Search results for: boosted multivariate trees

Authors: Abdullah H. Özcan, Dilara Hisar, Yetkin Sayar, Cem Ünsalan

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In this study, an automated building and tree detection method is proposed using DSM data and true orthophoto image. A multiscale matched filtering is used on DSM data. Therefore, first watershed transform is applied. Then, Otsu’s thresholding method is used as an adaptive threshold to segment each watershed region. Detected objects are masked with NDVI to separate buildings and trees. The proposed method is able to detect buildings and trees without entering any elevation threshold. We tested our method on ISPRS semantic labeling dataset and obtained promising results.

Keywords: building detection, local maximum filtering, matched filtering, multiscale

Procedia PDF Downloads 294

Authors: M. O. Adda

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In this paper, we propose a topology called Zoned fat tree (Z-Fat tree) which is a further extension to the classical fat trees. The extension relates to the provision of extra degree of connectivity to maximize the number of deployed ports per routing nodes, and hence increases the bisection bandwidth especially for slimmed fat trees. The extra links, when classical routing is used, tend, in deterministic environment, to be under-utilized for some traffic patterns, hence achieving poor performance. We suggest two versions of a dynamic round robin scheme that outperforms the classical D-mod-k and S-mod-K routing and show by simulation that our proposal utilize all the extra added links to the classical fat tree, and achieve better performance for general applications.

Keywords: deterministic routing, fat tree, interconnection, traffic pattern

Procedia PDF Downloads 455

Authors: Elahe Memari, Ahmad Aslizadeh, Ahmad Memari

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Today strategic planning is counted as the most important duties of senior directors in each organization. Strategic planning allows the organizations to implement compiled strategies and reach higher competitive benefits than their competitors. The present research work tries to prepare and rank the strategies form effective factors on strategic planning in fulfillment of the State Road Management and Transportation Organization in order to indicate the role of organizational factors in efficiency of the process to organization managers. Connection between six main factors in fulfillment of State Road Management and Transportation Organization were studied here, including Improvement of Strategic Thinking in senior managers, improvement of the organization business process, rationalization of resources allocation in different parts of the organization, coordination and conformity of strategic plan with organization needs, adjustment of organization activities with environmental changes, reinforcement of organizational culture. All said factors approved by implemented tests and then ranked using fuzzy multivariate decision-making technique.

Keywords: Fuzzy TOPSIS, improvement of organization business process, multivariate decision-making, strategic planning

Procedia PDF Downloads 375

Authors: Farshad Ghassemi Toosi, Nikola S. Nikolov

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We present a visualization technique for radial drawing of trees consisting of two slightly different algorithms. Both of them make use of node-link diagrams for visual encoding. This visualization creates clear drawings without edge crossing. One of the algorithms is suitable for real-time visualization of large trees, as it requires minimal recalculation of the layout if leaves are inserted or removed from the tree; while the other algorithm makes better utilization of the drawing space. The algorithms are very similar and follow almost the same procedure but with different parameters. Both algorithms assign angular coordinates for all nodes which are then converted into 2D Cartesian coordinates for visualization. We present both algorithms and discuss how they compare to each other.

Keywords: Radial drawing, Visualization, Algorithm, Use of node-link diagrams

Procedia PDF Downloads 304

Authors: Saba Arif, Anam Nadeem, Roman Kalvin, Muzaffar Ali, Burhan Ali, Juntakan Taweekun

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Globally transportation sector accounts for around 25% of energy demand and nearly 62% of oil consumed. Therefore, new energy sources are required to introduce for this huge demand replenishment of depleting conventional energy sources. Currently, biofuels such as Jatropha trees as an energy carrier for transportation sector are being utilized effectively round the globe. However, climate conditions at low altitudes with an average annual temperature above 20 degrees Celsius and rainfall of 300-1000mm are considered the most suitable environment for the efficient growth of Jatropha trees. The current study is providing a theoretical survey-based analysis to investigate the effect of rate of cultivation of jatropha trees on the reduction of fuel prices and its environmental benefits. The resulted study shows that jatropha tree’s 100 kg seeds give 80kg oil and the conversion process cost is very small as 890 PKR. Moreover, the extraction of oil from Jatropha tree is tax-free compared to other fuels. The analysis proved very essential for potential assessment of Jatropha regarding future energy fuel for transportation sector at global level. Additionally, it can be very beneficial for increment in the total amount of transportation fuel in Pakistan.

Keywords: jatropha tree, environmental impact, energy contents, theoretical survey

Procedia PDF Downloads 179

Authors: Faisal G. Khamis, Ghaleb A. El-Refae

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Many researchers have studied socioeconomic, health, and security variables in the developed countries; however, very few studies used multivariate analysis in developing countries. The current study contributes to the scarce literature about the determinants of the variance in socioeconomic, health, and security factors. Questions raised were whether the independent variables (IVs) of governorate and year impact the socioeconomic, health, and security dependent variables (DVs) in Jordan, whether the marginal mean of each DV in each governorate and in each year is significant, which governorates are similar in difference means of each DV, and whether these DVs vary. The main objectives were to determine the source of variances in DVs, collectively and separately, testing which governorates are similar and which diverge for each DV. The research design was time series and cross-sectional analysis. The main hypotheses are that IVs affect DVs collectively and separately. Multivariate and univariate analyses of variance were carried out to test these hypotheses. The population of 12 governorates in Jordan and the available data of 15 years (2000–2015) accrued from several Jordanian statistical yearbooks. We investigated the effect of two factors of governorate and year on the four DVs of divorce rate, mortality rate, unemployment percentage, and crime rate. All DVs were transformed to multivariate normal distribution. We calculated descriptive statistics for each DV. Based on the multivariate analysis of variance, we found a significant effect in IVs on DVs with p < .001. Based on the univariate analysis, we found a significant effect of IVs on each DV with p < .001, except the effect of the year factor on unemployment was not significant with p = .642. The grand and marginal means of each DV in each governorate and each year were significant based on a 95% confidence interval. Most governorates are not similar in DVs with p < .001. We concluded that the two factors produce significant effects on DVs, collectively and separately. Based on these findings, the government can distribute its financial and physical resources to governorates more efficiently. By identifying the sources of variance that contribute to the variation in DVs, insights can help inform focused variation prevention efforts.

Keywords: ANOVA, crime, divorce, governorate, hypothesis test, Jordan, MANOVA, means, mortality, unemployment, year

Procedia PDF Downloads 239

Authors: S. Benchelha, H. C. Aoudjehane, M. Hakdaoui, R. El Hamdouni, H. Mansouri, T. Benchelha, M. Layelmam, M. Alaoui

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The logistic regression (LR) and multivariate adaptive regression spline (MarSpline) are applied and verified for analysis of landslide susceptibility map in Oudka, Morocco, using geographical information system. From spatial database containing data such as landslide mapping, topography, soil, hydrology and lithology, the eight factors related to landslides such as elevation, slope, aspect, distance to streams, distance to road, distance to faults, lithology map and Normalized Difference Vegetation Index (NDVI) were calculated or extracted. Using these factors, landslide susceptibility indexes were calculated by the two mentioned methods. Before the calculation, this database was divided into two parts, the first for the formation of the model and the second for the validation. The results of the landslide susceptibility analysis were verified using success and prediction rates to evaluate the quality of these probabilistic models. The result of this verification was that the MarSpline model is the best model with a success rate (AUC = 0.963) and a prediction rate (AUC = 0.951) higher than the LR model (success rate AUC = 0.918, rate prediction AUC = 0.901).

Keywords: landslide susceptibility mapping, regression logistic, multivariate adaptive regression spline, Oudka, Taounate

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Authors: Jose Flores, Nadia Gamboa

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A proper water quality management requires the establishment of a monitoring network. Therefore, evaluation of the efficiency of water quality monitoring networks is needed to ensure high-quality data collection of critical quality chemical parameters. Unfortunately, in some Latin American countries water quality monitoring programs are not sustainable in terms of recording historical data or environmentally representative sites wasting time, money and valuable information. In this study, multivariate statistical techniques, such as principal components analysis (PCA) and hierarchical cluster analysis (HCA), are applied for identifying the most significant monitoring sites as well as critical water quality parameters in the monitoring network of the Jequetepeque River basin, in northern Peru. The Jequetepeque River basin, like others in Peru, shows socio-environmental conflicts due to economical activities developed in this area. Water pollution by trace elements in the upper part of the basin is mainly related with mining activity, and agricultural land lost due to salinization is caused by the extensive use of groundwater in the lower part of the basin. Since the 1980s, the water quality in the basin has been non-continuously assessed by public and private organizations, and recently the National Water Authority had established permanent water quality networks in 45 basins in Peru. Despite many countries use multivariate statistical techniques for assessing water quality monitoring networks, those instruments have never been applied for that purpose in Peru. For this reason, the main contribution of this study is to demonstrate that application of the multivariate statistical techniques could serve as an instrument that allows the optimization of monitoring networks using least number of monitoring sites as well as the most significant water quality parameters, which would reduce costs concerns and improve the water quality management in Peru. Main socio-economical activities developed and the principal stakeholders related to the water management in the basin are also identified. Finally, water quality management programs will also be discussed in terms of their efficiency and sustainability.

Keywords: PCA, HCA, Jequetepeque, multivariate statistical

Procedia PDF Downloads 329

Authors: Achut Manandhar, Kenneth D. Morton, Peter A. Torrione, Leslie M. Collins

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The current trends in affect recognition research are to consider continuous observations from spontaneous natural interactions in people using multiple feature modalities, and to represent affect in terms of continuous dimensions, incorporate spatio-temporal correlation among affect dimensions, and provide fast affect predictions. These research efforts have been propelled by a growing effort to develop affect recognition system that can be implemented to enable seamless real-time human-computer interaction in a wide variety of applications. Motivated by these desired attributes of an affect recognition system, in this work a multi-dimensional affect prediction approach is proposed by integrating multivariate Relevance Vector Machine (MVRVM) with a recently developed Output-associative Relevance Vector Machine (OARVM) approach. The resulting approach can provide fast continuous affect predictions by jointly modeling the multiple affect dimensions and their correlations. Experiments on the RECOLA database show that the proposed approach performs competitively with the OARVM while providing faster predictions during testing.

Keywords: dimensional affect prediction, output-associative RVM, multivariate regression, fast testing

Procedia PDF Downloads 260

Authors: Orhun Aydin, Mark V. Janikas, Rodrigo Alves, Renato Assuncao

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In this paper, a tree-based perturbation methodology for regionalization inference is presented. Regionalization is a constrained optimization problem that aims to create groups with similar attributes while satisfying spatial contiguity constraints. Similar to any constrained optimization problem, the spatial constraint may hinder convergence to some global minima, resulting in spatially contiguous members of a group with dissimilar attributes. This paper presents a general methodology for rigorously perturbing spatial constraints through the use of random spanning trees. The general framework presented can be used to quantify the effect of the spatial constraints in the overall regionalization result. We compare several types of stochastic spanning trees used in inference problems such as fuzzy regionalization and determining the number of regions. Performance of stochastic spanning trees is juxtaposed against the traditional permutation-based hypothesis testing frequently used in spatial statistics. Inference results for fuzzy regionalization and determining the number of regions is presented on the Local Area Personal Incomes for Texas Counties provided by the Bureau of Economic Analysis.

Keywords: regionalization, constrained clustering, probabilistic inference, fuzzy clustering

Procedia PDF Downloads 191

Authors: Riddhi Jangid, Gajendra Pratap Singh

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Petri nets, a mathematical tool, is used for modeling in different areas of computer sciences, biological networks, chemical systems and many other disciplines. A Petri net model of a given system is created by the graphical representation that describes the properties and behavior of the system. While looking for the behavior of any system, 1-safe Petri nets are of particular interest to many in the application part. Boolean Petri nets correspond to those class in 1- safe Petri nets that generate all the binary n-vectors in their reachability analysis. We study the class by changing different parameters like the token counts in the places and how the structure of the tree changes in the reachability analysis. We discuss here an extended class of Boolean Petri nets that generates n-ary trees in their reachability-based analysis.

Keywords: marking vector, n-vector, petri nets, reachability

Procedia PDF Downloads 53

Authors: Ishapathik Das

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The purpose of this article is to find a method of comparing designs for ordinal regression models using quantile dispersion graphs in the presence of linear predictor misspecification. The true relationship between response variable and the corresponding control variables are usually unknown. Experimenter assumes certain form of the linear predictor of the ordinal regression models. The assumed form of the linear predictor may not be correct always. Thus, the maximum likelihood estimates (MLE) of the unknown parameters of the model may be biased due to misspecification of the linear predictor. In this article, the uncertainty in the linear predictor is represented by an unknown function. An algorithm is provided to estimate the unknown function at the design points where observations are available. The unknown function is estimated at all points in the design region using multivariate parametric kriging. The comparison of the designs are based on a scalar valued function of the mean squared error of prediction (MSEP) matrix, which incorporates both variance and bias of the prediction caused by the misspecification in the linear predictor. The designs are compared using quantile dispersion graphs approach. The graphs also visually depict the robustness of the designs on the changes in the parameter values. Numerical examples are presented to illustrate the proposed methodology.

Keywords: model misspecification, multivariate kriging, multivariate logistic link, ordinal response models, quantile dispersion graphs

Procedia PDF Downloads 357

Authors: Tamrat Tesfaye, Bruce Sithole, K. Shabaridharan

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The use of natural dyes to replace synthetic dyes has been advocated for to circumvent the environmental problems associated with synthetic dyes. This paper is a preliminary study on the use of natural dyes extracted from eucalyptus and mango trees. Dyes extracted from eucalyptus bark gave more colourized material than the dyes extracted from eucalyptus leaves and mango pills and leaves. Additionally, the extracts exhibited a deeper colour shade. Cotton fiber dyed using the same dye but with different mordants resulted in fabric that exhibited different colours. It appears that natural dyes from these plants could be effective dyes for use on cotton fabrics especially considering that the dyes exhibited excellent colour fastness.

Keywords: natural dyes, mango, eucalyptus, cotton, mordants, colour fastness

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Authors: Prozorkevitch D., Mishurov A., Sokolov K., Karsakov L., Pestrikova L.

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The current body of knowledge about the marine environment and the dynamics of marine ecosystems includes a huge amount of heterogeneous data collected over decades. It generally includes a wide range of hydrological, biological and fishery data. Marine researchers collect these data and analyze how and why the ecosystem changes from past to present. Based on these historical records and linkages between the processes it is possible to predict future changes. Multivariate analysis of trends and their interconnection in the marine ecosystem may be used as an instrument for predicting further ecosystem evolution. A wide range of information about the components of the marine ecosystem for more than 50 years needs to be used to investigate how these arrays can help to predict the future.

Keywords: barents sea ecosystem, abiotic, biotic, data sets, trends, prediction

Procedia PDF Downloads 81

Authors: Azadeh Golshan, Craig Evans, Phillip Geary, Abigail Morrow, Zoe Rogers, Marcel Maeder

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22 physicochemical variables have been determined in water samples collected weekly from January to December in 2013 from three sampling stations located within a major drinking water reservoir. Classical Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) analysis was used to investigate the environmental factors associated with the physico-chemical variability of the water samples at each of the sampling stations. Matrix augmentation MCR-ALS (MA-MCR-ALS) was also applied, and the two sets of results were compared for interpretative clarity. Links between these factors, reservoir inflows and catchment land-uses were investigated and interpreted in relation to chemical composition of the water and their resolved geographical distribution profiles. The results suggested that the major factors affecting reservoir water quality were those associated with agricultural runoff, with evidence of influence on algal photosynthesis within the water column. Water quality variability within the reservoir was also found to be strongly linked to physical parameters such as water temperature and the occurrence of thermal stratification. The two methods applied (MCR-ALS and MA-MCR-ALS) led to similar conclusions; however, MA-MCR-ALS appeared to provide results more amenable to interpretation of temporal and geological variation than those obtained through classical MCR-ALS.

Keywords: drinking water reservoir, multivariate analysis, physico-chemical parameters, water quality

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Authors: Nida Rizvi, Deeksha Katyal, Varun Joshi

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River Hindon is an important river catering the demand of highly populated rural and industrial cluster of western Uttar Pradesh, India. Water quality of river Hindon is deteriorating at an alarming rate due to various industrial, municipal and agricultural activities. The present study aimed at identifying the pollution sources and quantifying the degree to which these sources are responsible for the deteriorating water quality of the river. Various water quality parameters, like pH, temperature, electrical conductivity, total dissolved solids, total hardness, calcium, chloride, nitrate, sulphate, biological oxygen demand, chemical oxygen demand and total alkalinity were assessed. Water quality data obtained from eight study sites for one year has been subjected to the two multivariate techniques, namely, principal component analysis and cluster analysis. Principal component analysis was applied with the aim to find out spatial variability and to identify the sources responsible for the water quality of the river. Three Varifactors were obtained after varimax rotation of initial principal components using principal component analysis. Cluster analysis was carried out to classify sampling stations of certain similarity, which grouped eight different sites into two clusters. The study reveals that the anthropogenic influence (municipal, industrial, waste water and agricultural runoff) was the major source of river water pollution. Thus, this study illustrates the utility of multivariate statistical techniques for analysis and elucidation of multifaceted data sets, recognition of pollution sources/factors and understanding temporal/spatial variations in water quality for effective river water quality management.

Keywords: cluster analysis, multivariate statistical techniques, river Hindon, water quality

Procedia PDF Downloads 430

Authors: Simeon Mayala, Ida Herdlevær, Jonas Bull Haugsøen, Shamundeeswari Anandan, Sonia Gavasso, Morten Brun

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In this paper, we propose a minimum spanning tree-based method for segmenting brain tumors. The proposed method performs interactive segmentation based on the minimum spanning tree without tuning parameters. The steps involve preprocessing, making a graph, constructing a minimum spanning tree, and a newly implemented way of interactively segmenting the region of interest. In the preprocessing step, a Gaussian filter is applied to 2D images to remove the noise. Then, the pixel neighbor graph is weighted by intensity differences and the corresponding minimum spanning tree is constructed. The image is loaded in an interactive window for segmenting the tumor. The region of interest and the background are selected by clicking to split the minimum spanning tree into two trees. One of these trees represents the region of interest and the other represents the background. Finally, the segmentation given by the two trees is visualized. The proposed method was tested by segmenting two different 2D brain T1-weighted magnetic resonance image data sets. The comparison between our results and the standard gold segmentation confirmed the validity of the minimum spanning tree approach. The proposed method is simple to implement and the results indicate that it is accurate and efficient.

Keywords: brain tumor, brain tumor segmentation, minimum spanning tree, segmentation, image processing

Procedia PDF Downloads 96

Authors: Chebouti-meziou Nadjiba1, And Chebouti Yahia2:

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the cultivation of Pistachios Pistacia vera of rare plants in Algeria and this point to see the lack of knowledge of techniques, which resulted in the proliferation of the tree to obtain a limited benefit does not exceed 0.75 tons / hectare, in addition to the enemy that lead to poor product on the one hand, one of which buds into wood and fruit Chaetoptelius vestitus. Since the tree is the raw sound production, while 25 kg of infected tree produces about 15 kg of any shortage of fact that this insect Chaetoptelius vestitus spend the amount of trouble going in the summer the young twigs of the trees into a sound the product by20% and due to the composition by the problem of spending in the newly formed branches, which lead to this loss in yield

Keywords: chaetoptelius vestitus, pistacia vera, spending, return, poor product.

Procedia PDF Downloads 44

Authors: Esraa Mohamed El-Fawal

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A facile solvothermal procedure was applied to fabricate zinc ferrite nanoparticles (ZnFe₂O₄ NPs). Activated carbon (AC) derived from peanut shells is synthesized using a microwave through the chemical activation method. The ZnFe₂O₄-AC composite is then mixed with a cerium-based metal-organic framework (CeMOF) by solid-state adding to formulate ZnFe₂O₄-AC/CeMOF composite. The synthesized photo materials were tested by scanning/transmission electron microscope (SEM/TEM), Photoluminescence (PL), (XRD) X-Ray diffraction, (FTIR) Fourier transform infrared, (UV-Vis/DRS) ultraviolet-visible/diffuse reflectance spectroscopy. The prepared ZnFe₂O₄-AC/CeMOFphotomaterial shows significantly boosted efficiency for photodegradation of methyl orange /methylene blue (MO/MB) compared with the pristine ZnFe₂O₄ and ZnFe₂O₄-AC composite under the irradiation of visible-light. The favorable ZnFe₂O₄-AC/CeMOFphotocatalyst displays the highest photocatalytic degradation efficiency of MB/MO (R: 91.5-88.6%, consecutively) compared with the other as-prepared materials after 30 min of visible-light irradiation. The apparent reaction rate K: 1.94-1.31 min-1 is also calculated. The boosted photocatalytic proficiency is ascribed to the heterojunction at the interface of prepared photo material that assists the separation of the charge carriers. To reach optimization, statistical analysis using response surface methodology was applied. The effect of independent parameters (such as A (pH), B (irradiation time), and (c) initial pollutants concentration on the response function (%)photodegradation of MB/MO dyes (as examples of azodyes) was investigated via using central composite design. At the optimum condition, the photodegradation efficiency (%) of the MB/MO is 99.8-97.8%, respectively. ZnFe2O₄-AC/CeMOF hybrid reveals good stability over four consecutive cycles.

Keywords: azo-dyes, photo-catalysis, zinc ferrite, response surface methodology

Procedia PDF Downloads 141

Authors: Feras Hanandeh, Izzat Alsmadi, Muhammad M. Kwafha

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Prefix trees or tries are data structures that are used to store data or index of data. The goal is to be able to store and retrieve data by executing queries in quick and reliable manners. In principle, the structure of the trie depends on having letters in nodes at the different levels to point to the actual words in the leafs. However, the exact structure of the trie may vary based on several aspects. In this paper, we evaluated different structures for building tries. Using datasets of words of different sizes, we evaluated the different forms of trie structures. Results showed that some characteristics may impact significantly, positively or negatively, the size and the performance of the trie. We investigated different forms and structures for the trie. Results showed that using an array of pointers in each level to represent the different alphabet letters is the best choice.

Keywords: data structures, indexing, tree structure, trie, information retrieval

Procedia PDF Downloads 423

Authors: Maria Toader, Gheorghe Valentin Roman

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The importance of fruit trees and bushes growing for Romania is due the concordance that exists between the different ecological conditions in natural basins, and the requirements of different species and varieties. There are, in Romania, natural areas dedicated to the main trees species: plum, apple, pear, cherry, sour cherry, finding optimal conditions for harnessing the potential of fruitfulness, making fruit quality both in terms of ratio commercial, and content in active principles. The share of fruits crops in the world economy of agricultural production is due primarily to the role of fruits in nourishment for human, and in the prevention and combating of diseases, in increasing the national income of cultivator countries and to improve comfort for human life. For Romania, the perspectives of the sector are positive, and are due to European funding opportunities, which provide farmers a specialized program that meets the needs of development and modernization of fruit growing industry, cultivation technology and equipment, organization and grouping of producers, creating storage facilities, conditioning, marketing and the joint use of fresh fruit. This paper shows the evolution of fruit growing, in Romania compared to other states. The document presents the current situation of the main tree species both in terms of surface but also of the productions and the role that this activity may have for the development of rural communities.

Keywords: fruit growing, fruits trees, productivity, rural development

Procedia PDF Downloads 234

Authors: Arlene López-Sampson, Tony Page, Betsy Jackes

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Aquilaria genus produces a highly valuable fragrant oleoresin known as agarwood. Agarwood forms in a few trees in the wild as a response to injure or pathogen attack. The resin is used in perfume and incense industry and medicine. Cultivation of Aquilaria species as a sustainable source of the resin is now a common strategy. Physiological traits are frequently used as a proxy of crop and tree productivity. Aquilaria species growing in Queensland, Australia were studied to investigate relationship between leaf-productivity traits with tree growth. Specifically, 28 trees, representing 12 plus trees and 16 trees from yield plots, were selected to conduct carbon isotope analysis (δ13C) and monitor six leaf attributes. Trees were grouped on four diametric classes (diameter at 150 mm above ground level) ensuring the variability in growth of the whole population was sampled. Model averaging technique based on the Akaike’s information criterion (AIC) was computed to identify whether leaf traits could assist in diameter prediction. Carbon isotope values were correlated with height classes and leaf traits to determine any relationship. In average four leaves per shoot were recorded. Approximately one new leaf per week is produced by a shoot. Rate of leaf expansion was estimated in 1.45 mm day-1. There were no statistical differences between diametric classes and leaf expansion rate and number of new leaves per week (p > 0.05). Range of δ13C values in leaves of Aquilaria species was from -25.5 ‰ to -31 ‰ with an average of -28.4 ‰ (± 1.5 ‰). Only 39% of the variability in height can be explained by δ13C in leaf. Leaf δ13C and nitrogen content values were positively correlated. This relationship implies that leaves with higher photosynthetic capacities also had lower intercellular carbon dioxide concentrations (ci/ca) and less depleted values of 13C. Most of the predictor variables have a weak correlation with diameter (D). However, analysis of the 95% confidence of best-ranked regression models indicated that the predictors that could likely explain growth in Aquilaria species are petiole length (PeLen), values of δ13C (true13C) and δ15N (true15N), leaf area (LA), specific leaf area (SLA) and number of new leaf produced per week (NL.week). The model constructed with PeLen, true13C, true15N, LA, SLA and NL.week could explain 45% (R2 0.4573) of the variability in D. The leaf traits studied gave a better understanding of the leaf attributes that could assist in the selection of high-productivity trees in Aquilaria.

Keywords: 13C, petiole length, specific leaf area, tree growth

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Authors: Xiao Zhang, HuanJun Jiang

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In order to overcome the problem of low precision caused by a single typical component, which is chosen to represent the overall fragility in the standard analysis, the continuous beam bridge is considered as a ternary system consisting of pier, abutment bearing, and pier bearing. After the main components undergo the seismic fragility analysis, the copula function in multivariate form is introduced. Based on the computation of the main components' fragility curves and the evaluation of the correlation between the main components, a method to solve the seismic vulnerability of ternary component systems is established.

Keywords: copula function, seismic fragility analysis, damage index, joint probability distribution function

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Authors: F. Lazzeri, I. Reiter

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Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: time-series, features engineering methods for forecasting, energy demand forecasting, Azure Machine Learning

Procedia PDF Downloads 272

Authors: Laetitia Lemiere, Marie Gosme, Gerard Subsol, Marc Jaeger

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Agroforestry is recognized as a way of developing sustainable and resilient agriculture that can fight against climate change. However, the number of species combinations, spatial configurations, and management options for trees and crops is vast. These choices must be adapted to the pedoclimatic and socio-economic contexts and to the objectives of the farmer, who therefore needs support in designing his system. Participative design workshops are a good way to integrate the knowledge of several experts in order to design such complex systems. The design of agroforestry systems should take into account both spatial aspects (e.g., spacing of trees within the lines and between lines, tree line orientation, tree-crop distance, species spatial patterns) and temporal aspects (e.g., crop rotations, tree thinning and pruning, tree planting in the case of successional agroforestry). Furthermore, the interactions between trees and crops evolve as the trees grow. However, agroforestry design workshops generally emphasize the spatial aspect only through the use of static tokens to represent the different species when designing the spatial configuration of the system. Augmented reality (AR) may overcome this limitation, allowing to visualize dynamic representations of trees and crops, and also their interactions, while at the same time retaining the possibility to physically interact with the system being designed (i.e., move trees, add or remove species, etc.). We propose an ergonomic digital solution capable of assisting a group of agroforestry experts to design an agroforestry system and to represent it. We investigated the use of web-based marker-based AR that does not require specific hardware and does not require specific installation so that all users could use their own smartphones right out of the pocket. We developed a prototype mobilizing the AR.js, ArToolKit.js, and Three.js open source libraries. In our implementation, we gradually build a virtual agroforestry system pattern scene from the users' interactions. A specific set of markers initialize the scene properties, and the various plant species are added and located during the workshop design session. The full virtual scene, including the trees positions with their neighborhood, are saved for further uses, such as virtual, augmented instantiation in the farmer fields. The number of tree species available in the application is gradually increasing; we mobilize 3D digital models for walnut, poplar, wild cherry, and other popular species used in agroforestry systems. The prototype allows shadow computations and the representation of trees at various growth stages, as well as different tree generations, and is thus able to visualize the dynamics of the system over time. Future work will focus on i) the design of complex patterns mobilizing several tree/shrub organizations, not restricted to lines; ii) the design of interfaces related to cultural practices, such as clearing or pruning; iii) the representation of tree-crop interactions. Beside tree shade (light competition), our objective is to represent also below-ground competitions (water, nitrogen) or other variables of interest for the design of agroforestry systems (e.g., predicted crop yield).

Keywords: agroforestry system design, augmented reality, marker-based AR, participative design, web-based AR

Procedia PDF Downloads 136

Authors: L. A. Toro, N. Prieto, J. J. Vargas

Abstract:

There are many control charts for monitoring mean and variance. Among these, the X y R, X y S, S2 Hotteling and Shewhart control charts, for mentioning some, are widely used for monitoring mean a variance in industrial processes. In particular, the Shewhart charts are based on the information about the process contained in the current observation only and ignore any information given by the entire sequence of points. Moreover, that the Shewhart chart is a control chart without memory. Consequently, Shewhart control charts are found to be less sensitive in detecting smaller shifts, particularly smaller than 1.5 times of the standard deviation. These kind of small shifts are important in many industrial applications. In this study and effective alternative to Shewhart control chart was implemented. In case of univariate process an Exponentially Moving Average (EWMA) control chart was developed and Multivariate Exponentially Moving Average (MEWMA) control chart in case of multivariate process. Both of these charts were based on memory and perform better that Shewhart chart while detecting smaller shifts. In these charts, information the past sample is cumulated up the current sample and then the decision about the process control is taken. The mentioned characteristic of EWMA and MEWMA charts, are of the paramount importance when it is necessary to control industrial process, because it is possible to correct or predict problems in the processes before they come to a dangerous limit.

Keywords: control charts, multivariate exponentially moving average (MEWMA), exponentially moving average (EWMA), industrial control process

Procedia PDF Downloads 327

Authors: A. B. Alarape, O. D. Aba

Abstract:

The use of legume tree pruning as mulch in agroforestry system is a common practice to maintain soil organic matter and improve soil fertility in the tropics. The study was conducted to determine the influence of agroforestry trees leafy biomass and nitrogen fertilizer on crop growth rate and relative growth rate of maize. The experiments were laid out as 3 x 4 x 2 factorial in a split-split plot design with three replicates. Control, biomass species (Parkia biglobosa and Albizia lebbeck) as main plots were considered, rates of nitrogen considered include (0, 40, 80, 120 kg N ha⁻¹) as sub-plots, and maize varieties (DMR-ESR-7 and 2009 EVAT) were used as sub-sub plots. Data were analyzed using descriptive and inferential statistics (ANOVA) at α = 0.05. Incorporation of leafy biomass was significant in 2015 on Relative Growth Rate (RGR), while nitrogen application was significant on Crop Growth Rate (CGR). 2009 EVAT had higher CGR in 2015 at 4-6 and 6-8 WAP. Incorporation of Albizia leaves enhanced the growth of maize than Parkia leaves. Farmers are, therefore, encouraged to use Albizia leaves as mulch to enrich their soil for maize production and most especially, in case of availability of inorganic fertilizers. Though, production of maize with biomass and application of 120 kg N ha⁻¹ will bring better growth of maize.

Keywords: agroforestry trees, fertilizer, growth, incorporation, leafy biomass

Procedia PDF Downloads 155

Authors: Plessen Mogens Graf

Abstract:

Within the context of multivariate time series segmentation, this paper proposes a method inspired by a posteriori optimal trading. After a normalization step, time series are treated channelwise as surrogate stock prices that can be traded optimally a posteriori in a virtual portfolio holding either stock or cash. Linear transaction costs are interpreted as hyperparameters for noise filtering. Trading signals, as well as trading signals obtained on the reversed time series, are used for unsupervised channelwise labeling before a consensus over all channels is reached that determines the final segmentation time instants. The method is model-free such that no model prescriptions for segments are made. Benefits of proposed approach include simplicity, computational efficiency, and adaptability to a wide range of different shapes of time series. Performance is demonstrated on synthetic and real-world data, including a large-scale dataset comprising a multivariate time series of dimension 1000 and length 2709. Proposed method is compared to a popular model-based bottom-up approach fitting piecewise affine models and to a recent model-based top-down approach fitting Gaussian models and found to be consistently faster while producing more intuitive results in the sense of segmenting time series at peaks and valleys.

Keywords: time series segmentation, model-free, trading-inspired, multivariate data

Procedia PDF Downloads 107

Authors: Ibrahim Suliman Hanaish

Abstract:

Disaggregation daily rainfall using stochastic models formulated based on multivariate approach (MuDRain) is discussed in this paper. Seven rain gauge stations are considered in this study for different distances from the referred station starting from 4 km to 160 km in Peninsular Malaysia. The hourly rainfall data used are covered the period from 1973 to 2008 and July and November months are considered as an example of dry and wet periods. The cross-correlation among the rain gauges is considered for the available hourly rainfall information at the neighboring stations or not. This paper discussed the applicability of the MuDRain model for disaggregation daily rainfall to hourly rainfall for both sources of cross-correlation. The goodness of fit of the model was based on the reproduction of fitting statistics like the means, variances, coefficients of skewness, lag zero cross-correlation of coefficients and the lag one auto correlation of coefficients. It is found the correlation coefficients based on extracted correlations that was based on daily are slightly higher than correlations based on available hourly rainfall especially for neighboring stations not more than 28 km. The results showed also the MuDRain model did not reproduce statistics very well. In addition, a bad reproduction of the actual hyetographs comparing to the synthetic hourly rainfall data. Mean while, it is showed a good fit between the distribution function of the historical and synthetic hourly rainfall. These discrepancies are unavoidable because of the lowest cross correlation of hourly rainfall. The overall performance indicated that the MuDRain model would not be appropriate choice for disaggregation daily rainfall.

Keywords: rainfall disaggregation, multivariate disaggregation rainfall model, correlation, stochastic model

Procedia PDF Downloads 471

Authors: K. Umbleja, M. Ichino, H. Yaguchi

Abstract:

In this paper, we propose a coloring method for multivariate data visualization by using parallel coordinates based on dissimilarity and tree structure information gathered during hierarchical clustering. The proposed method is an extension for proximity-based coloring that suffers from a few undesired side effects if hierarchical tree structure is not balanced tree. We describe the algorithm by assigning colors based on dissimilarity information, show the application of proposed method on three commonly used datasets, and compare the results with proximity-based coloring. We found our proposed method to be especially beneficial for symbolic data visualization where many individual objects have already been aggregated into a single symbolic object.

Keywords: data visualization, dissimilarity-based coloring, proximity-based coloring, symbolic data

Procedia PDF Downloads 137