hydroelectric system, located in the Paranapanema river basin. In this case study, the model showed good performance under the largest flood of the historical records.

关键词： flood control hydroelectric power optimal operation of hydrosystems multipurpose development of water resources network flow optimization reservoir operation water management

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