Real Data Case Research Articles

Effects of terrain-following vertical coordinates on high-resolution NWP simulations

With increasing resolution in numerical weather prediction (NWP) models, the model topography can be described with finer resolution and includes steeper slopes. Consequently, negative effects of the traditional terrain-following vertical coordinate on high-resolution numerical simulations become more distinct due to larger errors in the pressure gradient force (PGF) calculation and associated distortions of the gravity wave along the coordinate surface. A series of numerical experiments have been conducted in this study, including idealized test cases of gravity wave simulation over a complex mountain, error analysis of the PGF estimation over a real topography, and a suite of real-data test cases. The GRAPES-Meso model is utilized with four different coordinates, i.e., the traditional terrain-following vertical coordinate proposed by Gal-Chen and Somerville (hereinafter referred to as the Gal.C.S coordinate), the one-scale smoothed level (SLEVE1), the two-scale smoothed level (SLEVE2), and the COSINE (COS) coordinates. The results of the gravity wave simulation indicate that the GRAPES-Meso model generally can reproduce the mountain-induced gravity waves, which are consistent with the analytic solution. However, the shapes, vertical structures, and intensities of the waves are better simulated with the SLEVE2 coordinate than with the other three coordinates. The model with the COS coordinate also performs well, except at lower levels where it is not as effective as the SLEVE2 coordinate in suppressing the PGF errors. In contrast, the gravity waves simulated in both the Gal.C.S and SLEVE1 coordinates are relatively distorted. The estimated PGF errors in a rest atmosphere over the real complex topography are much smaller (even disappear at the middle and upper levels) in the GRAPES-Meso model using the SLEVE2 and COS coordinates than those using the Gal.C.S and SLEVE1 coordinates. The results of the real-data test cases conducted over a one-month period suggest that the three modified vertical coordinates (SLEVE1, SLEVE2, and COS coordinates) give better results than the traditional Gal.C.S coordinate in terms of forecasting bias and root mean square error, and forecasting anomaly correlation coefficients. In conclusion, the SLEVE2 coordinate is proved to be the best option for the GRAPES-Meso model.

New methodology for CFD three-dimensional simulation of a walking beam type reheating furnace in steady state

This study presents a new modelling methodology for three-dimensional simulation of a reheating furnace of steel billets with natural gas burners. The movement of the billets inside the furnace is a periodically transient phenomenon. A modification in the energy transport equation is introduced through source terms in the billet region to convert the transient movements of the billets into a steady-state simulation, which significantly reduces the computational time without any loss of information.The combustion simulation inside the burners is performed using a global combustion mechanism that takes into account the combustion of the main gaseous species present in natural gas following a kinetic and turbulent control of the reaction through the Eddy Dissipation Concept (EDC).The results are contrasted and validated with the data obtained in a real facility though the SCADA of the plant. Some parameters, such as the surface temperatures of billets at the outlet and the distribution of power supplied by the fuel inside the furnace are reasonably close to the real case data obtained in temperature fields of both billets and the gas phase as well as the energy balance, leading to the conclusion that the proposed methodology is adequate for the purpose of simulating this system.

Combining particle MCMC with Rao-Blackwellized Monte Carlo data association for parameter estimation in multiple target tracking

We consider state and parameter estimation in multiple target tracking problems with data association uncertainties and unknown number of targets. We show how the problem can be recast into a conditionally linear Gaussian state-space model with unknown parameters and present an algorithm for computationally efficient inference on the resulting model. The proposed algorithm is based on combining the Rao-Blackwellized Monte Carlo data association algorithm with particle Markov chain Monte Carlo algorithms to jointly estimate both parameters and data associations. Both particle marginal Metropolis–Hastings and particle Gibbs variants of particle MCMC are considered. We demonstrate the performance of the method both using simulated data and in a real-data case study of using multiple target tracking to estimate the brown bear population in Finland.

The prioritisation of provinces for public grants allocation by a decision-making methodology based on type-2 fuzzy sets

Regional development agencies (RDA) are the units established for accelerating regional development and increasing local capacity. They aim at activating the regional dynamics and to reduce the intra-regional and inter-regional development gap. In a region, each province may have a different development level. This would pose a problem for socio-economic development. In order to reduce the disparities among the provinces, financial support mechanisms of development agencies would be a beneficial influence only if supports are used with to correct strategies. In order to play a vital role to reduce intra-regional disparities, it is necessary to consider many criteria to categorise settlements in terms of socio-economic development. Some of these criteria are generally subjective and extremely difficult to express in numbers. However, fuzzy sets are a great help to decision makers in a prioritisation of provinces for public grants allocation process with linguistic variables and measurement challenges. In this study, a new city-ranking model has been proposed for development agencies operating in Turkey. To address ambiguities and relativities in real-world scenarios more conveniently, type-2 fuzzy sets and crisp sets have been simultaneously used in multicriteria decision making (MCDM) process of grants allocation. To illustrate the proposed model better, an application with real case data has been performed in the Middle Black Sea Development Agency in Turkey.

Improving Human Capacity–One of the Ways to Overcome the Decrease in Organic Farming

Aim: This paper tends to put a different perspective for the reasons that had contributed to the drop in interest for further development of organic farming in Macedonia. Anno Domini 2001 is the year considered as official beginning of organic farming in the Republic of Macedonia. Trainings were conducted, farmers associations were established and an umbrella National Association was established too. In 2005, organic farming got under the national subsidy program, but the very next 2006 was abandoned. But since then without interruptions organic farming enjoys steady and increasing governmental support. Since 2007, two National Programs were developed while the area under organic and number of organic growers recorded steady growth. Up to 2012, when first signs of losing the pace is recorded by having a drop in number of ha under organic was recorded and the number of organic growers too. The next year, 2013 was another disappointment for the sector, since the government had reduced the volume of support from € 2113000 in 2012 to € 1089430 in 2013. Reasons for the drop in interest are naturally various but one of the most significant is insufficient competent support farmers were receiving. Unfortunately, the national advisory service has very limited human capacity on this matter, and private advisors are much too small in number to cover the demand for advice. Methodology: In order to obtain real-case data number of farms were interviewed for the practice they apply in their daily operations, in order to check their understanding of what organic farming is all about. The area of intervention was the East Planning Region in Macedonia. Then, subject of analysis were the advisors at the National Extension Agency who were asked to express their opinions on answers listed in a carefully designed questionnaire, which needed to provide answers on the level of knowledge advisors possess on organic farming. Results: the results analysis on both of the surveys are driving to a conclusion that Ministry’s opinion for the drop in interest has little value, since the amount of subsidies is quite high, yet farmers are giving up. Conclusion: Specially designed program for introducing the principles of organic farming to the farmers is imperative before they would be invited to join this kind of agricultural production. Furthermore, in order to provide best possible competent support, advisors need to go through a carefully designed training procedure in order to increase their knowledge in the principles of organic farming.

Optimal Transport for Particle Image Velocimetry: Real Data and Postprocessing Algorithms

Particle image velocimetry (PIV) is a method used to measure the velocity field of a fluid flow. Traditionally, cross correlation algorithms are used to retrieve the flow velocity. Recently, a new method for PIV based on the L$^2$ optimal mass transportation problem (OT-PIV) was introduced and analyzed in [L. P. Saumier, B. Khouider, and M. Agueh, Comm. Math. Sci., 13 (2015), pp. 269--296] for the case of synthetically generated data and for particles of equal mass or brightness. Here, we extend the work of Saumier et al. to the case of particles with different masses and randomly seeded particles. More importantly, we compare the OT-PIV method with a typical cross-correlation algorithm for the case of real data. Using a combination of theory and numerical experiments, we demonstrate that in the presence of particles with different weights/brightness, the OT method is more accurate for the largest/brightest particles, and it is more faithful when the particles are far enough from each other. This makes it more suitable for the so-called particle tracking regime of PIV, i.e., when the seeding density is low. We demonstrate in particular that for low seeding densities, the OT method performs better than a typical cross-correlation algorithm. Based on these new results and the previous ones of Saumier et al., we propose a suite of postprocessing algorithms for the OT-PIV method. It is found that the OT-PIV with postprocessing performs very well on the synthetic and real data when compared to the cross-correlation method.

The Type I Generalized Half-Logistic Distribution Based on Upper Record Values

We consider the type I generalized half-logistic distribution and derive some new explicit expressions and recurrence relations for marginal and joint moment generating functions of upper record values. Here we show the computations for the first four moments and their variances. Next we show that results for record values of this distribution can be derived from our results as special cases. We obtain the characterization result of this distribution on using the recurrence relation for single moment and conditional expectation of upper record values. We obtain the maximum likelihood estimators of upper record values and their confidence intervals. Also, we compute the maximum likelihood estimates of the parameters of upper record values and their confidence intervals. At last, we present one real case data study to emphasize the results of this paper.

Market Response as a Function of Design, Competition, and Socio-political Condition: An Empirical Model

Some previous researches about market response focus on the design of the product, some others focus on market and competition, and a few researches consider politic and regulation. All of those factors affect the market response. Therefore, this study concerns to consider all of those factors. An empirical model of market response as a function of product design, company income, product trend, competitor's condition, marketing effort, socio-economic condition, and political condition is developed in this study, then a set of real case data of an automotive product are utilized to finalize and validate the model. Chi-square tests show that the output of the model is close to the real data. The final model developed can be used to assess current condition of the factors, to predict the future condition, and to help the decision maker in prioritizing the actions to maintain the market response.

Investment project evaluation by a decision making methodology based on type-2 fuzzy sets

Although investment projects supported by the state are extremely important in terms of national policy the projects to be transferred from the common public funds brings with it many problems. Highly transparent and comprehensive evaluation model are required to transfer the public resources to the right investment projects. It is necessary to consider many criteria for the evaluation of an investment project. These criteria are generally subjective and extremely difficult to express in numbers. However, using the fuzzy sets provide huge facilities to decision makers in project evaluation process with linguistic variables and measurement challenges. In this study, a new evaluation model for investment projects have been proposed for development agencies operating in Turkey. To address ambiguities and relativities in real world scenarios more conveniently, type-2 fuzzy sets and crisp sets have been simultaneously used. The proposed model for the investment project evaluation problem composed of type-2 fuzzy AHP and type-2 fuzzy TOPSIS methods. The proposed fuzzy MCDM method consists of three phases: (1) identify the criteria to be used in the model, (2) type-2 fuzzy AHP computations, (3) evaluation of investment projects with type-2 fuzzy TOPSIS and determination of the final rank. To perceive proposed model better, an application with real case data have been performed in Middle Black Sea Development Agency in Turkey. As a consequence of this application, it has been observed that the proposed model have proved effective in evaluation of alternatives in multi-criteria group decision making problems in a broader perspective and flexible fashion.

The Transient Processes Simulation of Kaplan Turbine Operating in Propeller Modes

In this paper simulation of basic elements of hydropower plant (penstock, turbine, and generator) is considered. A new integral method of water hammer effect representation with elastic water column is proposed. The model of Kaplan turbine in propeller modes, considering the change of rotor speed during the transient process, is developed in Simulink simulation environment and tested for the case of real experimental data.

Non-parametric regression for compositional data

Regression for compositional data has been considered only from a parametric point of view. We introduce local constant and local linear smoothing for this problem, and treat the cases when the response, the predictor or both of them are compositions. To this end, we introduce suitable series expansions of the regression function at a point, along with a class of simplicial kernels. Our methods are formulated according to the Aitchison geometry of the simplex and then, using some relevant properties of the isometric log-ratio transformation, are developed following the principle of ‘working on coordinates’. Asymptotic properties and real-data case studies show the effectiveness of the methods.

A Physically Based Precipitation–Nonprecipitation Radar Echo Classifier Using Polarimetric and Environmental Data in a Real-Time National System

Abstract Polarimetric radar observations provide information regarding the shape and size of scatterers in the atmosphere, which help users to differentiate between precipitation and nonprecipitation radar echoes. Identifying and removing nonprecipitation echoes in radar reflectivity fields is one critical step in radar-based quantitative precipitation estimation. An automated algorithm based on reflectivity, correlation coefficient, and temperature data is developed to perform reflectivity data quality control through a set of physically based rules. The algorithm was tested with a large number of real data cases across different geographical regions and seasons and showed a high accuracy (Heidke skill score of 0.83) in segregating precipitation and nonprecipitation echoes. The algorithm was compared with two other operational and experimental reflectivity quality control methodologies and showed a more effective removal of nonprecipitation echoes and a higher computational efficiency. The current methodology also demonstrated a satisfactory performance in a real-time national multiradar and multisensor system.

A new standardized interval-valued chart for fuzzy data

Purpose – Constructing a fuzzy control chart with interval-valued fuzzy data is an important topic in the fields of medical, sociological, economics, service and management. In particular, when the data illustrates uncertainty, inconsistency and is incomplete which is often the. case of real data. Traditionally, we use variable control chart to detect the process shift with real value. However, when the real data is composed of interval-valued fuzzy, it is not feasible to use such an approach of traditional statistical process control (SPC) to monitor the fuzzy control chart. The purpose of this paper is to propose the designed standardized fuzzy control chart for interval-valued fuzzy data set. Design/methodology/approach – The general statistical principles used on the standardized control chart are applied to fuzzy control chart for interval-valued fuzzy data. Findings – When the real data is composed of interval-valued fuzzy, it is not feasible to use such an approach of traditional SPC to monitor the fuzzy control chart. This study proposes the designed standardized fuzzy control chart for interval-valued fuzzy data set of vegetable price from January 2009 to September 2010 in Taiwan obtained from Council of Agriculture, Executive Yuan. Empirical studies are used to illustrate the application for designing standardized fuzzy control chart. More related practical phenomena can be explained by this appropriate definition of fuzzy control chart. Originality/value – This paper uses a simpler approach to construct the standardized interval-valued chart for fuzzy data based on traditional standardized control chart which is easy and straightforward. Moreover, the control limit of the designed standardized fuzzy control chart is an interval with (LCL, UCL), which consists of the conventional range of classical standardized control chart.

Understanding episode mining techniques: Benchmarking on diverse, realistic, artificial data

Frequent episode mining has been proposed as a data mining task for recovering sequential patterns from temporal data sequences and several approaches have been introduced over the last fifteen years. These techniques have however never been compared against each other in a large scale comparison, mainly because the existing real life data is prevented from entering the public domain by non-disclosure agreements. We perform such a comparison for the first time. To get around the problem of proprietary data, we employ a data generator based on a number of real life observations and capable of generating data that mimics real life data at our disposal. Artificial data offers the additional advantage that the underlying patterns are known, which is typically not the case for real life data. Thus, we can evaluate for the first time the ability of mining approaches to recover patterns that are embedded in noise. Our experiments indicate that temporal constraints are more important in affecting the effectiveness of episode mining than occurrence semantics. They also indicate that recovering underlying patterns when several phenomena are present at the same time is rather difficult and that there is need to develop better significance measures and techniques for dealing with sets of episodes.

Simulation Decision System on the Preparation of Emergency Resources Using System Dynamics

This paper establishes a simulation decision system for both emergency resource preparation and emergency resource replenishment. The decision system uses both system dynamics and goal programming. A system dynamics model is then developed and validated with real case data as simulation decision support to emergency resource replenishment. An optimization simulation using the data from the emergency resource preparation process in the Ya'an earthquake in China is reported. Copyright © 2014 John Wiley & Sons, Ltd.

Nonparametric Regression for Spherical Data

We develop nonparametric smoothing for regression when both the predictor and the response variables are defined on a sphere of whatever dimension. A local polynomial fitting approach is pursued, which retains all the advantages in terms of rate optimality, interpretability, and ease of implementation widely observed in the standard setting. Our estimates have a multi-output nature, meaning that each coordinate is separately estimated, within a scheme of a regression with a linear response. The main properties include linearity and rotational equivariance. This research has been motivated by the fact that very few models describe this kind of regression. Such current methods are surely not widely employable since they have a parametric nature, and also require the same dimensionality for prediction and response spaces, along with nonrandom design. Our approach does not suffer these limitations. Real-data case studies and simulation experiments are used to illustrate the effectiveness of the method.

Empirical Bayes estimation for the conditional extreme value model

A new estimation strategy for estimating the parameters of the Heffernan and Tawn conditional extreme value model is proposed. The technique makes use of empirical Bayes estimation for the conditional likelihood that otherwise does not have a simple closed‐form expression. The approach is tested on simulations from different types of extreme dependence (and independence) structures, as well as for two real data cases consisting of precipitation analysis conditional on extreme temperature in Boulder, Colorado, and Los Angeles, California, USA. The strategy generally has good coverage when informative priors are used for one of the parameters, except for the independence case where the coverage is low until the sample size reaches about 50. Results for the precipitation and temperature data are found to be consistent with the semi‐non‐parametric strategy. The presented model can be potentially applied in a wide variety of science fields, especially in earth, environment and climate sciences. Copyright © 2014 John Wiley & Sons, Ltd.

Vehicle routing for the eco-efficient collection of household plastic waste

Plastic waste is a special category of municipal solid waste. Plastic waste collection is featured with various alternatives of collection methods (curbside/drop-off) and separation methods (source-/post-separation). In the Netherlands, the collection routes of plastic waste are the same as those of other waste, although plastic is different than other waste in terms of volume to weight ratio. This paper aims for redesigning the collection routes and compares the collection options of plastic waste using eco-efficiency as performance indicator. Eco-efficiency concerns the trade-off between environmental impacts, social issues and costs. The collection problem is modeled as a vehicle routing problem. A tabu search heuristic is used to improve the routes. Collection alternatives are compared by a scenario study approach. Real distances between locations are calculated with MapPoint. The scenario study is conducted based on real case data of the Dutch municipality Wageningen. Scenarios are designed according to the collection alternatives with different assumptions in collection method, vehicle type, collection frequency and collection points, etc. Results show that the current collection routes can be improved in terms of eco-efficiency performance by using our method. The source-separation drop-off collection scenario has the best performance for plastic collection assuming householders take the waste to the drop-off points in a sustainable manner. The model also shows to be an efficient decision support tool to investigate the impacts of future changes such as alternative vehicle type and different response rates.

Application of Feature Calibration and Alignment to High-Resolution Analysis: Examples Using Observations Sensitive to Cloud and Water Vapor

Abstract Alignment errors [i.e., cases where coherent structures (“features”) of clouds or precipitation in the background have position errors] can lead to large and non-Gaussian background errors. Assimilation of cloud-affected radiances using additive increments derived by variational and/or ensemble methods can be problematic in these situations. To address this problem, the Feature Calibration and Alignment technique (FCA) is used here for correcting position errors by displacing background fields. A set of two-dimensional displacement vectors is applied to forecast fields to improve the alignment of features in the forecast and observations. These displacement vectors are obtained by a nonlinear minimization of a cost function that measures the misfit to observations, along with a number of additional constraints (e.g., smoothness and nondivergence of the displacement vectors) to prevent unphysical solutions. The method was applied in an idealized case using Weather Research and Forecasting Model (WRF) forecast fields for Hurricane Katrina. Application of the displacement vectors to the three-dimensional WRF fields resulted in improved predicted hurricane positions in subsequent forecasts. When applied to a set of high-resolution forecasts of deep moist convection over the central United States, displacements are able to efficiently characterize part of the ensemble spread. To test its application as an analysis preprocessor, FCA was applied to a real-data case of cloud-affected radiances of one of the Atmospheric Infrared Sounder (AIRS) channels. The displaced background resulted in an improved fit to the AIRS observations in all cloud-sensitive channels.

Respiratory motion guided four dimensional cone beam computed tomography: encompassing irregular breathing

Four dimensional cone beam computed tomography (4DCBCT) images suffer from angular under sampling and bunching of projections due to a lack of feedback between the respiratory signal and the acquisition system. To address this problem, respiratory motion guided 4DCBCT (RMG-4DCBCT) regulates the gantry velocity and projection time interval, in response to the patient’s respiratory signal, with the aim of acquiring evenly spaced projections in a number of phase or displacement bins during the respiratory cycle. Our previous study of RMG-4DCBCT was limited to sinusoidal breathing traces. Here we expand on that work to provide a practical algorithm for the case of real patient breathing data. We give a complete description of RMG-4DCBCT including full details on how to implement the algorithms to determine when to move the gantry and when to acquire projections in response to the patient’s respiratory signal. We simulate a realistic working RMG-4DCBCT system using 112 breathing traces from 24 lung cancer patients. Acquisition used phase-based binning and parameter settings typically used on commercial 4DCBCT systems (4 min acquisition time, 1200 projections across 10 respiratory bins), with the acceleration and velocity constraints of current generation linear accelerators. We quantified streaking artefacts and image noise for conventional and RMG-4DCBCT methods by reconstructing projection data selected from an oversampled set of Catphan phantom projections. RMG-4DCBCT allows us to optimally trade-off image quality, acquisition time and image dose. For example, for the same image quality and acquisition time as conventional 4DCBCT approximately half the imaging dose is needed. Alternatively, for the same imaging dose, the image quality as measured by the signal to noise ratio, is improved by 63% on average. C-arm cone beam computed tomography systems, with an acceleration up to 200°/s2, a velocity up to 100°/s and the acquisition of 80 projections per second, allow the image acquisition time to be reduced to below 60 s. We have made considerable progress towards realizing a system to reduce projection clustering in conventional 4DCBCT imaging and hence reduce the imaging dose to the patient.