Search results for: propagation of error

Authors: Yi-Xuan Huang, Kung-Ming Chung, Ping-Han Chung

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Pressure sensitive paint (PSP), which relies on the oxygen quenching of a luminescent molecule, is an optical technique used in wind-tunnel models. A full-field pressure pattern with low aerodynamic interference can be obtained, and it is becoming an alternative to pressure measurements using pressure taps. In this study, a polymer-ceramic PSP was used, using toluene as a solvent. The porous particle and polymer were silica gel (SiO₂) and RTV-118 (3g:7g), respectively. The compound was sprayed onto the model surface using a spray gun. The absorption and emission spectra for Ru(dpp) as a luminophore were respectively 441-467 nm and 597 nm. A Revox SLG-55 light source with a short-pass filter (550 nm) and a 14-bit CCD camera with a long-pass (600 nm) filter were used to illuminate PSP and to capture images. This study determines surface pressure patterns for a forebody of an AGARD B model in a compressible flow. Since there is no experimental data for surface pressure distributions available, numerical simulation is conducted using ANSYS Fluent. The lift and drag coefficients are calculated and in comparison with the data in the open literature. The experiments were conducted using a transonic wind tunnel at the Aerospace Science and Research Center, National Cheng Kung University. The freestream Mach numbers were 0.83, and the angle of attack ranged from -4 to 8 degree. Deviation between PSP and numerical simulation is within 5%. However, the effect of the setup of the light source should be taken into account to address the relative error.

Keywords: pressure sensitive paint, forebody, surface pressure, compressible flow

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Authors: Shiuh-Jer Huang, Chen-Zon Yan, C. K. Huang, Chun-Chien Ting

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Since the vision system application in industrial environment for autonomous purposes is required intensely, the image recognition technique becomes an important research topic. Here, deep learning algorithm is employed in image system to recognize the industrial object and integrate with a 7A6 Series Manipulator for object automatic gripping task. PC and Graphic Processing Unit (GPU) are chosen to construct the 3D Vision Recognition System. Depth Camera (Intel RealSense SR300) is employed to extract the image for object recognition and coordinate derivation. The YOLOv2 scheme is adopted in Convolution neural network (CNN) structure for object classification and center point prediction. Additionally, image processing strategy is used to find the object contour for calculating the object orientation angle. Then, the specified object location and orientation information are sent to robotic controller. Finally, a six-axis manipulator can grasp the specific object in a random environment based on the user command and the extracted image information. The experimental results show that YOLOv2 has been successfully employed to detect the object location and category with confidence near 0.9 and 3D position error less than 0.4 mm. It is useful for future intelligent robotic application in industrial 4.0 environment.

Keywords: deep learning, image processing, convolution neural network, YOLOv2, 7A6 series manipulator

Procedia PDF Downloads 248

Authors: Ofonime Ogba, Augustine Akpan

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Objectives and Goals: The study was aimed at determining pathogenic microbial carriage on the external body parts of Camponotus consobrinus which is also known as the banded sugar ant because of its liking for sugar and sweet food. The level of pathogenic microbial carriage of Camponotus consobrinus in association to the environment in which they have been collected is not known. Methods: The ants were purposively collected from four locations including the kitchens, bedroom of various homes, food shops, and bakeries. The sample collection took place within the hours of 6:30 pm to 11:00 pm. The ants were trapped in transparent plastic containers of which sugar, pineapple peels, sugar cane and soft drinks were used as bait. The ants were removed with a sterile spatula and put in 10mls of peptone water in sterile universal bottles. The containers were vigorously shaken to wash the external surface of the ant. It was left overnight and transported to the Microbiology Laboratory, University of Calabar Teaching Hospital for analysis. The overnight peptone broths were inoculated on Chocolate agar, Blood agar, Cystine Lactose Electrolyte-Deficient agar (CLED) and Sabouraud dextrose agar. Incubation was done aerobically and in a carbon dioxide jar for 24 to 48 hours at 37°C. Isolates were identified based on colonial characteristics, Gram staining, and biochemical tests. Results: Out of the 250 Camponotus consobrinus caught for the study, 90(36.0%) were caught in the kitchen, 75(30.0%) in the bedrooms 40(16.0%) in the bakery while 45(18.0%) were caught in the shops. A total of 82.0% prevalence of different microbial isolates was associated with the ants. The kitchen had the highest number of isolates 75(36.6%) followed by the bedroom 55(26.8%) while the bakery recorded the lowest number of isolates 35(17.1%). The profile of micro-organisms associated with Camponotus consobrinus was Escherichia coli 73(30.0%), Morganella morganii 45(18.0%), Candida species 25(10.0%), Serratia marcescens 10(4.0%) and Citrobacter freundii 10(4.0%). Conclusion: Most of the Camponotus consobrinus examined in the four locations harboured potential pathogens. The presence of ants in homes and shops can facilitate the propagation and spread of pathogenic microorganisms. Therefore, the development of basic preventive measures and the control of ants must be taken seriously.

Keywords: Camponotus consobrinus, potential pathogens, microbial isolates, spread

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Authors: Zelalem Bitew, Adane Kassa, Beyene Misgan

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In this study, a sensitive and selective voltammetric method based on poly(diphenylamine-4-sulfonic acid) modified glassy carbon electrode (poly(DPASA)/GCE) was developed for determination of gallic acid. Appearance of an irreversible oxidative peak at both bare GCE and poly(DPASA)/GCE for gallic acid with about three folds current enhancement and much reduced potential at poly(DPASA)/GCE showed catalytic property of the modifier towards oxidation of gallic acid. Under optimized conditions, Adsorptive stripping square wave voltammetric peak current response of the poly(DPASA)/GCE showed linear dependence with gallic acid concentration in the range 5.00 × 10-7 − 3.00 × 10-4 mol L-1 with limit of detection of 4.35 × 10-9. Spike recovery results between 94.62-99.63, 95.00-99.80 and 97.25-103.20% of gallic acid in honey, raw peanut, and commercial peanut butter samples respectively, interference recovery results with less than 4.11% error in the presence of uric acid and ascorbic acid, lower LOD and relatively wider dynamic range than most of the previously reported methods validated the potential applicability of the method based on poly(DPASA)/GCE for determination of gallic acid real samples including in honey and peanut samples.

Keywords: gallic acid, diphenyl amine sulfonic acid, adsorptive anodic striping square wave voltammetry, honey, peanut

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Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

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Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: artificial neural network, data mining, electroencephalogram, epilepsy, feature extraction, seizure detection, signal processing

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Authors: Bi-Huei Tsai

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Previous studies on financial distress prediction choose the conventional failing and non-failing dichotomy; however, the distressed extent differs substantially among different financial distress events. To solve the problem, “non-distressed”, “slightly-distressed” and “reorganization and bankruptcy” are used in our article to approximate the continuum of corporate financial health. This paper explains different financial distress events using the two-stage method. First, this investigation adopts firm-specific financial ratios, corporate governance and market factors to measure the probability of various financial distress events based on multinomial logit models. Specifically, the bootstrapping simulation is performed to examine the difference of estimated misclassifying cost (EMC). Second, this work further applies macroeconomic factors to establish the credit cycle index and determines the distressed cut-off indicator of the two-stage models using such index. Two different models, one-stage and two-stage prediction models, are developed to forecast financial distress, and the results acquired from different models are compared with each other, and with the collected data. The findings show that the two-stage model incorporating financial ratios, corporate governance and market factors has the lowest misclassification error rate. The two-stage model is more accurate than the one-stage model as its distressed cut-off indicators are adjusted according to the macroeconomic-based credit cycle index.

Keywords: Multinomial logit model, corporate governance, company failure, reorganization, bankruptcy

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Authors: Rita U. Onolemhemhen, Saheed L. Bello, Akin P. Iwayemi

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The effectiveness of energy demand policy depends on identifying the key drivers of energy demand both in the short-run and the long-run. This paper examines the influence of regional differences on the link between energy demand and other explanatory variables for Nigeria, China and USA using the Vector Error Correction Model (VECM) approach. This study employed annual time series data on energy consumption (ED), real gross domestic product (GDP) per capita (RGDP), real energy prices (P) and urbanization (N) for a thirty-six-year sample period. The utilized time-series data are sourced from World Bank’s World Development Indicators (WDI, 2016) and US Energy Information Administration (EIA). Results from the study, shows that all the independent variables (income, urbanization, and price) substantially affect the long-run energy consumption in Nigeria, USA and China, whereas, income has no significant effect on short-run energy demand in USA and Nigeria. In addition, the long-run effect of urbanization is relatively stronger in China. Urbanization is a key factor in energy demand, it therefore recommended that more attention should be given to the development of rural communities to reduce the inflow of migrants into urban communities which causes the increase in energy demand and energy excesses should be penalized while energy management should be incentivized.

Keywords: economic growth, energy demand, income, real GDP, urbanization, VECM

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Authors: Qiang Wang, Hongyang Yu

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Multi-human 3D pose estimation is a challenging task in computer vision, which aims to recover the 3D joint locations of multiple people from multi-view images. In contrast to traditional methods, which typically only use color (RGB) images as input, our approach utilizes both color and depth (D) information contained in RGB-D images. We also employ a transformer-based model as the backbone of our approach, which is able to capture long-range dependencies and has been shown to perform well on various sequence modeling tasks. Our method is trained and tested on the Carnegie Mellon University (CMU) Panoptic dataset, which contains a diverse set of indoor and outdoor scenes with multiple people in varying poses and clothing. We evaluate the performance of our model on the standard 3D pose estimation metrics of mean per-joint position error (MPJPE). Our results show that the transformer-based approach outperforms traditional methods and achieves competitive results on the CMU Panoptic dataset. We also perform an ablation study to understand the impact of different design choices on the overall performance of the model. In summary, our work demonstrates the effectiveness of using a transformer-based approach with RGB-D images for multi-human 3D pose estimation and has potential applications in real-world scenarios such as human-computer interaction, robotics, and augmented reality.

Keywords: multi-human 3D pose estimation, RGB-D images, transformer, 3D joint locations

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Authors: Anas Rao, Hao Duan, Fanhua Ma

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The dependency on the fossil fuels can be minimized by using the hydrogen enriched compressed natural gas (HCNG) in the transportation vehicles. However, the NOx emissions of HCNG engines are significantly higher, and this turned to be its major drawback. Therefore, the study of NOx emission of HCNG engines is a very important area of research. In this context, the experiments have been performed at the different hydrogen percentage, ignition timing, air-fuel ratio, manifold-absolute pressure, load and engine speed. Afterwards, the simulation has been accomplished by the quasi-dimensional combustion model of HCNG engine. In order to investigate the NOx emission, the NO mechanism has been coupled to the quasi-dimensional combustion model of HCNG engine. The three NOx mechanism: the thermal NOx, prompt NOx and N2O mechanism have been used to predict NOx emission. For the validation purpose, NO curve has been transformed into NO packets based on the temperature difference of 100 K for the lean-burn and 60 K for stoichiometric condition. While, the width of the packet has been taken as the ratio of crank duration of the packet to the total burnt duration. The combustion chamber of the engine has been divided into three zones, with the zone equal to the product of summation of NO packets and space. In order to check the accuracy of the model, the percentage error of NOx emission has been evaluated, and it lies in the range of ±6% and ±10% for the lean-burn and stoichiometric conditions respectively. Finally, the percentage contribution of each NO formation has been evaluated.

Keywords: quasi-dimensional combustion , thermal NO, prompt NO, NO packet

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Authors: M. Amparo Nunez-Andres, Nieves Lantada

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One of the most harmful geological risks is rockfalls. They cause both economic lost, damaged in buildings and infrastructures, and personal ones. Therefore, in order to estimate the risk of the exposed elements, it is necessary to know the mechanism of this kind of events, since the characteristics of the rock walls, to the propagation of fragments generated by the initial detached rock mass. In the framework of the research RockModels project, several inventories of rockfalls were carried out along the northeast of the Spanish peninsula and the Mallorca island. These inventories have general information about the events, although the important fact is that they contained detailed information about fragmentation. Specifically, the IBSD (Insitu Block Size Distribution) is obtained by photogrammetry from drone or TLS (Terrestrial Laser Scanner) and the RBSD (Rock Block Size Distribution) from the volume of the fragment in the deposit measured by hand. In order to share all this information with other scientists, engineers, members of civil protection, and stakeholders, it is necessary a platform accessible from the internet and following interoperable standards. In all the process, open-software have been used: PostGIS 2.1., Geoserver, and OpenLayers library. In the first step, a spatial database was implemented to manage all the information. We have used the data specifications of INSPIRE for natural risks adding specific and detailed data about fragmentation distribution. The next step was to develop a WMS with Geoserver. A previous phase was the creation of several views in PostGIS to show the information at different scales of visualization and with different degrees of detail. In the first view, the sites are identified with a point, and basic information about the rockfall event is facilitated. In the next level of zoom, at medium scale, the convex hull of the rockfall appears with its real shape and the source of the event and fragments are represented by symbols. The queries at this level offer a major detail about the movement. Eventually, the third level shows all elements: deposit, source, and blocks, in their real size, if it is possible, and in their real localization. The last task was the publication of all information in a web mapping site (www.rockdb.upc.edu) with data classified by levels using libraries in JavaScript as OpenLayers.

Keywords: geological risk, web mapping, WMS, rockfalls

Procedia PDF Downloads 159

Authors: Akalu Banbeta, Emmanuel Lesaffre, Reynaldo Martina, Joost Van Rosmalen

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Including data from previous studies (historical data) in the analysis of the current study may reduce the sample size requirement and/or increase the power of analysis. The most common example is incorporating historical control data in the analysis of a current clinical trial. However, this only applies when the historical control dataare similar enough to the current control data. Recently, several Bayesian approaches for incorporating historical data have been proposed, such as the meta-analytic-predictive (MAP) prior and the modified power prior (MPP) both for single control as well as for multiple historical control arms. Here, we examine the performance of the MAP and the MPP approaches for the analysis of (over-dispersed) count data. To this end, we propose a computational method for the MPP approach for the Poisson and the negative binomial models. We conducted an extensive simulation study to assess the performance of Bayesian approaches. Additionally, we illustrate our approaches on an overactive bladder data set. For similar data across the control arms, the MPP approach outperformed the MAP approach with respect to thestatistical power. When the means across the control arms are different, the MPP yielded a slightly inflated type I error (TIE) rate, whereas the MAP did not. In contrast, when the dispersion parameters are different, the MAP gave an inflated TIE rate, whereas the MPP did not.We conclude that the MPP approach is more promising than the MAP approach for incorporating historical count data.

Keywords: count data, meta-analytic prior, negative binomial, poisson

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Authors: Panupong Makvichian

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Global Navigation Satellite System (GNSS) is nowadays a common technology that improves navigation functions in our life. Additionally, GNSS is also being employed on behalf of an accurate atmospheric sensor these times. Meteorology is a practical application of GNSS, which is unnoticeable in the background of people’s life. GNSS Precise Point Positioning (PPP) is a positioning method that requires data from a single dual-frequency receiver and precise information about satellite positions and satellite clocks. In addition, careful attention to mitigate various error sources is required. All the above data are combined in a sophisticated mathematical algorithm. At this point, the research is going to demonstrate how GNSS and PPP method is capable to provide high-precision estimates, such as 3D positions or Zenith tropospheric delays (ZTDs). ZTDs combined with pressure and temperature information allows us to estimate the water vapor in the atmosphere as precipitable water vapor (PWV). If the process is replicated for a network of GNSS sensors, we can create thematic maps that allow extract water content information in any location within the network area. All of the above are possible thanks to the advances in GNSS data processing. Therefore, we are able to use GNSS data for climatic trend analysis and acquisition of the further knowledge about the atmospheric water content.

Keywords: GNSS, precise point positioning, Zenith tropospheric delays, precipitable water vapor

Procedia PDF Downloads 196

Authors: Baptiste Vallet-Simond, Léopold V. B. Diop, Olivier Isnard

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The antiferromagnetic itinerant-electron compound LaFe₁₂B₆ occupies a special place among rare-earth iron-rich intermetallic; it presents exotic magnetic and physical properties. The unusual amplitude-modulated spin configuration defined by a propagation vector k = (¼, ¼, ¼), remarkably weak Fe magnetic moment (0.43 μB) in the antiferromagnetic ground state, especially low magnetic ordering temperature TN = 36 K for an Fe-rich phase, a multicritical point in the complex magnetic phase diagram, both normal and inverse magnetocaloric effects, and huge hydrostatic pressure effects can be highlighted as the most relevant. Both antiferromagnetic (AFM) and paramagnetic (PM) states can be transformed into the ferromagnetic (FM) state via a field-induced first-order metamagnetic transition. Of particular interest is the low-temperature magnetization process. This process is discontinuous and evolves unexpected huge metamagnetic transitions consisting of a succession of steep magnetization jumps separated by plateaus, giving rise to an unusual avalanche-like behavior. The metamagnetic transition is accompanied by giant magnetoresistance and large magnetostriction. In the present work, we report on the intrinsic magnetic properties of the La₁₋ₓPrₓFe₁₂B₆ series of compounds exhibiting sharp metamagnetic transitions. The study of the structural, magnetic, magneto-transport, and magnetostrictive properties of the La₁₋ₓPrₓFe₁₂B₆ system was performed by combining a wide variety of measurement techniques. Magnetic measurements were performed up to µ0H = 10 T. It was found that the proportion of Pr had a strong influence on the magnetic properties of this series of compounds. At x=0.05, the ground state at 2K is that of an antiferromagnet, but the critical transition field Hc has been lowered from Hc = 6T at x = 0 to Hc = 2.5 Tat x=0.05. And starting from x=0.10, the ground state of this series of compounds is a coexistence of AFM and FM parts. At x=0.30, the AFM order has completely vanished, and only the FM part is left. However, we still observe meta-magnetic transitions at higher temperatures (above 100 K for x=0.30) from the paramagnetic (P) state to a forced FM state. And, of course, such transitions are accompanied by strong magneto-caloric, magnetostrictive, and magnetoresistance effects. The Curie temperatures for the probed compositions going from x=0.05 to x=0.30 were spread over the temperature range of 40 K up to 100 K.

Keywords: metamagnetism, RMB intermetallic, magneto-transport effect, metamagnetic transitions

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Authors: C. Kuoh, C. Lucas, T. Lopes, I. Mechie, J. Yoong, W. Yoong

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For all surgeons, there is one preventable but still highly occurring complication – wrong site surgeries. They can have potentially catastrophic, irreversible, or even fatal consequences on patients. With the exponential development of microsurgery and the use of advanced technological tools, the consequences of operating on the wrong side, anatomical part, or even person is seen as the most visible and destructive of all surgical errors and perhaps the error that is dreaded by most clinicians as it threatens their licenses and arouses feelings of guilt. Despite the implementation of the WHO surgical safety checklist more than a decade ago, the incidence of wrong-site surgeries remains relatively high, leading to tremendous physical and psychological repercussions for the clinicians involved, as well as a financial burden for the healthcare institution. In this presentation, the authors explore various factors which can lead to wrong site surgery – a combination of environmental and human factors and evaluate their impact amongst patients, practitioners, their families, and the medical industry. Major contributing factors to these “never events” include deviations from checklists, excessive workload, and poor communication. Two real-life cases are discussed, and systems that can be implemented to prevent these errors are highlighted alongside lessons learnt from other industries. The authors suggest that reinforcing speaking-up, implementing medical professional trainings, and higher patient’s involvements can potentially improve safety in surgeries and electrosurgeries.

Keywords: wrong side surgery, never events, checklist, workload, communication

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Authors: Jiao Sun, Li Pan, Shijun Liu

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Recommender systems have been widely used in contemporary industry, and plenty of work has been done in this field to help users to identify items of interest. Collaborative Filtering (CF, for short) algorithm is an important technology in recommender systems. However, less work has been done in automobile recommendation system with the sharp increase of the amount of automobiles. What’s more, the computational speed is a major weakness for collaborative filtering technology. Therefore, using MapReduce framework to optimize the CF algorithm is a vital solution to this performance problem. In this paper, we present a recommendation of the users’ comment on industrial automobiles with various properties based on real world industrial datasets of user-automobile comment data collection, and provide recommendation for automobile providers and help them predict users’ comment on automobiles with new-coming property. Firstly, we solve the sparseness of matrix using previous construction of score matrix. Secondly, we solve the data normalization problem by removing dimensional effects from the raw data of automobiles, where different dimensions of automobile properties bring great error to the calculation of CF. Finally, we use the MapReduce framework to optimize the CF algorithm, and the computational speed has been improved times. UV decomposition used in this paper is an often used matrix factorization technology in CF algorithm, without calculating the interpolation weight of neighbors, which will be more convenient in industry.

Keywords: collaborative filtering, recommendation, data normalization, mapreduce

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Authors: Hicham Gourgue, Ahmed Aharoune, Ahmed Ihlal

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We present in this work our model of road traffic emissions (line sources) and dispersion of these emissions, named DISPOLSPEM (Dispersion of Poly Sources and Pollutants Emission Model). In its emission part, this model was designed to keep the consistent bottom-up and top-down approaches. It also allows to generate emission inventories from reduced input parameters being adapted to existing conditions in Morocco and in the other developing countries. While several simplifications are made, all the performance of the model results are kept. A further important advantage of the model is that it allows the uncertainty calculation and emission rate uncertainty according to each of the input parameters. In the dispersion part of the model, an improved line source model has been developed, implemented and tested against a reference solution. It provides improvement in accuracy over previous formulas of line source Gaussian plume model, without being too demanding in terms of computational resources. In the case study presented here, the biggest errors were associated with the ends of line source sections; these errors will be canceled by adjacent sections of line sources during the simulation of a road network. In cases where the wind is parallel to the source line, the use of the combination discretized source and analytical line source formulas minimizes remarkably the error. Because this combination is applied only for a small number of wind directions, it should not excessively increase the calculation time.

Keywords: air pollution, dispersion, emissions, line sources, road traffic, urban transport

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Authors: Spandan Sahu, Amiya Kumar Pati, Kishanjit Kumar Khatua

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River is the main source of water. It is a form of natural open channel which gives rise to many complex phenomenon of sciences that needs to be tackled such as the critical flow conditions, boundary shear stress and depth averaged velocity. The development of society more or less solely depends upon the flow of rivers. The rivers are major sources of many sediments and specific ingredients which are much essential for human beings. During floods, part of a river is carried by the simple main channel and rest is carried by flood plains. For such compound asymmetric channels, the flow structure becomes complicated due to momentum exchange between main channel and adjoining flood plains. Distribution of boundary shear in subsections provides us with the concept of momentum transfer between the interface of main channel and the flood plains. Experimentally, to get better data with accurate results are very complex because of the complexity of the problem. Hence, Conveyance Estimation System (CES) software has been used to tackle the complex processes to determine the shear stresses at different sections of an open channel having asymmetric flood plains on both sides of the main channel and the results are compared with the symmetric flood plains for various geometrical shapes and flow conditions. Error analysis is also performed to know the degree of accuracy of the model implemented.

Keywords: depth average velocity, non prismatic compound channel, relative flow depth , velocity distribution

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Authors: Shogo Nagano, Yusuke Tanigaki, Hirokazu Fukuda

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Vegetables harvested early in the morning or late in the afternoon are valued in plant production, and so the time of harvest is important. The biological functions known as circadian clocks have a significant effect on this harvest timing. The purpose of this study was to non-destructively estimate the circadian clock and so construct a method for determining a suitable harvest time. We took eight samples of green busil (Perilla frutescens var. crispa) every 4 hours, six times for 1 day and analyzed all samples at the same time. A hyperspectral camera was used to collect spectrum intensities at 141 different wavelengths (350–1050 nm). Calculation of correlations between spectrum intensity of each wavelength and harvest time suggested the suitability of the hyperspectral camera for non-destructive estimation. However, even the highest correlated wavelength had a weak correlation, so we used machine learning to raise the accuracy of estimation and constructed a machine learning model to estimate the internal time of the circadian clock. Artificial neural networks (ANN) were used for machine learning because this is an effective analysis method for large amounts of data. Using the estimation model resulted in an error between estimated and real times of 3 min. The estimations were made in less than 2 hours. Thus, we successfully demonstrated this method of non-destructively estimating internal time.

Keywords: artificial neural network (ANN), circadian clock, green busil, hyperspectral camera, non-destructive evaluation

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Authors: Ejaz Ahmed, Huang Yong

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The operation of aero engines has a critical importance in the vicinity of lean blowout (LBO) limits. Lefebvre’s model of LBO based on empirical correlation has been extended to flame volume concept by the authors. The flame volume takes into account the effects of geometric configuration, the complex spatial interaction of mixing, turbulence, heat transfer and combustion processes inside the gas turbine combustion chamber. For these reasons, flame volume based LBO predictions are more accurate. Although LBO prediction accuracy has improved, it poses a challenge associated with Vf estimation in real gas turbine combustors. This work extends the approach of flame volume prediction previously based on fuel iterative approximation with cold flow simulations to reactive flow simulations. Flame volume for 11 combustor configurations has been simulated and validated against experimental data. To make prediction methodology robust as required in the preliminary design stage, reactive flow simulations were carried out with the combination of probability density function (PDF) and discrete phase model (DPM) in FLUENT 15.0. The criterion for flame identification was defined. Two important parameters i.e. critical injection diameter (Dp,crit) and critical temperature (Tcrit) were identified, and their influence on reactive flow simulation was studied for Vf estimation. Obtained results exhibit ±15% error in Vf estimation with experimental data.

Keywords: CFD, combustion, gas turbine combustor, lean blowout

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Authors: Mohamed Korany, Azza Gazy, Essam Khamis, Marwa Adel, Miranda Fawzy

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Background: Two new, simple and specific methods; First, a Zero-crossing first-derivative technique and second, a chemometric-assisted spectrophotometric artificial neural network (ANN) were developed and validated in accordance with ICH guidelines. Both methods were used for the simultaneous estimation of the two closely related antioxidant nutraceuticals ; Coenzyme Q10 (Q) ; also known as Ubidecarenone or Ubiquinone-10, and Vitamin E (E); alpha-tocopherol acetate, in their pharmaceutical binary mixture. Results: For first method: By applying the first derivative, both Q and E were alternatively determined; each at the zero-crossing of the other. The D1 amplitudes of Q and E, at 285 nm and 235 nm respectively, were recorded and correlated to their concentrations. The calibration curve is linear over the concentration range of 10-60 and 5.6-70 μg mL-1 for Q and E, respectively. For second method: ANN (as a multivariate calibration method) was developed and applied for the simultaneous determination of both analytes. A training set (or a concentration set) of 90 different synthetic mixtures containing Q and E, in wide concentration ranges between 0-100 µg/mL and 0-556 µg/mL respectively, were prepared in ethanol. The absorption spectra of the training sets were recorded in the spectral region of 230–300 nm. A Gradient Descend Back Propagation ANN chemometric calibration was computed by relating the concentration sets (x-block) to their corresponding absorption data (y-block). Another set of 45 synthetic mixtures of the two drugs, in defined range, was used to validate the proposed network. Neither chemical separation, preparation stage nor mathematical graphical treatment were required. Conclusions: The proposed methods were successfully applied for the assay of Q and E in laboratory prepared mixtures and combined pharmaceutical tablet with excellent recoveries. The ANN method was superior over the derivative technique as the former determined both drugs in the non-linear experimental conditions. It also offers rapidity, high accuracy, effort and money saving. Moreover, no need for an analyst for its application. Although the ANN technique needed a large training set, it is the method of choice in the routine analysis of Q and E tablet. No interference was observed from common pharmaceutical additives. The results of the two methods were compared together

Keywords: coenzyme Q10, vitamin E, chemometry, quantitative analysis, first derivative spectrophotometry, artificial neural network

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Authors: Nishal Parbhoo, John B. Dewar, Samantha Gildenhuys

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Rotavirus is the major cause of severe gastroenteritis worldwide in children aged 5 and younger. Death rates are high particularly in developing countries. The mature rotavirus is a non-enveloped triple-layered nucleocapsid containing 11 double-stranded RNA segments. Here a global view on the sequence and structure of the three main capsid proteins, VP7, VP6, and VP2 is taken by generating a consensus sequence for each of these rotavirus proteins, for each species obtained from published data of representative rotavirus genotypes from across the world and across species. The degree of conservation between species was represented on homology models for each of the proteins. VP7 shows the highest level of variation with 14 - 45 amino acids showing conservation of less than 60%. These changes are localized to the outer surface which is exposed to antibodies alluding to a possible mechanism in evading the immune system. The middle layer, VP6 shows lower variability with only 14-32 sites having lower than 70% conservation. The inner structural layer made up of VP2 showed the lowest variability with only 1-16 sites having less than 70% conservation across species. The results correlate with proteins’ multiple structural roles. Although the nucleotide sequences vary due to an error-prone replication and lack of proofreading, the corresponding amino acid sequence of VP2, 6 and 7 remains conserved. Sequence conservation maintained for the virus results in stable protein structures, fit for function. This can be exploited in drug design, molecular studies and biotechnological applications.

Keywords: amino acid sequence conservation, capsid protein, protein structure, vaccine candidate

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Authors: Kuan-Jen Lai, Fang-Yi Lin, Shang-Rong Huang, Yun-Zheng Zeng, Po-Chieh Hsu, Jay Wu

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The normalized glandular dose (DgN) estimates the energy deposition of mammography in clinical practice. The Monte Carlo simulations frequently use uniformly mixed phantom for calculating the conversion factor. However, breast tissues are not uniformly distributed, leading to errors of conversion factor estimation. This study constructed a three-layer phantom to estimated more accurate of normalized glandular dose. In this study, MCNP code (Monte Carlo N-Particles code) was used to create the geometric structure. We simulated three types of target/filter combinations (Mo/Mo, Mo/Rh, Rh/Rh), six voltages (25 ~ 35 kVp), six HVL parameters and nine breast phantom thicknesses (2 ~ 10 cm) for the three-layer mammographic phantom. The conversion factor for 25%, 50% and 75% glandularity was calculated. The error of conversion factors compared with the results of the American College of Radiology (ACR) was within 6%. For Rh/Rh, the difference was within 9%. The difference between the 50% average glandularity and the uniform phantom was 7.1% ~ -6.7% for the Mo/Mo combination, voltage of 27 kVp, half value layer of 0.34 mmAl, and breast thickness of 4 cm. According to the simulation results, the regression analysis found that the three-layer mammographic phantom at 0% ~ 100% glandularity can be used to accurately calculate the conversion factors. The difference in glandular tissue distribution leads to errors of conversion factor calculation. The three-layer mammographic phantom can provide accurate estimates of glandular dose in clinical practice.

Keywords: Monte Carlo simulation, mammography, normalized glandular dose, glandularity

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Authors: Siphat Lim

Abstract:

This study used Autoregressive Distributed Lag (ARDL) approach to cointegration. In the long-run the general price level and exchange rate have a positively significant effect on domestic output. The estimated result further revealed that fiscal stimulus help stimulate domestic output in the long-run, but not in the short-run, while monetary expansion help to stimulate output in both short-run and long-run. The result is complied with the theory which is the macroeconomic policies, fiscal and monetary policy; help to stimulate domestic output in the long-run. The estimated result of the Vector Error Correction Model (VECM) has indicated more clearly that the consumer price index has a positive effect on output with highly statistically significant. Increasing in the general price level would increase the competitiveness among producers than increase in the output. However, the exchange rate also has a positive effect and highly significant on the gross domestic product. The exchange rate depreciation might increase export since the purchasing power of foreigners has increased. More importantly, fiscal stimulus would help stimulate the domestic output in the long-run since the coefficient of government expenditure is positive. In addition, monetary expansion would also help stimulate the output and the result is highly significant. Thus, fiscal stimulus and monetary expansionary would help stimulate the domestic output in the long-run in Cambodia.

Keywords: fiscal policy, monetary policy, ARDL, VECM

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Authors: Mogale Sabone, Thabiso Ntlole

Abstract:

The pressure from globalization has made manufacturing organizations to move towards three major competitive arenas: quality, cost, and responsiveness. Quality is a universal value and has become a global issue. In order to survive and be able to provide customers with good products, manufacturing organizations’ supporting systems, tools, and structures it uses must grow or evolve. The majority of quality management concepts and strategies that are practiced recently are aimed at detecting and correcting problems which already exist and serve to limit losses. In agile manufacturing environment there is no room for defect and error so it needs a quality management which is proactively directed at problem prevention. This proactive quality management avoids losses by focusing on failure prevention, virtual elimination of the possibility of premature failure, mistake-proofing, and assuring consistently high quality in the definition and design of creation processes. To achieve this, a decision support tool for quality control and management is suggested. Current decision support tools/methods used by most manufacturing companies in Botswana for quality management and control are not integrated, for example they are not consistent since some tests results data is recorded manually only whilst others are recorded electronically. It is only a set of procedures not a tool. These procedures cannot offer interactive decision support. This point brings to light the aim of this research which is to develop a framework which will help manufacturing companies in Botswana build a decision support tool for quality control and management.

Keywords: decision support tool, manufacturing, quality control, quality management

Procedia PDF Downloads 564

Authors: Amr Sadek, Abdelrahaman Al-Qahtany, Turkey Salem Al-Qahtany

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In this study, a backpropagation artificial neural network (ANN) model has been used to predict the cooling and heating capacities of air conditioners (AC) under different conditions. Sufficiently large measurement results were obtained from the national energy-efficiency laboratories in Saudi Arabia and were used for the learning process of the ANN model. The parameters affecting the performance of the AC, including temperature, humidity level, specific heat enthalpy indoors and outdoors, and the air volume flow rate of indoor units, have been considered. These parameters were used as inputs for the ANN model, while the cooling and heating capacity values were set as the targets. A backpropagation ANN model with two hidden layers and one output layer could successfully correlate the input parameters with the targets. The characteristics of the ANN model including the input-processing, transfer, neurons-distance, topology, and training functions have been discussed. The performance of the ANN model was monitored over the training epochs and assessed using the mean squared error function. The model was then used to predict the performance of the AC under conditions that were not included in the measurement results. The optimum performance of the AC was also predicted under the different environmental conditions in Saudi Arabia. The uncertainty of the ANN model predictions has been evaluated taking into account the randomness of the data and lack of learning.

Keywords: artificial neural network, uncertainty of model predictions, efficiency of air conditioners, cooling and heating capacities

Procedia PDF Downloads 72

Authors: Azam Fazelpour, Saeed R. Dehghani, Vlastimil Masek, Yuri S. Muzychka

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This study employs a method based on image analyses and structure information to detect accumulated ice on known structures. The icing of marine vessels and offshore structures causes significant reductions in their efficiency and creates unsafe working conditions. Image processing methods are used to measure ice loads automatically. Most image processing methods are developed based on captured image analyses. In this method, ice loads on structures are calculated by defining structure coordinates and processing captured images. A pyramidal structure is designed with nine cylindrical bars as the known structure of experimental setup. Unsymmetrical ice accumulated on the structure in a cold room represents the actual case of experiments. Camera intrinsic and extrinsic parameters are used to define structure coordinates in the image coordinate system according to the camera location and angle. The thresholding method is applied to capture images and detect iced structures in a binary image. The ice thickness of each element is calculated by combining the information from the binary image and the structure coordinate. Averaging ice diameters from different camera views obtains ice thicknesses of structure elements. Comparison between ice load measurements using this method and the actual ice loads shows positive correlations with an acceptable range of error. The method can be applied to complex structures defining structure and camera coordinates.

Keywords: camera calibration, ice detection, ice load measurements, image processing

Procedia PDF Downloads 367

Authors: M Ananthakrishnan, Sunil K Patil, Koti Naveen, Inuganti Hemanth Kumar

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State estimation output of EMS forms the base case for all other advanced applications used in real time by a power system operator. Ensuring tuning of state estimator is a repeated process and cannot be left once a good solution is obtained. This paper attempts to demonstrate methods to improve state estimator solution by identifying incorrect modelling and telemetry inputs to the application. In this work, identification of database topology modelling error by plotting static network using node-to-node connection details is demonstrated with examples. Analytical methods to identify wrong transmission parameters, incorrect limits and mistakes in pseudo load and generator modelling are explained with various cases observed. Further, methods used for active and reactive power tuning using bus summation display, reactive power absorption summary, and transformer tap correction are also described. In a large power system, verifying all network static data and modelling parameter on regular basis is difficult .The proposed tuning methods can be easily used by operators to quickly identify errors to obtain the best possible state estimation performance. This, in turn, can lead to improved decision-support capabilities, ultimately enhancing the safety and reliability of the power grid.

Keywords: active power tuning, database modelling, reactive power, state estimator

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Authors: Davit Shahnazaryan, Diogo Gomes, Mher Safaryan

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Many symbolic computations and manipulations required in the analysis of partial differential equations (PDE) or systems of PDEs are tedious and error-prone. These computations arise when determining conservation laws, entropies or integral identities, which are essential tools for the study of PDEs. Here, we discuss a new Mathematica package for the symbolic analysis of PDEs that automate multiple tasks, saving time and effort. Methodologies: During the research, we have used concepts of linear algebra and partial differential equations. We have been working on creating algorithms based on theoretical mathematics to find results mentioned below. Major Findings: Our package provides the following functionalities; finding symmetry group of different PDE systems, generation of polynomials invariant with respect to different symmetry groups; simplification of integral quantities by integration by parts and null Lagrangian cleaning, computing general forms of expressions by integration by parts; finding equivalent forms of an integral expression that are simpler or more symmetric form; determining necessary and sufficient conditions on the coefficients for the positivity of a given symbolic expression. Conclusion: Using this package, we can simplify integral identities, find conserved and dissipated quantities of time-dependent PDE or system of PDEs. Some examples in the theory of mean-field games and semiconductor equations are discussed.

Keywords: partial differential equations, symbolic computation, conserved and dissipated quantities, mathematica

Procedia PDF Downloads 162

Authors: Kossigan Bernard Dedey, David Grenier, Tiphaine Lucas

Abstract:

Bread dough is often described as a dispersion of gas cells in a continuous gluten/starch matrix. The final bread crumb structure is strongly related to gas cell walls (GCWs) rupture during baking. At the end of proofing and during baking, part of the thinnest GCWs between expanding gas cells is reduced to a gluten film of about the size of a starch granule. When such size is reached gluten and starch granules must be considered as interacting phases in order to account for heterogeneities and appropriately describe GCW rupture. Among experimental investigations carried out to assess GCW rupture, no experimental work was performed to observe the GCW rupture in the baking conditions at GCW scale. In addition, attempts to numerically understand GCW rupture are usually not performed at the GCW scale and often considered GCWs as continuous. The most relevant paper that accounted for heterogeneities dealt with the gluten/starch interactions and their impact on the mechanical behavior of dough film. However, stress concentration in GCW was not discussed. In this study, both experimental and numerical approaches were used to better understand GCW rupture in bread dough during baking. Experimentally, a macro-scope placed in front of a two-chamber device was used to observe the rupture of a real GCW of 200 micrometers in thickness. Special attention was paid in order to mimic baking conditions as far as possible (temperature, gas pressure and moisture). Various differences in pressure between both sides of GCW were applied and different modes of fracture initiation and propagation in GCWs were observed. Numerically, the impact of gluten/starch interactions (cohesion or non-cohesion) and rheological moduli ratio on the mechanical behavior of GCW under unidirectional extension was assessed in 2D/3D. A non-linear viscoelastic and hyperelastic approach was performed to match the finite strain involved in GCW during baking. Stress concentration within GCW was identified. Simulated stresses concentration was discussed at the light of GCW failure observed in the device. The gluten/starch granule interactions and rheological modulus ratio were found to have a great effect on the amount of stress possibly reached in the GCW.

Keywords: dough, experimental, numerical, rupture

Procedia PDF Downloads 121

Authors: Ochuko K. Overen, Edson L. Meyer

Abstract:

Uncontrolled heat transfer between the inner and outer space of low-cost housings through the thermal envelope result in indoor thermal discomfort. As a result, an excessive amount of energy is consumed for space heating and cooling. Thermo-optical properties are the ability of paints to reduce the rate of heat transfer through the thermal envelope. The aim of this study is to analyze the thermal performance of a low-cost house with its walls inner surface coated with transparent infrared reflective paint. The thermo-optical properties of the paint were analyzed using Scanning Electron Microscopy/ Energy Dispersive X-ray spectroscopy (SEM/EDX), Fourier Transform Infra-Red (FTIR) and thermal photographic technique. Meteorological indoor and ambient parameters such as; air temperature, relative humidity, solar radiation, wind speed and direction of a low-cost house in Golf-course settlement, South Africa were monitored. The monitoring period covers both winter and summer period before and after coating. The thermal performance of the coated walls was evaluated using time lag and decrement factor. The SEM image shows that the coat is transparent to light. The presence of Al as Al2O and other elements were revealed by the EDX spectrum. Before coating, the average decrement factor of the walls in summer was found to be 0.773 with a corresponding time lag of 1.3 hours. In winter, the average decrement factor and corresponding time lag were 0.467 and 1.6 hours, respectively. After coating, the average decrement factor and corresponding time lag were 0.533 and 2.3 hour, respectively in summer. In winter, an average decrement factor of 1.120 and corresponding time lag of 3 hours was observed. The findings show that the performance of the coats is influenced by the seasons. With a 74% reduction in decrement factor and 1.4 time lag increase in winter, it implies that the coatings have more ability to retain heat within the inner space of the house than preventing heat flow into the house. In conclusion, the results have shown that transparent infrared reflective paint has the ability to reduce the propagation of heat flux through building walls. Hence, it can serve as a remedy to the poor thermal performance of low-cost housings in South Africa.

Keywords: energy efficiency, decrement factor, low-cost housing, paints, rural development, thermal comfort, time lag

Procedia PDF Downloads 282