A Review of Pipe Network Optimization Techniques

1992 ◽  
pp. 3-13 ◽  
Author(s):  
Godfrey A. Walters
Keyword(s):  
Network Optimization ◽  
Optimization Techniques ◽  
Pipe Network

scholarly journals Investigating the Potential of Network Optimization for a Constrained Object Detection Problem

2021 ◽  
Vol 7 (4) ◽  
pp. 64
Author(s):  
Tanguy Ophoff ◽  
Cédric Gullentops ◽  
Kristof Van Beeck ◽  
Toon Goedemé
Keyword(s):  
Computational Complexity ◽  
Object Detection ◽  
Network Optimization ◽  
Real Life ◽  
Optimization Techniques ◽  
Training Data ◽  
Single Shot ◽  
Standard Object ◽  
Number Of Classes

Object detection models are usually trained and evaluated on highly complicated, challenging academic datasets, which results in deep networks requiring lots of computations. However, a lot of operational use-cases consist of more constrained situations: they have a limited number of classes to be detected, less intra-class variance, less lighting and background variance, constrained or even fixed camera viewpoints, etc. In these cases, we hypothesize that smaller networks could be used without deteriorating the accuracy. However, there are multiple reasons why this does not happen in practice. Firstly, overparameterized networks tend to learn better, and secondly, transfer learning is usually used to reduce the necessary amount of training data. In this paper, we investigate how much we can reduce the computational complexity of a standard object detection network in such constrained object detection problems. As a case study, we focus on a well-known single-shot object detector, YoloV2, and combine three different techniques to reduce the computational complexity of the model without reducing its accuracy on our target dataset. To investigate the influence of the problem complexity, we compare two datasets: a prototypical academic (Pascal VOC) and a real-life operational (LWIR person detection) dataset. The three optimization steps we exploited are: swapping all the convolutions for depth-wise separable convolutions, perform pruning and use weight quantization. The results of our case study indeed substantiate our hypothesis that the more constrained a problem is, the more the network can be optimized. On the constrained operational dataset, combining these optimization techniques allowed us to reduce the computational complexity with a factor of 349, as compared to only a factor 9.8 on the academic dataset. When running a benchmark on an Nvidia Jetson AGX Xavier, our fastest model runs more than 15 times faster than the original YoloV2 model, whilst increasing the accuracy by 5% Average Precision (AP).

Optimal planning model for rehabilitation of water networks

2004 ◽  
Vol 4 (3) ◽  
pp. 133-148 ◽  
Author(s):  
J.-H. Kim ◽  
C.-W. Baek ◽  
D.-J. Jo ◽  
E.-S. Kim ◽  
M.-J. Park
Keyword(s):  
Harmony Search ◽  
Optimization Techniques ◽  
Optimal Decision ◽  
Optimal Planning ◽  
Planning Model ◽  
Pipe Network ◽  
Water Networks ◽  
Network Systems ◽  
Capital Costs ◽  
Hydraulic Reliability

An optimal planning model for rehabilitation of water networks is presented. Capital costs (replacement, rehabilitation and repairing costs), benefits (by the reduction of pumping cost and leakage cost), and hydraulic reliability are used for making an optimal decision for the rehabilitation plan of a water pipe network. KYPIPE is used for checking the hydraulic reliability. A multi-objective optimization model is successfully developed in this study. And the task is tackled using a new meta-heuristic algorithm, Harmony Search, for solving a large optimization problem to which conventional optimization techniques are poorly suited. Five different models with different objective functions are developed and tested according to various conditions considered in this study. These models provide more options for the rehabilitation of pipe network systems compared to previously suggested models in the literature.

scholarly journals Review on Distribution Network Optimization under Uncertainty

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3369 ◽  
Author(s):  
Huilian Liao
Keyword(s):  
Network Optimization ◽  
Optimization Problems ◽  
Distribution Network ◽  
Network Planning ◽  
Distribution Networks ◽  
Optimization Techniques ◽  
Optimization Under Uncertainty ◽  
Distribution Network Planning ◽  
Optimization Search ◽  
Planning And Operation

With the increase of renewable energy in electricity generation and increased engagement from demand sides, distribution network planning and operation face great challenges in the provision of stable, secure and dedicated service under a high level of uncertainty in network behaviors. Distribution network planning and operation, at the same time, also benefit from the changes of current and future distribution networks in terms of the availability of increased resources, diversity, smartness, controllability and flexibility of the distribution networks. This paper reviews the critical optimization problems faced by distribution planning and operation, including how to cope with these changes, how to integrate an optimization process in a problem-solving framework to efficiently search for optimal strategy and how to optimize sources and flexibilities properly in order to achieve cost-effective operation and provide quality of services as required, among other factors. This paper also discusses the approaches to reduce the heavy computation load when solving large-scale network optimization problems, for instance by integrating the prior knowledge of network configuration in optimization search space. A number of optimization techniques have been reviewed and discussed in the paper. This paper also discusses the changes, challenges and opportunities in future distribution networks, analyzes the possible problems that will be faced by future network planning and operations and discusses the potential strategies to solve these optimization problems.

Solving the Pipe Network Analysis Problem Using Optimization Techniques

1978 ◽  
Vol 24 (7) ◽  
pp. 747-760 ◽  
Author(s):  
M. Collins ◽  
L. Cooper ◽  
R. Helgason ◽  
J. Kennington ◽  
L. LeBlanc
Keyword(s):  
Network Analysis ◽  
Optimization Techniques ◽  
Pipe Network ◽  
Analysis Problem

scholarly journals Noncrossover Dither Creeping Mutation-Based Genetic Algorithm for Pipe Network Optimization

2014 ◽  
Vol 140 (4) ◽  
pp. 553-557 ◽  
Author(s):  
Feifei Zheng ◽  
Aaron C. Zecchin ◽  
Angus R. Simpson ◽  
Martin F. Lambert
Keyword(s):  
Genetic Algorithm ◽  
Network Optimization ◽  
Pipe Network
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