scholarly journals Using injection points in reformulation local search for solving continuous location problems

2017 ◽  
Vol 27 (3) ◽  
pp. 291-300 ◽  
Author(s):  
Jack Brimberg ◽  
Zvi Drezner ◽  
Nenad Mladenovic ◽  
Said Salhi
Keyword(s):  
Local Search ◽  
Main Idea ◽  
Continuous Model ◽  
Continuous Phase ◽  
Location Problems ◽  
Data Sets ◽  
Continuous Location ◽  
New Approach ◽  
Discrete Phase ◽  
Discrete Counterpart

Reformulation local search (RLS) has been recently proposed as a new approach for solving continuous location problems. The main idea, although not new, is to exploit the relation between the continuous model and its discrete counterpart. The RLS switches between the continuous model and a discrete relaxation in order to expand the search. In each iteration new points obtained in the continuous phase are added to the discrete formulation. Thus, the two formulations become equivalent in a limiting sense. In this paper we introduce the idea of adding 'injection points' in the discrete phase of RLS in order to escape a current local solution. Preliminary results are obtained on benchmark data sets for the multi-source Weber problem that support further investigation of the RLS framework.

scholarly journals A new local search for continuous location problems

2014 ◽  
Vol 232 (2) ◽  
pp. 256-265 ◽  
Author(s):  
Jack Brimberg ◽  
Zvi Drezner ◽  
Nenad Mladenović ◽  
Said Salhi
Keyword(s):  
Local Search ◽  
Location Problems ◽  
Continuous Location

scholarly journals Effects of Blade Parameters on the Flow Field and Classification Performance of the Vertical Roller Mill via Numerical Investigations

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Chang Liu ◽  
Zuobing Chen ◽  
Weili Zhang ◽  
Chenggang Yang ◽  
Ya Mao ◽  
...  
Keyword(s):  
Flow Field ◽  
Pressure Difference ◽  
Continuous Phase ◽  
Classification Performance ◽  
Field Analysis ◽  
Discrete Phase Model ◽  
Roller Mill ◽  
Discrete Phase ◽  
Vertical Roller ◽  
Classification Efficiency

The vertical roller mill is an important crushing and grading screening device widely used in many industries. Its classification efficiency and the pressure difference determine the entire producing capacity and power consumption, respectively, which makes them the two key indicators describing the mill performance. Based on the DPM (Discrete Phase Model) and continuous phase coupling model, the flow field characteristics in the vertical roller mill including the velocity and pressure fields and the discrete phase distributions had been analyzed. The influence of blade parameters like the shape, number, and rotating speed on the flow field and classification performance had also been comprehensively explored. The numerical simulations showed that there are vortices in many zones in the mill and the blades are of great significance to the mill performance. The blade IV not only results in high classification efficiency but also reduces effectively the pressure difference in the separator and also the whole machine. The conclusions of the flow field analysis and the blade effects on the classification efficiency and the pressure difference could guide designing and optimizing the equipment structure and the milling process, which is of great importance to obtain better overall performance of the vertical roller mill.

Survey Data Quality in Analyzing Harmonized Indicators of Protest Behavior: A Survey Data Recycling Approach

2021 ◽  
pp. 000276422110216
Author(s):  
Kazimierz M. Slomczynski ◽  
Irina Tomescu-Dubrow ◽  
Ilona Wysmulek
Keyword(s):  
Data Processing ◽  
Data Quality ◽  
Survey Data ◽  
A Priori ◽  
Data Sets ◽  
New Approach ◽  
Survey Quality ◽  
Survey Error ◽  
Ex Post ◽  
The Impact

This article proposes a new approach to analyze protest participation measured in surveys of uneven quality. Because single international survey projects cover only a fraction of the world’s nations in specific periods, researchers increasingly turn to ex-post harmonization of different survey data sets not a priori designed as comparable. However, very few scholars systematically examine the impact of the survey data quality on substantive results. We argue that the variation in source data, especially deviations from standards of survey documentation, data processing, and computer files—proposed by methodologists of Total Survey Error, Survey Quality Monitoring, and Fitness for Intended Use—is important for analyzing protest behavior. In particular, we apply the Survey Data Recycling framework to investigate the extent to which indicators of attending demonstrations and signing petitions in 1,184 national survey projects are associated with measures of data quality, controlling for variability in the questionnaire items. We demonstrate that the null hypothesis of no impact of measures of survey quality on indicators of protest participation must be rejected. Measures of survey documentation, data processing, and computer records, taken together, explain over 5% of the intersurvey variance in the proportions of the populations attending demonstrations or signing petitions.

Recent and Planned Developments of the Program OxCal

Radiocarbon ◽  
2013 ◽  
Vol 55 (2) ◽  
pp. 720-730 ◽  
Author(s):  
Christopher Bronk Ramsey ◽  
Sharen Lee
Keyword(s):  
Statistical Analysis ◽  
Statistical Methods ◽  
Software Package ◽  
Model Averaging ◽  
Data Sets ◽  
Radiocarbon Dates ◽  
New Approach ◽  
New Models ◽  
Multiphase Models ◽  
Deposition Models

OxCal is a widely used software package for the calibration of radiocarbon dates and the statistical analysis of 14C and other chronological information. The program aims to make statistical methods easily available to researchers and students working in a range of different disciplines. This paper will look at the recent and planned developments of the package. The recent additions to the statistical methods are primarily aimed at providing more robust models, in particular through model averaging for deposition models and through different multiphase models. The paper will look at how these new models have been implemented and explore the implications for researchers who might benefit from their use. In addition, a new approach to the evaluation of marine reservoir offsets will be presented. As the quantity and complexity of chronological data increase, it is also important to have efficient methods for the visualization of such extensive data sets and methods for the presentation of spatial and geographical data embedded within planned future versions of OxCal will also be discussed.

Quasi-2D inversion of DCR and TDEM data for shallow investigations

Geophysics ◽  
2011 ◽  
Vol 76 (4) ◽  
pp. F239-F250 ◽  
Author(s):  
Fernando A. Monteiro Santos ◽  
Hesham M. El-Kaliouby
Keyword(s):  
Joint Inversion ◽  
Synthetic Data ◽  
Data Sets ◽  
Complex Environments ◽  
Inversion Algorithm ◽  
2D Inversion ◽  
New Approach ◽  
Earth Models ◽  
Time Domain Electromagnetic ◽  
Inversion Techniques

Joint or sequential inversion of direct current resistivity (DCR) and time-domain electromagnetic (TDEM) data commonly are performed for individual soundings assuming layered earth models. DCR and TDEM have different and complementary sensitivity to resistive and conductive structures, making them suitable methods for the application of joint inversion techniques. This potential joint inversion of DCR and TDEM methods has been used by several authors to reduce the ambiguities of the models calculated from each method separately. A new approach for joint inversion of these data sets, based on a laterally constrained algorithm, was found. The method was developed for the interpretation of soundings collected along a line over a 1D or 2D geology. The inversion algorithm was tested on two synthetic data sets, as well as on field data from Saudi Arabia. The results show that the algorithm is efficient and stable in producing quasi-2D models from DCR and TDEM data acquired in relatively complex environments.

Multi-Instance Dimensionality Reduction via Sparsity and Orthogonality

2018 ◽  
Vol 30 (12) ◽  
pp. 3281-3308
Author(s):  
Hong Zhu ◽  
Li-Zhi Liao ◽  
Michael K. Ng
Keyword(s):  
Dimensionality Reduction ◽  
Optimization Problem ◽  
Augmented Lagrangian ◽  
Main Idea ◽  
Real Data ◽  
Learning Performance ◽  
High Dimensional ◽  
Data Sets ◽  
Outer Loop ◽  
Orthogonality Constraints

We study a multi-instance (MI) learning dimensionality-reduction algorithm through sparsity and orthogonality, which is especially useful for high-dimensional MI data sets. We develop a novel algorithm to handle both sparsity and orthogonality constraints that existing methods do not handle well simultaneously. Our main idea is to formulate an optimization problem where the sparse term appears in the objective function and the orthogonality term is formed as a constraint. The resulting optimization problem can be solved by using approximate augmented Lagrangian iterations as the outer loop and inertial proximal alternating linearized minimization (iPALM) iterations as the inner loop. The main advantage of this method is that both sparsity and orthogonality can be satisfied in the proposed algorithm. We show the global convergence of the proposed iterative algorithm. We also demonstrate that the proposed algorithm can achieve high sparsity and orthogonality requirements, which are very important for dimensionality reduction. Experimental results on both synthetic and real data sets show that the proposed algorithm can obtain learning performance comparable to that of other tested MI learning algorithms.

Lighting Equilibrium Distribution Maps and Their Application to Face Recognition Under Difficult Lighting Conditions

2017 ◽  
Vol 31 (03) ◽  
pp. 1756003 ◽  
Author(s):  
Jun Dong ◽  
Xue Yuan ◽  
Fanlun Xiong
Keyword(s):  
Face Recognition ◽  
Equilibrium Distribution ◽  
Main Idea ◽  
Data Sets ◽  
Test Image ◽  
Distribution Maps ◽  
Lighting Conditions ◽  
Map Construction ◽  
Compensation Methods ◽  
Approximation Coefficients

In this paper, a novel facial-patch based recognition framework is proposed to deal with the problem of face recognition (FR) on the serious illumination condition. First, a novel lighting equilibrium distribution maps (LEDM) for illumination normalization is proposed. In LEDM, an image is analyzed in logarithm domain with wavelet transform, and the approximation coefficients of the image are mapped according to a reference-illumination map in order to normalize the distribution of illumination energy due to different lighting effects. Meanwhile, the detail coefficients are enhanced to achieve detail information emphasis. The LEDM is obtained by blurring the distances between the test image and the reference illumination map in the logarithm domain, which may express the entire distribution of illumination variations. Then, a facial-patch based framework and a credit degree based facial patches synthesizing algorithm are proposed. Each normalized face images is divided into several stacked patches. And, all patches are individually classified, then each patch from the test image casts a vote toward the parent image classification. A novel credit degree map is established based on the LEDM, which is deciding a credit degree for each facial patch. The main idea of credit degree map construction is the over-and under-illuminated regions should be assigned lower credit degree than well-illuminated regions. Finally, results are obtained by the credit degree based facial patches synthesizing. The proposed method provides state-of-the-art performance on three data sets that are widely used for testing FR under different illumination conditions: Extended Yale-B, CAS-PEAL-R1, and CMUPIE. Experimental results show that our FR frame outperforms several existing illumination compensation methods.

Unsteady Modeling of Powdered Activated Carbon Deposition on Collection Electrode of a Lab-Scale Electrostatic Precipitator (ESP)

2012 ◽  
Author(s):  
Yasmin Khakpour ◽  
Herek L. Clack
Keyword(s):  
Activated Carbon ◽  
Particle Deposition ◽  
Electrostatic Precipitator ◽  
Activated Carbons ◽  
Collection Efficiency ◽  
Continuous Phase ◽  
Powdered Activated Carbon ◽  
Computational Domain ◽  
Discrete Phase Model ◽  
Discrete Phase

Particulate sampling in the flue gas at the Electrostatic Precipitator (ESP) outlet during injection of powdered activated carbons (PACs) has provided strong anecdotal evidence indicating that injected PACs can penetrate the ESP in significant concentrations. The low resistivity of PAC is consistent with poor collection efficiency in an ESP and lab-scale testing has revealed significantly different collection behavior of PAC in an ESP as compared to fly ash. The present study illustrates the use of a commercial CFD package — FLUENT — to investigate precipitation of powdered activated carbon (PAC) in the presence and absence of electric field. The computational domain is designed to represent a 2-D wire-plate ESP channel. The governing equations include those covering continuous phase transport, electric potential, air ionization, and particle charging. The particles are tracked using a Lagrangian Discrete Phase Model (DPM). In addition, a custom user-defined function (UDF) uses a deforming boundary condition and a prescribed critical particle velocity to account for particle deposition and dust-cake growth on the electrodes. The effect of Electrohydrodynamics (EHD) induced flow on the ESP collection efficiency under various flow and particle characteristics as well as different ESP configurations are illustrated.

scholarly journals Local Search Heuristics for k-Median and Facility Location Problems

2004 ◽  
Vol 33 (3) ◽  
pp. 544-562 ◽  
Author(s):  
Vijay Arya ◽  
Naveen Garg ◽  
Rohit Khandekar ◽  
Adam Meyerson ◽  
Kamesh Munagala ◽  
...  
Keyword(s):  
Local Search ◽  
Facility Location ◽  
Location Problems ◽  
Facility Location Problems ◽  
Local Search Heuristics ◽  
Search Heuristics
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