Search results for: covariance matrix estimation

Authors: Saja M. Nabat Al-Ajrash, Charles Browning, Rose Eckerle, Li Cao, Robyn L. Bradford, Donald Klosterman

Abstract:

An additive manufacturing process and subsequent pyrolysis cycle were used to fabricate SiC matrix/carbon fiber hybrid composites. The matrix was fabricated using a mixture of preceramic polymer and acrylate monomers, while polyacrylonitrile (PAN) precursor was used to fabricate fibers via electrospinning. The precursor matrix and reinforcing fibers at 0, 2, 5, or 10 wt% were printed using digital light processing, and both were simultaneously pyrolyzed to yield the final ceramic matrix composite structure. After pyrolysis, XRD and SEAD analysis proved the existence of SiC nanocrystals and turbostratic carbon structure in the matrix, while the reinforcement phase was shown to have a turbostratic carbon structure similar to commercial carbon fibers. Thermogravimetric analysis (TGA) in the air up to 1400 °C was used to evaluate the oxidation resistance of this material. TGA results showed some weight loss due to oxidation of SiC and/or carbon up to about 900 °C, followed by weight gain to about 1200 °C due to the formation of a protective SiO2 layer. Although increasing carbon fiber content negatively impacted the total mass loss for the first heating cycle, exposure of the composite to second-run air revealed negligible weight chance. This is explained by SiO2 layer formation, which acts as a protective film that prevents oxygen diffusion. Oxidation of SiC and the formation of a glassy layer has been proven to protect the sample from further oxidation, as well as provide healing of surface cracks and defects, as revealed by SEM analysis.

Keywords: silicon carbide, carbon fibers, additive manufacturing, composite

Procedia PDF Downloads 45

Authors: Davies Obaromi, Qin Yongsong, James Ndege, Azeez Adeboye, Akinwumi Odeyemi

Abstract:

The basic model usually used in disease mapping, is the Besag, York and Mollie (BYM) model and which combines the spatially structured and spatially unstructured priors as random effects. Bayesian Conditional Autoregressive (CAR) model is a disease mapping method that is commonly used for smoothening the relative risk of any disease as used in the Besag, York and Mollie (BYM) model. This model (CAR), which is also usually assigned as a prior to one of the spatial random effects in the BYM model, successfully uses information from adjacent sites to improve estimates for individual sites. To our knowledge, there are some unrealistic or counter-intuitive consequences on the posterior covariance matrix of the CAR prior for the spatial random effects. In the conventional BYM (Besag, York and Mollie) model, the spatially structured and the unstructured random components cannot be seen independently, and which challenges the prior definitions for the hyperparameters of the two random effects. Therefore, the main objective of this study is to construct and utilize an extended Bayesian spatial CAR model for studying tuberculosis patterns in the Eastern Cape Province of South Africa, and then compare for flexibility with some existing CAR models. The results of the study revealed the flexibility and robustness of this alternative extended CAR to the commonly used CAR models by comparison, using the deviance information criteria. The extended Bayesian spatial CAR model is proved to be a useful and robust tool for disease modeling and as a prior for the structured spatial random effects because of the inclusion of an extra hyperparameter.

Keywords: Besag2, CAR models, disease mapping, INLA, spatial models

Procedia PDF Downloads 243

Authors: Arpita Mondal, Aurobinda Routray, Sreeraj Puravankara, Rajashree Biswas

Abstract:

The most difficult part of designing a battery management system (BMS) is battery modeling. A good battery model can capture the dynamics which helps in energy management, by accurate model-based state estimation algorithms. So far the most suitable and fruitful model is the equivalent circuit model (ECM). However, in real-time applications, the model parameters are time-varying, changes with current, temperature, state of charge (SOC), and aging of the battery and this make a great impact on the performance of the model. Therefore, to increase the equivalent circuit model performance, the parameter estimation has been carried out in the frequency domain. The battery is a very complex system, which is associated with various chemical reactions and heat generation. Therefore, it’s very difficult to select the optimal model structure. As we know, if the model order is increased, the model accuracy will be improved automatically. However, the higher order model will face the tendency of over-parameterization and unfavorable prediction capability, while the model complexity will increase enormously. In the time domain, it becomes difficult to solve higher order differential equations as the model order increases. This problem can be resolved by frequency domain analysis, where the overall computational problems due to ill-conditioning reduce. In the frequency domain, several dominating frequencies can be found in the input as well as output data. The selective frequency domain estimation has been carried out, first by estimating the frequencies of the input and output by subspace decomposition, then by choosing the specific bands from the most dominating to the least, while carrying out the least-square, recursive least square and Kalman Filter based parameter estimation. In this paper, a second order battery model consisting of three resistors, two capacitors, and one SOC controlled voltage source has been chosen. For model identification and validation hybrid pulse power characterization (HPPC) tests have been carried out on a 2.6 Ah LiFePO₄ battery.

Keywords: equivalent circuit model, frequency estimation, parameter estimation, subspace decomposition

Procedia PDF Downloads 108

Authors: Abdel-Aziz M. Mohamed

Abstract:

This research proposes a methodology to estimate the customer order lead time in the supply chain based on the process capability index. The cases when the process output is normally distributed and when it is not are considered. The relationships between the system capability indices in both service and manufacturing applications, delivery system reliability and the percentages of orders delivered after their promised due dates are presented. The proposed method can be used to examine the current process capability to deliver the orders before the promised lead-time. If the system was found to be incapable, the method can be used to help revise the current lead-time to a proper value according to the service reliability level selected by the management. Numerical examples and a case study describing the lead time estimation methodology and testing the system capability of delivering the orders before their promised due date are illustrated.

Keywords: lead-time estimation, process capability index, delivery system reliability, statistical analysis, service achievement index, service quality

Procedia PDF Downloads 533

Authors: Farzad Khajavi

Abstract:

Studies about the interaction between active pharmaceutical ingredients (API) and excipients are so important in the pre-formulation stage of development of all dosage forms. Analytical techniques such as differential scanning calorimetry (DSC), Thermal gravimetry (TG), and Furrier transform infrared spectroscopy (FTIR) are commonly used tools for investigating regarding compatibility and incompatibility of APIs with excipients. Sometimes the interpretation of data obtained from these techniques is difficult because of severe overlapping of API spectrum with excipients in their mixtures. Principal component analysis (PCA) as a powerful factor analytical method is used in these situations to resolve data matrices acquired from these analytical techniques. Binary mixtures of API and interested excipients are considered and produced. Peaks of FTIR, DSC, or TG of pure API and excipient and their mixtures at different mole ratios will construct the rows of the data matrix. By applying PCA on the data matrix, the number of principal components (PCs) is determined so that it contains the total variance of the data matrix. By plotting PCs or factors obtained from the score of the matrix in two-dimensional spaces if the pure API and its mixture with the excipient at the high amount of API and the 1:1mixture form a separate cluster and the other cluster comprise of the pure excipient and its blend with the API at the high amount of excipient. This confirms the existence of compatibility between API and the interested excipient. Otherwise, the incompatibility will overcome a mixture of API and excipient.

Keywords: API, compatibility, DSC, TG, interactions

Procedia PDF Downloads 94

Authors: Yemane Hailu Fissuh, Zhongzhan Zhang

Abstract:

In biomedical researches and randomized clinical trials, the most commonly interested outcomes are time-to-event so-called survival data. The importance of robust models in this context is to compare the effect of randomly controlled experimental groups that have a sense of causality. Causal estimation is the scientific concept of comparing the pragmatic effect of treatments conditional to the given covariates rather than assessing the simple association of response and predictors. Hence, the causal effect based semiparametric transformation model was proposed to estimate the effect of treatment with the presence of possibly time-varying covariates. Due to its high flexibility and robustness, the semiparametric transformation model which shall be applied in this paper has been given much more attention for estimation of a causal effect in modeling left-truncated and right censored survival data. Despite its wide applications and popularity in estimating unknown parameters, the maximum likelihood estimation technique is quite complex and burdensome in estimating unknown parameters and unspecified transformation function in the presence of possibly time-varying covariates. Thus, to ease the complexity we proposed the modified estimating equations. After intuitive estimation procedures, the consistency and asymptotic properties of the estimators were derived and the characteristics of the estimators in the finite sample performance of the proposed model were illustrated via simulation studies and Stanford heart transplant real data example. To sum up the study, the bias of covariates was adjusted via estimating the density function for truncation variable which was also incorporated in the model as a covariate in order to relax the independence assumption of failure time and truncation time. Moreover, the expectation-maximization (EM) algorithm was described for the estimation of iterative unknown parameters and unspecified transformation function. In addition, the causal effect was derived by the ratio of the cumulative hazard function of active and passive experiments after adjusting for bias raised in the model due to the truncation variable.

Keywords: causal estimation, EM algorithm, semiparametric transformation models, time-to-event outcomes, time-varying covariate

Procedia PDF Downloads 95

Authors: Farzaneh Khorram

Abstract:

This paper endeavors to construct a block model to assess grinding energy consumption (CCE) and pinpoint blocks with the highest potential for energy usage during the grinding process within a specified region. Leveraging geostatistical techniques, particularly joint estimation, or simulation, based on geometallurgical data from various mineral processing stages, our objective is to forecast CCE across the study area. The dataset encompasses variables obtained from 2754 drill samples and a block model comprising 4680 blocks. The initial analysis encompassed exploratory data examination, variography, multivariate analysis, and the delineation of geological and structural units. Subsequent analysis involved the assessment of contacts between these units and the estimation of CCE via cokriging, considering its correlation with SPI. The selection of blocks exhibiting maximum CCE holds paramount importance for cost estimation, production planning, and risk mitigation. The study conducted exploratory data analysis on lithology, rock type, and failure variables, revealing seamless boundaries between geometallurgical units. Simulation methods, such as Plurigaussian and Turning band, demonstrated more realistic outcomes compared to cokriging, owing to the inherent characteristics of geometallurgical data and the limitations of kriging methods.

Keywords: geometallurgy, multivariate analysis, plurigaussian, turning band method, cokriging

Procedia PDF Downloads 17

Authors: S. Meguenni, A. Djahbar, K. Tounsi

Abstract:

Electric railway traction systems are complex; they have electrical couplings, magnetic and solid mechanics. These couplings impose several constraints that complicate the modeling and analysis of these systems. An example of drive systems, which combine the advantages of the use of multiphase machines, power electronics and computing means, is mono convert isseur multi-machine system which can control a fully decoupled so many machines whose electric windings are connected in series. In this approach, our attention especially on modeling and independent control of two five phase synchronous machine with permanent magnet connected in series and fed by a matrix converter application to the rail traction (bogie of a locomotive BB 36000).

Keywords: synchronous machine, vector control Multi-machine/ Multi-inverter, matrix inverter, Railway traction

Procedia PDF Downloads 344

Authors: Sourita Ghosh, Falguni Pati, Suhanya Duraiswamy

Abstract:

Spheroid, a simple three-dimensional cellular aggregate, allows us to simulate the in-vivo complexity of cellular signaling and interactions in greater detail than traditional 2D cell culture. It can be used as an in-vitro model for drug toxicity testing, tumor modeling and many other such applications specifically for cancer. Our work is focused on the development of an affordable, user-friendly, robust, reproducible, high throughput microfluidic device for water in oil droplet production, which can, in turn, be used for spheroids manufacturing. Here, we have investigated the droplet breakup between two non-Newtonian fluids, viz. silicone oil and decellularized liver matrix, which acts as our extra cellular matrix (ECM) for spheroids formation. We performed some biochemical assays to characterize the liver ECM, as well as rheological studies on our two fluids and observed a critical dependence of capillary number (Ca) on droplet breakup and homogeneous drop formation

Keywords: chip less, droplets, extracellular matrix, liver spheroid

Procedia PDF Downloads 55

Authors: Shih-Pin Chen, Shih-Syuan You

Abstract:

This study proposes a regression model with flexible spreads for fuzzy input-output data to cope with the situation that the existing measures cannot reflect the actual estimation error. The main idea is that a dissemblance index (DI) is carefully identified and defined for precisely measuring the actual estimation error. Moreover, the graded mean integration (GMI) representation is adopted for determining more representative numeric regression coefficients. Notably, to comprehensively compare the performance of the proposed model with other ones, three different criteria are adopted. The results from commonly used test numerical examples and an application to Taiwan's business monitoring indicator illustrate that the proposed dissemblance index method not only produces valid fuzzy regression models for fuzzy input-output data, but also has satisfactory and stable performance in terms of the total estimation error based on these three criteria.

Keywords: dissemblance index, forecasting, fuzzy sets, linear regression

Procedia PDF Downloads 330

Authors: Rajeev, N. K. Raigar

Abstract:

In this study, one dimensional phase change problem (a Stefan problem) is considered and a numerical solution of this problem is discussed. First, we use similarity transformation to convert the governing equations into ordinary differential equations with its boundary conditions. The solutions of ordinary differential equation with the associated boundary conditions and interface condition (Stefan condition) are obtained by using a numerical approach based on operational matrix of differentiation of shifted second kind Chebyshev wavelets. The obtained results are compared with existing exact solution which is sufficiently accurate.

Keywords: operational matrix of differentiation, similarity transformation, shifted second kind chebyshev wavelets, stefan problem

Procedia PDF Downloads 380

Authors: Harry Aginta

Abstract:

Using Phillips curve framework, this paper seeks for new empirical evidence on the relationship between inflation and output in a major emerging economy. By exploiting sub-national data, the contribution of this paper is threefold. First, it resolves the issue of using on-target national inflation rates that potentially causes weakening inflation-output nexus. This is very relevant for Indonesia as its central bank has been adopting inflation targeting framework based on national consumer price index (CPI) inflation. Second, the study tests the relevance of mining sector in output gap estimation. The test for mining sector is important to control for the effects of mining regulation and nominal effects of coal prices on real economic activities. Third, the paper applies panel econometric method by incorporating regional variation that help to improve model estimation. The results from this paper confirm the strong presence of Phillips curve in Indonesia. Positive output gap that reflects excess demand condition gives rise to the inflation rates. In addition, the elasticity of output gap is higher if the mining sector is excluded from output gap estimation. In addition to inflation adaptation, the dynamics of exchange rate and international commodity price are also found to affect inflation significantly. The results are robust to the alternative measurement of output gap

Keywords: Phillips curve, inflation, Indonesia, panel data

Procedia PDF Downloads 95

Authors: C. H. S. Vidyasagar, D. B. Karunakar

Abstract:

In this present work, five different composite samples with AA2024 as matrix and varying amounts of yttrium (0.1-0.5 wt.%) as reinforcement are developed through cold compaction. The microstructures of the developed composite samples revealed that the yttrium reinforcement caused grain refinement up to 0.3 wt.% and beyond which the refinement is not effective. The microstructure revealed Al2Cu precipitation which strengthened the composite up to 0.3 wt.% yttrium reinforcement. Upon further increase in yttrium reinforcement, the intermetallics and the precipitation coarsen and their corresponding strengthening effect decreases. The mechanical characterization revealed that the composite sample reinforced with 0.3 wt.% yttrium showed highest mechanical properties like 82 HV of hardness, 276 MPa Ultimate Tensile Strength (UTS), 229 MPa Yield Strength (YS) and an elongation (EL) of 18.9% respectively. However, the relative density of the developed composites decreased with the increase in yttrium reinforcement.

Keywords: mechanical properties, AA 2024 matrix, yttrium reinforcement, cold compaction, precipitation

Procedia PDF Downloads 120

Authors: Ping Tan, Xiaomeng Su, Yi Shen

Abstract:

The motion intention in the motor imagery braincomputer interface is identified by classifying the event-related desynchronization (ERD) and event-related synchronization ERS characteristics of sensorimotor rhythm (SMR) in EEG signals. When the subject imagines different limbs or different parts moving, the rhythm components and bandwidth will change, which varies from person to person. How to find the effective sensorimotor frequency band of subjects is directly related to the classification accuracy of brain-computer interface. To solve this problem, this paper proposes a Minimum Distance to Riemannian Mean Classification method based on Non-Uniform Filter Banks. During the training phase, the EEG signals are decomposed into multiple different bandwidt signals by using multiple band-pass filters firstly; Then the spatial covariance characteristics of each frequency band signal are computered to be as the feature vectors. these feature vectors will be classified by the MDRM (Minimum Distance to Riemannian Mean) method, and cross validation is employed to obtain the effective sensorimotor frequency bands. During the test phase, the test signals are filtered by the bandpass filter of the effective sensorimotor frequency bands, and the extracted spatial covariance feature vectors will be classified by using the MDRM. Experiments on the BCI competition IV 2a dataset show that the proposed method is superior to other classification methods.

Keywords: non-uniform filter banks, motor imagery, brain-computer interface, minimum distance to Riemannian mean

Procedia PDF Downloads 81

Authors: Nicolas Bigot, M'hamed Boutaous, Nahiene Hamila, Shihe Xin

Abstract:

Composite materials with polymer matrices are widely used in most industrial areas, particularly in aeronautical and automotive ones. Thanks to the development of a high-performance thermoplastic semicrystalline polymer matrix, those materials exhibit more and more efficient properties. The polymer matrix in composite materials can manifest a specific crystalline structure characteristic of crystallization in a fibrous medium. In order to guarantee a good mechanical behavior of structures and to optimize their performances, it is necessary to define realistic mechanical constitutive laws of such materials considering their physical structure. The interaction between fibers and matrix is a key factor in the mechanical behavior of composite materials. Transcrystallization phenomena which develops in the matrix around the fibers constitute the interphase which greatly affects and governs the nature of the fiber-matrix interaction. Hence, it becomes fundamental to quantify its impact on the thermo-mechanical behavior of composites material in relationship with processing conditions. In this work, we propose a numerical model coupling the thermal and crystallization kinetics in long fiber-based composite materials, considering both the spherulitic and transcrystalline types of the induced structures. After validation of the model with comparison to results from the literature and noticing a good correlation, a parametric study has been led on the effects of the thermal kinetics, the fibers volume fractions, the deformation, and the pressure on the crystallization rate in the material, under processing conditions. The ratio of the transcrystallinity is highlighted and analyzed with regard to the thermal kinetics and gradients in the material. Experimental results on the process are foreseen and pave the way to establish a mechanical constitutive law describing, with the introduction of the role on the crystallization rates and types on the thermo-mechanical behavior of composites materials.

Keywords: composite materials, crystallization, heat transfer, modeling, transcrystallization

Procedia PDF Downloads 169

Authors: Baljinder Singh, Navneet Sharma

Abstract:

The skin can be used as the site for drug administration for continuous transdermal drug infusion into the systemic circulation. For the continuous diffusion/penetration of the drugs through the intact skin surface membrane-moderated systems, matrix dispersion type systems, adhesive diffusion controlled systems and micro reservoir systems have been developed. Various penetration enhancers are used for the drug diffusion through skin. In matrix dispersion type systems, the drug is dispersed in the solvent along with the polymers and solvent allowed to evaporate forming a homogeneous drug-polymer matrix. Matrix type systems were developed in the present study. In the present work, an attempt has been made to develop a matrix-type transdermal therapeutic system comprising of ondansetron-HCl with different ratios of hydrophilic and hydrophobic polymeric combinations using solvent evaporation technique. The physicochemical compatibility of the drug and the polymers was studied by infrared spectroscopy. The results obtained showed no physical-chemical incompatibility between the drug and the polymers. The patches were further subjected to various physical evaluations along with the in-vitro permeation studies using rat skin. On the basis of results obtained form the in vitro study and physical evaluation, the patches containing hydrophilic polymers i.e. polyvinyl alcohol and poly vinyl pyrrolidone with oleic acid as the penetration enhancer(5%) were considered as suitable for large scale manufacturing with a backing layer and a suitable adhesive membrane.

Keywords: transdermal drug delivery, penetration enhancers, hydrophilic and hydrophobic polymers, ondansetron HCl

Procedia PDF Downloads 296

Authors: Tasir Khan, Yejuan Wan, Kalim Ullah

Abstract:

Hydrological frequency analysis has been conducted to estimate the minimum flow elevation of the Indus River in Pakistan to protect the ecosystem. The Maximum likelihood estimation (MLE) technique is used to estimate the best-fitted distribution for Minimum Ecological Flows at nine stations of the Indus River in Pakistan. The four selected distributions, Generalized Extreme Value (GEV) distribution, Generalized Logistics (GLO) distribution, Generalized Pareto (GPA) distribution, and Pearson type 3 (PE3) are fitted in all sites, usually used in hydro frequency analysis. Compare the performance of these distributions by using the goodness of fit tests, such as the Kolmogorov Smirnov test, Anderson darling test, and chi-square test. The study concludes that the Maximum Likelihood Estimation (MLE) method recommended that GEV and GPA are the most suitable distributions which can be effectively applied to all the proposed sites. The quantiles are estimated for the return periods from 5 to 1000 years by using MLE, estimations methods. The MLE is the robust method for larger sample sizes. The results of these analyses can be used for water resources research, including water quality management, designing irrigation systems, determining downstream flow requirements for hydropower, and the impact of long-term drought on the country's aquatic system.

Keywords: minimum ecological flow, frequency distribution, indus river, maximum likelihood estimation

Procedia PDF Downloads 53

Authors: Kyugneun Lee, Ikjin Lee

Abstract:

Parameter estimation with inverse problem often suffers from unfavorable conditions in the real world. Useless data and many input parameters make the problem complicated or insoluble. Data refinement and reformulation of the problem can solve that kind of difficulties. In this research, a method to solve the rank deficient inverse problem is suggested. A multi-physics system which has rank deficiency caused by response correlation is treated. Impeditive information is removed and the problem is reformulated to sequential estimations using Bayesian network (BN) and subset groups. At first, subset grouping of the responses is performed. Feature selection with singular value decomposition (SVD) is used for the grouping. Next, BN inference is used for sequential conditional estimation according to the group hierarchy. Directed acyclic graph (DAG) structure is organized to maximize the estimation ability. Variance ratio of response to noise is used to pairing the estimable parameters by each response.

Keywords: Bayesian network, feature selection, rank deficiency, statistical inverse analysis

Procedia PDF Downloads 284

Authors: V. J. Pillewan, D. N. Raut, K. N. Patil, D. K. Shinde

Abstract:

Forging, milling, turning, grinding and shaping etc. are the various industrial manufacturing processes which generate the metal waste. Grinding is extensively used in the finishing operation. The waste generated contains significant impurities apart from the metal particles. Due to these significant impurities, it becomes difficult to process and gets usually dumped in the landfills which create environmental problems. Therefore, it becomes essential to reuse metal waste to create value added products. Powder injection molding process is used for producing the porous metal matrix framework. This paper discusses the presented design of the porous framework to be used for the liquid filter application. Different parameters are optimized to obtain the better strength framework with variable porosity. Carbon nanotubes are used as reinforcing materials to enhance the strength of the metal matrix framework.

Keywords: grinding waste, powder injection molding (PIM), carbon nanotubes (CNTs), matrix composites (MMCs)

Procedia PDF Downloads 276

Authors: Nigus Maregu Demewoz, Shu-Kai Yeh

Abstract:

Low-density nanocellular foam is a fascinating new-generation advanced material due to its mechanical strength and thermal insulation properties. In nanocellular foam, reducing the density increases the insulation ability. However, producing a nanocellular foam of densities less than 0.3 with a cell size of less than 100 nm is very challenging. In this study, poly (methyl methacrylate) (PMMA) was blended with Polyether block amide (PEBAX) to study the effects of PEBAX on the nanocellular foam structure of the PMMA matrix. We added 2 wt% of PEBAX in the PMMA matrix, and the PEBAX nanostructured domain size of 45 nm was well dispersed in the PMMA matrix. The foaming result produced a new generation special bouquet-like nanocellular foam of cell size less than 50 nm with a relative density of 0.24. Also, we were able to produce a nanocellular foam of a relative density of about 0.17. In addition to thermal insulation applications, bouquet-like nanocellular foam may be expected for filtration applications.

Keywords: nanocellular foam, low-density, cell size, relative density, PMMA/PEBAX

Procedia PDF Downloads 50

Authors: Muhammad Jahanzaib, Muhammad Z. A. Khan, Junaid Khayyam

Abstract:

Islamabad is the capital of Pakistan with the population of 1.365 million people and with a vehicular fleet size of 0.75 million. The vehicular fleet size is growing annually by the rate of 11%. Vehicular emissions are major source of Black carbon (BC). In developing countries like Pakistan, most of the vehicles consume conventional fuels like Petrol, Diesel, and CNG. These fuels are the major emitters of pollutants like CO, CO2, NOx, CH4, VOCs, and particulate matter (PM10). Carbon dioxide and methane are the leading contributor to the global warming with a global share of 9-26% and 4-9% respectively. NOx is the precursor of nitrates which ultimately form aerosols that are noxious to human health. In this study, COPERT (Computer program to Calculate Emissions from Road Transport) was used for vehicular emission estimation in Islamabad. COPERT is a windows based program which is developed for the calculation of emissions from the road transport sector. The emissions were calculated for the year of 2016 include pollutants like CO, NOx, VOC, and PM and energy consumption. The different variable was input to the model for emission estimation including meteorological parameters, average vehicular trip length and respective time duration, fleet configuration, activity data, degradation factor, and fuel effect. The estimated emissions for CO, CH4, CO2, NOx, and PM10 were found to be 9814.2, 44.9, 279196.7, 3744.2 and 304.5 tons respectively.

Keywords: COPERT Model, emission estimation, PM10, vehicular emission

Procedia PDF Downloads 228

Authors: Rui Huang, Thomas Richard, Masood Mostofi

Abstract:

The lifetime of impregnated diamond bits and their drilling efficiency are in part governed by the bit wear conditions, not only the extent of the diamonds’ wear but also their exposure or protrusion out of the matrix bonding. As much as individual diamonds wear, the bonding matrix does also wear through two-body abrasion (direct matrix-rock contact) and three-body erosion (cuttings trapped in the space between rock and matrix). Although there is some work dedicated to the study of diamond bit wear, there is still a lack of understanding on how matrix erosion and diamond exposure relate to the bit drilling response and drilling efficiency, as well as no literature on the process that governs bit sharpening a procedure commonly implemented by drillers when the extent of diamond polishing yield extremely low rate of penetration. The aim of this research is (i) to derive a correlation between the wear state of the bit and the drilling performance but also (ii) to gain a better understanding of the process associated with tool sharpening. The research effort combines specific drilling experiments and precise mapping of the tool-cutting face (impregnated diamond bits and segments). Bit wear is produced by drilling through a rock sample at a fixed rate of penetration for a given period of time. Before and after each wear test, the bit drilling response and thus efficiency is mapped out using a tailored design experimental protocol. After each drilling test, the bit or segment cutting face is scanned with an optical microscope. The test results show that, under the fixed rate of penetration, diamond exposure increases with drilling distance but at a decreasing rate, up to a threshold exposure that corresponds to the optimum drilling condition for this feed rate. The data further shows that the threshold exposure scale with the rate of penetration up to a point where exposure reaches a maximum beyond which no more matrix can be eroded under normal drilling conditions. The second phase of this research focuses on the wear process referred as bit sharpening. Drillers rely on different approaches (increase feed rate or decrease flow rate) with the aim of tearing worn diamonds away from the bit matrix, wearing out some of the matrix, and thus exposing fresh sharp diamonds and recovering a higher rate of penetration. Although a common procedure, there is no rigorous methodology to sharpen the bit and avoid excessive wear or bit damage. This paper aims to gain some insight into the mechanisms that accompany bit sharpening by carefully tracking diamond fracturing, matrix wear, and erosion and how they relate to drilling parameters recorded while sharpening the tool. The results show that there exist optimal conditions (operating parameters and duration of the procedure) for sharpening that minimize overall bit wear and that the extent of bit sharpening can be monitored in real-time.

Keywords: bit sharpening, diamond exposure, drilling response, impregnated diamond bit, matrix erosion, wear rate

Procedia PDF Downloads 62

Authors: Nhat-To Huynh, Chen-Fu Chien

Abstract:

Textile batch dyeing scheduling problem is complicated which includes batch formation, batch assignment on machines, batch sequencing with sequence-dependent setup time. Most manufacturers schedule their orders manually that are time consuming and inefficient. More power methods are needed to improve the solution. Motivated by the real needs, this study aims to propose approaches in which genetic algorithm is developed with multi-subpopulation and hybridised with estimation of distribution algorithm to solve the constructed problem for minimising the makespan. A heuristic algorithm is designed and embedded into the proposed algorithms to improve the ability to get out of the local optima. In addition, an empirical study is conducted in a textile company in Taiwan to validate the proposed approaches. The results have showed that proposed approaches are more efficient than simulated annealing algorithm.

Keywords: estimation of distribution algorithm, genetic algorithm, multi-subpopulation, scheduling, textile dyeing

Procedia PDF Downloads 273

Authors: Leon Mishnaevsky Jr., Gaoming Dai

Abstract:

3D numerical simulations of fatigue damage of multiscale fiber reinforced polymer composites with secondary nanoclay reinforcement are carried out. Macro-micro FE models of the multiscale composites are generated automatically using Python based software. The effect of the nanoclay reinforcement (localized in the fiber/matrix interface (fiber sizing) and distributed throughout the matrix) on the crack path, damage mechanisms and fatigue behavior is investigated in numerical experiments.

Keywords: computational mechanics, fatigue, nanocomposites, composites

Procedia PDF Downloads 577

Authors: Sarabjeet Singh Sidhu, Ajay Batish, Sanjeev Kumar

Abstract:

This paper reports the optimal process conditions for machining of three different types of metal matrix composites (MMCs): 65vol%SiC/A356.2; 10vol%SiC-5vol%quartz/Al and 30vol%SiC/A359 using PMEDM process. Metal removal rate (MRR), tool wear rate (TWR), surface roughness (SR) and surface integrity (SI) were evaluated after each trial and contributing process parameters were identified. The four responses were then collectively optimized using the technique for order preference by similarity to ideal solution (TOPSIS) and optimal process conditions were identified for each type of MMCS. The density of reinforced particles shields the matrix material from spark energy hence the high MRR and SR was observed with lowest reinforced particle. TWR was highest with Cu-Gr electrode due to disintegration of the weakly bonded particles in the composite electrode. Each workpiece was examined for surface integrity and ranked as per severity of surface defects observed and their rankings were used for arriving at the most optimal process settings for each workpiece.

Keywords: metal matrix composites (MMCS), metal removal rate (MRR), surface roughness (SR), surface integrity (SI), tool wear rate (TWR), technique for order preference by similarity to ideal solution (TOPSIS)

Procedia PDF Downloads 260

Authors: Zhiyong Zheng, Xu Li, Liang Huang, Zhengliang Sun, Jianhua Xu

Abstract:

Yaw angle plays a significant role in many vehicle safety applications, such as collision avoidance and lane-keeping system. Although the estimation of the yaw angle has been extensively studied in existing literature, it is still the main challenge to simultaneously achieve a reliable and cost-effective solution in complex urban environments. This paper proposes a multi-stage learning framework to estimate the yaw angle with a monocular camera, which can deal with the challenge in a more reliable manner. In the first stage, an efficient road detection network is designed to extract the road region, providing a highly reliable reference for the estimation. In the second stage, a variational auto-encoder (VAE) is proposed to learn the distribution patterns of road regions, which is particularly suitable for modeling the changing patterns of yaw angle under different driving maneuvers, and it can inherently enhance the generalization ability. In the last stage, a gated recurrent unit (GRU) network is used to capture the temporal correlations of the learned patterns, which is capable to further improve the estimation accuracy due to the fact that the changes of deflection angle are relatively easier to recognize among continuous frames. Afterward, the yaw angle can be obtained by combining the estimated deflection angle and the road direction stored in a roadway map. Through effective multi-stage learning, the proposed framework presents high reliability while it maintains better accuracy. Road-test experiments with different driving maneuvers were performed in complex urban environments, and the results validate the effectiveness of the proposed framework.

Keywords: gated recurrent unit, multi-stage learning, reliable estimation, variational auto-encoder, yaw angle

Procedia PDF Downloads 107

Authors: Pyung Soo Kim, Eung Hyuk Lee, Mun Suck Jang

Abstract:

In the current paper, an indoor positioning system is proposed with consideration of measurement delay. Firstly, an estimation filter with a measurement delay is designed for the indoor positioning mechanism under a weighted least square criterion, which utilizes only finite measurements on the most recent window. The proposed estimation filtering based scheme gives the filtered estimates for position, velocity and acceleration of moving target in real-time, while removing undesired noisy effects and preserving desired moving positions. Secondly, the proposed scheme is shown to have good inherent properties such as unbiasedness, efficiency, time-invariance, deadbeat, and robustness due to the finite memory structure. Finally, computer simulations shows that the performance of the proposed estimation filtering based scheme can outperform to the existing infinite memory filtering based mechanism.

Keywords: indoor positioning system, wireless sensor networks, measurement delay

Procedia PDF Downloads 441

Authors: Nigus Maregu Demewoz, Shu-Kai Yeh

Abstract:

Low-density nanocellular foam is a fascinating new-generation advanced material due to its mechanical strength and thermal insulation properties. In nanocellular foam, reducing the density increases the insulation ability. However, producing a nanocellular foam of densities less than 0.3 with a cell size of less than 100 nm is very challenging. In this study, poly (methyl methacrylate) (PMMA) was blended with Polyether block amide (PEBAX) to study the effects of PEBAX on the nanocellular foam structure of the PMMA matrix. We added 2 wt% of PEBAX in the PMMA matrix, and the PEBAX nanostructured domain size of 45 nm was well dispersed in the PMMA matrix. The foaming result produced a new generation special bouquet-like nanocellular foam of cell size less than 50 nm with a relative density of 0.24. Also, we were able to produce a nanocellular foam of a relative density of about 0.17. In addition to thermal insulation applications, bouquet-like nanocellular foam may be expected for filtration applications.

Keywords: nanocellular foam, low-density, cell size, relative density, PMMA/PEBAX blend

Procedia PDF Downloads 52

Authors: Abdullah Eqal Al Mazrooei

Abstract:

In this paper, we introduce a mathematical algorithm which is used for estimating the states in the bilinear systems. This algorithm uses a special linearization of the second-order term by using the best available information about the state of the system. This technique makes our algorithm generalizes the well-known Kalman estimators. The system which is used here is of the bilinear class, the evolution of this model is linear-bilinear in the state of the system. Our algorithm can be used with linear and bilinear systems. We also here introduced a real application for the new algorithm to prove the feasibility and the efficiency for it.

Keywords: estimation algorithm, bilinear systems, Kakman filter, second order linearization

Procedia PDF Downloads 449

Authors: Naime Boudemagh, Zoheir Hammoudi

Abstract:

The parameters estimation of the K-distribution is an essential part in radar detection. In fact, presence of interfering targets in reference cells causes a decrease in detection performances. In such situation, the estimate of the shape and the scale parameters are far from the actual values. In the order to avoid interfering targets, we propose an Automatic Censoring (AC) algorithm of radar interfering targets in K-distribution. The censoring technique used in this work offers a good discrimination between homogeneous and non-homogeneous environments. The homogeneous population is then used to estimate the unknown parameters by the classical Method of Moment (MOM). The AC algorithm does not need any prior information about the clutter parameters nor does it require both the number and the position of interfering targets. The accuracy of the estimation parameters obtained by this algorithm are validated and compared to various actual values of the shape parameter, using Monte Carlo simulations, this latter show that the probability of censing in multiple target situations are in good agreement.

Keywords: parameters estimation, method of moments, automatic censoring, K distribution

Procedia PDF Downloads 349