Search results for: density estimation

Authors: Jasbir Arora, Indu Talwar, Daisy Sahni, Vidya Rattan

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Introduction: Age estimation is a crucial step to build the identity of a person, both in case of deceased and alive. In adults, age can be estimated on the basis of six regressive (Attrition, Secondary dentine, Dentine transparency, Root resorption, Cementum apposition and Periodontal Disease) changes in teeth qualitatively using scoring system and quantitatively by micrometric method. The present research was designed to establish the reliability of qualitative (method 1) and quantitative (method 2) of age estimation among North Indians and to compare the efficacy of these two methods. Method: 250 single-rooted extracted teeth (18-75 yrs.) were collected from Department of Oral Health Sciences, PGIMER, Chandigarh. Before extraction, periodontal score of each tooth was noted. Labiolingual sections were prepared and examined under light microscope for regressive changes. Each parameter was scored using Gustafson’s 0-3 point score system (qualitative), and total score was calculated. For quantitative method, each regressive change was measured quantitatively in form of 18 micrometric parameters under microscope with the help of measuring eyepiece. Age was estimated using linear and multiple regression analysis in Gustafson’s method and Kedici’s method respectively. Estimated age was compared with actual age on the basis of absolute mean error. Results: In pooled data, by Gustafson’s method, significant correlation (r= 0.8) was observed between total score and actual age. Total score generated an absolute mean error of ±7.8 years. Whereas, for Kedici’s method, a value of correlation coefficient of r=0.5 (p<0.01) was observed between all the eighteen micrometric parameters and known age. Using multiple regression equation, age was estimated, and an absolute mean error of age was found to be ±12.18 years. Conclusion: Gustafson’s (qualitative) method was found to be a better predictor for age estimation among North Indians.

Keywords: forensic odontology, age estimation, North India, teeth

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Authors: H. Ben Salah, A. Gannoun, C. de Peretti, A. Trabelsi

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The Downside Risk (DSR) model for portfolio optimisation allows to overcome the drawbacks of the classical mean-variance model concerning the asymetry of returns and the risk perception of investors. This model optimization deals with a positive definite matrix that is endogenous with respect to portfolio weights. This aspect makes the problem far more difficult to handle. For this purpose, Athayde (2001) developped a new recurcive minimization procedure that ensures the convergence to the solution. However, when a finite number of observations is available, the portfolio frontier presents an appearance which is not very smooth. In order to overcome that, Athayde (2003) proposed a mean kernel estimation of the returns, so as to create a smoother portfolio frontier. This technique provides an effect similar to the case in which we had continuous observations. In this paper, taking advantage on the the robustness of the median, we replace the mean estimator in Athayde's model by a nonparametric median estimator of the returns. Then, we give a new version of the former algorithm (of Athayde (2001, 2003)). We eventually analyse the properties of this improved portfolio frontier and apply this new method on real examples.

Keywords: Downside Risk, Kernel Method, Median, Nonparametric Estimation, Semivariance

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Authors: Douglas Yeboah, Monishka Narayan, Jai Singh

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One of the key parameters in determining the power conversion efficiency (PCE) of organic solar cells (OSCs) is the open-circuit voltage, however, it is still not well understood. In order to examine the performance of OSCs, it is necessary to understand the losses associated with the open-circuit voltage and how best it can be improved. Here, an analytical expression for the open-circuit voltage of bulk heterojunction (BHJ) OSCs is derived from the charge carrier densities without considering the drift-diffusion current. The open-circuit voltage thus obtained is dependent on the donor-acceptor band gap, the energy difference between the highest occupied molecular orbital (HOMO) and the hole quasi-Fermi level of the donor material, temperature, the carrier density (electrons), the generation rate of free charge carriers and the bimolecular recombination coefficient. It is found that open-circuit voltage increases when the carrier density increases and when the temperature decreases. The calculated results are discussed in view of experimental results and agree with them reasonably well. Overall, this work proposes an alternative pathway for improving the open-circuit voltage in BHJ OSCs.

Keywords: charge carrier density, open-circuit voltage, organic solar cells, temperature

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Authors: Muhammad Jabbar

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In this paper we propose two ratio and ratio type exponential estimator for the estimation of population coefficient of variation using the auxiliary information under two-stage sampling. The properties of these estimators are derived up to first order of approximation. The efficiency conditions under which suggested estimator are more efficient, are obtained. Numerical and simulated studies are conducted to support the superiority of the estimators. Theoretically and numerically, we have found that our proposed estimator is always more efficient as compared to its competitor estimator.

Keywords: two-stage sampling, coefficient of variation, ratio type exponential estimator

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Authors: Arif Armagan Gozutok, Alper Kule, Burak Tos, Selman Demirel

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Wireless communication chain requires effective ways to keep throughput efficiency high while it suffers location-dependent, time-varying burst errors. Several techniques are developed in order to assure that the receiver recovers the transmitted information without errors. The most fundamental approaches are error checking and correction besides re-transmission of the non-acknowledged packets. In this paper, stop & wait (SAW) and chase combined (CC) hybrid automated repeat request (HARQ) protocols are compared and analyzed in terms of throughput and average delay for the usage of low earth orbit (LEO) mega-constellations case. Several assumptions and technological implementations are considered as well as usage of low-density parity check (LDPC) codes together with several constellation orbit configurations.

Keywords: HARQ, LEO, satellite constellation, throughput

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Authors: Mohammad Javad Mollakazemi, Farhad Asadi, Aref Ghafouri

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Performance of different filtering approaches depends on modeling of dynamical system and algorithm structure. For modeling and smoothing the data the evaluation of posterior distribution in different filtering approach should be chosen carefully. In this paper different filtering approaches like filter KALMAN, EKF, UKF, EKS and smoother RTS is simulated in some trajectory tracking of path and accuracy and limitation of these approaches are explained. Then probability of model with different filters is compered and finally the effect of the noise variance to estimation is described with simulations results.

Keywords: Gaussian approximation, Kalman smoother, parameter estimation, noise variance

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Authors: Ekin Nurbaş

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One of the essential challenges in array signal processing, which has drawn enormous research interest over the past several decades, is estimating the direction of arrival (DOA) of plane waves impinging on an array of sensors. In recent years, the Compressive Sensing based DoA estimation methods have been proposed by researchers, and it has been discovered that the Compressive Sensing (CS)-based algorithms achieved significant performances for DoA estimation even in scenarios where there are multiple coherent sources. On the other hand, the Genetic Algorithm, which is a method that provides a solution strategy inspired by natural selection, has been used in sparse representation problems in recent years and provides significant improvements in performance. With all of those in consideration, in this paper, a method that combines the Genetic Algorithm (GA) and the Multi-Criteria Decision Making (MCDM) approaches for Direction of Arrival (DoA) estimation in the Compressive Sensing (CS) framework is proposed. In this method, we generate a multi-objective optimization problem by splitting the norm minimization and reconstruction loss minimization parts of the Compressive Sensing algorithm. With the help of the Genetic Algorithm, multiple non-dominated solutions are achieved for the defined multi-objective optimization problem. Among the pareto-frontier solutions, the final solution is obtained with the multiple MCDM methods. Moreover, the performance of the proposed method is compared with the CS-based methods in the literature.

Keywords: genetic algorithm, direction of arrival esitmation, multi criteria decision making, compressive sensing

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Authors: L. Mouzai, M. Bouhadef

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Rills begin to be generated once overland flow shear capacity overcomes the soil surface resistance. This resistance depends on soil texture, the arrangement of soil particles and on chemical and physical properties. The rill density could affect soil erosion, especially when the distance between the rills (interrill) contributes to the variation of the rill characteristics, and consequently on sediment concentration. To investigate this point, agricultural sandy soil, a soil tray of 0.2x1x3m³ and a piece of hardwood rectangular in shape to build up rills were the base of this work. The results have shown that small lines have been developed between the rills and the flow acceleration increased in comparison to the flow on the flat surface (interrill). Sediment concentration increased with increasing rill number (density).

Keywords: artificial rainfall, experiments, rills, soil erosion, transport capacity

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Authors: Erhan Ayas, Buse Katipoğlu, Eda Metin, Rifat Yılmaz

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The search for new materials has begun to be used even higher than the service temperature (~1150ᵒC) where nickel-based superalloys are currently used. This search should also meet the increasing demands for energy efficiency improvements. The materials studied for aerospace applications are expected to have good oxidation resistance. Mo-Si-B alloys, which have higher operating temperatures than nickel-based superalloys, are candidates for ultra-high temperature materials used in gas turbine and jet engines. Because the Moss and Mo₅SiB₂ (T2) phases exhibit high melting temperature, excellent high-temperature creep strength and oxidation resistance properties, however, low fracture toughness value at room temperature is a disadvantage for these materials, but this feature can be improved with optimum Moss phase and microstructure control. High-density value is also a problem for structural parts. For example, in turbine rotors, the higher the weight, the higher the centrifugal force, which reduces the creep life of the material. The density value of the nickel-based superalloys and the T2 phase, which is the Mo-Si-B alloy phase, is in the range of 8.6 - 9.2 g/cm³. But under these conditions, T2 phase Moss (density value 10.2 g/cm³), this value is above the density value of nickel-based superalloys. So, with some ceramic-based contributions, this value is enhanced by optimum values.

Keywords: molybdenum, composites, in-situ, mmc

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Authors: Huy Hieu Pham, Houssam Salmane, Louahdi Khoudour, Alain Crouzil, Pablo Zegers, Sergio Velastin

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We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from color video sequences. Our approach proceeds along two stages. In the first, we run a real-time 2D pose detector to determine the precise pixel location of important key points of the body. A two-stream neural network is then designed and trained to map detected 2D keypoints into 3D poses. In the second, we deploy the Efficient Neural Architecture Search (ENAS) algorithm to find an optimal network architecture that is used for modeling the Spatio-temporal evolution of the estimated 3D poses via an image-based intermediate representation and performing action recognition. Experiments on Human3.6M, Microsoft Research Redmond (MSR) Action3D, and Stony Brook University (SBU) Kinect Interaction datasets verify the effectiveness of the proposed method on the targeted tasks. Moreover, we show that our method requires a low computational budget for training and inference.

Keywords: human action recognition, pose estimation, D-CNN, deep learning

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Authors: Diego Luján Villarreal

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Retinal prosthetic devices aim to repair some vision in visual impairment patients by stimulating electrically neural cells in the visual system. In this study, the 3D linear electrode carrier is presented. A simulation framework was developed by placing the 3D carrier 1 mm away from the fovea center at the highest-density cell. Cell stimulation is verified in COMSOL Multiphysics by developing a 3D computational model which includes the relevant retinal interface elements and dynamics of the voltage-gated ionic channels. Current distribution resulting from low threshold amplitudes produces a small volume equivalent to the volume confined by individual cells at the highest-density cell using small-sized electrodes. Delicate retinal tissue is protected by excessive charge density

Keywords: retinal prosthetic devices, visual devices, retinal implants., visual prosthetic devices

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Authors: Charmaine Lamiel, Van Hoa Nguyen, Marjorie Baynosa, Jae-Jin Shim

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Electrochemical supercapacitors have attracted considerable attention because of their high potential as an efficient energy storage system. The combination of carbon-based material and transition metal oxides/sulfides are studied because they have long and improved cycle life as well as high energy and power densities. In this study, a hierarchical mesoporous carbon sphere/nickel cobalt sulfide (CS/Ni-Co-S) core/shell structure was synthesized using a facile hydrothermal method without any further sulfurization or post-heat treatment. The CS/Ni-Co-S core/shell microstructures exhibited a high capacitance of 724 F g−1 at 2 A g−1 in a 6 M KOH electrolyte. After 2000 charge-discharge cycles, it retained 86.1% of its original capacitance, with high Coulombic efficiency of 97.9%. The electrode exhibited a high energy density of 58.0 Wh kg−1 at an energy density of 1440 W kg−1, and high power density of 7200 W kg−1 at an energy density of 34.2 Wh kg−1. The successful synthesis was considered to be simple and cost-effective which supports the viability of this composite as an alternative activated material for high performance supercapacitors.

Keywords: carbon sphere, electrochemical, hydrothermal, nickel cobalt sulfide, supercapacitor

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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Current real-estate value estimation, difficult for laymen, usually is performed by specialists. This paper presents an automated estimation process based on big data and machine-learning technology that calculates influences of building conditions on real-estate price measurement. The present study analyzed actual building sales sample data for Nonhyeon-dong, Gangnam-gu, Seoul, Korea, measuring the major influencing factors among the various building conditions. Further to that analysis, a prediction model was established and applied using RapidMiner Studio, a graphical user interface (GUI)-based tool for derivation of machine-learning prototypes. The prediction model is formulated by reference to previous examples. When new examples are applied, it analyses and predicts accordingly. The analysis process discerns the crucial factors effecting price increases by calculation of weighted values. The model was verified, and its accuracy determined, by comparing its predicted values with actual price increases.

Keywords: apartment complex, big data, life-cycle building value analysis, machine learning

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Authors: Md. Rashidul Hasan, Atikur Rahman Baizid

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The Bayesian estimation approach is a non-classical estimation technique in statistical inference and is very useful in real world situation. The aim of this paper is to study the Bayes estimators of the parameter of exponential distribution under different loss functions and then compared among them as well as with the classical estimator named maximum likelihood estimator (MLE). In our real life, we always try to minimize the loss and we also want to gather some prior information (distribution) about the problem to solve it accurately. Here the gamma prior is used as the prior distribution of exponential distribution for finding the Bayes estimator. In our study, we also used different symmetric and asymmetric loss functions such as squared error loss function, quadratic loss function, modified linear exponential (MLINEX) loss function and non-linear exponential (NLINEX) loss function. Finally, mean square error (MSE) of the estimators are obtained and then presented graphically.

Keywords: Bayes estimator, maximum likelihood estimator (MLE), modified linear exponential (MLINEX) loss function, Squared Error (SE) loss function, non-linear exponential (NLINEX) loss function

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Authors: Jae Hong Kim, Sang Cheol Um, Jong Kook Lee

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Strong and biocompatible wollastonite (CaSiO3) was fabricated by pressureless sintering at temperature range of 1250~ 1300 ℃ and phase transition of to β-wollastonite with an addition of MgSiO3. Starting pure α-wollastonite powder were prepared by solid state reaction, and MgSiO3 powder was added to α-wollastonite powder to induce the phase transition α to β-wollastonite over 1250℃. Sintered wollastonite samples at 1250℃ with 5 and 10 wt% MgSiO3 were α+β phase and β phase respectively, and showed higher densification rate than that of α or β-wollastonite, which are almost the same as the theoretical density. Hardness and Young’s modulus of sintered wollastonite were dependent on the apparent density and the amount of β-wollastonite. Young’s modulus (78GPa) of β-wollastonite added 10 wt% MgSiO3 was almost double time of sintered α-wollastonite. From the in-vitro test, biphasic (α+β) wollastonite with 5wt% MgSiO3 addition had good bioactivity in simulated body fluid solution.

Keywords: β-wollastonite, high density, MgSiO3, phase transition

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Authors: Kouzi Katia

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This paper proposes a Direct Torque Control (DTC) algorithm of dual Stator Induction Motor (DSIM) drive using two approach intelligent techniques: Artificial Neural Network (ANN) approach replaces the switching table selector block of conventional DTC and Mamdani Fuzzy Logic controller (FLC) is used for stator resistance estimation. The fuzzy estimation method is based on an online stator resistance correction through the variations of stator current estimation error and its variation. The fuzzy logic controller gives the future stator resistance increment at the output. The main advantage of suggested algorithm control is to reduce the hardware complexity of conventional selectors, to avoid the drive instability that may occur in certain situation and ensure the tracking of the actual of the stator resistance. The effectiveness of the technique and the improvement of the whole system performance are proved by results.

Keywords: artificial neural network, direct torque control, dual stator induction motor, fuzzy logic estimator, switching table

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Authors: Khandaker Fariha Ahmed, Md. Noman Munshi, Tarin Sultana, Md. Zoynul Abedin

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Suitability number (SN) is perhaps one of the most important parameters of coarse-grained soil in assessing its appropriateness to use as a backfill in retaining structures, sand compaction pile, Vibro compaction, and other similar foundation and ground improvement works. Though determined in an empirical manner, it is imperative to study SN to understand its relation with other aggregate properties like fineness modulus (FM), and strength and density properties of sandy soil. The present paper reports the findings of the study on the examination of the properties of sandy soil, as mentioned. Random numbers were generated to obtain the percent fineness on various sieve sizes, and fineness modulus and suitability numbers were predicted. Sand samples were collected from the field, and test samples were prepared to determine maximum density, minimum density and shear strength parameter φ against particular fineness modulus and corresponding suitability number Five samples of SN value of excellent (0-10) and three samples of SN value fair (20-30) were taken and relevant tests were done. The data obtained from the laboratory tests were statistically analyzed. Results show that with the increase of SN, the value of FM decreases. Within the SN value rated as excellent (0-10), there is a decreasing trend of φ for a higher value of SN. It is found that SN is dependent on various combinations of grain size properties like D10, D30, and D20, D50. Strong linear relationships were obtained between SN and FM (R²=.0.93) and between SN value and φ (R²=.94). Correlation equations are proposed to define relationships among SN, φ, and FM.

Keywords: density, fineness modulus, shear strength parameter, suitability number

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Authors: Zakiul Fuady, Michaela Spiske

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Inundation events, such as storms and tsunamis can leave onshore sedimentary evidence like sand deposits or large boulders. These deposits store indirect information on the related inundation parameters (e.g., flow velocity, flow depth, wave height). One tool to reveal these parameters are inverse models that use the physical characteristics of the deposits to refer to the magnitude of inundation. This study used boulders of the 2004 Indian Ocean Tsunami from Thailand (Pakarang Cape) and form a historical tsunami event that inundated the outer British Virgin Islands (Anegada). For the largest boulder found in Pakarang Cape with a volume of 26.48 m³ the required tsunami wave height is 0.44 m and storm wave height are 1.75 m (for a bulk density of 1.74 g/cm³. In Pakarang Cape the highest tsunami wave height is 0.45 m and storm wave height are 1.8 m for transporting a 20.07 m³ boulder. On Anegada, the largest boulder with a diameter of 2.7 m is the asingle coral head (species Diploria sp.) with a bulk density of 1.61 g/cm³, and requires a minimum tsunami wave height of 0.31 m and storm wave height of 1.25 m. The highest required tsunami wave height on Anegada is 2.12 m for a boulder with a bulk density of 2.46 g/cm³ (volume 0.0819 m³) and the highest storm wave height is 5.48 m (volume 0.216 m³) from the same bulk density and the coral type is limestone. Generally, the higher the bulk density, volume, and weight of the boulders, the higher the minimum tsunami and storm wave heights required to initiate transport. It requires 4.05 m/s flow velocity by Nott’s equation (2003) and 3.57 m/s by Nandasena et al. (2011) to transport the largest boulder in Pakarang Cape, whereas on Anegada, it requires 3.41 m/s to transport a boulder with diameter 2.7 m for both equations. Thus, boulder equations need to be handled with caution because they make many assumptions and simplifications. Second, the physical boulder parameters, such as density and volume need to be determined carefully to minimize any errors.

Keywords: tsunami wave height, storm wave height, flow velocity, boulders, Anegada, Pakarang Cape

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Authors: Yanto Santosa, Dede Aulia Rahman, Cory Wulan, Abdul Haris Mustari

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The global demand for long-tailed macaques for medical experimentation has continued to increase. Fulfillment of Indonesian export demands has been mostly from natural habitats, based on a harvesting quota. This quota has been determined according to the total catch for a given year, and not based on consideration of any demographic parameters or physical environmental factors with regard to the animal; hence threatening the sustainability of the various populations. It is therefore necessary to formulate a method for calculating a sustainable harvesting quota, based on population parameters in natural habitats. Considering the possibility of variations in habitat characteristics and population parameters, a time series observation of demographic and physical/biotic parameters, in various habitats, was performed on 13 groups of long-tailed macaques, distributed throughout the West Java, Lampung and Yogyakarta areas of Indonesia. These provinces were selected for comparison of the influence of human/tourism activities. Data on population parameters that was collected included data on life expectancy according to age class, numbers of individuals by sex and age class, and ‘ratio of infants to reproductive females’. The estimation of population growth was based on a population dynamic growth model: the Leslie matrix. The harvesting quota was calculated as being the difference between the actual population size and the MVP (minimum viable population) for each sex and age class. Observation indicated that there were variations within group size (24 – 106 individuals), gender (sex) ratio (1:1 to 1:1.3), life expectancy value (0.30 to 0.93), and ‘ratio of infants to reproductive females’ (0.23 to 1.56). Results of subsequent calculations showed that sustainable harvesting quotas for each studied group of long-tailed macaques, ranged from 29 to 110 individuals. An estimation model of the MVP for each age class was formulated as Log Y = 0.315 + 0.884 Log Ni (number of individual on ith age class). This study also found that life expectancy for the juvenile age class was affected by the humidity under tree stands, and dietary plants’ density at sapling, pole and tree stages (equation: Y= 2.296 – 1.535 RH + 0.002 Kpcg – 0.002 Ktg – 0.001 Kphn, R2 = 89.6% with a significance value of 0.001). By contrast, for the sub-adult-adult age class, life expectancy was significantly affected by slope (equation: Y=0.377 = 0.012 Kml, R2 = 50.4%, with significance level of 0.007). The infant to reproductive female ratio was affected by humidity under tree stands, and dietary plant density at sapling and pole stages (equation: Y = -1.432 + 2.172 RH – 0.004 Kpcg + 0.003 Ktg, R2 = 82.0% with significance level of 0.001). This research confirmed the importance of population parameters in determining the minimum viable population, and that MVP varied according to habitat characteristics (especially food availability). It would be difficult therefore, to formulate a general mathematical equation model for determining a harvesting quota for the species as a whole.

Keywords: harvesting, long-tailed macaque, population, quota

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Authors: Osamu Komori, Shinto Eguchi, Hiroshi Okamura, Momoko Ichinokawa

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The long time-series data on population assessments are essential for global ecosystem assessment because the temporal change of biomass in such a database reflects the status of global ecosystem properly. However, the available assessment data usually have limited sample sizes and the ratio of populations with low abundance of biomass (collapsed) to those with high abundance (non-collapsed) is highly imbalanced. To allow for the imbalance and uncertainty involved in the ecological data, we propose a binary regression model with mixed effects for inferring ecosystem status through an asymmetric logistic model. In the estimation equation, we observe that the weights for the non-collapsed populations are relatively reduced, which in turn puts more importance on the small number of observations of collapsed populations. Moreover, we extend the asymmetric logistic regression model using propensity score to allow for the sample biases observed in the labeled and unlabeled datasets. It robustified the estimation procedure and improved the model fitting.

Keywords: double robust estimation, ecological binary data, mixed effect logistic regression model, propensity score

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Authors: Kun Huang

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This paper investigates whether the combination of local and stochastic volatility models can be calibrated exactly to any arbitrage-free implied volatility surface of European option. The risk neutral Brownian Bridge density is applied for calibration of the leverage function of our Hybrid model. Furthermore, the tails of marginal risk neutral density are generated by Generalized Extreme Value distribution in order to capture the properties of asset returns. The local volatility is generated from the arbitrage-free implied volatility surface using stochastic volatility inspired parameterization.

Keywords: arbitrage free implied volatility, calibration, extreme value distribution, hybrid model, local volatility, risk-neutral density, stochastic volatility

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Authors: Idit Avrahami, Alex Schechter, Lev Zakhvatkin

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This research focuses on developing a compact and light Hydrogen Generator (HG), coupled with fuel cells (FC) to provide a High-Energy-Density Power-Pack (HEDPP) solution, which is 10 times Li-Ion batteries. The HEDPP is designed for portable & off-grid power applications such as Drones, UAVs, stationary off-grid power sources, unmanned marine vehicles, and more. Hydrogen gas provided by this device is delivered in the safest way as a chemical powder at room temperature and ambient pressure is activated only when the power is on. Hydrogen generation is based on a stabilized chemical reaction of Sodium Borohydride (SBH) and water. The proposed solution enables a ‘No Storage’ Hydrogen-based Power Pack. Hydrogen is produced and consumed on-the-spot, during operation; therefore, there’s no need for high-pressure hydrogen tanks, which are large, heavy, and unsafe. In addition to its high energy density, ease of use, and safety, the presented power pack has a significant advantage of versatility and deployment in numerous applications and scales. This patented HG was demonstrated using several prototypes in our lab and was proved to be feasible and highly efficient for several applications. For example, in applications where water is available (such as marine vehicles, water and sewage infrastructure, and stationary applications), the Energy Density of the suggested power pack may reach 2700-3000 Wh/kg, which is again more than 10 times higher than conventional lithium-ion batteries. In other applications (e.g., UAV or small vehicles) the energy density may exceed 1000 Wh/kg.

Keywords: hydrogen energy, sodium borohydride, fixed-wing UAV, energy pack

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Authors: David A. Padilla, Rodolfo Villamizar

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In recent years, closed loop control strategies have been developed in order to establish a healthy glucose profile in type 1 diabetic mellitus (T1DM) patients. However, the controller itself is unable to define a suitable reference trajectory for glucose. In this paper, a control strategy Is proposed where the shape of the reference trajectory is generated bases in the amount of carbohydrates present during the digestive process, due to the effect of carbohydrate intake. Since there no exists a sensor to measure the amount of carbohydrates consumed, an estimator is proposed. Thus this paper presents the entire process of designing a carbohydrate estimator, which allows estimate disturbance for a predictive controller (MPC) in a T1MD patient, the estimation will be used to establish a profile of reference and improve the response of the controller by providing the estimated information of ingested carbohydrates. The dynamics of the diabetic model used are due to the equations described by the UVA/Padova model of the T1DMS simulator, the system was developed and simulated in Simulink, taking into account the noise and limitations of the glucose control system actuators.

Keywords: estimation, glucose control, predictive controller, MPC, UVA/Padova

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Authors: Amitha Puranik, V. S. Binu, Seena Biju

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Missing data is a common problem in spatial analysis especially at the aggregate level. Missing can either occur in covariate or in response variable or in both in a given location. Many missing data techniques are available to estimate the missing data values but not all of these methods can be applied on spatial data since the data are autocorrelated. Hence there is a need to develop a method that estimates the missing values in both response variable and covariates in spatial data by taking account of the spatial autocorrelation. The present study aims to develop a model to estimate the missing data points at the aggregate level in spatial data by accounting for (a) Spatial autocorrelation of the response variable (b) Spatial autocorrelation of covariates and (c) Correlation between covariates and the response variable. Estimating the missing values of spatial data requires a model that explicitly account for the spatial autocorrelation. The proposed model not only accounts for spatial autocorrelation but also utilizes the correlation that exists between covariates, within covariates and between a response variable and covariates. The precise estimation of the missing data points in spatial data will result in an increased precision of the estimated effects of independent variables on the response variable in spatial regression analysis.

Keywords: spatial regression, missing data estimation, spatial autocorrelation, simulation analysis

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Authors: A. B. Bolkhir, A. Elshafie, T. K. Yousif

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This paper highlights the methods of error estimation in finite element analysis (FEA) results. It indicates that the modeling error could be eliminated by performing finite element analysis with successively finer meshes or by extrapolating response predictions from an orderly sequence of relatively low degree of freedom analysis results. In addition, the paper eliminates the round-off error by running the code at a higher precision. The paper provides application in finite element analysis results. It draws a conclusion based on results of application of methods of error estimation.

Keywords: finite element analysis (FEA), discretization error, round-off error, mesh refinement, richardson extrapolation, monotonic convergence

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Authors: Olukunle C. Olawole, Dilip Kumar De

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We have modified Richardson-Dushman equation considering thermal expansion of lattice and change of chemical potential with temperature in material. The corresponding modified Richardson-Dushman (MRDE) equation fits quite well the experimental data of thermoelectronic current density (J) vs T from carbon nanotubes. It provides a unique technique for accurate determination of W0 Fermi energy, EF0 at 0 K and linear thermal expansion coefficient of carbon nano-tube in good agreement with experiment. From the value of EF0 we obtain the charge carrier density in excellent agreement with experiment. We describe application of the equations for the evaluation of performance of concentrated solar thermionic energy converter (STEC) with emitter made of carbon nanotube for future applications.

Keywords: carbon nanotube, modified Richardson-Dushman equation, fermi energy at 0 K, charge carrier density

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Authors: Kristian Bautista, Ruben A. Idoy

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A bio-inspired metaheuristic algorithm inspired by the theory of collective animal behavior (CAB) was integrated to density-based clustering modeled as multimodal optimization problem. The algorithm was tested on synthetic, Iris, Glass, Pima and Thyroid data sets in order to measure its effectiveness relative to CDE-based Clustering algorithm. Upon preliminary testing, it was found out that one of the parameter settings used was ineffective in performing clustering when applied to the algorithm prompting the researcher to do an investigation. It was revealed that fine tuning distance δ3 that determines the extent to which a given data point will be clustered helped improve the quality of cluster output. Even though the modification of distance δ3 significantly improved the solution quality and cluster output of the algorithm, results suggest that there is no difference between the population mean of the solutions obtained using the original and modified parameter setting for all data sets. This implies that using either the original or modified parameter setting will not have any effect towards obtaining the best global and local animal positions. Results also suggest that CDE-based clustering algorithm is better than CAB-density clustering algorithm for all data sets. Nevertheless, CAB-density clustering algorithm is still a good clustering algorithm because it has correctly identified the number of classes of some data sets more frequently in a thirty trial run with a much smaller standard deviation, a potential in clustering high dimensional data sets. Thus, the researcher recommends further investigation in the post-processing stage of the algorithm.

Keywords: clustering, metaheuristics, collective animal behavior algorithm, density-based clustering, multimodal optimization

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Authors: Peng Li, Luan Wang, Haifeng Fei, Renhong Xie, Yibin Rui, Shanhong Guo

Abstract:

The applications of satellite-based automatic identification system (S-AIS) pave the road for wide-range maritime traffic monitoring and management. But the coverage of satellite’s view includes multiple AIS self-organizing networks, which leads to the collision of AIS signals from different cells. The contribution of this work is to propose an improved multi-channel blind source separation algorithm based on Artificial Bee Colony (ABC) and advanced stochastic optimization to perform separation of the mixed AIS signals. The proposed approach adopts modified ABC algorithm to get an optimized initial separating matrix, which can expedite the initialization bias correction, and utilizes the Adaptive Moment Estimation (Adam) to update the separating matrix by adjusting the learning rate for each parameter dynamically. Simulation results show that the algorithm can speed up convergence and lead to better performance in separation accuracy.

Keywords: satellite-based automatic identification system, blind source separation, artificial bee colony, adaptive moment estimation

Procedia PDF Downloads 183

Authors: Jun Su Park, Byung Kwan Oh, Jin Woo Hwang, Yousok Kim, Hyo Seon Park

Abstract:

For the structural safety, the maximum stress calculated from the stress distribution of a structure is widely used. The stress distribution can be estimated by deformed shape of the structure obtained from measurement. Although the estimation of stress is strongly affected by the location and number of sensing points, most studies have conducted the stress estimation without reasonable basis on sensing plan such as the location and number of sensors. In this paper, an optimal sensing technique for estimating the stress distribution is proposed. This technique proposes the optimal location and number of sensing points for a 2-D frame structure while minimizing the error of stress distribution between analytical model and estimation by cubic smoothing splines using genetic algorithm. To verify the proposed method, the optimal sensor measurement technique is applied to simulation tests on 2-D steel frame structure. The simulation tests are performed under various loading scenarios. Through those tests, the optimal sensing plan for the structure is suggested and verified.

Keywords: genetic algorithm, optimal sensing, optimizing sensor placements, steel frame structure

Procedia PDF Downloads 529

Authors: Young Man Cho, Sang Hyun Kim

Abstract:

Even though various management practices such as sediment dredging were attempted to improve water quality of Dongcheon located in Busan, the environmental condition of this stream was deteriorated. Therefore, Busan metropolitan city had pumped and diverted sea water to upstream of Dongcheon for several years. This study explored hydraulic characteristics of Dongcheon to configure the best management practice for ecological restoration and water quality improvement of a man-made urban stream. Intensive field investigation indicates that average flow velocities at depths of 20% and 80% from the water surface ranged 5 to 10 cm/s and 2 to 5 cm/s, respectively. Concentrations of dissolved oxygen for all depths were less than 0.25 mg/l during low tidal period. Even though density difference can be found along stream depth, density current seems rarely generated in Dongcheon. Short period of high tidal portion and shallow depths are responsible for well-mixing nature of Doncheon.

Keywords: hydraulic, tidal river, density current, sea water

Procedia PDF Downloads 222